WO2016154110A1 - Companion diagnostic for p97 inhibitor therapy and methods of use thereof - Google Patents

Companion diagnostic for p97 inhibitor therapy and methods of use thereof Download PDF

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WO2016154110A1
WO2016154110A1 PCT/US2016/023405 US2016023405W WO2016154110A1 WO 2016154110 A1 WO2016154110 A1 WO 2016154110A1 US 2016023405 W US2016023405 W US 2016023405W WO 2016154110 A1 WO2016154110 A1 WO 2016154110A1
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methyl
benzylamino
pyrimidin
hgnc
indol
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PCT/US2016/023405
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French (fr)
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Daniel Anderson
Mark Rolfe
Stan LETOVSKY
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Cleave Biosciences, Inc.
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/914—Hydrolases (3)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2500/00—Screening for compounds of potential therapeutic value
    • G01N2500/04—Screening involving studying the effect of compounds C directly on molecule A (e.g. C are potential ligands for a receptor A, or potential substrates for an enzyme A)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • biomarkers correlated with patient response to drug treatment for example, response to trastuzumab in breast carcinomas and metastases (Baselga J., Science 312: 1175, 2006), response to Gleevec (imatinib) in Chronic Myelogenous Leukemia (CML) (Giles et al, Semin Oncol 35(1 Suppl 1):S1 -17, 2008) and radiosensitivity (Torres-Roca et al, Cancer Res. 65:7169-76, 2005).
  • CML Chronic Myelogenous Leukemia
  • gene expression profiling has been used to distinguish different molecular subtypes of various diseases, e.g., diffuse large B-cell lymphoma (Alizadeh et al, Nature 403 :503-511, 2000, Rosenwald et al, N EnglJ Med 346: 1937 -1947, 2002, Wright et al, Proc Natl Acad Sci USA 100:9991-9996, 2003), breast cancer (Sorlie et al, Proc Natl AcadSci USA 98: 10869-10874, 2001, Sorlie et al, Proc Natl Acad Sci USA 100:8418-8423, 2003), and multiple myeloma (MMprofiler, SkylineDx BV).
  • diffuse large B-cell lymphoma Alizadeh et al, Nature 403 :503-511, 2000, Rosenwald et al, N EnglJ Med 346: 1937 -1947, 2002, Wright et al, Proc Natl Acad Sci USA 100
  • Gene expression patterns that characterize the different subtypes of a disease can also be used to identify potential therapeutic targets or pathways (Lenz et al., Proc Natl Acad Sci USA 105: 13520-13525, 2008). Most importantly, there is a need for distinguishing responders from non-responders even before starting treatment to allow for an increased chance of benefit for the treated patients. Additionally, exclusion of potential non responders will protect patients from unnecessary treatment and toxicities.
  • Figure 1 Genomic features of 86 significant signature genes correlate with p97 inhibitor Compound 1 sensitivity.
  • Figure 3 Linear regression predictive model using training and hold back sets.
  • Figure 4 Multivariate linear regression models for predicting ECso of Compound 1 using various numbers of genes (5-90).
  • Figure 5 External validation of linear regression model built with 26 genes.
  • Figure 6 Correlation between predicted ECso and actual ECso for p97 inhibitor Compound 2 and proteasome inhibitor bortezomib (Compound 4).
  • Figure 7 Predictive model for alternate p97 inhibitor Compound 2 compared to proteasome inhibitor bortezomib (Compound 4).
  • Figure 8 Model for p97 inhibitor Compound 1 using gene expression, mutation and copy number features.
  • Figure 9 Support vector machine classifier using 50 gene expression features.
  • Figure 10 Correlation between sensitivity to Compound 1 and sensitivity to a p97 allosteric inihibitor NMS-873.
  • the present invention provides a method of predicting sensitivity to p97 inhibition by a p97 inhibitor in a cell or tissue or body fluid sample from a subject.
  • the method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in the cell or tissue or body fluid sample.
  • the present invention provides a method for selecting a subject for treatment of a disease or condition with a therapy comprising a p97 inhibitor.
  • the method comprises (a) assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject; and (b) selecting the subject for treatment with a therapy comprising a p97 inhibitor based on the assigned sensitivity score.
  • the present invention provides a method of prognosis of a disease or condition suitable for treatment with a therapy comprising a p97 inhibitor in a patient.
  • the method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject.
  • the prognosis of the patient with the disease or condition is based on the assigned sensitivity score.
  • the present invention provides a method of predicting a response to a p97 inhibitor in a patient.
  • the method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject.
  • the patient is predicted to respond to or not respond to a p97 inhibitor therapy based on the assigned sensitivity score.
  • the present invention provides a method for predicting efficacy of, or monitoring treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition.
  • the method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from a subject who is or has been treated with the therapy comprising the p97 inhibitor.
  • the assigned sensitivity score indicates whether the treatment is effective or is likely to be effective, or is an indicator of the progress of treatment.
  • the method further comprises altering treatment based on the assigned sensitivity score.
  • the present invention provides a method for improving clinical outcome of treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition.
  • the method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject.
  • the method comprises developing appropriate treatment based on the assigned sensitivity score thereby improving clinical outcome.
  • the method further comprises altering treatment based on the assigned sensitivity score.
  • the methods can further comprise obtaining the cell or tissue or body fluid sample from the subject.
  • the methods can comprise analyzing the cell or tissue or body fluid sample from the subject for genomic features of the at least two signature genes.
  • the methods comprises obtaining the cell or tissue or body fluid sample from the subject, and analyzing the cell or tissue or body fluid sample from the subject for genomic features of the at least two signature genes.
  • the present invention provides a computer-implemented method of identifying genes associated with sensitivity to p97 inhibition.
  • the method comprises (a) analyzing a cell or tissue or body fluid sample from a subject for genomic features of one or more subsets of genes; (b) assigning a sensitivity score to p97 inhibition in the cell or tissue or body fluid sample based on the genomic features of each of the one or more subsets of genes; and (c) identifying a subset comprising at least two signature genes, the genomic features of which are correlated with the sensitivity to p97 inhibition.
  • the assigning the sensitivity score comprises determining expression levels of at least two signature genes.
  • the assigned sensitivity score is a predicted ICso, wherein an increase in the predicted ICso indicates a decrease in sensitivity to p97 inhibition and a decrease in the predicted ICso indicates an increase in sensitivity to p97 inhibition.
  • the assigned sensitivity score is expression levels of at least two signature genes.
  • the methods further comprise comparing the assigned sensitivity score to a reference sensitivity score.
  • the reference sensitivity is determined from a reference sample.
  • the reference sample is a sample from a healthy subject, is a sample from an individual not having the disease or condition, is a baseline sample from the subject prior to treatment with a therapy comprising a p97 inhibitor, is a sample from a subject prior to the last dose of a therapy comprising a p97 inhibitor, or is a tissue or body fluid sample from an individual not having the disease or condition.
  • the reference sensitivity score is a predicted IC50 of 1000 nM. In some embodiments, the reference sensitivity score is a predicted IC50 of 500 nM.
  • the reference sensitivity score is a predicted IC50 of 250 nM.
  • the present invention provides a microarray comprising a substrate and one or more individually addressable hybridizable array elements arranged thereon, wherein the individually addressable hybridizable array elements are selective for at least two signature genes described herein.
  • the microarray further comprises a hybridizable array element selective doe an internal normalization control gene.
  • the present invention provides a microfluidic device comprising a substrate and one or more reaction chambers, wherein the reaction chambers comprise reagents for selective quantification of at least two signature genes described herein.
  • the microfluidic device further comprises a reaction chamber comprising reagents for selective quantification of an internal normalization control gene.
  • the present invention provides a database comprising data on the genomic features of at least two signature genes described herein in a cell or tissue or body fluid sample from a subject.
  • the database further comprises data regarding the administration of a treatment to the cell or tissue or body fluid sample from a subject.
  • the treatment comprises administering a p97 inhibitor.
  • the present invention provides a computer-readable medium bearing instructions executable by a processor for assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes described herein in a cell or tissue or body fluid sample from a subject.
  • the present invention provides a computer-readable medium bearing instructions executable by the processor for: analyzing genomic features of one or more subsets of genes in a cell or tissue or body fluid sample; assigning a sensitivity score to p97 inhibition in the cell or tissue or body fluid sample based on the genomic features of the one or more subsets of genes; and identifying a subset of genes comprising at least two signature genes, the genomic features of which are correlated with the sensitivity score.
  • the present invention provides a kit comprising reagents for the specific quantification of genomic features of at least two signature genes described herein in a cell or tissue or body fluid sample.
  • the kit further comprises a microarray comprising a substrate and one or more individually addressable hybridizable array elements arranged thereon, wherein the individually addressable hybridizable array elements are selective for the at least two signature genes.
  • the kit further comprises a microfluidic device comprising a substrate and one or more reaction chambers, wherein the reaction chambers comprise reagents for selective quantification of the at least two signature genes.
  • the disease or condition can be a cancer.
  • the cancer is a solid tumor malignancy.
  • the cancer is a hematological malignancy.
  • the therapy is a combination therapy.
  • the combination therapy comprises a p97 inhibitor and a proteasome inhibitor.
  • the proteasome inhibitor is bortezomib or carfilzomib.
  • the assigning the sensitivity score can comprise applying a linear regression model to the genomic features of at least two signature genes; and optionally combining the genomic features into a predictive model using a multivariate algorithm.
  • the linear regression model is a multivariate linear regression model.
  • the genomic features can comprise a feature selected from the group consisting of gene expression (mRNA expression or protein expression), gene copy number, and activating or deactivating point mutation.
  • the sensitivity score is assigned based on genomic features of at least 5, 10, or 25 signature genes.
  • the at least two signature genes comprise at least two genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3.
  • the at least two signature genes comprise at least two genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C.
  • the at least two signature genes comprise at least two genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the at least two signature genes comprise at least two genes selected the group consisting of the genes listed in Table 2A. In some embodiments, the at least two signature genes comprise at least two genes selected from the group consisting of the genes listed in Table 3.
  • the at least two signature genes comprise MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3.
  • the at least two signature genes comprise MUCL1, BCCIP, RNF38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the at least two signature genes comprise MUCL1, BCCIP, RNF38, TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the sensitivity score is assigned based on genomic features of all 26 genes listed in Table 3.
  • the genomic features can be gene expression.
  • the genomic feature is mRNA expression.
  • the genomic feature is protein expression.
  • the genomic feature is gene copy number.
  • the genomic feature is an activating or deactivating point mutation.
  • genomic features of at least five, at least ten, at least fifteen, at least twenty, at least twenty five, at least thirty, at least thirty five, at least forty, at least forty five, at least fifty, at least fifty five, at least sixty, at least sixty five, at least seventy, at least seventy five, at least eighty, at least eighty five, at least ninety, at least ninety five, at least one hundred, at least two hundred, or more signature genes can be utilized.
  • genomic features of less than two hundreds, less than one hundred, less than ninety five, less than ninety, less than eighty five, less than eighty, less than seventy five, less than seventy, less than sixty five, less than sixty, less than fifty five, less than fifty, less than forty five, less than forty, less than thirty five, less than thirty, less than twenty five, less than twenty, less than fifteen, less than ten, less than nine, less than eight, less than seven, less than six, less than five, or less than four signature genes are utilized.
  • the p97 inhibitor can be a small molecule.
  • the small molecule p97 inhibitor is a fused pyrimidines and substituted quinazoline compound as described in US20140024661.
  • the small molecule p97 inhibitor is a compound as described in Cervi et al., Journal of Medicinal Chemistry 57: 10443-10454, 2014, Chou et al, Proceedings of the National Academy of Sciences 108:4834-4839, 201 1 , Chou et al , ChemMedChem 8:297-312, 2013, Magnaghi et al., Nat Chem Biol 9:548-556, 2013, Polucci et al., Journal of Medicinal Chemistry 56:437-450, 2013, or US8865708.
  • the small molecule p97 inhibitor is "Eeyarestatin-I" (Eer-I; 3-(4-Chlorophenyl)-4-[[[(4- chlorophenyl)amino]carbonyl]hydroxyamino]-5,5-dimethyl-2-oxo-l-imidazolidineacetic acid 2-[3-(5- nitro-2-furanyl)-2-propen-l-ylidene] hydrazide), "DBEQ” (N2, N4- Dibenzylquinazoline-2,4-diamine), "Syk-inhibitor III” (3,4-Methylenedioxy-P- nitrostyrene), "NMS-873” (3-(3-(c clopent lthio)-5-(((2-methyl-4'-(methylsulfonyl)- [ 1 , 1 '-biphenyl]-4-y l)oxy)methyl)-4H- 1 ,2,4
  • the p97 inhibitor is an antibody, a protein, a peptide, or a p97 inhibitor introduced by gene therapy.
  • the sample is a biopsy sample from a solid tumor or a bone marrow aspirate.
  • the sample is a fluid sample that is a blood, serum, plasma, ascites, urine, sweat, semen, saliva, cerebral spinal fluid, or lymph sample.
  • the sample is obtained by needle biopsy, CT- guided needle biopsy, aspiration biopsy, endoscopic biopsy, bronchoscopic biopsy, bronchial lavage, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, skin biopsy, bone marrow biopsy, and the Loop Electrosurgical Excision Procedure (LEEP).
  • LEEP Loop Electrosurgical Excision Procedure
  • p97 or "p97 ATPase” refers to transitional endoplasmic reticulum ATPase also known as VCP.
  • the p97 protein is a ubiquitous protein and is a member of the AAA-ATPase super family, wherein "AAA” refers to ATPase Associated with a variety of cellular Activities.
  • the genomic sequence of human p97 has a Gene bank accession number AC004472; Gene ID: 7415, which maps to 9ql3 -pi 2 (Locus tag: HGNC: 12666; MIM: 601023).
  • the mRNA sequence encoding p97 is found at gene bank accession NM_007126.
  • p97 orthologues have been identified including, but not limited to, Human p97 (GenBank Accession No. NP_009057.1 GL6005942; AAI21795.1 GI: 111305821); Rat p97 (Genbank Accession No. NP_446316.1 GI: 17865351); Mouse p97 (Genbank Accession No. AAH49114.1 GI: 29144989).
  • the term p97 includes proteins that share at least 70%, 80%, 90%, 95%, 97%, 98%, 99% or 100% sequence identity with the human, mouse or rat p97.
  • the p97 is characterized by the presences of two conserved energy generating ATPases.
  • p97 or "p97 ATPase” refers to natural p97 protein (e.g., a human p97 protein), variants and mutations thereof (e.g., natural variants, somatic, germline, or induced mutations).
  • Natural human p97 variants include R95G (Watts et al., Nat. Genet. 36:377-381, 2004), R155C (Schroeder et al, Ann. Neurol. 57:457-461, 2005; Watts et al., Nat. Genet. 36:377-381, 2004), R155H (Watts et al., Nat. Genet. 36:377- 381, 2004; Johnson et al., Neuron 68:857-864, 2010), R155P (Watts et al, Nat. Genet.
  • sensitivity to p97 inhibition refers to the sensitivity of a cell or tissue or body fluid sample or a subject in response to a p97 inhibitor (alone or in combination with other drugs or treatments).
  • sensitivity to p97 inhibition refers to an outcome whereby a disease or condition responds favorably (e.g., cellular growth inhibition, decreased adverse symptoms in a subject, a reduction of tumor burden in a subject) to a p97 inhibitor (alone or in combination with other drugs or treatments).
  • sensitivity to p97 inhibition refers to the ability of a cell or tissue or body fluid sample or a subject to interact with a p97 inhibitor.
  • sensitivity to p97 inhibition is determined by measuring genomic features of specific genes (e.g., signature genes).
  • a "sensitivity score” can be calculated based on the sensitivity of a cell or tissue or body fluid sample or a subject to a p97 inhibitor.
  • a "sensitivity score” can be calculated using an algorithm using the values of genomic features of one or more signature genes.
  • the sensitivity score is calculated based on the gene expression (mRNA expression or protein expression), gene copy number, activating or deactivating point mutation, or a combination thereof.
  • the sensitivity score is expressed as a predicted IC50.
  • sensitivity scores are utilized in personalized medicine, or the use of an appropriate treatment to each individual case.
  • IC50 refers to the predicted concentration required for 50% of cellular growth inhibition.
  • ICn refers to Inhibitory Concentration. It is the concentration of a compound (e.g., a p97 inhibitor) in vivo or in vitro needed to inhibit cellular growth (e.g., cancer cell growth) by n %.
  • IC50 refers to the concentration of a compound at which cellular growth is inhibited by 50% of the level observed in the absence of the compound.
  • IC75 refers to the concentration of a compound (e.g., a p97 inhibitor) at which cellular growth is inhibited by 75% of the level observed in the absence of the compound
  • IC90 refers to the concentration of a compound at which cellular growth is inhibited by 90% of the level observed in the absence of the compound.
  • genomic feature refers to a physical, chemical, or genetic characteristic of a gene (e.g., a signature gene).
  • genomic features include, but are not limited to, expression levels (mRNA or protein) or expression level variations, expression pattern (mRNA or protein), activity levels, structure variations (e.g., post- translational modifications, such as phosphorylation), nucleic acid or protein mutations (e.g., point mutations including activating or deactivating point mutation, deletions, germline or somatic mutation, mRNA mutation, rRNA mutation, tRNA mutation), copy number or copy number variations, methylation status or methylation profiles, cancer pathway alterations,
  • the term "subject" refers to an animal, such as a mammal, for example a human.
  • the methods described herein can be useful in both human therapeutics, pre-clinical, and veterinary applications.
  • the subject is a mammal, and in some embodiments, the subject is human.
  • the term "healthy subject” refers to a subject that does not have a disease (e.g., a cancer).
  • a healthy subject has not been diagnosed as having a disease and is not presenting with two or more (e.g., two, three, four, or five) symptoms of a disease state.
  • the healthy subject does not have cancer.
  • the present invention describes a novel model of predicting sensitivity of a disease to p97 inhibition in various cells, tissues, body fluid sample, and subjects. Based on the genomic features of a set of signature genes, the model (e.g., a multi-gene model) assigns a sensitivity score that is correlated with sensitivity of a disease to p97 inhibition.
  • the multi- gene model of the present invention can be used to individualize therapy comprising a p97 inhibitor.
  • the model provides an opportunity to individualize p97 inhibitor dose parameters based on intrinsic p97 inhibition sensitivity.
  • the model provides a unique framework to understand the differences between responders and non-responders. This allows more accurate identification of patients that benefit from a p97 inhibitor therapy, and evaluation of the likelihood that a p97 inhibitor therapy will be effective in treating a disease or condition.
  • the present invention provides methods, compositions, devices, databases, and kits for predicting sensitivity to p97 inhibition in a cell or tissue or body fluid sample from a subject.
  • the invention provides methods for selecting a subject for treatment of a disease or condition with a therapy comprising a p97 inhibitor, methods of diagnosis or prognosis of a disease or condition suitable for treatment with a therapy comprising a p97 inhibitor in a subject, methods for predicting efficacy of treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition, methods for monitoring treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition, and methods of screening candidate p97 inhibitors effective for treating a disease or condition sensitive to p97 inhibition.
  • these methods are based on analysis of genomic features of signature genes in the cell or tissue or body fluid sample from a subject.
  • a sensitivity score to p97 inhibition such as a predicted ICso can be assigned using the results of genomic feature analysis.
  • the signature genes can be determined using computer-implemented methods, which identify genes associated with sensitivity to p97 inhibition based on the correlation between the genomic features and the sensitivity to p97 inhibition.
  • kits, microarrays and microfluidic devices can be made for detection and quantification of the signature genes.
  • the present invention also provides databases comprising data on the genomic features of the signature genes, and computer-readable medium bearing instructions executable by a processor for assigning a sensitivity score to p97 inhibition based on genomic features of the signature genes.
  • the present invention provides methods of predicting sensitivity to p97 inhibition in a cell or tissue or body fluid sample from a subject by assigning a sensitivity score to p97 inhibition based on genomic features of two or more signature genes in the cell or tissue or body fluid sample.
  • the methods use a model to assign the sensitivity score.
  • the methods further include obtaining a cell or tissue or body fluid sample from the subject, and analyzing the cell or tissue or body fluid sample for genomic features of the at least two signature genes.
  • the cell can be a living cell, such as a cultured cell, a normal cell from a subject, a cancer cell from a patient, a tumor cell from a patient.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes. In some embodiments, the methods include determining and analyzing genomic features of five, ten, fifteen, twenty, twenty five, thirty, thirty five, forty, forty five, fifty, fifty five, sixty, sixty five, seventy, seventy five, eighty, eighty five, ninety, ninety five, one hundred, or two hundred signature genes.
  • the methods include determining and analyzing genomic features of at least five, at least ten, at least fifteen, at least twenty, at least twenty five, at least thirty, at least thirty five, at least forty, at least forty five, at least fifty, at least fifty five, at least sixty, at least sixty five, at least seventy, at least seventy five, at least eighty, at least eighty five, at least ninety, at least ninety five, at least one hundred, at least two hundred, or more signature genes.
  • the methods include determining and analyzing genomic features of at least two signature genes, but less than five hundreds, less than two hundreds, less than one hundred, less than ninety five, less than ninety, less than eighty five, less than eighty, less than seventy five, less than seventy, less than sixty five, less than sixty, less than fifty five, less than fifty, less than forty five, less than forty, less than thirty five, less than thirty, less than twenty five, less than twenty, less than fifteen, less than ten, less than nine, less than eight, less than seven, less than six, less than five, or less than four signature genes.
  • the methods include determining and analyzing genomic features of twenty six, ten, five, four, three, or two signature genes.
  • the signature genes include p97 gene. In some embodiments, the signature genes do not include the p97 gene.
  • experiments conducted during the course of development of the present invention identified two or more exemplary signature genes whose genomic features are correlated with the sensitivity to p97 inhibition, including, but not limited to, genes listed in Tables 2A, 2B, 2C, and 3.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes listed in Tables 2A, 2B, and 2C.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes listed in Tables 2A and 2B.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3.
  • the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, EVIPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, T FRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3.
  • one or more signature genes selected from TYW3, EVIPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, T FRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, RNF38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, RNF38, TYW3, EVIPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, T FRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3.
  • the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
  • signature genes may be identified using any suitable methods, e.g., those disclosed herein, and can be used in the present invention.
  • signature genes can be identified as being correlated with sensitivity of a disease to p97 inhibition or resistance using any one or more of the methods including, but are not limited to, gene expression microarray methods, gene copy number values collected by hybrid capture methods, activating or deactivating point mutation analysis, or a combination thereof.
  • signature genes identified as being correlated with sensitivity of a disease to p97 inhibition using any of the suitable methods can be further characterized using other methods, e.g., siRNA or antisense RNA inhibition, immunohistochemistry, tissue microarray, and Northern blot analysis.
  • genes described herein are the human genes, and thus are best suited for use in human cells, a person of ordinary skill in the art could readily identify mammalian homologs using database searches (for known sequences) or routine molecular biological techniques (to identify additional sequences). In general, genes are considered homologs if they show at least 80%, e.g., 85%, 90%, 95%, 98%, 99% or more, identity in conserved regions (e.g., biologically important regions).
  • the present invention provides a modeling method to assigning sensitivity score based on the genomic features of two or more signature genes.
  • the modeling method applies a linear regression model or a non-linear regression model to the genomic features of at least two signature genes to assign a sensitivity score to the cell or tissue or body fluid sample from a subject.
  • the model is a multivariate regression model, e.g., a linear or non-linear multivariate regression model.
  • the modeling method according to the present invention includes a cross validation process.
  • the cross validation process is an internal cross validation process. For example, a certain portion of a given genomic feature data set can be used as a test set whereas the rest of the data set is used to determine genomics features of greatest significance and build models. The derived models are then applied to the test set to calculate the sensitivity score. The validation process can be reiterated (e.g., 5 times, 10 times, 20 times) using different portions of the data set, respectively.
  • the cross validation process is an external cross validation process.
  • the cross validation process can be an external cross validation process with additional cellular viability data other than the genomic features used for building models.
  • the external cross validation process includes applying the models to an independent set of cell lines.
  • the sensitivity scores e.g., actual ICso
  • the sensitivity scores assigned by the models are then compared to the sensitivity scores of the independent set of cell lines to determine whether there is correlation between them.
  • cross validation processes can be used.
  • in vivo validation in cellular or animal models can be used.
  • the genomic features include one or more features such as expression levels (mRNA or protein) or expression level variations, expression profile or pattern (mRNA or protein), activity levels, structure variations (e.g., post- translational modifications, such as phosphorylation), nucleic acid or protein mutations (e.g., point mutations including activating or deactivating point mutation, deletions, germline or somatic mutations, mRNA mutation, rRNA mutation, tRNA mutation), copy number or copy number variations, methylation status or methylation profiles, cancer pathway alterations, translocations, intra-chromosomal inversions, cytogenetic abnormalities, non-reciprocal translocations, rearrangements, and intra- chromosomal inversions.
  • the genomic features include one or more of gene expression (mRNA expression or protein expression), gene copy number, and activating or deactivating point mutation, and optionally in combination with other
  • Sensitivity of a disease or condition to p97 inhibition can be predicted by measuring genomic features of specific genes such as signature genes according to the methods of the present invention.
  • the sensitivity to p97 inhibition by a p97 inhibitor can result in a change in a disease or condition, e.g., decreased adverse symptoms in a subject.
  • the sensitivity to p97 inhibition is cellular growth inhibition. In some embodiments, the sensitivity to p97 inhibition is a combination of the exemplary sensitivities described in the present invention.
  • a sensitivity score can be assigned. For example, a sensitivity score can be calculated using an algorithm based on the values of genomic features of one or more signature genes (e.g., a scaled value between 0 and 100).
  • the sensitivity score can be expression levels (protein or mRNA) of certain signature genes, or an expression profile or expression pattern of certain signature genes.
  • the sensitivity score can also be a parameter for measuring cellular growth inhibition.
  • ICn i.e., inhibitory
  • the sensitivity score is a predicted ICn, e.g., a predicted ICio, ICis, IC20, IC25, IC30, IC35, IC40, IC45, IC50, IC55, IC 6 o, IC 6 5, IC70, IC75, IC80, IC 8 5, IC90, IC95, or IC99.
  • the sensitivity score is a predicted IC50.
  • the sensitivity score is a combination score of ICn (e.g., IC50) and one or more other sensitivity scores (e.g., expression levels of certain genes).
  • the predicted sensitivity score is compared to a reference sensitivity score.
  • the reference sensitivity score can be a sensitivity score determined by any methods, for example, an empirical score or an arbitrarily chosen score.
  • the reference sensitivity score is a reference ICn, for example, a reference IC50.
  • the reference sensitivity score is an ICn (e.g., ICso, IC9o, IC75) of 5- 5000 nM, 50-2000 nM, 100-1000 nM, 300-800 nM, 300-500 nM, 200-400 nM, 200-300 nM, 100-300 nM, 100-200 nM, 50-150 nM, or 50-200 nM.
  • the reference sensitivity score is an ICn (e.g., ICso, IC90, IC75) of 800 nM, 750 nM, 700 nM, 650 nM, 600 nM, 550 nM, 500 nM, 450 nM, 400 nM, 350 nM, 300 nM, 250 nM, 200 nM, 150 nM, 100 nM, or 50 nM.
  • Substantial similarity between the predicted sensitivity score and the reference sensitivity score indicates that the cell or tissue or body fluid sample from the subject is sensitive to p97 inhibition and that a disease or condition will be modified as a result of treatment by a p97 inhibitor.
  • the reference sensitivity score can be used threshold values for various detection, diagnostic, or therapeutic applications.
  • the reference sensitivity score can also be determined from a reference sample, e.g., a normal cell or a cancer cell.
  • a reference sample e.g., a normal cell or a cancer cell.
  • Any suitable sample can be used as a reference sample in the present invention.
  • the reference sample can be a sample from a healthy subject, from an asymptomatic individual, from an individual not having the disease or condition, or from an individual having a disease or condition.
  • Preferable samples from an individual having a disease or condition include a baseline sample from the subject prior to treatment with a therapy comprising a p97 inhibitor, or a sample from a subject prior to the last dose of a therapy comprising a p97 inhibitor.
  • a p97 inhibitor can be a small molecule or a macromolecule.
  • a p97 inhibitor can be an antibody to p97, a dominant negative variant of p97, or a p97 inhibitor introduced by gene therapy (e.g., a siRNA or an antisense nucleic acid that suppress expression of p97, or a nucleic acid molecule encoding a p97 inhibitor).
  • gene therapy e.g., a siRNA or an antisense nucleic acid that suppress expression of p97, or a nucleic acid molecule encoding a p97 inhibitor.
  • the p97 inhibitor is a small molecule compound.
  • p97 inhibitors includes, but are not limited to, "Eeyarestatin-I” (Eer-I; 3-(4-Chlorophenyl)-4- [[[(4-chlorophenyl)amino]carbonyl]hydroxyamino]-5,5-dimethyl-2-oxo-l- imidazolidineacetic acid 2-[3-(5- nitro-2-furanyl)-2-propen-l-ylidene] hydrazide), "DBEQ” (N2, N4-Dibenzylquinazoline-2,4-diamine), "Syk-inhibitor III” (3,4- Methylenedioxy-p-nitrostyrene), "NMS-873” (3-(3-(cyclopentylthio)-5-(((2-methyl-4'- (methy lsulfony l)-[ 1 , 1 '-biphen
  • the small molecule p97 inhibitor is a fused pyrimidine compound of Formula I or a salt or hydrate thereof
  • the A ring is fused to the pyrimidine ring and is a saturated or unsaturated five or six membered ring having zero, one, two or three heteroatoms in the ring, the remaining atoms of the ring being carbon, each heteroatom being independently selected from the group consisting of nitrogen, oxygen and sulfur;
  • G is N, O or (CR'R 2 )!,;
  • R 1 and R 2 are each independently hydrogen or alkyl of one to four carbons in length;
  • n is zero or an integer from 1 to 4 and when G is not N or O and n is zero, G is a single covalent bond;
  • R 3 is selected from the group consisting of hydrogen, an aliphatic component and an aromatic component, each component being substituted by zero, one or two aliphatic or aromatic components;
  • R 4 and R 5 are each independently bound to carbon or nitrogen and are each independently selected from the group consisting of hydrogen, an aliphatic component, a functional component, an aromatic component, and a combination thereof;
  • R 6
  • the p97 inhibitor is a fused pyrimidine compound of Formula II or a salt or hydrate thereof
  • A is CH2, NR 1 , O or S; m is an integer of 1-3; n is 0 or an integer of 1-2; the ring containing A is a five or six member ring; Y is selected from the group consisting of hydrogen, halogen, R c , OR c , CN, CO2H, CON(R c ) 2 , C( R C )N(R C ) 2 , CH 2 N(R C ) 2 , S0 2 N(R c ) 2 and S0 2 R c wherein each R c is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl,
  • Z is selected from the group consisting of halogen, unsubstituted alkyl of 1 to 6 carbons, substituted alkyl or alkenyl of 1 to 4 carbons, and substituted alkoxy of 1 to 4 carbons; wherein the substituted alkyl or alkenyl group is substituted with OR a , SR a , OC(O) R a , C(0)R a , C(0)OR a , OC(0)N(R a ) 2 , C(0)N(R a ) 2 , N(R a )C(0)OR a , N(R a )C(0)R a , N(R a )C(0)N(R a ) 2 ,
  • each R a is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof; and, the substituted alkoxy group is substituted with OR b , R b , OC(0)R b , N(R b ) 2 , C(0)R b , C(0)OR b , OC(0)N(R b ) 2 , C(0)N(R b ) 2 , N(R b )C(0)OR b , N(R b )C(0)R
  • each R b is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof.
  • R 1 is selected from a group consisting of hydrogen, unsubstituted alkyl of 1 to 6 carbons, substituted alkyl of 1 to 4 carbons and -C(0)R d ; wherein, the substituted alkyl is substituted with OR d , SR d , OC(0) R d , C(0)R d , C(0)OR d ,- OC(0)N(R d ) 2 , C(0)N(R d ) 2 , N(R d )C(0)OR d , N(R d )C(0)R d , N(R d )C(0)N(R d ) 2 ,
  • each R d is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl alkenyl, alkynyl or any combination thereof.
  • Each t is independently selected from an integer of 1 or 2.
  • Ar is a phenyl, thiophenyl, pyridinyl, pyrrolyl, furanyl, or a substituted version thereof wherein the substituent is optional, independent and optionally multiple and is an aliphatic, functional or aromatic component.
  • the small molecule p97 inhibitor is a fused pyrimidine compound of Formulas Ilia or Illb or a salt or hydrate thereof
  • A is CH 2 , NR 1 , O or S; m is an integer of 1-3; n is 0 or an integer of 1-2; the sum of m+n is no more than 4 and no less than 1; Y is selected from the group consisting of H, CN, CO2H, CON(R c ) 2 , C(NR C )N(R C ) 2 , CH 2 N(R C ) 2 , S0 2 N(R c ) 2 and S0 2 R c wherein each R c is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl and any combination thereof; Z is selected from the group consisting of unsubstituted alkyl of 1 to 6 carbons, substituted alkyl or alkenyl of 1 to 4 carbon
  • the substituted alkoxy group is substituted with OR b , SR b , OC(O) R b , N(R b ) 2 , C(0)R b , C(0)OR b , OC(0)N(R b ) 2 , C(0)N(R b ) 2 , N(R b )C(0)OR b , N(R b )C(0)R b , N(R b )C(0)N(R b ) 2 , N(R b )C( R b )N(R b ) 2 , N(R b )S(0)tR b , S(0)tOR b , S(0)tN(R b ) 2 , N(Rb) 2 or P0 3 (R b ) 2 wherein each R b is independently hydrogen, alkyl, fluoroalkyl, carbocycly
  • Z is selected from the group consisting of methyl, ethyl, propyl, cyclopropyl , methoxy, ethoxy, propoxy, methoxy methyl, methoxyethyl,
  • alkylenyl group is -(CH 2 ) n - of one to six carbons.
  • Y is selected from the group consisting of hydrogen, cyano, methyl, ethyl, propyl, butyl, amino, methylamino, dimethylamino, aminoalkylenyl, methylaminoalkylenyl, dimethylaminoalkylenyl, hydroxyalkylenyl, methoxy, ethoxy, propoxy, methoxy methyl, methoxyethyl, methoxyethoxy, N-alkylenylacetamide, N- alkylenylurea, N-alkylenylcarbamate, methyl N-alkylenylcarbamate, N- alkylenylsulfonamide, N-alkylenylpropynamide, N-alkylenylacrylamide, morpholinyl, piperidinyl, piperazinyl, pyrrolidonyl, pyrrolidinyl, N-al
  • the aromatic component (Ar) is substituted by a functional component selected from the group consisting of hydroxy, halo, cyano, trifluoromethyl, trifluoromethoxy, nitro, trimethylsilanyl, OR a , SR a , OC(O) R a , N(R a ) 2 , C(0)R a , C(0)OR a ,-OC(0)N(R a ) 2 , C(0)N(R a ) 2 , , N(R a )C(0)OR a , N(R a )C(0)R a , N(R a )C(0)N(R a ) 2 ,
  • each R a is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof; and wherein each t independently is an integer of 1 or 2.
  • Ar is an unsubstituted phenyl.
  • the indole group at the 2 position of the fused pyrimidine is a 2-alkylindolyl, a 2-cyanoindolyl, a 2-haloindolyl, a 2-(hydroxyalkyl)indolyl, a 2- (alkoxy)indolyl, a 2-(aminoalkyl)indolyl, a 2-(alkylaminoalkyl)indolyl, a 2- (dialkylaminoalkyl)indolyl,a 2-(acylamidoalkyl)indolyl, a 2- (alkoxycarbonylaminoalkyl)indolyl, a 2-(sulfonamidoalkyl)indolyl, a 2-( ⁇ - cyanoalkenyl)indolyl, a 2-(P-cyano-P-carboxyamidoalkenyl)indolyl, a 2-( ⁇ - cyanoalken
  • A is CH 2 . In some embodiments, A is R 1 . In some embodiments, A is O.
  • the small molecule p97 inhibitor is a fused pyrimidine compound having any of the following RJPAC names, or a salt or hydrate thereof: N-benzyl- 2-(2-methyl-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; N-benzyl-2-(2-ethyl-lH- indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; 2-[2-(aminomethyl)-lH-indol-l-yl]-N- benzyl-5,6,7,8-tetrahydroquinazolin-4-amine; 2-[2-(l-aminoethyl)-lH-indol-l-yl]-N-benzyl- 5,6,7,8-tetrahydroquinazolin-4-amine; 2-[5-(aminomethyl)
  • the small molecule p97 inhibitor is a fused pyrimidine compound having any of the following IUPAC names, or a salt or hydrate thereof: l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-l,3-benzodiazole-4- carbonitrile; N-benzyl-2-(2-methoxy-lH-l,3-benzodiazol-l-yl)-5,6,7,8-tetrahydroquinazolin- 4-amine; 2-[2-(aminomethyl)- 1H- 1 ,3 -benzodiazol- 1 -yl]-N-benzyl-5,6,7, 8- tetrahydroquinazolin-4-amine; 2-[2-( 1 -aminoethyl)- 1H- 1 ,3 -benzodiazol- 1 -yl]-N-benzyl- 5,6,7,
  • the small molecule p97 inhibitor is a fused pyrimidine compound having any of the following IUPAC names, or a salt or hydrate thereof: l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-carbonitrile; l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-carboxamide; l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-indole-4-carboxamide; 1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-ethoxy-lH-indole-4-carboxamide
  • the p97 inhibitor is a fused pyrimidine compound having the following IUPAC name, or a salt or hydrate thereof: l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-2-[(carbamoylamino)methyl]-lH-indole-4-carboxamide.
  • the p97 inhibitor is a fused pyrimidine compound having the following IUPAC name, or a salt or hydrate thereof: l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2- methyl-lH-indole-4-carboxamide.
  • the p97 inhibitor is a fused pyrimidine compound having the following IUPAC name, or a salt or hydrate thereof: l-[4- (benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carboxamide.
  • the small molecule p97 inhibitor is a p97 allosteric inhibitor.
  • p97 allosteric inhibitors include MS-873 (Magnaghi et al., Nat Chem Biol., 9:548-556, 2013) and allosteric indole amide inhibitors (Alverez et al., ACS Med. Chem. Lett., 7: 182-187, 2016).
  • the present invention provides methods of assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in the cell or tissue or body fluid sample.
  • sensitivity scores find use in a variety of prognostic, diagnostic and therapeutic applications, e.g., for evaluating the likelihood that a p97 therapy will be effective in treating a disease or condition. Exemplary, non-limiting examples of such applications are described herein.
  • the sensitivity score assigned to a cell or tissue or body fluid sample from the subject can be used for selecting a subject for treatment of a disease or condition suitable for a therapy comprising a p97 inhibitor.
  • the method include a step of assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from the subject, and a step of selecting the subject for treatment with a therapy comprising a p97 inhibitor based on the assigned sensitivity score.
  • the subject can be selected for treatment based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern.
  • the subject can also be selected for treatment when the sensitivity score is at, within, above or below certain threshold values or threshold ranges.
  • the threshold value or threshold range is a reference sensitivity score.
  • the method can be practiced with or without a reference sensitivity score.
  • the subject can be selected for treatment without a reference sensitivity score.
  • the assigned sensitivity score is the presence or absence of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns
  • the subject can be selected without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence of certain characteristics.
  • the subject can be selected for treatment based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the selecting comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of signature genes, comparing the assigned sensitivity score to a reference sensitivity score, and selecting the subject for treatment with a therapy comprising a p97 inhibitor based on the assigned sensitivity.
  • the reference sensitivity score can be a threshold value or threshold range, or any sensitivity score obtained from a reference sample.
  • the sensitivity score can be used for diagnosis or prognosis of a disease or condition suitable for treatment with a therapy comprising of a p97 inhibitor in a subject.
  • the method includes assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from the subject. The prognosis or diagnosis of the subject with the disease or condition can be based on the assigned sensitivity score.
  • the prognosis or diagnosis of the subject with the disease or condition can be based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern.
  • the prognosis or diagnosis of the subject with the disease or condition can also be based on a sensitivity score that is at, within, above or below certain threshold values or threshold ranges.
  • the threshold value or threshold range is a reference sensitivity score.
  • the method can be practiced with or without a reference sensitivity score.
  • the prognosis or diagnosis of the subject with the disease or condition can be done without a reference sensitivity score.
  • the assigned sensitivity score is the presence or absence or magnitude of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns
  • the prognosis or diagnosis of the subject can be done without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence or magnitude of certain
  • the prognosis or diagnosis of the subject can be based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the diagnosis or prognosis comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of signature genes, comparing the assigned sensitivity score to a reference sensitivity score, wherein the prognosis or diagnosis of the subject is based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the sensitivity score can be used for predicting a response to a p97 inhibitor in a subject.
  • the method includes assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from the subject.
  • the subject is predicted to respond to or not respond to a p97 inhibitor therapy based on the assigned sensitivity score
  • the subject can be predicted to respond to or not respond to a p97 inhibitor therapy based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern.
  • the subject can also be predicted to respond to or not respond to a p97 inhibitor therapy based on a sensitivity score that is at, within, above or below certain threshold values or threshold ranges.
  • the threshold value or threshold range is a reference sensitivity score.
  • the method can be practiced with or without a reference sensitivity score.
  • the subject is predicted to respond to or not respond to a p97 inhibitor therapy without a reference sensitivity score.
  • the assigned sensitivity score is the presence or absence or magnitude of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns
  • the subject can be predicted to respond to or not respond to a p97 inhibitor therapy without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence or magnitude of certain characteristics.
  • the subject can be predicted to respond to or not respond to a p97 inhibitor therapy based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the prediction comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of signature genes, comparing the assigned sensitivity score to a reference sensitivity score, wherein the subject is predicted to respond to or not respond to a p97 inhibitor therapy based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the sensitivity score can be used for predicting efficacy of treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition.
  • the method includes assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from a subject who is or has been treated with the therapy comprising the p97 inhibitor.
  • the treatment can be predicted as effective or likely to be effective based on the assigned sensitivity score.
  • the treatment can be predicted as effective or likely to be effective based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern.
  • the treatment can also be predicted as effective or likely to be effective when the sensitivity score is at, within, above or below certain threshold values or threshold ranges.
  • the threshold value or threshold range is a reference sensitivity score.
  • the method can be practiced with or without a reference sensitivity score.
  • the treatment can be predicted as effective or likely to be effective without a reference sensitivity score.
  • the assigned sensitivity score is the presence or absence of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns
  • the treatment can be predicted as effective or likely to be effective by without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence of certain characteristics.
  • the treatment can be predicted as effective or likely to be effective based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the prediction comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from a subject who is or has been treated with the therapy comprising the p97 inhibitor, comparing the assigned sensitivity score to a reference sensitivity score, and predicting whether or not the treatment is effective or likely to be effective based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the reference sensitivity score can be a threshold value or threshold range, or any sensitivity score obtained from a reference sample.
  • the sensitivity score can be used for monitoring treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition.
  • the method includes assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject.
  • the assigned sensitivity score can be used as an indicator of the progress of treatment.
  • the progress of treatment can be assessed based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern.
  • the progress of treatment can also be assessed based on whether the sensitivity score is at, within, above or below certain threshold values or threshold ranges.
  • the threshold value or threshold range is a reference sensitivity score.
  • the method can be practiced with or without a reference sensitivity score.
  • the progress of treatment can be monitored without a reference sensitivity score. For example, when the assigned sensitivity score is the presence or absence of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns, the progress of treatment can be monitored without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence of certain characteristics.
  • the progress of treatment can be monitored based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the monitoring comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of signature genes, comparing the assigned sensitivity score to a reference sensitivity score, and assessing the progress of the treatment based on the comparison between the assigned sensitivity score and the reference sensitivity score.
  • the reference sensitivity score can be a threshold value or threshold range, or any sensitivity score obtained from a reference sample.
  • the assigned sensitivity score can be used as basis for altering treatment. For example, when treatment with a therapy comprising a p97 inhibitor is predicted as effective or is likely to be effective or ineffective or is likely to be ineffective, the treatment regimen can be altered accordingly based on the assigned sensitivity score. Similarly, when the progress of treatment is assessed and the disease or condition is predicted as having been ameliorated or having worsened over the course of the treatment, the treatment regimen can be altered accordingly based on the assigned sensitivity score.
  • the treatment can be continued or escalated by increasing the dosage and/or dose schedule of the p97 inhibitor. If the therapy is predicted as ineffective or is likely to be ineffective, or the disease or condition is predicted as having worsened over the course of the treatment, the treatment can be either continued, or reduced by decreasing the dosage and/or dose schedule or terminating treatment of the p97 inhibitor.
  • the method can be practiced with or without a reference sensitivity score.
  • the treatment can be altered directly based on the assigned sensitivity score without comparing it to a reference sensitivity score.
  • the assigned sensitivity score is the presence or absence of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns
  • the treatment can be altered based on the presence or absence of certain characteristics, i.e., without the aid of a reference sensitivity score.
  • the treatment can be altered based on the comparison between the assigned sensitivity score and a reference sensitivity score.
  • the reference sensitivity score can be a threshold value or threshold range, or any sensitivity score obtained from a reference sample.
  • a higher sensitivity score can indicate higher sensitivity to p97 inhibition (e.g., when the sensitivity score is cell death rate, or a scaled value between 0 and 100 wherein a higher scaled value indicates higher sensitivity), or can indicate lower sensitivity to p97 inhibition (e.g., when the sensitivity score is IC50, or a scaled value between 0 and 100 wherein a higher scaled value indicates lower sensitivity).
  • the treatment can be continued or escalated by increasing the dosage and/or dose schedule of the p97 inhibitor if the assigned sensitivity score is at or above the reference sensitivity score, alternatively, the treatment can be either continued, or reduced by decreasing the dosage and/or dose schedule or terminating treatment of the p97 inhibitor if the assigned sensitivity score is at or below the reference sensitivity score.
  • the treatment can be continued or escalated by increasing the dosage and/or dose schedule of the p97 inhibitor if the assigned sensitivity score is at or below the reference sensitivity score, alternatively, the treatment can be either continued, or reduced by decreasing the dosage and/or dose schedule or terminating treatment of the p97 inhibitor if the assigned sensitivity score is at or above the reference sensitivity score.
  • compositions, devices, databases and kits of the present invention can be applied to any disease or condition that is or becoming sensitive to p97 inhibition.
  • Exemplary diseases and conditions include various cancers, e.g., solid tumor malignancy and hematological malignancy.
  • Exemplary solid tumors include but are not limited to lung cancer, colon cancer, CNS cancer, melanoma, ovarian cancer, renal cancer, prostate cancer, head and neck cancer, testicular cancer, germ-line cancers, endocrine tumors, uterine cancer, breast cancer, sarcomas, gastric cancer, hepatic cancer, esophageal cancer and pancreatic cancer.
  • Exemplary hematological malignancies include but are not limited to multiple myeloma, acute myeloid leukemia, high-risk acute myeloid leukemia, and diffuse large B-cell lymphoma.
  • the p97 inhibitor can be administered as a monotherapy or in a combination treatment, for example, for the treatment of a disease or condition sensitive to p97 inhibition, such as cancers.
  • the p97 inhibitor can be co-formulated or co-administered together with, prior to, intermittently with, or subsequent to, other therapeutic or pharmacologic agents or treatments, such as procedures.
  • agents include, but are not limited to, biologies, anticancer agents, other small molecule compounds, dispersing agents, anesthetics,
  • the p97 inhibitor can be administered in combination with a proteasome inhibitor (e.g., bortezomib and carfilzomib) or additional standard of care agents for various cancers (e.g., lenalidomide and dexamethasone for multiple myeloma).
  • a proteasome inhibitor e.g., bortezomib and carfilzomib
  • additional standard of care agents for various cancers e.g., lenalidomide and dexamethasone for multiple myeloma
  • Exemplary proteasome inhibitors include bortezomib, CEP- 18770 (See Pwa et al., Blood, 1 1:2765-75, 2008), carfilzomib or ixazomib (MLN9708).
  • a sample e.g., a tissue or body fluid sample
  • a cell e.g., a living cell
  • a tumor e.g., from a biopsy or bone marrow aspirate or circulating tumor cells
  • a normal cell e.g., a normal cell
  • a cultured cell e.g., a cell from a tumor
  • tumor cells include surgical (the use of tissue taken from the tumor after removal of all or part of the tumor) and needle biopsies.
  • Commonly used methods to obtain hematological malignancy cells include collection of bone marrow aspirate, isolation of peripheral blood, and isolation of circulating dendritic cells from peripheral blood.
  • the samples can be treated in a way that preserves intact the gene expression levels or genomic material of the living cells to allow for analysis, e.g., flash freezing or chemical fixation, e.g., formalin fixation.
  • Examples of a cell, tissue, or body fluid sample useful in the present invention include one or more samples from urine, stool, tears, whole blood, serum, plasma, ascites, sweat, plasma, blood constituent, bone marrow, tissue, cells, organs, saliva, semen, cheek swab, hair follicle, lymph fluid, cerebrospinal fluid, lesion exudates and other fluids produced by the body.
  • the sample can be a biopsy sample, frozen, fixed or fresh, or a marrow aspirate from a subject having a hematological malignancy.
  • the cell, tissue, or body fluid sample from a subject is a sample from a biopsy from a solid tumor.
  • the cell, tissue, or body fluid sample can be obtained by needle biopsy, CT-guided needle biopsy, aspiration biopsy, endoscopic biopsy, bronchoscopic biopsy, bronchial lavage, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, skin biopsy, bone marrow biopsy, and the Loop Electrosurgical Excision Procedure (LEEP).
  • LEEP Loop Electrosurgical Excision Procedure
  • Genomic features of the genes contained in the cell, tissue, or fluidic sample from a subject can be determined in many different ways.
  • genomic features can be determined by detection and/or quantification of expression levels (mRNA or protein) or expression level variations, expression pattern or profile (mRNA or protein), activity levels, structure variations (e.g., post-translational modifications, such as phosphorylation), nucleic acid or protein mutations (e.g., point mutations including activating or deactivating point mutations, deletions, germline or somatic mutations, mRNA mutation, rRNA mutation, tRNA mutation), copy number or copy number variations, methylation status or methylation profiles, cancer pathway alterations, translocations, intra-chromosomal inversions, cytogenetic abnormalities, non-reciprocal translocations, rearrangements, and intra- chromosomal inversions.
  • gene expression levels can be determined by the quantification of fluorescence of hybridized mRNA on glass slides, Northern blot analysis, real-time reverse transcription PCR (RT-PCR), RNA sequencing (RNAseq), or other measures of gene expression abundance.
  • expression levels can be evaluated by obtaining a sample from a subject and contacting the sample with a compound or an agent capable of detecting mRNA for the signature genes, or protein encoded by the signature genes, such that the level of the protein or nucleic acid is detected in the sample.
  • the level of expression of the signature genes can be measured by, for example, measuring the mRNA encoded by the signature genes; measuring the amount of protein encoded by the signature genes; or measuring the activity of the protein encoded by the signature genes.
  • the level of mRNA corresponding to the signature gene in a cell can be determined both by in situ and by in vitro formats.
  • the isolated mRNA can be used in hybridization or amplification assays, e.g., Southern or Northern analyses, polymerase chain reaction analyses and probe arrays.
  • One exemplary diagnostic method for the detection of mRNA levels involves contacting the isolated mRNA with a nucleic acid molecule (probe) that can hybridize to the mRNA encoded by the signature gene being detected.
  • the nucleic acid probe can be, for example, a full-length nucleic acid or an oligonucleotide of at least 7, 15, 30, 50, 100, 250 or 500 nucleotides in length and sufficient to specifically hybridize under stringent conditions to mRNA for a signature gene.
  • Other suitable probes for use in the diagnostic assays are known in the art.
  • mRNA (or cDNA) from the sample is immobilized on a surface and contacted with the probes, for example by running the isolated mRNA on an agarose gel and transferring the mRNA from the gel to a membrane, such as nitrocellulose.
  • the probes are immobilized on a surface and the mRNA (or cDNA) from the sample is contacted with the probes, for example, in a two-dimensional gene chip array. Any other known mRNA detection methods can be adapted for use in detecting the level of mRNA encoded by the signature genes.
  • the level of mRNA encoded by the signature genes in a sample can also be evaluated with nucleic acid amplification, e.g., by RT-PCR (Mullis (1987) U.S. Patent No. 4,683,202), ligase chain reaction (Barany (1991) Proc. Natl. Acad. Sci. USA 88: 189- 193), self-sustained sequence replication (Guatelli et al, (1990) Proc. Natl. Acad. Sci. USA 87: 1874-1878), transcriptional amplification system (Kwoh et al., (1989), Proc. Natl. Acad. Sci.
  • qPCR quantitative RT-PCR
  • a signature gene or expression thereof can be detected using a microarray.
  • differential gene expression can be identified or confirmed using the microarray technique.
  • Polynucleotide sequences of interest including cDNAs and oligonucleotides
  • the arrayed sequences are then hybridized with specific nucleic acids from cells or tissues of interest.
  • PCR amplified inserts of cDNA clones are applied to a substrate in a dense array.
  • Preferably at least 10,000 nucleotide sequences are applied to the substrate.
  • the microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions.
  • Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array.
  • the microarray chip is scanned by a device such as, confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pair-wise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols.
  • a variety of methods can be used to determine the levels of proteins encoded by the selected signature genes. In general, these methods include contacting an agent that selectively binds to the protein, such as an antibody, and evaluating the level of protein in the sample. In some embodiments, the antibody bears a detectable label.
  • Antibodies can be polyclonal or monoclonal. An intact antibody, or a fragment thereof (e.g., Fab or F(ab')2) can be used. Examples of detectable substances are known in the art, as are methods of quantifying levels of proteins detected thereby.
  • the detection methods can be used to detect signature protein in a sample in vitro as well as in vivo.
  • In vitro techniques for detection of signature protein include enzyme linked immunosorbent assays (ELISAs), immunoprecipitations, immunofluorescence, enzyme immunoassay (EIA), radioimmunoassay (RIA), and Western blot analysis.
  • In vivo techniques for detection of protein encoded by a signature gene include introducing into a subject a labeled anti-signature antibody.
  • the antibody can be labeled with a radioactive marker whose presence and location in a subject can be detected by standard imaging techniques.
  • a protein encoded by a signature gene can be detected in a sample using an immunoassay assay.
  • An exemplary method includes the steps of contacting the sample with the antibody and allowing the antibody to form a complex of with the antigen in the sample, washing the sample and detecting the antibody-antigen complex with a detection reagent.
  • the protein encoded by a signature gene can be detected using an indirect assay, in which a second, labeled antibody is used to detect bound marker- specific antibody.
  • Exemplary detectable labels include magnetic beads (e.g.,
  • DYNABEADSTM DYNABEADSTM
  • fluorescent dyes e.g., fluorescent dyes, radiolabels, enzymes (e.g., horseradish peroxidase, alkaline phosphatase and others commonly used), and calorimetric labels such as colloidal gold or colored glass or plastic beads.
  • enzymes e.g., horseradish peroxidase, alkaline phosphatase and others commonly used
  • calorimetric labels such as colloidal gold or colored glass or plastic beads.
  • the marker in the sample can be detected using and/or in a competition or inhibition assay wherein, for example, a monoclonal antibody which binds to a distinct epitope of the marker is incubated simultaneously with the mixture.
  • the amount of a protein encoded by a signature gene can also be determined by immunoassays.
  • Methods for measuring the amount of antibody-marker complex include, e.g., fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, birefringence or refractive index (e.g., surface plasm on resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry).
  • these regents are used with optical detection methods, such as various forms of microscopy, imaging methods and non-imaging methods.
  • Electrochemical methods include voltametry and amperometry methods.
  • Radio frequency methods include multipolar resonance spectroscopy.
  • a protein encoded by a signature gene can be detected in a sample using an immunohistochemistry assay.
  • Antibodies specific for each protein encoded by a signature gene are used to detect expression of the protein in a sample.
  • the antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as, biotin, or an enzyme such as horseradish peroxidase or alkaline phosphatase.
  • unlabeled primary antibody is used in conjunction with a labeled secondary antibody, comprising antisera, polyclonal antisera or a monoclonal antibody specific for the primary antibody.
  • a protein encoded by a signature gene can be detected in a sample using flow cytometry.
  • This technology is routinely used in the diagnosis of health disorders, especially hematological malignancy.
  • Fluorescence-activated cell sorting (FACS) is a specialized type of flow cytometry that often uses the aid of fl ore scent-labeled antibodies to detect antigens on cell of interest. This additional feature of antibody labeling use in FACS provides for simultaneous multiparametric analysis and quantification based upon the specific light scattering and fluorescent characteristics of each cell florescent-labeled cell and it provides physical separation of the population of cells of interest as well as traditional flow cytometry does.
  • a protein encoded by a signature gene can be detected in a sample using other methods of single cell multiparametric protein detection analysis technology such as mass cytometry.
  • mass cytometry antibodies are tagged with isotopically pure rare earth elements, allowing simultaneous measurement of greater than 40 parameters while circumventing the issue of spectral overlap which is observed with fluorophores.
  • the multi-atom metal tags are ionized, for example by passage through an argon plasma, and then analyzed by mass spectrometry. See, e.g., Bandura et al. Analytical Chemistry 81 :6813-6822, 2009; Ornatsky et al. Journal of Immunological Methods 361 : 1- 20, 2010; Bendall et al. Science 332(6030):687-696, 2011.
  • a signature gene or a protein encoded by a signature gene can be detected in a sample using a biochip.
  • biochip technology nucleic acids or proteins are attached to the surface of the biochip in an ordered array format. The grid pattern of the test regions allowed analyzed by imaging software to rapidly and simultaneously quantify the individual analytes at their predetermined locations (addresses).
  • the CCD camera is a sensitive and high-resolution sensor able to accurately detect and quantify very low levels of light on the chip.
  • Biochips can be designed with immobilized nucleic acid and proteins. A biochip could be designed to detect multiple macromolecule types (e.g., nucleic acid molecules and proteins) on one chip. The biochip can be used simultaneously analyze a panel of signature genes or proteins encoded thereby in a single sample, producing a subjects profile.
  • An exemplary biochip is a protein microarray.
  • the microarray includes a support surface such as a glass slide, nitrocellulose membrane, bead, or microtitre plate, to which an array of capture proteins are bound in an arrayed format onto a solid surface.
  • Detection probe molecules typically labeled with a fluorescent dye, are added to the array. Any reaction between the probe and the immobilized protein emits a fluorescent signal that is read by a laser scanner.
  • analytical microarrays also known as capture arrays
  • functional protein microarrays also known as target protein arrays
  • RPA reverse phase protein microarray
  • a signature gene or a protein encoded by a signature gene can be detected in a sample using mass spectrometry.
  • Suitable mass spectrometry methods to be used with the present invention include but are not limited to, one or more of electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), tandem liquid chromatography-mass spectrometry (LC-MS/MS) mass spectrometry,
  • ESI-MS electrospray ionization mass spectrometry
  • MALDI-TOF-MS matrix-assisted laser desorption ionization time-of-flight mass spectrometry
  • SELDI-TOF-MS surface-en
  • DIOS desorption/ionization on silicon
  • SFMS secondary ion mass spectrometry
  • Q-TOF quadrupole time-of-flight
  • APCI-MS atmospheric pressure chemical ionization mass spectrometry
  • APCI-MS/MS APCI-(MS)
  • APPI-MS atmospheric pressure photoionization mass spectrometry
  • APPI-MS APPI-MS/MS
  • APPI-(MS)n quadrupole mass spectrometry
  • FTMS Fourier transform mass spectrometry
  • ion trap mass spectrometry where n is an integer greater than zero.
  • any method known in the art can be used to extract material, e.g., protein or nucleic acid (e.g., mRNA) from the sample.
  • material e.g., protein or nucleic acid (e.g., mRNA)
  • mechanical or enzymatic cell disruption can be used, followed by a solid phase method (e.g., using a column) or phenol-chloroform extraction, e.g., guanidinium thiocyanate-phenol-chloroform extraction of the RNA.
  • phenol-chloroform extraction e.g., guanidinium thiocyanate-phenol-chloroform extraction of the RNA.
  • a number of kits are commercially available for use in isolation of mRNA. Purification can also be used if desired. See, e.g., Peirson and Butler, Methods Mol. Biol. 2007; 362:315-27.
  • cDNA can be transcribed from the mRNA.
  • Computing devices and systems can be used to implement the methods of the present invention.
  • Computing device can be any forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
  • Computing device can include a processor, memory, a storage device, an interface connecting to memory and expansion ports, and an interface connecting to bus and storage device. Each of the components are interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate.
  • the processor can process instructions for execution within the computing device, including instructions stored in the memory or on the storage device to display graphical information for a GUI on an external input/output device, such as display coupled to high speed interface.
  • multiple processors and/or multiple buses can be used, as appropriate, along with multiple memories and types of memory.
  • multiple computing devices can be connected, with each device providing portions of the operations (e.g., as a server bank, a group of blade servers, or a multi -processor system).
  • Computing device can also be any forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, and other similar computing devices.
  • Computing device includes a processor, memory, an input/output device such as a display, a communication interface, and a transceiver, among other components.
  • the device can also be provided with a storage device, such as a microdrive or other device, to provide additional storage.
  • a storage device such as a microdrive or other device, to provide additional storage.
  • Each of the components are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate.
  • the systems and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, and structural equivalents thereof, or in combinations of them.
  • the systems and the functional operations can be implemented as one or more computer program products, i.e., one or more computer programs tangibly embodied in an information carrier, e.g., in a machine readable storage device or in a propagated signal, for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.
  • a computer program (also known as a program, software, software application, or code) can be written in any form of programming language, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
  • the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform the described functions by operating on input data and generating output.
  • the processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
  • processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.
  • a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
  • Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.
  • semiconductor memory devices e.g., EPROM, EEPROM, and flash memory devices
  • magnetic disks e.g., internal hard disks or removable disks
  • magneto optical disks e.g., CD ROM and DVD-ROM disks.
  • aspects of the described techniques can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
  • keyboard and a pointing device e.g., a mouse or a trackball
  • Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • a computer-implemented methods can be implemented for identifying genes associated with sensitivity to p97 inhibition.
  • the methods include a step of analyzing a cell or tissue or body fluid sample from a subject for genomic features of one or more subsets of genes, a step of assigning a sensitivity score to p97 inhibition to the cell or tissue or body fluid sample based on the genomic features of each of the one or more subsets of genes, as described above, and a step of identifying a subset comprising one or more (e.g., at least two) signature genes, the genomic features of which are correlated with the sensitivity to p97 inhibition.
  • the methods can further include obtaining a cell or a tissue or body fluid sample from a subject, and/or analyzing the cell or the tissue or body fluid sample for genomic features of certain signature genes.
  • a database comprising a plurality of records.
  • Each record includes data on the genomic features of one or more signature genes in a cell or tissue or body fluid sample from a subject.
  • the record can also include data on a preselected factor relating to a subject who has a disease or condition.
  • exemplary preselected factors include the presence of a treatment (e.g., the administration of a therapy such as a therapy comprising a p97 inhibitor, vitamin, food or dietary supplement); the presence of an environmental factor (e.g., the presence of a substance in the environment); the presence of a genetic factor or physical factor such as age and somatic or germline mutations.
  • the database includes at least two records, and the preselected factor in each of the records differs from the other record.
  • the preselected factor can be administration of a compound and in one record the preselected factor includes administration of the compound and in the other record the compound is not administered, is administered at a different dose and/or a different compound is administered.
  • the preselected factor can be an environmental factor and in one record the factor is present and in the other record the environmental factor is not present or is present at a different level.
  • the preselected factor can be a genetic factor such as somatic or germline mutations and in one record the genetic factor is present and in the other record the genetic factor is not present or is present at a different level.
  • the preselected factor can be a physical factor such as age and the age in one record varies from the age in the other record, e.g., a difference in age of at least 5, 10, 15, 20 years or more.
  • each record of the database includes data on at least two preselected factors relating to the subject.
  • the database includes at least two records, and at least one preselected factor in each of the records differs from the other record.
  • the database includes at least two records and at least one preselected factor in the records differ and at least one of the other preselected factors is the same.
  • the database can include at least two records and each record includes at least one preselected factor and at least one preselected condition.
  • the database includes at least two records, wherein each record includes information regarding genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3.
  • the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, RNF38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
  • the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
  • each record additionally includes other information such as administration of a therapy (e.g., dose of p97 inhibitor administered), and/or response to that dose (e.g., survival fraction in response to the dose).
  • a therapy e.g., dose of p97 inhibitor administered
  • response to that dose e.g., survival fraction in response to the dose
  • each record can further include data on the genomic features of at least one internal control gene.
  • the present invention provides a computer-readable medium bearing instructions executable by the processor for determining sensitivity to p97 inhibition based on genomic features of at least two signature genes in a cell or a tissue or body fluid sample from a subject, as described above.
  • the computer-readable media refers to any medium that can be read and accessed directly by a machine, e.g., a digital computer or analogue computer.
  • Non-limiting examples of a computer include a desktop PC, laptop, mainframe, server (e.g., a web server, network server, or server farm), handheld digital assistant, pager, mobile telephone, and the like.
  • one or more (e.g., at least two) signature genes are selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3.
  • the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, R F38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
  • the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
  • the present invention further provides microarrays useful for detecting and quantifying genomic features of the signature genes (e.g., levels of mRNA or protein corresponding to the signature genes).
  • the microarray comprises a substrate and
  • the microarray will include a plurality of individually addressable areas including hybridizable array elements selective for the selected signature genes.
  • the microarray will include a plurality of individually addressable areas including reagents for the detection of one or more proteins encoded by the signature genes, e.g., antibodies.
  • the microarrays include hybridizable array elements selective for one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3.
  • the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, and EVIPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, FMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
  • the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
  • the hybridizable array elements are individually addressable hybridizable array elements selective for the signature genes.
  • the microarrays also include one or more hybridizable array elements selective for an internal normalization control. In some embodiments, the microarrays do not include hybridizable array elements selective for other genes.
  • Microarray refers to a substrate having an ordered arrangement of hybridizable array elements arranged thereon. In some embodiments, the array elements are arranged so that there are preferably at least about 10 different array elements, on a 1 cm 2 substrate surface. The maximum number of array elements is unlimited, but can be upwards of at least 100,000 array elements. Furthermore, a hybridization signal from each of the array elements is individually distinguishable. In some embodiments, the array elements comprise polynucleotide probes.
  • Hybridization causes a denatured polynucleotide probe and a denatured
  • Hybridization methods are well known to those skilled in the art (See, e.g., Laboratory Techniques in Biochemistry and Molecular Biology, Vol. 24: Hybridization With Nucleic Acid Probes, P. Tijssen, ed. Elsevier Science, New York, N.Y. (1993)).
  • Conditions can be selected for hybridization where exactly complementary target and polynucleotide probe can hybridize, i.e., each base pair must interact with its complementary base pair.
  • conditions can be selected where target and polynucleotide probes have mismatches but are still able to hybridize.
  • Suitable conditions can be selected, for example, by varying the concentrations of salt or formamide in the prehybridization, hybridization and wash solutions, or by varying the hybridization and wash temperatures.
  • Hybridization can be performed at low stringency with buffers, such as 6 X SSPE with 0.005% Triton X-100 at 37°C, which permits hybridization between target and polynucleotide probes that contain some mismatches to form target polynucleotide/probe complexes. Subsequent washes are performed at higher stringency with buffers, such as 0.5 X SSPE with 0.005% Triton X-100 at 50°C, to retain hybridization of only those
  • target/probe complexes that contain exactly complementary sequences.
  • hybridization can be performed with buffers, such as 5 X SSC/0.2%) SDS at 60°C and washes are performed in 2 X SSC/0.2% SDS and then in 0.1 X SSC.
  • Stringency can also be increased by adding agents such as formamide.
  • Background signals can be reduced by the use of detergent, such as sodium dodecyl sulfate, Sarcosyl or Triton X-100, or a blocking agent, such as sperm DNA.
  • Hybridization specificity can be evaluated by comparing the hybridization of specificity-control polynucleotide probes to specificity-control target polynucleotides that are added to a sample in a known amount.
  • the specificity-control target polynucleotides may have one or more sequence mismatches compared with the corresponding polynucleotide probes. In this manner, whether only complementary target polynucleotides are hybridizing to the polynucleotide probes or whether mismatched hybrid duplexes are forming is determined.
  • the microarray is washed to remove non-hybridized nucleic acids and complex formation between the hybridizable array elements and the target polynucleotides is detected.
  • the target polynucleotides are labeled with a fluorescent label and
  • fluorescence microscopy preferably confocal fluorescence microscopy.
  • An argon ion laser excites the fluorescent label, emissions are directed to a photomultiplier and the amount of emitted light detected and quantitated.
  • the detected signal should be proportional to the amount of probe/target polynucleotide complex at each position of the microarray.
  • the fluorescence microscope can be associated with a computer-driven scanner device to generate a quantitative two-dimensional image of hybridization intensity. The scanned image is examined to determine the abundance/expression level of each hybridized target polynucleotide.
  • microarray fluorescence intensities can be normalized to take into account variations in hybridization intensities when more than one microarray is used under similar test conditions.
  • individual polynucleotide probe/target complex hybridization intensities are normalized using the intensities derived from internal normalization controls contained on each microarray, e.g., control genes.
  • the present invention further provides microfluidic device useful for detecting and quantifying genomic features of the signature genes (e.g., levels of mRNA or protein corresponding to the signature genes.
  • the microfluidic device comprises a substrate and one or more reaction chambers comprising reagents for selective quantification of at least two signature genes.
  • the microfluidic device will include a plurality of reaction chambers comprising reagents for selective quantification of the selected signature genes.
  • the microfluidic device will include a plurality of reaction chambers comprising reagents for selective quantification of one or more proteins encoded by the signature genes, e.g., antibodies.
  • General methods for making and using microfluidic devices are known in the art, see, e.g., U.S. Pat. Nos. 6,960,437 and 7,250,260.
  • the microfluidic device also include a reaction chamber comprising reagents for selective quantification of an internal normalization control. In some embodiments, the microfluidic device also includes a reaction chamber comprising reagents for selective quantification of other genes.
  • the microfluidic device also includes a reaction chamber comprising reagents for selective quantification of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3.
  • the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, R F38, TYW3, and EVIPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, EVIPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3.
  • the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
  • the present invention provides methods for determining the liklihood of improving a disease or condition with a p97 inhibitor.
  • the present invention provides methods of screening p97 inhibitors useful for treating a disease or condition sensitive to p97 inhibition.
  • the present invention provides methods of screening p97 inhibitors that alter (e.g., increase or decrease) the genomic features (e.g., expression) of at least two signature genes.
  • the present invention provides methods of providing personalized medicine (e.g., choice of a particular p97 inhibitor) for individuals with a given drug sensitivity profile. In some embodiments, the effect of therapeutics on the growth and/or progression of cancers with specific drug sensitivity profiles are assessed.
  • candidate p97 inhibitors are evaluated for their ability to alter the genomic profiles (e.g., expression) of at least two signature genes by contacting a p97 inhibitor with a cell expressing signature genes and then assaying for the effect of the candidate p97 inhibitors on genomic features.
  • the candidate p97 inhibitor compounds of the present invention can be obtained using any of the numerous approaches in combinatorial library methods known in the art, including spatially addressable parallel solid phase or solution phase libraries; synthetic library methods requiring deconvolution; the One-bead one-compound' library method; and synthetic library methods using affinity chromatography selection, biological libraries;
  • peptoid libraries libraries of molecules having the functionalities of peptides, but with a novel, non-peptide backbone, which are resistant to enzymatic degradation but which nevertheless remain bioactive; see, e.g., Zuckennann et al., J. Med. Chem. 37:2678-85, 1994).
  • an assay is a cell-based assay in which a cell that displays genomic features of certain signature genes is contacted with a candidate p97 inhibitor compound, and the ability of the candidate compound to the alter the genomic features of the cell is determined.
  • This invention further pertains to novel agents identified by the above-described screening assays. Accordingly, the present invention further provides a method of determining the efficacy, toxicity, side effects, or mechanism of action, of treatment with the novel agents identified as described herein in an appropriate animal model. Furthermore, novel agents identified by the above-described screening assays can be, e.g., used for treatments of a disease or condition sensitive to p97 inhibition.
  • the present invention provides a method of screening candidate p97 inhibitors, comprising: obtaining a candidate p97 inhibitor compound and a cell or tissue or body fluid sample from a subject, and determining the effectiveness of the candidate compound in treating a disease or condition sensitive to p97 inhibition.
  • the cell or tissue or body fluid sample has a known p97 inhibitor sensitivity profile, comprising genomic features of at least two signature genes.
  • the method further comprises the step of determining the effect of the candidate compound on the genomic features of at least two signature genes in the cell or tissue or body fluid sample.
  • the method further comprises the step of determining the effect of the candidate compound on sensitive score to p97 inhibition based on the genomic features of at least two signature genes.
  • one or more (e.g., at least two) signature genes are selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3.
  • the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, RNF38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, R F38, TYW3, FMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
  • the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
  • the invention provides kits for detection and quantification of genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample.
  • the invention provides kits for detecting and quantifying one or more selected signature genes as described herein (e.g., mRNA or protein corresponding to the signature genes) in a biological sample.
  • the kit includes a compound or agent capable of detecting mRNA or protein corresponding to the signature genes in a sample; and a standard; and optionally one or more reagents necessary for performing detection, quantification, or amplification.
  • the compounds, agents, and/or reagents can be packaged in a suitable container.
  • the kit can further comprise instructions for using the kit to detect and quantify signature protein or nucleic acid.
  • the kit is an antibody-based kit.
  • the antibody-based kit according to the present invention can include a first antibody (e.g., attached to a solid support) which binds to a polypeptide corresponding to a signature gene; and optionally a second, different antibody which binds to either the polypeptide or the first antibody and is conjugated to a detectable agent.
  • the kit can also include a buffering agent, a preservative, and/or a protein stabilizing agent.
  • the kit can also include components necessary for detecting the detectable agent (e.g., an enzyme or a substrate).
  • the kit can also contain a control sample or a series of control samples which can be assayed and compared to the test sample contained.
  • Each component of the kit can be enclosed within an individual container and all of the various containers can be within a single package, along with instructions for interpreting the results of the assays performed using the kit.
  • the kit is oligonucleotide-based kit
  • the oligonucleotide- based kit according to the present invention can include an oligonucleotide, e.g., a detectably labeled oligonucleotide, which hybridizes to a nucleic acid sequence corresponding to a signature gene; or a pair of primers useful for amplifying a nucleic acid molecule
  • kits include a microarray comprising a substrate and one or more individually addressable hybridizable array elements arranged thereon, wherein the individually addressable hybridizable array elements are selective for the at least two signature genes.
  • the kits include a microfluidic device comprising a substrate and one or more reaction chambers, wherein the reaction chambers comprise reagents for selective quantification of the at least two signature genes.
  • kits include reagents (e.g., primers or antibodies) for specific detection and quantification of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B.
  • the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3.
  • the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ⁇ 6, NPM3.
  • one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ⁇ 6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, and EVIPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3.
  • the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, R F38, TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
  • kits also include reagents for specific detection and quantification of a housekeeping or control gene.
  • Example 1 Materials and methods [0159] Cell line sensitivity to test compounds
  • Example 2 Analysis conducted on 209 cancer cell lines
  • Multivariate models were built to predict EC50 of Compound 1 using linear regression. Genes to be used in multivariate model were selected in the order of significance of correlation with sensitivity or resistance to Compound 1. Example of this approach using 50 gene expression parameters and 1/5 hold back for validation is shown in Figure 3. To determine the optimal number of genes needed to build multivariate predictive models of Compound 1 sensitivity, gene numbers were varied from 5-90 of the most significant correlating genes. For each set of gene numbers, a predictive model was built randomly excluding 1/5 of cell line lines 200 times. The model was then applied to the hold back set of cell lines and the correlation between predicted EC50 and actual EC50 was calculated (Figure 4). Greater than 10 genes and less than 90 genes appeared to be optimal to build the most robust predictive models.
  • GFR is the value of the readout of genomic features for each gene, and the gene expression is linear and normalized centered around zero.
  • Table 2 Top 549 gene expression correlated with Compound 1 sensitivity or resistance in solid tumor cancer cell lines.
  • Hs.152717 similarity 65 member A acetylglucosaminyltransferase, isozyme A [Source:HGNC Symbol; Acc :HGNC : 70471 premature ovarian failure, IB
  • TMEM15 (gene/p seudogene)
  • E74-like factor 3 (ets domain transcription factor, epithelial-

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Abstract

The present application discloses methods of assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in the cell or tissue or body fluid sample. Such sensitivity scores find use in a variety of detection, diagnostic and therapeutic applications.

Description

COMPANION DIAGNOSTIC FOR P97 INHIBITOR THERAPY AND
METHODS OF USE THEREOF
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority to U.S. Provisional Patent Application No.
62/136,421, filed March 20, 2015, the contents of which are incorporated by reference in the entirety.
BACKGROUND OF THE INVENTION
[0002] High-throughput technologies (e.g., microarrays and proteomics) allow for the detailed characterization of the genomic and proteomic makeup of cancers. These approaches have led to the identification of biomarkers in various cancers (Beer et al., Nat e<f 8:816-24, 2002; Alizadeh et al., Nature 403 :503-11, 2000; Bild et al., Nature 439:353- 7, 2006; van de Vijver et al., New Eng. J. Med. 347: 1999-2009, 2002; Shedden et al, Nat Med 14:822-7, 2008), e.g., breast cancer (van 't Veer et al, Nature 415:530-6, 2002), head and neck cancer (Chung et al, Cancer Cell 5:489-500, 2004), and colon cancer (Eschrich et al, J Clin Oncol 23 :3526-35, 2005). Several studies have identified biomarkers correlated with patient response to drug treatment, for example, response to trastuzumab in breast carcinomas and metastases (Baselga J., Science 312: 1175, 2006), response to Gleevec (imatinib) in Chronic Myelogenous Leukemia (CML) (Giles et al, Semin Oncol 35(1 Suppl 1):S1 -17, 2008) and radiosensitivity (Torres-Roca et al, Cancer Res. 65:7169-76, 2005). Large-scale gene expression data has been used to establish gene expression patterns for the prediction of treatment outcome, e.g., in advanced Philadelphia-chromosome-positive (Ph+) acute lymphoblastic leukemia (Hofmann et al, Lancet 359:481, 2002), childhood leukemia (Holleman et al, N. Engl. J. Med. 351 :533, 2004), breast cancer (Iwao-Koizumi et al, J. Clin. Oncol. 23 :422, 2005), and inflammatory breast cancer (Bertucci et al, Cancer Res. 64:8558, 2004). In addition, gene expression profiling has been used to distinguish different molecular subtypes of various diseases, e.g., diffuse large B-cell lymphoma (Alizadeh et al, Nature 403 :503-511, 2000, Rosenwald et al, N EnglJ Med 346: 1937 -1947, 2002, Wright et al, Proc Natl Acad Sci USA 100:9991-9996, 2003), breast cancer (Sorlie et al, Proc Natl AcadSci USA 98: 10869-10874, 2001, Sorlie et al, Proc Natl Acad Sci USA 100:8418-8423, 2003), and multiple myeloma (MMprofiler, SkylineDx BV). Gene expression patterns that characterize the different subtypes of a disease can also be used to identify potential therapeutic targets or pathways (Lenz et al., Proc Natl Acad Sci USA 105: 13520-13525, 2008). Most importantly, there is a need for distinguishing responders from non-responders even before starting treatment to allow for an increased chance of benefit for the treated patients. Additionally, exclusion of potential non responders will protect patients from unnecessary treatment and toxicities.
BRIEF DESCRIPTION OF THE FIGURES
[0003] Figure 1 : Genomic features of 86 significant signature genes correlate with p97 inhibitor Compound 1 sensitivity.
[0004] Figure 2: Clustering of most significant gene correlates.
[0005] Figure 3 : Linear regression predictive model using training and hold back sets.
[0006] Figure 4: Multivariate linear regression models for predicting ECso of Compound 1 using various numbers of genes (5-90).
[0007] Figure 5: External validation of linear regression model built with 26 genes.
[0008] Figure 6: Correlation between predicted ECso and actual ECso for p97 inhibitor Compound 2 and proteasome inhibitor bortezomib (Compound 4).
[0009] Figure 7: Predictive model for alternate p97 inhibitor Compound 2 compared to proteasome inhibitor bortezomib (Compound 4).
[0010] Figure 8: Model for p97 inhibitor Compound 1 using gene expression, mutation and copy number features.
[0011] Figure 9: Support vector machine classifier using 50 gene expression features.
[0012] Figure 10: Correlation between sensitivity to Compound 1 and sensitivity to a p97 allosteric inihibitor NMS-873.
SUMMARY OF THE CLAIMED INVENTION
[0013] In one aspect, the present invention provides a method of predicting sensitivity to p97 inhibition by a p97 inhibitor in a cell or tissue or body fluid sample from a subject. The method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in the cell or tissue or body fluid sample. [0014] In another aspect, the present invention provides a method for selecting a subject for treatment of a disease or condition with a therapy comprising a p97 inhibitor. The method comprises (a) assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject; and (b) selecting the subject for treatment with a therapy comprising a p97 inhibitor based on the assigned sensitivity score.
[0015] In another aspect, the present invention provides a method of prognosis of a disease or condition suitable for treatment with a therapy comprising a p97 inhibitor in a patient. The method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject. In some embodiments, the prognosis of the patient with the disease or condition is based on the assigned sensitivity score.
[0016] In another aspect, the present invention provides a method of predicting a response to a p97 inhibitor in a patient. The method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject. In some embodiments, the patient is predicted to respond to or not respond to a p97 inhibitor therapy based on the assigned sensitivity score.
[0017] In another aspect, the present invention provides a method for predicting efficacy of, or monitoring treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition. The method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from a subject who is or has been treated with the therapy comprising the p97 inhibitor. In some embodiments, the assigned sensitivity score indicates whether the treatment is effective or is likely to be effective, or is an indicator of the progress of treatment. In some embodiments, the method further comprises altering treatment based on the assigned sensitivity score.
[0018] In another aspect, the present invention provides a method for improving clinical outcome of treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition. The method comprises assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject. In some embodiments, the method comprises developing appropriate treatment based on the assigned sensitivity score thereby improving clinical outcome. In some embodiments, the method further comprises altering treatment based on the assigned sensitivity score.
[0019] In any of the above methods, the methods can further comprise obtaining the cell or tissue or body fluid sample from the subject. In any of the above methods, the methods can comprise analyzing the cell or tissue or body fluid sample from the subject for genomic features of the at least two signature genes. In some embodiments, the methods comprises obtaining the cell or tissue or body fluid sample from the subject, and analyzing the cell or tissue or body fluid sample from the subject for genomic features of the at least two signature genes.
[0020] In another aspect, the present invention provides a computer-implemented method of identifying genes associated with sensitivity to p97 inhibition. The method comprises (a) analyzing a cell or tissue or body fluid sample from a subject for genomic features of one or more subsets of genes; (b) assigning a sensitivity score to p97 inhibition in the cell or tissue or body fluid sample based on the genomic features of each of the one or more subsets of genes; and (c) identifying a subset comprising at least two signature genes, the genomic features of which are correlated with the sensitivity to p97 inhibition.
[0021] In some embodiments, the assigning the sensitivity score comprises determining expression levels of at least two signature genes. In some embodiments, the assigned sensitivity score is a predicted ICso, wherein an increase in the predicted ICso indicates a decrease in sensitivity to p97 inhibition and a decrease in the predicted ICso indicates an increase in sensitivity to p97 inhibition. In some embodiments, the assigned sensitivity score is expression levels of at least two signature genes.
[0022] In some embodiments, the methods further comprise comparing the assigned sensitivity score to a reference sensitivity score. In some embodiments, the reference sensitivity is determined from a reference sample. In some embodiments, the reference sample is a sample from a healthy subject, is a sample from an individual not having the disease or condition, is a baseline sample from the subject prior to treatment with a therapy comprising a p97 inhibitor, is a sample from a subject prior to the last dose of a therapy comprising a p97 inhibitor, or is a tissue or body fluid sample from an individual not having the disease or condition. In some embodiments, the reference sensitivity score is a predicted IC50 of 1000 nM. In some embodiments, the reference sensitivity score is a predicted IC50 of 500 nM. In some embodiments, the reference sensitivity score is a predicted IC50 of 250 nM. [0023] In another aspect, the present invention provides a microarray comprising a substrate and one or more individually addressable hybridizable array elements arranged thereon, wherein the individually addressable hybridizable array elements are selective for at least two signature genes described herein. In some embodiments, the microarray further comprises a hybridizable array element selective doe an internal normalization control gene.
[0024] In another aspect, the present invention provides a microfluidic device comprising a substrate and one or more reaction chambers, wherein the reaction chambers comprise reagents for selective quantification of at least two signature genes described herein. In some embodiments, the microfluidic device further comprises a reaction chamber comprising reagents for selective quantification of an internal normalization control gene.
[0025] In another aspect, the present invention provides a database comprising data on the genomic features of at least two signature genes described herein in a cell or tissue or body fluid sample from a subject. In some embodiments, the database further comprises data regarding the administration of a treatment to the cell or tissue or body fluid sample from a subject. In some embodiments, the treatment comprises administering a p97 inhibitor.
[0026] In another aspect, the present invention provides a computer-readable medium bearing instructions executable by a processor for assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes described herein in a cell or tissue or body fluid sample from a subject. In another aspect, the present invention provides a computer-readable medium bearing instructions executable by the processor for: analyzing genomic features of one or more subsets of genes in a cell or tissue or body fluid sample; assigning a sensitivity score to p97 inhibition in the cell or tissue or body fluid sample based on the genomic features of the one or more subsets of genes; and identifying a subset of genes comprising at least two signature genes, the genomic features of which are correlated with the sensitivity score.
[0027] In another aspect, the present invention provides a kit comprising reagents for the specific quantification of genomic features of at least two signature genes described herein in a cell or tissue or body fluid sample. In some embodiments, the kit further comprises a microarray comprising a substrate and one or more individually addressable hybridizable array elements arranged thereon, wherein the individually addressable hybridizable array elements are selective for the at least two signature genes. In some embodiments, the kit further comprises a microfluidic device comprising a substrate and one or more reaction chambers, wherein the reaction chambers comprise reagents for selective quantification of the at least two signature genes.
[0028] In any of above methods, microarrays, microfluidic devices, databases, computer- readable medium bearing instructions, or kits, the disease or condition can be a cancer. In some embodiments, the cancer is a solid tumor malignancy. In some embodiments, the cancer is a hematological malignancy. In some embodiments, the therapy is a combination therapy. In some embodiments, the combination therapy comprises a p97 inhibitor and a proteasome inhibitor. In some embodiments, the proteasome inhibitor is bortezomib or carfilzomib.
[0029] In any of above methods, microarrays, microfluidic devices, databases, computer- readable medium bearing instructions, or kits, the assigning the sensitivity score can comprise applying a linear regression model to the genomic features of at least two signature genes; and optionally combining the genomic features into a predictive model using a multivariate algorithm. In some embodiments, the linear regression model is a multivariate linear regression model. In some embodiments, the linear regression model is represented by the following algorithm: Predicted ECso = 0.4698 + -0.0014(GFRMUCLI) + -0.0329(GFRBCCIP) + 0.0883(GFRRNF38) + -0.0546(GFRTYW3) + -0.0340(GFRIMPDH2) + 0.0323(GFRSLC4A8) + 0.01 1 1(GFRZFP3) + 0.0190(GFRDACHI) + 0.0081(GFRUBE2GI) + 0.0176(GFRTTC27) + - 0.0224(GFRMPP6) + -0.0028(GFRBAG2) + 0.0209(GFRNRCAM) + -0.0200(GFRNOC3L) + - 0.0076(GFRZNF652) + -0.0219(GFRTNFRSFIOB) + -0.0157(GFRSSR3) + 0.0021(GFRAK2) + - 0.0052(GFRDCLKI) + 0.0255(GFRRABGGTO) + 0.0301(GFRKLHDC9) + 0.01 10 (GFREBNAIBP2) + 0.0006(GFRNOHFDIL) + 0.0005(GFRDNM3) + -0.0147(GFRZNHIT6) + 0.0016(GFRNPM3), wherein GFR is the value of the readout of genomic features for each gene
[0030] In any of above methods, microarrays, microfluidic devices, databases, computer- readable medium bearing instructions, or kits, the genomic features can comprise a feature selected from the group consisting of gene expression (mRNA expression or protein expression), gene copy number, and activating or deactivating point mutation. In some embodiments, the sensitivity score is assigned based on genomic features of at least 5, 10, or 25 signature genes. In some embodiments, the at least two signature genes comprise at least two genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3. In some embodiments, the at least two signature genes comprise at least two genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C. In some embodiments, the at least two signature genes comprise at least two genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the at least two signature genes comprise at least two genes selected the group consisting of the genes listed in Table 2A. In some embodiments, the at least two signature genes comprise at least two genes selected from the group consisting of the genes listed in Table 3. In some embodiments, the at least two signature genes comprise MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3. In some embodiments, the at least two signature genes comprise MUCL1, BCCIP, RNF38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the at least two signature genes comprise MUCL1, BCCIP, RNF38, TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the sensitivity score is assigned based on genomic features of all 26 genes listed in Table 3.
[0031] In any of above methods, microarrays, microfluidic devices, databases, computer- readable medium bearing instructions, or kits, the genomic features can be gene expression. In some embodiments, the genomic feature is mRNA expression. In some embodiments, the genomic feature is protein expression. In some embodiments, the genomic feature is gene copy number. In some embodiments, the genomic feature is an activating or deactivating point mutation.
[0032] In any of above methods, microarrays, microfluidic devices, databases, computer- readable medium bearing instructions, or kits, genomic features of at least five, at least ten, at least fifteen, at least twenty, at least twenty five, at least thirty, at least thirty five, at least forty, at least forty five, at least fifty, at least fifty five, at least sixty, at least sixty five, at least seventy, at least seventy five, at least eighty, at least eighty five, at least ninety, at least ninety five, at least one hundred, at least two hundred, or more signature genes can be utilized. In some embodiments, genomic features of less than two hundreds, less than one hundred, less than ninety five, less than ninety, less than eighty five, less than eighty, less than seventy five, less than seventy, less than sixty five, less than sixty, less than fifty five, less than fifty, less than forty five, less than forty, less than thirty five, less than thirty, less than twenty five, less than twenty, less than fifteen, less than ten, less than nine, less than eight, less than seven, less than six, less than five, or less than four signature genes are utilized.
[0033] In any of above methods, microarrays, microfluidic devices, databases, computer- readable medium bearing instructions, or kits, the p97 inhibitor can be a small molecule. In some embodiments, the small molecule p97 inhibitor is a fused pyrimidines and substituted quinazoline compound as described in US20140024661. In some embodiments, the small molecule p97 inhibitor is a compound as described in Cervi et al., Journal of Medicinal Chemistry 57: 10443-10454, 2014, Chou et al, Proceedings of the National Academy of Sciences 108:4834-4839, 201 1 , Chou et al , ChemMedChem 8:297-312, 2013, Magnaghi et al., Nat Chem Biol 9:548-556, 2013, Polucci et al., Journal of Medicinal Chemistry 56:437-450, 2013, or US8865708. In some embodiments, the small molecule p97 inhibitor is "Eeyarestatin-I" (Eer-I; 3-(4-Chlorophenyl)-4-[[[(4- chlorophenyl)amino]carbonyl]hydroxyamino]-5,5-dimethyl-2-oxo-l-imidazolidineacetic acid 2-[3-(5- nitro-2-furanyl)-2-propen-l-ylidene] hydrazide), "DBEQ" (N2, N4- Dibenzylquinazoline-2,4-diamine), "Syk-inhibitor III" (3,4-Methylenedioxy-P- nitrostyrene), "NMS-873" (3-(3-(c clopent lthio)-5-(((2-methyl-4'-(methylsulfonyl)- [ 1 , 1 '-biphenyl]-4-y l)oxy)methyl)-4H- 1 ,2,4-triazol-4-yl)pyridine), or 2-Anilino-4-ary 1- 1 ,3-thiazole compounds (Bursavich et al , Bioorg Med Chem Lett. 20: 1677-9. Epub 2010).
[0034] In some embodiments, the p97 inhibitor is an antibody, a protein, a peptide, or a p97 inhibitor introduced by gene therapy. In some embodiments, the sample is a biopsy sample from a solid tumor or a bone marrow aspirate. In some embodiments, the sample is a fluid sample that is a blood, serum, plasma, ascites, urine, sweat, semen, saliva, cerebral spinal fluid, or lymph sample. In some embodiments, the sample is obtained by needle biopsy, CT- guided needle biopsy, aspiration biopsy, endoscopic biopsy, bronchoscopic biopsy, bronchial lavage, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, skin biopsy, bone marrow biopsy, and the Loop Electrosurgical Excision Procedure (LEEP).
DEFINITIONS
[0035] The term "p97" or "p97 ATPase" refers to transitional endoplasmic reticulum ATPase also known as VCP. The p97 protein is a ubiquitous protein and is a member of the AAA-ATPase super family, wherein "AAA" refers to ATPase Associated with a variety of cellular Activities. The genomic sequence of human p97 has a Gene bank accession number AC004472; Gene ID: 7415, which maps to 9ql3 -pi 2 (Locus tag: HGNC: 12666; MIM: 601023). The mRNA sequence encoding p97 is found at gene bank accession NM_007126. A number of p97 orthologues have been identified including, but not limited to, Human p97 (GenBank Accession No. NP_009057.1 GL6005942; AAI21795.1 GI: 111305821); Rat p97 (Genbank Accession No. NP_446316.1 GI: 17865351); Mouse p97 (Genbank Accession No. AAH49114.1 GI: 29144989). As used herein, the term p97 includes proteins that share at least 70%, 80%, 90%, 95%, 97%, 98%, 99% or 100% sequence identity with the human, mouse or rat p97. The p97 is characterized by the presences of two conserved energy generating ATPases. As used herein, the term "p97" or "p97 ATPase" refers to natural p97 protein (e.g., a human p97 protein), variants and mutations thereof (e.g., natural variants, somatic, germline, or induced mutations). Natural human p97 variants include R95G (Watts et al., Nat. Genet. 36:377-381, 2004), R155C (Schroeder et al, Ann. Neurol. 57:457-461, 2005; Watts et al., Nat. Genet. 36:377-381, 2004), R155H (Watts et al., Nat. Genet. 36:377- 381, 2004; Johnson et al., Neuron 68:857-864, 2010), R155P (Watts et al, Nat. Genet.
36:377-381, 2004), R159H (Haubenberger et al, Neurology 65: 1304-1305, 2005), R191Q (Watts et al, Nat. Genet. 36:377-381, 2004; Johnson et al, Neuron 68:857-864, 2010), and A232E (Watts et al, Nat. Genet. 36:377-381, 2004).
[0036] The term "sensitivity to p97 inhibition" refers to the sensitivity of a cell or tissue or body fluid sample or a subject in response to a p97 inhibitor (alone or in combination with other drugs or treatments). For example, in some embodiments, sensitivity to p97 inhibition refers to an outcome whereby a disease or condition responds favorably (e.g., cellular growth inhibition, decreased adverse symptoms in a subject, a reduction of tumor burden in a subject) to a p97 inhibitor (alone or in combination with other drugs or treatments). In some embodiments, sensitivity to p97 inhibition refers to the ability of a cell or tissue or body fluid sample or a subject to interact with a p97 inhibitor. In some embodiments, sensitivity to p97 inhibition is determined by measuring genomic features of specific genes (e.g., signature genes).
[0037] The term "signature gene" refers to a gene whose genomic features (e.g., expression level), alone or in combination with other genes, are correlated with the sensitivity to p97 inhibition. [0038] In some embodiments, a "sensitivity score" can be calculated based on the sensitivity of a cell or tissue or body fluid sample or a subject to a p97 inhibitor. For example, in some embodiments, a "sensitivity score" can be calculated using an algorithm using the values of genomic features of one or more signature genes. In some embodiments, the sensitivity score is calculated based on the gene expression (mRNA expression or protein expression), gene copy number, activating or deactivating point mutation, or a combination thereof. In some embodiments, the sensitivity score is expressed as a predicted IC50. In some embodiments, sensitivity scores are utilized in personalized medicine, or the use of an appropriate treatment to each individual case.
[0039] The term "predicted IC50" refers to the predicted concentration required for 50% of cellular growth inhibition. "ICn" refers to Inhibitory Concentration. It is the concentration of a compound (e.g., a p97 inhibitor) in vivo or in vitro needed to inhibit cellular growth (e.g., cancer cell growth) by n %. Thus, "IC50" refers to the concentration of a compound at which cellular growth is inhibited by 50% of the level observed in the absence of the compound. Similarly, "IC75" refers to the concentration of a compound (e.g., a p97 inhibitor) at which cellular growth is inhibited by 75% of the level observed in the absence of the compound, and "IC90" refers to the concentration of a compound at which cellular growth is inhibited by 90%) of the level observed in the absence of the compound.
[0040] The term "genomic feature" refers to a physical, chemical, or genetic characteristic of a gene (e.g., a signature gene). In some embodiments, genomic features include, but are not limited to, expression levels (mRNA or protein) or expression level variations, expression pattern (mRNA or protein), activity levels, structure variations (e.g., post- translational modifications, such as phosphorylation), nucleic acid or protein mutations (e.g., point mutations including activating or deactivating point mutation, deletions, germline or somatic mutation, mRNA mutation, rRNA mutation, tRNA mutation), copy number or copy number variations, methylation status or methylation profiles, cancer pathway alterations,
translocations, intra-chromosomal inversions, cytogenetic abnormalities, non-reciprocal translocations, rearrangements, intra-chromosomal inversions, and protein phosphorylation. Slight variations in the codon compositions of the direct and indirect genes relating to p97 are possible and occur from time to time, the genomic features (e.g., expression levels) of these genes rather than slight variations in their codons enable their selection and designation whether a patient would benefit from a p97 inhibitor. Therefore, codon variations of these genes will not affect the algorithm developed to determine sensitivity to p97 inhibition. [0041] The term "subject" refers to an animal, such as a mammal, for example a human. The methods described herein can be useful in both human therapeutics, pre-clinical, and veterinary applications. In some embodiments, the subject is a mammal, and in some embodiments, the subject is human.
[0042] The term "healthy subject" refers to a subject that does not have a disease (e.g., a cancer). In some embodiments, a healthy subject has not been diagnosed as having a disease and is not presenting with two or more (e.g., two, three, four, or five) symptoms of a disease state. In some embodiments, the healthy subject does not have cancer.
DETAILED DESCRIPTION OF THE INVENTION
[0043] The present invention describes a novel model of predicting sensitivity of a disease to p97 inhibition in various cells, tissues, body fluid sample, and subjects. Based on the genomic features of a set of signature genes, the model (e.g., a multi-gene model) assigns a sensitivity score that is correlated with sensitivity of a disease to p97 inhibition. The multi- gene model of the present invention can be used to individualize therapy comprising a p97 inhibitor. For example, the model provides an opportunity to individualize p97 inhibitor dose parameters based on intrinsic p97 inhibition sensitivity. Importantly, the model provides a unique framework to understand the differences between responders and non-responders. This allows more accurate identification of patients that benefit from a p97 inhibitor therapy, and evaluation of the likelihood that a p97 inhibitor therapy will be effective in treating a disease or condition.
[0044] Accordingly, the present invention provides methods, compositions, devices, databases, and kits for predicting sensitivity to p97 inhibition in a cell or tissue or body fluid sample from a subject. For example, the invention provides methods for selecting a subject for treatment of a disease or condition with a therapy comprising a p97 inhibitor, methods of diagnosis or prognosis of a disease or condition suitable for treatment with a therapy comprising a p97 inhibitor in a subject, methods for predicting efficacy of treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition, methods for monitoring treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition, and methods of screening candidate p97 inhibitors effective for treating a disease or condition sensitive to p97 inhibition. In one aspect of the invention, these methods are based on analysis of genomic features of signature genes in the cell or tissue or body fluid sample from a subject. A sensitivity score to p97 inhibition such as a predicted ICso can be assigned using the results of genomic feature analysis. The signature genes can be determined using computer-implemented methods, which identify genes associated with sensitivity to p97 inhibition based on the correlation between the genomic features and the sensitivity to p97 inhibition. In another aspect of the invention, kits, microarrays and microfluidic devices can be made for detection and quantification of the signature genes. The present invention also provides databases comprising data on the genomic features of the signature genes, and computer-readable medium bearing instructions executable by a processor for assigning a sensitivity score to p97 inhibition based on genomic features of the signature genes.
[0045] Method of predicting sensitivity to p97 inhibition
[0046] In one aspect, the present invention provides methods of predicting sensitivity to p97 inhibition in a cell or tissue or body fluid sample from a subject by assigning a sensitivity score to p97 inhibition based on genomic features of two or more signature genes in the cell or tissue or body fluid sample. In some embodiments, the methods use a model to assign the sensitivity score. In some embodiments, the methods further include obtaining a cell or tissue or body fluid sample from the subject, and analyzing the cell or tissue or body fluid sample for genomic features of the at least two signature genes. The cell can be a living cell, such as a cultured cell, a normal cell from a subject, a cancer cell from a patient, a tumor cell from a patient.
[0047] In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes. In some embodiments, the methods include determining and analyzing genomic features of five, ten, fifteen, twenty, twenty five, thirty, thirty five, forty, forty five, fifty, fifty five, sixty, sixty five, seventy, seventy five, eighty, eighty five, ninety, ninety five, one hundred, or two hundred signature genes. In some embodiments, the methods include determining and analyzing genomic features of at least five, at least ten, at least fifteen, at least twenty, at least twenty five, at least thirty, at least thirty five, at least forty, at least forty five, at least fifty, at least fifty five, at least sixty, at least sixty five, at least seventy, at least seventy five, at least eighty, at least eighty five, at least ninety, at least ninety five, at least one hundred, at least two hundred, or more signature genes. In some embodiments, the methods include determining and analyzing genomic features of at least two signature genes, but less than five hundreds, less than two hundreds, less than one hundred, less than ninety five, less than ninety, less than eighty five, less than eighty, less than seventy five, less than seventy, less than sixty five, less than sixty, less than fifty five, less than fifty, less than forty five, less than forty, less than thirty five, less than thirty, less than twenty five, less than twenty, less than fifteen, less than ten, less than nine, less than eight, less than seven, less than six, less than five, or less than four signature genes. In some embodiments, the methods include determining and analyzing genomic features of twenty six, ten, five, four, three, or two signature genes. In some embodiments, the signature genes include p97 gene. In some embodiments, the signature genes do not include the p97 gene.
[0048] As detailed in the Examples below, experiments conducted during the course of development of the present invention identified two or more exemplary signature genes whose genomic features are correlated with the sensitivity to p97 inhibition, including, but not limited to, genes listed in Tables 2A, 2B, 2C, and 3. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes listed in Tables 2A, 2B, and 2C. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes listed in Tables 2A and 2B. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3. In some embodiments, the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, EVIPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, T FRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, RNF38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, RNF38, TYW3, EVIPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, T FRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3. In some embodiments, the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
[0049] Additional signature genes may be identified using any suitable methods, e.g., those disclosed herein, and can be used in the present invention. In some embodiments, signature genes can be identified as being correlated with sensitivity of a disease to p97 inhibition or resistance using any one or more of the methods including, but are not limited to, gene expression microarray methods, gene copy number values collected by hybrid capture methods, activating or deactivating point mutation analysis, or a combination thereof. In some embodiments, signature genes identified as being correlated with sensitivity of a disease to p97 inhibition using any of the suitable methods can be further characterized using other methods, e.g., siRNA or antisense RNA inhibition, immunohistochemistry, tissue microarray, and Northern blot analysis.
[0050] Although the exemplary signature genes described herein are the human genes, and thus are best suited for use in human cells, a person of ordinary skill in the art could readily identify mammalian homologs using database searches (for known sequences) or routine molecular biological techniques (to identify additional sequences). In general, genes are considered homologs if they show at least 80%, e.g., 85%, 90%, 95%, 98%, 99% or more, identity in conserved regions (e.g., biologically important regions).
[0051] In some embodiments, the present invention provides a modeling method to assigning sensitivity score based on the genomic features of two or more signature genes. In some embodiments, the modeling method applies a linear regression model or a non-linear regression model to the genomic features of at least two signature genes to assign a sensitivity score to the cell or tissue or body fluid sample from a subject. In some embodiments, the model is a multivariate regression model, e.g., a linear or non-linear multivariate regression model. In some embodiments, the linear regression model is represented by the following algorithm: ECso = 0.4698 + -0.0014(GFRMUCLI) + - 0.0329(GFRBCCIP) + 0.0883(GFRRNF38) + -0.0546(GFRTYW3) + -0.0340(GFRIMPDH2) + 0.0323(GFRSLC4A8) + 0.0111(GFRZFP3) + 0.0190(GFRDACHI) + 0.0081(GFRUBE2GI) + 0.0176(GFRTTC27) + -0.0224(GFRMPP6) + -0.0028(GFRBAG2) + 0.0209(GFRNRCAM) + - 0.0200(GFRNOC3L) + -0.0076(GFRZNF652) + -0.0219(GFRTNFRSFIOB) + -0.0157(GFRSSR3) + 0.0021(GFRAK2) + -0.0052(GFRDCLKI) + 0.0255(GFRRABGGTO) + 0.0301(GFRKLHDC9) + 0.01 10 (GFREBNAIBP2) + 0.0006(GFRMTHFDIL) + 0.0005(GFRDNM3) + -0.0147(GFRZNHIT6) + 0.0016(GFRNPM3), wherein GFR is the value of the readout of genomic features for each gene.
[0052] In some embodiments, the modeling method according to the present invention includes a cross validation process. In some embodiments, the cross validation process is an internal cross validation process. For example, a certain portion of a given genomic feature data set can be used as a test set whereas the rest of the data set is used to determine genomics features of greatest significance and build models. The derived models are then applied to the test set to calculate the sensitivity score. The validation process can be reiterated (e.g., 5 times, 10 times, 20 times) using different portions of the data set, respectively.
[0053] In some embodiments, the cross validation process is an external cross validation process. For example, the cross validation process can be an external cross validation process with additional cellular viability data other than the genomic features used for building models. In some embodiments, the external cross validation process includes applying the models to an independent set of cell lines. The sensitivity scores (e.g., actual ICso) of the independent set of cell lines can be obtained using a different method (e.g., cell titer glo luminescent cell viability assay). The sensitivity scores assigned by the models are then compared to the sensitivity scores of the independent set of cell lines to determine whether there is correlation between them.
[0054] Other cross validation processes can be used. For example, in vivo validation in cellular or animal models can be used.
[0055] The modeling methods of the present invention can be applied to various genomic features measured at molecular or cellular levels. In some embodiments, the genomic features include one or more features such as expression levels (mRNA or protein) or expression level variations, expression profile or pattern (mRNA or protein), activity levels, structure variations (e.g., post- translational modifications, such as phosphorylation), nucleic acid or protein mutations (e.g., point mutations including activating or deactivating point mutation, deletions, germline or somatic mutations, mRNA mutation, rRNA mutation, tRNA mutation), copy number or copy number variations, methylation status or methylation profiles, cancer pathway alterations, translocations, intra-chromosomal inversions, cytogenetic abnormalities, non-reciprocal translocations, rearrangements, and intra- chromosomal inversions. In some embodiments, the genomic features include one or more of gene expression (mRNA expression or protein expression), gene copy number, and activating or deactivating point mutation, and optionally in combination with other genomic features.
[0056] Sensitivity of a disease or condition to p97 inhibition can be predicted by measuring genomic features of specific genes such as signature genes according to the methods of the present invention. The sensitivity to p97 inhibition by a p97 inhibitor can result in a change in a disease or condition, e.g., decreased adverse symptoms in a subject. In some
embodiments, the sensitivity to p97 inhibition is cellular growth inhibition. In some embodiments, the sensitivity to p97 inhibition is a combination of the exemplary sensitivities described in the present invention.
[0057] Based on the results of the genomic feature-based modeling, a sensitivity score can be assigned. For example, a sensitivity score can be calculated using an algorithm based on the values of genomic features of one or more signature genes (e.g., a scaled value between 0 and 100). In some embodiments, the sensitivity score can be expression levels (protein or mRNA) of certain signature genes, or an expression profile or expression pattern of certain signature genes. The sensitivity score can also be a parameter for measuring cellular growth inhibition.
[0058] One way for measuring cellular growth inhibition is ICn, i.e., inhibitory
concentration of a compound. Accordingly, in some embodiments, the sensitivity score is a predicted ICn, e.g., a predicted ICio, ICis, IC20, IC25, IC30, IC35, IC40, IC45, IC50, IC55, IC6o, IC65, IC70, IC75, IC80, IC85, IC90, IC95, or IC99. Preferably, the sensitivity score is a predicted IC50. In some embodiments, the sensitivity score is a combination score of ICn (e.g., IC50) and one or more other sensitivity scores (e.g., expression levels of certain genes).
[0059] In some embodiments, the predicted sensitivity score is compared to a reference sensitivity score. The reference sensitivity score can be a sensitivity score determined by any methods, for example, an empirical score or an arbitrarily chosen score. In some
embodiments, the reference sensitivity score is a reference ICn, for example, a reference IC50. In some embodiments, the reference sensitivity score is an ICn (e.g., ICso, IC9o, IC75) of 5- 5000 nM, 50-2000 nM, 100-1000 nM, 300-800 nM, 300-500 nM, 200-400 nM, 200-300 nM, 100-300 nM, 100-200 nM, 50-150 nM, or 50-200 nM. In some embodiments, the reference sensitivity score is an ICn (e.g., ICso, IC90, IC75) of 800 nM, 750 nM, 700 nM, 650 nM, 600 nM, 550 nM, 500 nM, 450 nM, 400 nM, 350 nM, 300 nM, 250 nM, 200 nM, 150 nM, 100 nM, or 50 nM. Substantial similarity between the predicted sensitivity score and the reference sensitivity score indicates that the cell or tissue or body fluid sample from the subject is sensitive to p97 inhibition and that a disease or condition will be modified as a result of treatment by a p97 inhibitor. As discussed below, the reference sensitivity score can be used threshold values for various detection, diagnostic, or therapeutic applications.
[0060] The reference sensitivity score can also be determined from a reference sample, e.g., a normal cell or a cancer cell. Any suitable sample can be used as a reference sample in the present invention. For example, the reference sample can be a sample from a healthy subject, from an asymptomatic individual, from an individual not having the disease or condition, or from an individual having a disease or condition. Preferable samples from an individual having a disease or condition include a baseline sample from the subject prior to treatment with a therapy comprising a p97 inhibitor, or a sample from a subject prior to the last dose of a therapy comprising a p97 inhibitor.
[0061] A p97 inhibitor can be a small molecule or a macromolecule. For example, a p97 inhibitor can be an antibody to p97, a dominant negative variant of p97, or a p97 inhibitor introduced by gene therapy (e.g., a siRNA or an antisense nucleic acid that suppress expression of p97, or a nucleic acid molecule encoding a p97 inhibitor). In some
embodiments, the p97 inhibitor is a small molecule compound. Examples of p97 inhibitors includes, but are not limited to, "Eeyarestatin-I" (Eer-I; 3-(4-Chlorophenyl)-4- [[[(4-chlorophenyl)amino]carbonyl]hydroxyamino]-5,5-dimethyl-2-oxo-l- imidazolidineacetic acid 2-[3-(5- nitro-2-furanyl)-2-propen-l-ylidene] hydrazide), "DBEQ" (N2, N4-Dibenzylquinazoline-2,4-diamine), "Syk-inhibitor III" (3,4- Methylenedioxy-p-nitrostyrene), "NMS-873" (3-(3-(cyclopentylthio)-5-(((2-methyl-4'- (methy lsulfony l)-[ 1 , 1 '-biphenyl]-4-y l)oxy)methyl)-4H- 1 ,2,4-triazol-4-yl)pyridine), and 2-Anilino-4-aryl-l ,3-thiazole compounds (Bursavich et al., Bioorg Med Chem Lett. 20: 1677-9. Epub 2010), compounds as described in US8865708, fused pyrimidines and substituted quinazoline compounds as described in US20140024661, compounds as described in Cervi et al , Journal of Medicinal Chemistry 57: 10443-10454, 2014, compounds as described in Chou et al, Proceedings of the National Academy of
Sciences 108:4834-4839, 201 1, compounds as described in Chou et al, ChemMedChem 8:297-312, 2013, compounds as described in Magnaghi et al, Nat Chem Biol 9:548-556, 2013, and compounds as described in Polucci et al, Journal of Medicinal Chemistry 56:437-450, 2013 (each of which is incorporated herein by reference for all purposes). Exemplary methods for identifying p97 inhibitors are described in Chou et al. , The Journal of Biological Chemistry, 286, 16546, 201 1 (incorporated herein by reference for all purposes).
[0062] In some embodiments, the small molecule p97 inhibitor is a fused pyrimidine compound of Formula I or a salt or hydrate thereof
Figure imgf000019_0001
Formula I
[0063] wherein: the A ring is fused to the pyrimidine ring and is a saturated or unsaturated five or six membered ring having zero, one, two or three heteroatoms in the ring, the remaining atoms of the ring being carbon, each heteroatom being independently selected from the group consisting of nitrogen, oxygen and sulfur; G is N, O or (CR'R2)!,; R1 and R2 are each independently hydrogen or alkyl of one to four carbons in length; n is zero or an integer from 1 to 4 and when G is not N or O and n is zero, G is a single covalent bond; R3 is selected from the group consisting of hydrogen, an aliphatic component and an aromatic component, each component being substituted by zero, one or two aliphatic or aromatic components; R4 and R5 are each independently bound to carbon or nitrogen and are each independently selected from the group consisting of hydrogen, an aliphatic component, a functional component, an aromatic component, and a combination thereof; R6 is a covalent bond joining nitrogen to Ar or is an alkyl group of 1 to 4 carbons or an alkenyl group of 2 to 4 carbons; Ar is an aromatic component; Het is a saturated or unsaturated 5 :5 or 5 :6 bicyclic ring having zero, one, two or three heteroatoms in the bicyclic ring, the remaining atoms being carbon, the bicyclic ring being substituted with zero, one, two or three substituents each independently selected from the group consisting of an aliphatic group, an aromatic group and any combination thereof; provided that when the A ring is benzo or substituted benzo, the Het ring is not unsubstituted indolinyl, unsubstituted benzoxazol-2-one, unsubstituted 2- aminobenzimidazole, 5,6-dimethyl-2-aminobenzamidazole, unsubstituted benzimidazole or an unsubstituted 2-aminoimidazole fused to a unsubstituted cyclopentane, cyclohexane or cycloheptane ring; and when the A ring is an unsubstituted cyclobutane, cyclopentane, cyclohexane or cycloheptane ring containing a ring oxygen, a ring aminomethyl, a ring aminoethyl or a ring aminophenyl moiety, the Het ring is not a 2-aminobenzamidazole with no substituent or with a methyl, fluoro, chloro, bromo or methoxyl substituent.
[0064] In some embodiments, the p97 inhibitor is a fused pyrimidine compound of Formula II or a salt or hydrate thereof
Figure imgf000020_0001
Formula II
[0065] wherein: A is CH2, NR1, O or S; m is an integer of 1-3; n is 0 or an integer of 1-2; the ring containing A is a five or six member ring; Y is selected from the group consisting of hydrogen, halogen, Rc, ORc, CN, CO2H, CON(Rc)2, C( RC)N(RC)2, CH2N(RC)2, S02N(Rc)2 and S02Rc wherein each Rc is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl,
heterocyclylalkyl, heteroaryl, heteroarylalkyl and any combination thereof; Z is selected from the group consisting of halogen, unsubstituted alkyl of 1 to 6 carbons, substituted alkyl or alkenyl of 1 to 4 carbons, and substituted alkoxy of 1 to 4 carbons; wherein the substituted alkyl or alkenyl group is substituted with ORa , SRa, OC(O) Ra, C(0)Ra, C(0)ORa, OC(0)N(Ra)2, C(0)N(Ra)2, N(Ra)C(0)ORa, N(Ra)C(0)Ra, N(Ra)C(0)N(Ra)2,
N(Ra)C( Ra)N(Ra)2, N(Ra)S(0)tRa, S(0)tORa, S(0)tN(Ra)2, N(Ra)2 or P03(Ra)2 wherein each Ra is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof; and, the substituted alkoxy group is substituted with ORb , Rb, OC(0)Rb, N(Rb)2, C(0)Rb, C(0)ORb , OC(0)N(Rb)2, C(0)N(Rb)2, N(Rb)C(0)ORb, N(Rb)C(0)Rb,
N(Rb)C(0)N(Rb)2, N(Rb)C( Rb)N(Rb)2, N(Rb)S(0)tRb, S(0)tORb, S(0)tN(Rb)2, N(Rb)2 or P03(Rb)2 wherein each Rb is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof. R1 is selected from a group consisting of hydrogen, unsubstituted alkyl of 1 to 6 carbons, substituted alkyl of 1 to 4 carbons and -C(0)Rd; wherein, the substituted alkyl is substituted with ORd, SRd, OC(0) Rd, C(0)Rd, C(0)ORd ,- OC(0)N(Rd)2, C(0)N(Rd)2, N(Rd)C(0)ORd, N(Rd)C(0)Rd, N(Rd)C(0)N(Rd)2,
N(Rd)C( Rd)N(Rd)2, N(Rd)S(0)tR, S(0)tORd, S(0)tN(Rd)2, N(Rd)2 or P03(Rd)2; and wherein each Rd is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl alkenyl, alkynyl or any combination thereof. Each t is independently selected from an integer of 1 or 2. Ar is a phenyl, thiophenyl, pyridinyl, pyrrolyl, furanyl, or a substituted version thereof wherein the substituent is optional, independent and optionally multiple and is an aliphatic, functional or aromatic component.
[0066] In some embodiments, the small molecule p97 inhibitor is a fused pyrimidine compound of Formulas Ilia or Illb or a salt or hydrate thereof
Figure imgf000021_0001
Formula IIIA Formula 11 IB
[0067] wherein: A is CH2, NR1, O or S; m is an integer of 1-3; n is 0 or an integer of 1-2; the sum of m+n is no more than 4 and no less than 1; Y is selected from the group consisting of H, CN, CO2H, CON(Rc)2, C(NRC)N(RC)2, CH2N(RC)2, S02N(Rc)2 and S02Rc wherein each Rc is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl and any combination thereof; Z is selected from the group consisting of unsubstituted alkyl of 1 to 6 carbons, substituted alkyl or alkenyl of 1 to 4 carbons and substituted alkoxy of 1 to 4 carbons; wherein, the substituted alkyl or alkenyl group is substituted with ORa , SR a, OC(O) Ra, C(0)Ra, C(0)ORa, OC(0)N(Ra)2, C(0)N(Ra)2, N(Ra)C(0)ORa, N(Ra)C(0)Ra, N(Ra)C(0)N(Ra)2, N(Ra)C( Ra)N(Ra)2, N(Ra)S(0)tRa, S(0)tORa, S(0)tN(Ra)2, N(Ra)2 or P03(Ra)2 wherein each Ra is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl,
heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof; and, The substituted alkoxy group is substituted with ORb , SRb, OC(O) Rb, N(Rb)2, C(0)Rb, C(0)ORb , OC(0)N(Rb)2, C(0)N(Rb)2, N(Rb)C(0)ORb, N(Rb)C(0)Rb, N(Rb)C(0)N(Rb)2, N(Rb)C( Rb)N(Rb)2, N(Rb)S(0)tRb, S(0)tORb, S(0)tN(Rb)2, N(Rb)2 or P03(Rb)2 wherein each Rb is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof; R1 is selected from a group consisting of hydrogen, unsubstituted alkyl of 1 to 6 carbons, substituted alkyl of 1 to 4 carbons and -C(0)Rd; wherein, The substituted alkyl is substituted with ORd, SRd, OC(0)-Rd, C(0)Rd, C(0)ORd , OC(0)N(Rd)2, C(0)N(Rd)2, N(Rd)C(0)ORd, N(Rd)C(0)Rd, N(Rd)C(0)N(Rd)2, N(Rd)C( Rd)N(Rd)2, N(Rd)S(0)tR, S(0)tORd, S(0)tN(Rd)2, N(Rd)2 or P03(Rd)2; wherein each Rd is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl alkenyl, alkynyl or any combination thereof; each t is independently selected from a group of integers consisting of 1 or 2; B is CH2, CH, C=0, N or O; D and E are each independently selected from C or N; the dash line signifies a single or double bond according to the valence bond requirements of the molecular identities of the bonded atoms; and, Ar is an unsubstituted or substituted aromatic component.
[0068] In some embodiments, Z is selected from the group consisting of methyl, ethyl, propyl, cyclopropyl , methoxy, ethoxy, propoxy, methoxy methyl, methoxyethyl,
methoxymethoxy, methoxyethoxy, N-alkylenylacetamide, N-alkylenylurea, N- alkylenylcarbamate, methyl N-alkylenylcarbamate, N-alkylenylsulfonamide, N- alkylenylpropynamide, N-alkylenylacrylamide, morpholinyl, piperidinyl, piperazinyl, pyrrolidonyl, pyrrolidinyl, N-alkylenylmorpholine, trifluoromethyl, pentafluoroethyl, wherein the alkylenyl group is -(CH2)n- of one to six carbons.
[0069] In some embodiments, wherein Y is selected from the group consisting of hydrogen, cyano, methyl, ethyl, propyl, butyl, amino, methylamino, dimethylamino, aminoalkylenyl, methylaminoalkylenyl, dimethylaminoalkylenyl, hydroxyalkylenyl, methoxy, ethoxy, propoxy, methoxy methyl, methoxyethyl, methoxyethoxy, N-alkylenylacetamide, N- alkylenylurea, N-alkylenylcarbamate, methyl N-alkylenylcarbamate, N- alkylenylsulfonamide, N-alkylenylpropynamide, N-alkylenylacrylamide, morpholinyl, piperidinyl, piperazinyl, pyrrolidonyl, pyrrolidinyl, N-alkylenylmorpholine, trifluoromethyl, pentafluoroethyl, cyanoalkylenyl, fluoro, chloro, bromo, carboxylic acid, sulfonic acid, carboxamide, sulfonamide, N-alkyl carboxamide, Ν,Ν-dialkylcarboxamide, N- alkylsulfonamide, Ν,Ν-dialkylsulfonamide, wherein the alkylenyl group is -(CH2)n- of one to six carbons and the alkyl group is 1 to 4 carbons. [0070] In some embodiments, the aromatic component (Ar) is substituted by a functional component selected from the group consisting of hydroxy, halo, cyano, trifluoromethyl, trifluoromethoxy, nitro, trimethylsilanyl, ORa, SRa, OC(O) Ra, N(Ra)2, C(0)Ra, C(0)ORa ,-OC(0)N(Ra)2, C(0)N(Ra)2, , N(Ra)C(0)ORa, N(Ra)C(0)Ra, N(Ra)C(0)N(Ra)2,
N(Ra)C( Ra)N(Ra)2, N(Ra)S(0)tRa, S(0)tORa, S(0)tN(Ra)2, -RaN(Ra)2, P03(Ra)2 and any combination thereof; wherein each Ra is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof; and wherein each t independently is an integer of 1 or 2. In some embodiments, Ar is an unsubstituted phenyl.
[0071] In some embodiments, the indole group at the 2 position of the fused pyrimidine is a 2-alkylindolyl, a 2-cyanoindolyl, a 2-haloindolyl, a 2-(hydroxyalkyl)indolyl, a 2- (alkoxy)indolyl, a 2-(aminoalkyl)indolyl, a 2-(alkylaminoalkyl)indolyl, a 2- (dialkylaminoalkyl)indolyl,a 2-(acylamidoalkyl)indolyl, a 2- (alkoxycarbonylaminoalkyl)indolyl, a 2-(sulfonamidoalkyl)indolyl, a 2-(β- cyanoalkenyl)indolyl, a 2-(P-cyano-P-carboxyamidoalkenyl)indolyl, a 2-(β- alkylsulfonylalkenyl)indolyl, a 2-(heterocycylalkyl)indolyl, a 2- (aminocarbonylaminoalkyl)indolyl, a 2-(alkynylcarboxamidoalkyl)indolyl, or a 2- (heterocy clyl)indolyl .
[0072] In some embodiments, A is CH2. In some embodiments, A is R1. In some embodiments, A is O.
[0073] In some embodiments, the small molecule p97 inhibitor is a fused pyrimidine compound having any of the following RJPAC names, or a salt or hydrate thereof: N-benzyl- 2-(2-methyl-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; N-benzyl-2-(2-ethyl-lH- indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; 2-[2-(aminomethyl)-lH-indol-l-yl]-N- benzyl-5,6,7,8-tetrahydroquinazolin-4-amine; 2-[2-(l-aminoethyl)-lH-indol-l-yl]-N-benzyl- 5,6,7,8-tetrahydroquinazolin-4-amine; 2-[5-(aminomethyl)-4H-pyrrolo[2,3-d][l,3]thiazol-4- yl]-N-benzyl-5,6,7,8-tetrahydroquinazolin-4-amine; N-benzyl-2-(2-methoxy-lH-indol-l-yl)- 5,6,7,8-tetrahydroquinazolin-4-amine; { l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2- yl]-lH-indol-2-yl}methanol; N-benzyl-2-[2-(methoxymethyl)-lH-indol-l-yl]-5,6,7,8- tetrahydroquinazolin-4-amine; N-benzyl-2-{2-[(methylamino)methyl]-lH-indol-l-yl}- 5,6,7,8-tetrahydroquinazolin-4-amine; N-benzyl-2-{2-[(dimethylamino)methyl]-lH-indol-l- yl}-5,6,7,8-tetrahydroquinazolin-4-amine; N-({ l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-lH-indol-2-yl}methyl)acetamide; ({ l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-lH-indol-2-yl}methyl)urea; methyl N-({ l-[4-(benzylamino)- 5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2-yl}methyl)carbamate; N-({ l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2- yl}methyl)methanesulfonamide; 4-N-benzyl-2-N-[l-(lH-indol-2-yl)ethyl]-5,6,7,8- tetrahydroquinazoline-2,4-diamine; N-benzyl-2-[2-(morpholin-4-ylmethyl)-lH-indol-l-yl]- 5,6,7,8-tetrahydroquinazolin-4-amine; N-benzyl-2-(2 -methyl- lH-indol-3-yl)-5, 6,7,8- tetrahydroquinazolin-4-amine; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2- methyl-lH-indole-4-carbonitrile; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2- methoxy-lH-indole-4-carbonitrile; N-benzyl-2-(2-ethoxy-lH-indol-l-yl)-5, 6,7,8- tetrahydroquinazolin-4-amine; N-benzyl-2-[2-(trifluoromethyl)-lH-indol-l-yl]-5, 6,7,8- tetrahydroquinazolin-4-amine; N-benzyl-2-(2-chloro-lH-indol- l-yl)-5, 6,7,8- tetrahydroquinazolin-4-amine; N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]- lH-indol-2-yl}methyl)prop-2-ynamide; N-({ l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-lH-indol-2-yl}methyl)prop-2-enamide; l-[4-(benzylamino)- 5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-carboxamide; l-[4-(benzylamino)- 5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-indole-4-carboxamide; 2-(aminomethyl)-l- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indole-4-carboxamide; l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-carboxylic acid; 1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-sulfonamide; N- benzyl-2-(4-methanesulfonyl-2-methyl-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-ethyl-lH-indole-4-carboxamide; N- benzyl-2-[2-methyl-4-(lH-l,2,3,4-tetrazol-5-yl)-lH-indol-l-yl]-5,6,7,8-tetrahydroquinazolin- 4-amine; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-(2-methoxyethoxy)-lH- indole-4-carboxamide; 2-[4-(aminomethyl)-2-methyl-lH-indol-l-yl]-N-benzyl-5, 6,7,8- tetrahydroquinazolin-4-amine; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2- (propan-2-yl)-lH-indole-4-carboxamide; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2- yl]-2-cyclopropyl-lH-indole-4-carboxamide; l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-N,2-dimethyl-lH-indole-4-carboxamide; l-[4-(benzylamino)- 5,6,7,8-tetrahydroquinazolin-2-yl]-N,N,2-trimethyl-lH-indole-4-carboxamide; l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-N-ethyl-2-methyl-lH-indole-4- carboxamide; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-N-(2-methoxyethyl)-2- methyl-lH-indole-4-carboxamide; N-(2-aminoethyl)-l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-carboxamide; l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-2-ethoxy-lH-indole-4-carboxamide; l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-2-(2-methoxyethoxy)-lH-indole-4-carbonitrile; 2-[2-(l- aminoethyl)-lH-indol-l-yl]-N-benzyl-5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine; N-benzyl- 2-(2-methoxy-lH-indol-l-yl)-5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine; N-benzyl-2-(2- methyl-lH-indol-l-yl)-5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine; 2-[2-(aminomethyl)-lH- indol-l-yl]-N-benzyl-5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine; l-[4-(benzylamino)- 5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methoxy-lH-indole-4-carbonitrile; l-[4- (benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carbonitrile; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methoxy-lH-indole-4- carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxamide; 2-(aminomethyl)-l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3- d]pyrimidin-2-yl]-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3- d]pyrimidin-2-yl]-2-[(dimethylamino)methyl]-lH-indole-4-carboxamide; l-[4- (benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-(hydroxymethyl)-lH-indole-4- carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N,2-dimethyl- lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]- N,N,2-trimethyl-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3- d]pyrimidin-2-yl]-2-methyl-N-(propan-2-yl)-lH-indole-4-carboxamide; l-[4-(benzylamino)- 5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N-(butan-2-yl)-2-methyl-lH-indole-4-carboxamide;
1- [4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N-[2-(dimethylamino)ethyl]-2- methyl-lH-indole-4-carboxamide; N-benzyl-2-{2-methyl-4-[(morpholin-4-yl)carbonyl]-lH- indol-l-yl}-5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine; N-benzyl-2-{2-methyl-4- [(piperazin-l-yl)carbonyl]-lH-indol-l-yl}-5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine; N-(2- aminoethyl)-l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2- methyl-lH-indole-4-carboxylic acid; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-
2- yl]-2-methyl-lH-indole-4-sulfonamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3- d]pyrimidin-2-yl]-2-ethyl-lH-indole-4-carboxamide; N-benzyl-2-(4-methanesulfonyl-2- methyl-lH-indol-l-yl)-5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine; l-[4-(benzylamino)- 5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carboximidamide; N-benzyl- 2-[2-methyl-4-(lH-l,2,3,4-tetrazol-5-yl)-lH-indol-l-yl]-5H,7H,8H-pyrano[4,3-d]pyrimidin- 4-amine; l-(4-{[(4-fluorophenyl)methyl]amino}-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl)-2- methyl-lH-indole-4-carboxamide; l-(4-{[(2-fluorophenyl)methyl]amino}-5H,7H,8H- pyrano[4,3-d]pyrimidin-2-yl)-2-methyl-lH-indole-4-carboxamide; 2-[4-(aminomethyl)-2- methyl-lH-indol-l-yl]-N-benzyl-5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine; l-[4- (benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-[(carbamoylamino)methyl]-lH- indole-4-carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2- (propan-2-yl)-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3- d]pyrimidin-2-yl]-2-cyclopropyl-lH-indole-4-carboxamide; l-(4-{[(3- fluorophenyl)methyl]amino}-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl)-2-methyl-lH-indole- 4-carboxamide; 2-[2-(l-aminoethyl)-lH-indol-l-yl]-N-benzyl-5H,6H,8H-pyrano[3,4- d]pyrimidin-4-amine; N-benzyl-2-(2 -methyl- lH-indol-1 -yl)-5H,7H-furo[3,4-d]pyrimidin-4- amine; N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine;
1- [4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-6- yl]ethan-l-one; 2-[2-(l-aminoethyl)-lH-indol-l-yl]-N-benzyl-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine; N-benzyl-2-(2-methoxy-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine; 2-[2-(aminomethyl)-lH-indol-l-yl]-N-benzyl-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-4-amine; 2-[2-(aminomethyl)-lH-indol-l-yl]-N-[(4- fluorophenyl)methyl]-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; N-benzyl-2-(2-ethoxy- lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; N-benzyl-2-[2-(morpholin-4- ylmethyl)- lH-indol- 1 -yl]-5H,6H,7H, 8H-pyrido[4,3 -d]pyrimidin-4-amine; N-benzyl-2-(2- methoxy-lH-indol-l-yl)-6-methyl-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; { l-[4- (benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indol-2-yl}methanol; 1-{ 1- [4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indol-2-yl}ethan-l-ol; N- benzyl-2-[2-(methoxymethyl)-lH-indol-l-yl]-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4- amine; N-benzyl-2-{2-[(dimethylamino)methyl]-lH-indol-l-yl}-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine; N-({ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]- lH-indol-2-yl}methyl)acetamide; ({ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-lH-indol-2-yl}methyl)urea; methyl N-({ l-[4-(benzylamino)- 5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indol-2-yl}methyl)carbamate; N-({ l-[4- (benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indol-2- yl}methyl)methanesulfonamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-
2- yl]-2,3-dihydro-lH-indol-2-one; { l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-lH-indol-2-yl}methyl carbamate; N-benzyl-2-(2,4-dimethyl-lH-indol-l- yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; N-benzyl-2-(4-fluoro-2-methyl-lH-indol- l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; l-[4-(benzylamino)-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carbonitrile; l-[4-(benzylamino)- 5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-6-carbonitrile; N-benzyl-2- (4-methoxy-2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; N- benzyl-2-[2-(trifluoromethyl)-lH-indol-l-yl]-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; N-benzyl-6-methyl-2 2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4- amine; N-benzyl-2-[2-(propan-2-yl)-lH-indol-l-yl]-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4- amine; N-benzyl-2-(2-ethyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine;
1- [4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indole-2-carboxamide; N-benzyl-2-(4-chloro-2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4- amine; N-benzyl-6-ethyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin- 4-amine; N-benzyl-2-(2-methyl-lH-indol-l-yl)-6-(propan-2-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine; N-benzyl-2-(2-methyl-lH-indol-l-yl)-6-propyl-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-4-amine; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-
2- yl]-2-methyl-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-2-methoxy-lH-indole-4-carbonitrile; 4-(benzylamino)-2-(2 -methyl- 1H- indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-6-ol; l-[4-(benzylamino)-6-methyl- 5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carbonitrile; N-benzyl-2- [2-methyl-4-(trifluoromethyl)-lH-indol-l-yl]-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; N-benzyl-2-(2-chloro-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; l-[4- (benzylamino)-6-ethyl-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4- carbonitrile; l-[4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-6-yl]prop-2-yn- 1 -one; 1 -[4-(benzylamino)-2-(2-methyl- lH-indol- 1 -yl)- 5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-6-yl]prop-2-en-l-one; 4-(benzylamino)-2-(2-methyl- lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidine-6-carbaldehyde; N-{ l-[4- (benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indol-4- yl}acetamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-N,2-dimethyl- lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]- N,N,2-trimethyl-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-2-methyl-N-(propan-2-yl)-lH-indole-4-carboxamide; l-[4-(benzylamino)- 5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-N-(butan-2-yl)-2-methyl-lH-indole-4- carboxamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indole-4- carboxamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-2,3- dihydro-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-2-methyl-2,3-dihydro-lH-indole-4-carbonitrile; l-[6-(2-aminoacetyl)-4- (benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4- carbonitrile; l-[4-(benzylamino)-6-(2-methoxyacetyl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin- 2-yl]-2-methyl-lH-indole-4-carbonitrile; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-2-methoxy-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-sulfonamide; l-[4-(benzylamino)- 5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carboxylic acid; N- benzyl-2-(4-methanesulfonyl-2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-(2- methoxyethoxy)-lH-indole-4-carboxamide; N-benzyl-2-(2-methyl-lH-indol-l-yl)- 5H,6H,7H,8H-pyrido[3,4-d]pyrimidin-4-amine; tert-butyl 4-(benzylamino)-2-(2-methyl-lH- indol-l-yl)-5H,6H,7H,8H-pyrido[3,4-d]pyrimidine-7-carboxylate; l-[4-(benzylamino)-2-(2- methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4-d]pyrimidin-7-yl]ethan-l-one; tert-butyl 4- (benzylamino)-2-(2-methoxy-lH-indol-l-yl)-8-oxo-5H,6H,7H,8H-pyrido[3,4-d]pyrimidine- 7-carboxylate; 2-[2-(aminomethyl)-lH-indol-l-yl]-N-benzyl-5H,6H,7H,8H-pyrido[3,4- d]pyrimidin-4-amine; N-benzyl-2-(2-methoxy-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4- d]pyrimidin-4-amine; l-{2-[2-(aminomethyl)-lH-indol-l-yl]-4-(benzylamino)- 5H,6H,7H, 8H-pyrido[3 ,4-d]pyrimidin-7-yl } ethan- 1 -one; 2-[2-(aminomethyl)- IH-indol- 1-yl]- N-benzyl-7-ethyl-5H,6H,7H,8H-pyrido[3,4-d]pyrimidin-4-amine; methyl 2-[2- (aminomethyl)-lH-indol-l-yl]-4-(benzylamino)-5H,6H,7H,8H-pyrido[3,4-d]pyrimidine-7- carboxylate; N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4-d]pyrimidin-4- amine; N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H-pyrrolo[3,4-d]pyrimidin-4-amine; 1- [4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H-pyrrolo[3,4-d]pyrimidin-6-yl]ethan- 1-one; l-[4-(benzylamino)-5H,6H,7H-pyrrolo[3,4-d]pyrimidin-2-yl]-2-methyl-lH-indole-4- carboxamide; l-[4-(benzylamino)-6-methyl-5H,6H,7H-pyrrolo[3,4-d]pyrimidin-2-yl]-2- methyl-lH-indole-4-carboxamide; l-[6-acetyl-4-(benzylamino)-5H,6H,7H-pyrrolo[3,4- d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carboxamide.
[0074] In some embodiments, the small molecule p97 inhibitor is a fused pyrimidine compound having any of the following IUPAC names, or a salt or hydrate thereof: l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-l,3-benzodiazole-4- carbonitrile; N-benzyl-2-(2-methoxy-lH-l,3-benzodiazol-l-yl)-5,6,7,8-tetrahydroquinazolin- 4-amine; 2-[2-(aminomethyl)- 1H- 1 ,3 -benzodiazol- 1 -yl]-N-benzyl-5,6,7, 8- tetrahydroquinazolin-4-amine; 2-[2-( 1 -aminoethyl)- 1H- 1 ,3 -benzodiazol- 1 -yl]-N-benzyl- 5,6,7,8-tetrahydroquinazolin-4-amine; N-benzyl-2-(2 -methyl- lH-1, 3 -benzodiazol- 1 -yl)- 5,6,7,8-tetrahydroquinazolin-4-amine; { l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2- yl]-lH-l,3-benzodiazol-2-yl}methanol; N-benzyl-2-{2-[(dimethylamino)methyl]-lH-l,3- benzodiazol-l-yl}-5,6,7,8-tetrahydroquinazolin-4-amine; N-benzyl-2-[2-(morpholin-4- ylmethyl)-lH-l,3-benzodiazol-l-yl]-5,6,7,8-tetrahydroquinazolin-4-amine; N-({ l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-l,3-benzodiazol-2- yl}methyl)acetamide; ({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-l,3- benzodiazol-2-yl }methyl)urea; N-benzyl-2-[2-(morpholin-4-yl)- 1H- 1 ,3 -benzodiazol- 1 -yl]- 5,6,7,8-tetrahydroquinazolin-4-amine; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2- yl]-2-methyl-lH-l,3-benzodiazole-4-carbonitrile; l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-2-methyl-lH-l,3-benzodiazole-4-carboxamide; 2-(aminomethyl)-
1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-l,3-benzodiazole-4-carbonitrile;
2- (aminomethyl)-l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-l,3- benzodiazole-4-carboxamide; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2- methoxy-lH-l,3-benzodiazole-4-carboxamide; l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-2-methoxy-lH-l,3-benzodiazole-4-carboxylic acid; N-benzyl-2- (2-ethoxy-lH-l,3-benzodiazol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; N- benzyl-2-(2-methoxy-lH-l,3-benzodiazol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4- amine; N-benzyl-2-(2-methyl-lH-l,3-benzodiazol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine; { l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH- l,3-benzodiazol-2-yl}methanol; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin- 2-yl]-lH-l,3-benzodiazol-2-yl carbamate; { l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-lH-l,3-benzodiazol-2-yl}urea; N-benzyl-2-[2-(trifluoromethyl)-lH-l,3- benzodiazol-l-yl]-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine; l-[4-(benzylamino)- 5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-l,3-benzodiazole-4-carbonitrile 1- [4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH-l,3- b enzodi azol e-4-carb oxami de .
[0075] In some embodiments, the small molecule p97 inhibitor is a fused pyrimidine compound having any of the following IUPAC names, or a salt or hydrate thereof: l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-carbonitrile; l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-carboxamide; l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-indole-4-carboxamide; 1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-ethoxy-lH-indole-4-carboxamide; 1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-(2-methoxyethoxy)-lH-indole-4- carbonitrile; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-cyclopropyl-lH- indole-4-carboxamide; N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol- 2-yl}methyl)prop-2-ynamide; N-benzyl-2-[2-methyl-4-(lH-l,2,3,4-tetrazol-5-yl)-lH-indol-l- yl]-5,6,7,8-tetrahydroquinazolin-4-amine; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3- d]pyrimidin-2-yl]-2-methoxy-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,7H,8H- pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carboxamide; l-(4-{[(3- fluorophenyl)methyl]amino}-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl)-2-methyl-lH-indole- 4-carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxylic acid; N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2- methyl-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carboxylic acid; 2-[4-(aminomethyl)-2-methyl-lH- indol-l-yl]-N-benzyl-5,6,7,8-tetrahydroquinazolin-4-amine; l-[4-(benzylamino)-2-(2-methyl- lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4-d]pyrimidin-7-yl]prop-2-yn-l-one; l-[4- (benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4-sulfonamide; N- benzyl-2-(4-methanesulfonyl-2-methyl-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; l-[4-(benzylamino)-5,6,7,84etrahydroquinazolin-2-yl]-N-methyl-2-methyl-lH-indole-4- carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N,2-dimethyl- lH-indole-4-carboxamide; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2- methoxy-lH-l,3-benzodiazole-4-carboxylic acid; l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-2-methoxy-lH-l,3-benzodiazole-4-carboxamide; N-benzyl-2-(2- methoxy-lH-l,3-benzodiazol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine.
[0076] In some embodiments, the p97 inhibitor is a fused pyrimidine compound having the following IUPAC name, or a salt or hydrate thereof: l-[4-(benzylamino)-5, 6,7,8- tetrahydroquinazolin-2-yl]-2-[(carbamoylamino)methyl]-lH-indole-4-carboxamide. In some embodiments, the p97 inhibitor is a fused pyrimidine compound having the following IUPAC name, or a salt or hydrate thereof: l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2- methyl-lH-indole-4-carboxamide. In some embodiments, the p97 inhibitor is a fused pyrimidine compound having the following IUPAC name, or a salt or hydrate thereof: l-[4- (benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carboxamide.
[0077] In some embodiments, the small molecule p97 inhibitor is a p97 allosteric inhibitor. Examples of p97 allosteric inhibitors include MS-873 (Magnaghi et al., Nat Chem Biol., 9:548-556, 2013) and allosteric indole amide inhibitors (Alverez et al., ACS Med. Chem. Lett., 7: 182-187, 2016). [0078] Prognostic, Diagnostic and Therapeutic Applications
[0079] As discussed above, the present invention provides methods of assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in the cell or tissue or body fluid sample. Such sensitivity scores find use in a variety of prognostic, diagnostic and therapeutic applications, e.g., for evaluating the likelihood that a p97 therapy will be effective in treating a disease or condition. Exemplary, non-limiting examples of such applications are described herein.
[0080] In one aspect, the sensitivity score assigned to a cell or tissue or body fluid sample from the subject can be used for selecting a subject for treatment of a disease or condition suitable for a therapy comprising a p97 inhibitor. In some embodiments, the method include a step of assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from the subject, and a step of selecting the subject for treatment with a therapy comprising a p97 inhibitor based on the assigned sensitivity score. For example, the subject can be selected for treatment based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern. The subject can also be selected for treatment when the sensitivity score is at, within, above or below certain threshold values or threshold ranges. In some embodiments, the threshold value or threshold range is a reference sensitivity score.
[0081] The method can be practiced with or without a reference sensitivity score. In some embodiments, the subject can be selected for treatment without a reference sensitivity score. For example, when the assigned sensitivity score is the presence or absence of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns, the subject can be selected without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence of certain characteristics. In some embodiments, the subject can be selected for treatment based on the comparison between the assigned sensitivity score and the reference sensitivity score. For example, the selecting comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of signature genes, comparing the assigned sensitivity score to a reference sensitivity score, and selecting the subject for treatment with a therapy comprising a p97 inhibitor based on the assigned sensitivity. The reference sensitivity score can be a threshold value or threshold range, or any sensitivity score obtained from a reference sample.
[0082] In another aspect of the present invention, the sensitivity score can be used for diagnosis or prognosis of a disease or condition suitable for treatment with a therapy comprising of a p97 inhibitor in a subject. In some embodiments, the method includes assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from the subject. The prognosis or diagnosis of the subject with the disease or condition can be based on the assigned sensitivity score. For example, the prognosis or diagnosis of the subject with the disease or condition can be based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern. The prognosis or diagnosis of the subject with the disease or condition can also be based on a sensitivity score that is at, within, above or below certain threshold values or threshold ranges. In some embodiments, the threshold value or threshold range is a reference sensitivity score.
[0083] The method can be practiced with or without a reference sensitivity score. In some embodiments, the prognosis or diagnosis of the subject with the disease or condition can be done without a reference sensitivity score. For example, when the assigned sensitivity score is the presence or absence or magnitude of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns, the prognosis or diagnosis of the subject can be done without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence or magnitude of certain
characteristics. In some embodiments, the prognosis or diagnosis of the subject can be based on the comparison between the assigned sensitivity score and the reference sensitivity score. For example, the diagnosis or prognosis comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of signature genes, comparing the assigned sensitivity score to a reference sensitivity score, wherein the prognosis or diagnosis of the subject is based on the comparison between the assigned sensitivity score and the reference sensitivity score.
[0084] In another aspect of the present invention, the sensitivity score can be used for predicting a response to a p97 inhibitor in a subject. In some embodiments, the method includes assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from the subject. The subject is predicted to respond to or not respond to a p97 inhibitor therapy based on the assigned sensitivity score For example, the subject can be predicted to respond to or not respond to a p97 inhibitor therapy based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern. The subject can also be predicted to respond to or not respond to a p97 inhibitor therapy based on a sensitivity score that is at, within, above or below certain threshold values or threshold ranges. In some embodiments, the threshold value or threshold range is a reference sensitivity score.
[0085] The method can be practiced with or without a reference sensitivity score. In some embodiments, the subject is predicted to respond to or not respond to a p97 inhibitor therapy without a reference sensitivity score. For example, when the assigned sensitivity score is the presence or absence or magnitude of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns, the subject can be predicted to respond to or not respond to a p97 inhibitor therapy without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence or magnitude of certain characteristics. In some embodiments, the subject can be predicted to respond to or not respond to a p97 inhibitor therapy based on the comparison between the assigned sensitivity score and the reference sensitivity score. For example, the prediction comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of signature genes, comparing the assigned sensitivity score to a reference sensitivity score, wherein the subject is predicted to respond to or not respond to a p97 inhibitor therapy based on the comparison between the assigned sensitivity score and the reference sensitivity score.
[0086] In another aspect of the present invention, the sensitivity score can be used for predicting efficacy of treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition. In some embodiments, the method includes assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from a subject who is or has been treated with the therapy comprising the p97 inhibitor. The treatment can be predicted as effective or likely to be effective based on the assigned sensitivity score. For example, the treatment can be predicted as effective or likely to be effective based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern. The treatment can also be predicted as effective or likely to be effective when the sensitivity score is at, within, above or below certain threshold values or threshold ranges. In some embodiments, the threshold value or threshold range is a reference sensitivity score.
[0087] The method can be practiced with or without a reference sensitivity score. In some embodiments, the treatment can be predicted as effective or likely to be effective without a reference sensitivity score. For example, when the assigned sensitivity score is the presence or absence of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns, the treatment can be predicted as effective or likely to be effective by without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence of certain characteristics. In some
embodiments, the treatment can be predicted as effective or likely to be effective based on the comparison between the assigned sensitivity score and the reference sensitivity score. For example, the prediction comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample from a subject who is or has been treated with the therapy comprising the p97 inhibitor, comparing the assigned sensitivity score to a reference sensitivity score, and predicting whether or not the treatment is effective or likely to be effective based on the comparison between the assigned sensitivity score and the reference sensitivity score. The reference sensitivity score can be a threshold value or threshold range, or any sensitivity score obtained from a reference sample.
[0088] In another aspect of the present invention, the sensitivity score can be used for monitoring treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition. In some embodiments, the method includes assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject. The assigned sensitivity score can be used as an indicator of the progress of treatment. For example, the progress of treatment can be assessed based on an expression pattern (e.g., an expression profile) of certain signature genes, wherein the cell or tissue or body fluid sample can be classified as having certain characteristics or phenotypes based on the expression pattern. The progress of treatment can also be assessed based on whether the sensitivity score is at, within, above or below certain threshold values or threshold ranges. In some embodiments, the threshold value or threshold range is a reference sensitivity score. [0089] The method can be practiced with or without a reference sensitivity score. In some embodiments, the progress of treatment can be monitored without a reference sensitivity score. For example, when the assigned sensitivity score is the presence or absence of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns, the progress of treatment can be monitored without a reference sensitivity score based on the assigned sensitivity score, i.e., the presence or absence of certain characteristics. In some embodiments, the progress of treatment can be monitored based on the comparison between the assigned sensitivity score and the reference sensitivity score. For example, the monitoring comprises a step of assigning a sensitivity score to p97 inhibition based on genomic features of signature genes, comparing the assigned sensitivity score to a reference sensitivity score, and assessing the progress of the treatment based on the comparison between the assigned sensitivity score and the reference sensitivity score. The reference sensitivity score can be a threshold value or threshold range, or any sensitivity score obtained from a reference sample.
[0090] In another aspect of the present invention, the assigned sensitivity score can be used as basis for altering treatment. For example, when treatment with a therapy comprising a p97 inhibitor is predicted as effective or is likely to be effective or ineffective or is likely to be ineffective, the treatment regimen can be altered accordingly based on the assigned sensitivity score. Similarly, when the progress of treatment is assessed and the disease or condition is predicted as having been ameliorated or having worsened over the course of the treatment, the treatment regimen can be altered accordingly based on the assigned sensitivity score. For example, if the therapy is predicted as effective or is likely to be effective, or the disease or condition is predicted as having been ameliorated over the course of the treatment, the treatment can be continued or escalated by increasing the dosage and/or dose schedule of the p97 inhibitor. If the therapy is predicted as ineffective or is likely to be ineffective, or the disease or condition is predicted as having worsened over the course of the treatment, the treatment can be either continued, or reduced by decreasing the dosage and/or dose schedule or terminating treatment of the p97 inhibitor.
[0091] The method can be practiced with or without a reference sensitivity score. In some embodiments, the treatment can be altered directly based on the assigned sensitivity score without comparing it to a reference sensitivity score. For example, when the assigned sensitivity score is the presence or absence of certain genomic features, the presence or absence of certain phenotypes, the presence or absence of certain profiles or patterns, the treatment can be altered based on the presence or absence of certain characteristics, i.e., without the aid of a reference sensitivity score. In some embodiments, the treatment can be altered based on the comparison between the assigned sensitivity score and a reference sensitivity score. For example, the reference sensitivity score can be a threshold value or threshold range, or any sensitivity score obtained from a reference sample.
[0092] Depending on the choices of the sensitivity score, a higher sensitivity score can indicate higher sensitivity to p97 inhibition (e.g., when the sensitivity score is cell death rate, or a scaled value between 0 and 100 wherein a higher scaled value indicates higher sensitivity), or can indicate lower sensitivity to p97 inhibition (e.g., when the sensitivity score is IC50, or a scaled value between 0 and 100 wherein a higher scaled value indicates lower sensitivity). When a higher sensitivity score indicates higher sensitivity to p97 inhibition, the treatment can be continued or escalated by increasing the dosage and/or dose schedule of the p97 inhibitor if the assigned sensitivity score is at or above the reference sensitivity score, alternatively, the treatment can be either continued, or reduced by decreasing the dosage and/or dose schedule or terminating treatment of the p97 inhibitor if the assigned sensitivity score is at or below the reference sensitivity score. When a higher sensitivity score indicates lower sensitivity to p97 inhibition, the treatment can be continued or escalated by increasing the dosage and/or dose schedule of the p97 inhibitor if the assigned sensitivity score is at or below the reference sensitivity score, alternatively, the treatment can be either continued, or reduced by decreasing the dosage and/or dose schedule or terminating treatment of the p97 inhibitor if the assigned sensitivity score is at or above the reference sensitivity score.
[0093] The methods, compositions, devices, databases and kits of the present invention can be applied to any disease or condition that is or becoming sensitive to p97 inhibition.
Exemplary diseases and conditions include various cancers, e.g., solid tumor malignancy and hematological malignancy. Exemplary solid tumors include but are not limited to lung cancer, colon cancer, CNS cancer, melanoma, ovarian cancer, renal cancer, prostate cancer, head and neck cancer, testicular cancer, germ-line cancers, endocrine tumors, uterine cancer, breast cancer, sarcomas, gastric cancer, hepatic cancer, esophageal cancer and pancreatic cancer. Exemplary hematological malignancies include but are not limited to multiple myeloma, acute myeloid leukemia, high-risk acute myeloid leukemia, and diffuse large B-cell lymphoma. [0094] The p97 inhibitor can be administered as a monotherapy or in a combination treatment, for example, for the treatment of a disease or condition sensitive to p97 inhibition, such as cancers. The p97 inhibitor can be co-formulated or co-administered together with, prior to, intermittently with, or subsequent to, other therapeutic or pharmacologic agents or treatments, such as procedures. Such agents include, but are not limited to, biologies, anticancer agents, other small molecule compounds, dispersing agents, anesthetics,
vasoconstrictors and surgery, radiotherapies, and combinations thereof. Such other agents and treatments that are available for the treatment of a disease or condition, including all those exemplified herein, are known to one of skill in the art or can be empirically determined. In some embodiments, the p97 inhibitor can be administered in combination with a proteasome inhibitor (e.g., bortezomib and carfilzomib) or additional standard of care agents for various cancers (e.g., lenalidomide and dexamethasone for multiple myeloma).
[0095] Exemplary proteasome inhibitors include bortezomib, CEP- 18770 (See Pwa et al., Blood, 1 1:2765-75, 2008), carfilzomib or ixazomib (MLN9708).
[0096] Any method known in the art for obtaining a sample (e.g., a tissue or body fluid sample) comprising at least one cell (e.g., a living cell) such as a cell from a tumor (e.g., from a biopsy or bone marrow aspirate or circulating tumor cells), or a normal cell, or a cultured cell, can be used. Commonly used methods to obtain tumor cells include surgical (the use of tissue taken from the tumor after removal of all or part of the tumor) and needle biopsies. Commonly used methods to obtain hematological malignancy cells include collection of bone marrow aspirate, isolation of peripheral blood, and isolation of circulating dendritic cells from peripheral blood. The samples can be treated in a way that preserves intact the gene expression levels or genomic material of the living cells to allow for analysis, e.g., flash freezing or chemical fixation, e.g., formalin fixation.
[0097] Examples of a cell, tissue, or body fluid sample useful in the present invention include one or more samples from urine, stool, tears, whole blood, serum, plasma, ascites, sweat, plasma, blood constituent, bone marrow, tissue, cells, organs, saliva, semen, cheek swab, hair follicle, lymph fluid, cerebrospinal fluid, lesion exudates and other fluids produced by the body. For example, the sample can be a biopsy sample, frozen, fixed or fresh, or a marrow aspirate from a subject having a hematological malignancy. In some embodiments, the cell, tissue, or body fluid sample from a subject is a sample from a biopsy from a solid tumor. The cell, tissue, or body fluid sample can be obtained by needle biopsy, CT-guided needle biopsy, aspiration biopsy, endoscopic biopsy, bronchoscopic biopsy, bronchial lavage, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, skin biopsy, bone marrow biopsy, and the Loop Electrosurgical Excision Procedure (LEEP).
[0098] Assays for Determining Genomic Features
[0099] Genomic features of the genes contained in the cell, tissue, or fluidic sample from a subject can be determined in many different ways. For example, genomic features can be determined by detection and/or quantification of expression levels (mRNA or protein) or expression level variations, expression pattern or profile (mRNA or protein), activity levels, structure variations (e.g., post-translational modifications, such as phosphorylation), nucleic acid or protein mutations (e.g., point mutations including activating or deactivating point mutations, deletions, germline or somatic mutations, mRNA mutation, rRNA mutation, tRNA mutation), copy number or copy number variations, methylation status or methylation profiles, cancer pathway alterations, translocations, intra-chromosomal inversions, cytogenetic abnormalities, non-reciprocal translocations, rearrangements, and intra- chromosomal inversions.
[0100] For example, gene expression levels can be determined by the quantification of fluorescence of hybridized mRNA on glass slides, Northern blot analysis, real-time reverse transcription PCR (RT-PCR), RNA sequencing (RNAseq), or other measures of gene expression abundance. In some embodiments, expression levels can be evaluated by obtaining a sample from a subject and contacting the sample with a compound or an agent capable of detecting mRNA for the signature genes, or protein encoded by the signature genes, such that the level of the protein or nucleic acid is detected in the sample. The level of expression of the signature genes can be measured by, for example, measuring the mRNA encoded by the signature genes; measuring the amount of protein encoded by the signature genes; or measuring the activity of the protein encoded by the signature genes. The level of mRNA corresponding to the signature gene in a cell can be determined both by in situ and by in vitro formats.
[0101] The isolated mRNA can be used in hybridization or amplification assays, e.g., Southern or Northern analyses, polymerase chain reaction analyses and probe arrays. One exemplary diagnostic method for the detection of mRNA levels involves contacting the isolated mRNA with a nucleic acid molecule (probe) that can hybridize to the mRNA encoded by the signature gene being detected. The nucleic acid probe can be, for example, a full-length nucleic acid or an oligonucleotide of at least 7, 15, 30, 50, 100, 250 or 500 nucleotides in length and sufficient to specifically hybridize under stringent conditions to mRNA for a signature gene. Other suitable probes for use in the diagnostic assays are known in the art.
[0102] In one format, mRNA (or cDNA) from the sample is immobilized on a surface and contacted with the probes, for example by running the isolated mRNA on an agarose gel and transferring the mRNA from the gel to a membrane, such as nitrocellulose. In an alternative format, the probes are immobilized on a surface and the mRNA (or cDNA) from the sample is contacted with the probes, for example, in a two-dimensional gene chip array. Any other known mRNA detection methods can be adapted for use in detecting the level of mRNA encoded by the signature genes.
[0103] The level of mRNA encoded by the signature genes in a sample can also be evaluated with nucleic acid amplification, e.g., by RT-PCR (Mullis (1987) U.S. Patent No. 4,683,202), ligase chain reaction (Barany (1991) Proc. Natl. Acad. Sci. USA 88: 189- 193), self-sustained sequence replication (Guatelli et al, (1990) Proc. Natl. Acad. Sci. USA 87: 1874-1878), transcriptional amplification system (Kwoh et al., (1989), Proc. Natl. Acad. Sci. USA 86: 1173-1177), Q-Beta Replicase (Lizardi et al., (1988) Bio/Technology 6: 1197), rolling circle replication (Lizardi et al, U.S. Patent No. 5,854,033) or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques known in the art. For example, the signature genes and expression thereof can be detected in a sample using quantitative RT-PCR (qPCR), which can be used to compare mRNA levels in different sample populations, in normal and tumor tissues, with or without drug treatment, to characterize patterns of gene expression, to discriminate between closely related mRNAs, and to analyze RNA structure.
[0104] In some embodiments, a signature gene or expression thereof can be detected using a microarray. For example, differential gene expression can be identified or confirmed using the microarray technique. Polynucleotide sequences of interest (including cDNAs and oligonucleotides) are plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific nucleic acids from cells or tissues of interest.
[0105] In an exemplary embodiment of the microarray technique, PCR amplified inserts of cDNA clones are applied to a substrate in a dense array. Preferably at least 10,000 nucleotide sequences are applied to the substrate. The microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the microarray chip is scanned by a device such as, confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pair-wise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols.
[0106] A variety of methods can be used to determine the levels of proteins encoded by the selected signature genes. In general, these methods include contacting an agent that selectively binds to the protein, such as an antibody, and evaluating the level of protein in the sample. In some embodiments, the antibody bears a detectable label. Antibodies can be polyclonal or monoclonal. An intact antibody, or a fragment thereof (e.g., Fab or F(ab')2) can be used. Examples of detectable substances are known in the art, as are methods of quantifying levels of proteins detected thereby.
[0107] The detection methods can be used to detect signature protein in a sample in vitro as well as in vivo. In vitro techniques for detection of signature protein include enzyme linked immunosorbent assays (ELISAs), immunoprecipitations, immunofluorescence, enzyme immunoassay (EIA), radioimmunoassay (RIA), and Western blot analysis. In vivo techniques for detection of protein encoded by a signature gene include introducing into a subject a labeled anti-signature antibody. For example, the antibody can be labeled with a radioactive marker whose presence and location in a subject can be detected by standard imaging techniques.
[0108] In some embodiments, a protein encoded by a signature gene can be detected in a sample using an immunoassay assay. An exemplary method includes the steps of contacting the sample with the antibody and allowing the antibody to form a complex of with the antigen in the sample, washing the sample and detecting the antibody-antigen complex with a detection reagent. Alternatively, the protein encoded by a signature gene can be detected using an indirect assay, in which a second, labeled antibody is used to detect bound marker- specific antibody. Exemplary detectable labels include magnetic beads (e.g.,
DYNABEADS™), fluorescent dyes, radiolabels, enzymes (e.g., horseradish peroxidase, alkaline phosphatase and others commonly used), and calorimetric labels such as colloidal gold or colored glass or plastic beads. The marker in the sample can be detected using and/or in a competition or inhibition assay wherein, for example, a monoclonal antibody which binds to a distinct epitope of the marker is incubated simultaneously with the mixture.
[0109] The amount of a protein encoded by a signature gene can also be determined by immunoassays. Methods for measuring the amount of antibody-marker complex include, e.g., fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, birefringence or refractive index (e.g., surface plasm on resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry). In general these regents are used with optical detection methods, such as various forms of microscopy, imaging methods and non-imaging methods. Electrochemical methods include voltametry and amperometry methods. Radio frequency methods include multipolar resonance spectroscopy.
[0110] In some embodiments, a protein encoded by a signature gene can be detected in a sample using an immunohistochemistry assay. Antibodies specific for each protein encoded by a signature gene are used to detect expression of the protein in a sample. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as, biotin, or an enzyme such as horseradish peroxidase or alkaline phosphatase. Alternatively, unlabeled primary antibody is used in conjunction with a labeled secondary antibody, comprising antisera, polyclonal antisera or a monoclonal antibody specific for the primary antibody.
[0111] In some embodiments, a protein encoded by a signature gene can be detected in a sample using flow cytometry. This technology is routinely used in the diagnosis of health disorders, especially hematological malignancy. Fluorescence-activated cell sorting (FACS) is a specialized type of flow cytometry that often uses the aid of fl ore scent-labeled antibodies to detect antigens on cell of interest. This additional feature of antibody labeling use in FACS provides for simultaneous multiparametric analysis and quantification based upon the specific light scattering and fluorescent characteristics of each cell florescent-labeled cell and it provides physical separation of the population of cells of interest as well as traditional flow cytometry does. In some embodiments, a protein encoded by a signature gene can be detected in a sample using other methods of single cell multiparametric protein detection analysis technology such as mass cytometry. In mass cytometry, antibodies are tagged with isotopically pure rare earth elements, allowing simultaneous measurement of greater than 40 parameters while circumventing the issue of spectral overlap which is observed with fluorophores. The multi-atom metal tags are ionized, for example by passage through an argon plasma, and then analyzed by mass spectrometry. See, e.g., Bandura et al. Analytical Chemistry 81 :6813-6822, 2009; Ornatsky et al. Journal of Immunological Methods 361 : 1- 20, 2010; Bendall et al. Science 332(6030):687-696, 2011.
[0112] In some embodiments, a signature gene or a protein encoded by a signature gene can be detected in a sample using a biochip. In biochip technology nucleic acids or proteins are attached to the surface of the biochip in an ordered array format. The grid pattern of the test regions allowed analyzed by imaging software to rapidly and simultaneously quantify the individual analytes at their predetermined locations (addresses). The CCD camera is a sensitive and high-resolution sensor able to accurately detect and quantify very low levels of light on the chip. Biochips can be designed with immobilized nucleic acid and proteins. A biochip could be designed to detect multiple macromolecule types (e.g., nucleic acid molecules and proteins) on one chip. The biochip can be used simultaneously analyze a panel of signature genes or proteins encoded thereby in a single sample, producing a subjects profile.
[0113] An exemplary biochip is a protein microarray. The microarray includes a support surface such as a glass slide, nitrocellulose membrane, bead, or microtitre plate, to which an array of capture proteins are bound in an arrayed format onto a solid surface. Detection probe molecules, typically labeled with a fluorescent dye, are added to the array. Any reaction between the probe and the immobilized protein emits a fluorescent signal that is read by a laser scanner. There are at least three types of protein microarrays that are currently used to study the biochemical activities of proteins: analytical microarrays (also known as capture arrays), functional protein microarrays (also known as target protein arrays), and reverse phase protein microarray (RPA).
[0114] In some embodiments, a signature gene or a protein encoded by a signature gene can be detected in a sample using mass spectrometry. Suitable mass spectrometry methods to be used with the present invention include but are not limited to, one or more of electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), tandem liquid chromatography-mass spectrometry (LC-MS/MS) mass spectrometry,
desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SFMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS), atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-(MS)n, quadrupole mass spectrometry, fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry, where n is an integer greater than zero.
[0115] Any method known in the art can be used to extract material, e.g., protein or nucleic acid (e.g., mRNA) from the sample. For example, mechanical or enzymatic cell disruption can be used, followed by a solid phase method (e.g., using a column) or phenol-chloroform extraction, e.g., guanidinium thiocyanate-phenol-chloroform extraction of the RNA. A number of kits are commercially available for use in isolation of mRNA. Purification can also be used if desired. See, e.g., Peirson and Butler, Methods Mol. Biol. 2007; 362:315-27. A number of methods are also known in the art to obtain proteins from cells, see, e.g., "Protein Methods," 2nd Edition by Bollag et al, Wiley Pub. (1996). Optionally, cDNA can be transcribed from the mRNA.
[0116] Computer Software Hardware
[0117] Computing devices and systems can be used to implement the methods of the present invention. Computing device can be any forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Computing device can include a processor, memory, a storage device, an interface connecting to memory and expansion ports, and an interface connecting to bus and storage device. Each of the components are interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate. The processor can process instructions for execution within the computing device, including instructions stored in the memory or on the storage device to display graphical information for a GUI on an external input/output device, such as display coupled to high speed interface. In other implementations, multiple processors and/or multiple buses can be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices can be connected, with each device providing portions of the operations (e.g., as a server bank, a group of blade servers, or a multi -processor system).
[0118] Computing device can also be any forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, and other similar computing devices.
Computing device includes a processor, memory, an input/output device such as a display, a communication interface, and a transceiver, among other components. The device can also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the components are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate.
[0119] Where appropriate, the systems and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, and structural equivalents thereof, or in combinations of them. The systems and the functional operations can be implemented as one or more computer program products, i.e., one or more computer programs tangibly embodied in an information carrier, e.g., in a machine readable storage device or in a propagated signal, for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program (also known as a program, software, software application, or code) can be written in any form of programming language, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
[0120] The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform the described functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.
[0121] To provide for interaction with a user, aspects of the described techniques can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.
[0122] In one aspect of the present invention, a computer-implemented methods can be implemented for identifying genes associated with sensitivity to p97 inhibition. In some embodiments, the methods include a step of analyzing a cell or tissue or body fluid sample from a subject for genomic features of one or more subsets of genes, a step of assigning a sensitivity score to p97 inhibition to the cell or tissue or body fluid sample based on the genomic features of each of the one or more subsets of genes, as described above, and a step of identifying a subset comprising one or more (e.g., at least two) signature genes, the genomic features of which are correlated with the sensitivity to p97 inhibition. In some embodiments, the methods can further include obtaining a cell or a tissue or body fluid sample from a subject, and/or analyzing the cell or the tissue or body fluid sample for genomic features of certain signature genes.
[0123] Databases
[0124] In one aspect of the present invention, a database comprising a plurality of records is provided. Each record includes data on the genomic features of one or more signature genes in a cell or tissue or body fluid sample from a subject. In some embodiments, the record can also include data on a preselected factor relating to a subject who has a disease or condition. Exemplary preselected factors include the presence of a treatment (e.g., the administration of a therapy such as a therapy comprising a p97 inhibitor, vitamin, food or dietary supplement); the presence of an environmental factor (e.g., the presence of a substance in the environment); the presence of a genetic factor or physical factor such as age and somatic or germline mutations. [0125] In some embodiments, the database includes at least two records, and the preselected factor in each of the records differs from the other record. For example, in some embodiments, the preselected factor can be administration of a compound and in one record the preselected factor includes administration of the compound and in the other record the compound is not administered, is administered at a different dose and/or a different compound is administered. In some embodiments, the preselected factor can be an environmental factor and in one record the factor is present and in the other record the environmental factor is not present or is present at a different level. In some embodiments, the preselected factor can be a genetic factor such as somatic or germline mutations and in one record the genetic factor is present and in the other record the genetic factor is not present or is present at a different level. In some embodiments, the preselected factor can be a physical factor such as age and the age in one record varies from the age in the other record, e.g., a difference in age of at least 5, 10, 15, 20 years or more.
[0126] In some embodiments, each record of the database includes data on at least two preselected factors relating to the subject. In some embodiments, the database includes at least two records, and at least one preselected factor in each of the records differs from the other record. In some embodiments, the database includes at least two records and at least one preselected factor in the records differ and at least one of the other preselected factors is the same. In some embodiments, the database can include at least two records and each record includes at least one preselected factor and at least one preselected condition.
[0127] In some embodiments, the database includes at least two records, wherein each record includes information regarding genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3. In some embodiments, the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, RNF38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
[0128] In some embodiments, each record additionally includes other information such as administration of a therapy (e.g., dose of p97 inhibitor administered), and/or response to that dose (e.g., survival fraction in response to the dose). In some embodiments, each record can further include data on the genomic features of at least one internal control gene.
[0129] In another aspect, the present invention provides a computer-readable medium bearing instructions executable by the processor for determining sensitivity to p97 inhibition based on genomic features of at least two signature genes in a cell or a tissue or body fluid sample from a subject, as described above. The computer-readable media refers to any medium that can be read and accessed directly by a machine, e.g., a digital computer or analogue computer. Non-limiting examples of a computer include a desktop PC, laptop, mainframe, server (e.g., a web server, network server, or server farm), handheld digital assistant, pager, mobile telephone, and the like.
[0130] In some embodiments, one or more (e.g., at least two) signature genes are selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3. In some embodiments, the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, R F38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
MTHFD1L, DNM3, ΖΝΉΙΤ6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
[0131] Microarrays/Microfluidic Devices
[0132] The present invention further provides microarrays useful for detecting and quantifying genomic features of the signature genes (e.g., levels of mRNA or protein corresponding to the signature genes). The microarray comprises a substrate and
hybridizable array elements. For the detection and quantification of mRNA, the microarray will include a plurality of individually addressable areas including hybridizable array elements selective for the selected signature genes. For the detection and quantification of protein, the microarray will include a plurality of individually addressable areas including reagents for the detection of one or more proteins encoded by the signature genes, e.g., antibodies. [0133] In some embodiments, the microarrays include hybridizable array elements selective for one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3. In some embodiments, the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, and EVIPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, FMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of all 26 genes listed in Table 3. Typically the hybridizable array elements are individually addressable hybridizable array elements selective for the signature genes. In some embodiments, the microarrays also include one or more hybridizable array elements selective for an internal normalization control. In some embodiments, the microarrays do not include hybridizable array elements selective for other genes. [0134] Microarray refers to a substrate having an ordered arrangement of hybridizable array elements arranged thereon. In some embodiments, the array elements are arranged so that there are preferably at least about 10 different array elements, on a 1 cm2 substrate surface. The maximum number of array elements is unlimited, but can be upwards of at least 100,000 array elements. Furthermore, a hybridization signal from each of the array elements is individually distinguishable. In some embodiments, the array elements comprise polynucleotide probes.
[0135] Hybridization causes a denatured polynucleotide probe and a denatured
complementary target to form a stable duplex through base pairing. Hybridization methods are well known to those skilled in the art (See, e.g., Laboratory Techniques in Biochemistry and Molecular Biology, Vol. 24: Hybridization With Nucleic Acid Probes, P. Tijssen, ed. Elsevier Science, New York, N.Y. (1993)). Conditions can be selected for hybridization where exactly complementary target and polynucleotide probe can hybridize, i.e., each base pair must interact with its complementary base pair. Alternatively, conditions can be selected where target and polynucleotide probes have mismatches but are still able to hybridize.
Suitable conditions can be selected, for example, by varying the concentrations of salt or formamide in the prehybridization, hybridization and wash solutions, or by varying the hybridization and wash temperatures.
[0136] Hybridization can be performed at low stringency with buffers, such as 6 X SSPE with 0.005% Triton X-100 at 37°C, which permits hybridization between target and polynucleotide probes that contain some mismatches to form target polynucleotide/probe complexes. Subsequent washes are performed at higher stringency with buffers, such as 0.5 X SSPE with 0.005% Triton X-100 at 50°C, to retain hybridization of only those
target/probe complexes that contain exactly complementary sequences. Alternatively, hybridization can be performed with buffers, such as 5 X SSC/0.2%) SDS at 60°C and washes are performed in 2 X SSC/0.2% SDS and then in 0.1 X SSC. Stringency can also be increased by adding agents such as formamide. Background signals can be reduced by the use of detergent, such as sodium dodecyl sulfate, Sarcosyl or Triton X-100, or a blocking agent, such as sperm DNA.
[0137] Hybridization specificity can be evaluated by comparing the hybridization of specificity-control polynucleotide probes to specificity-control target polynucleotides that are added to a sample in a known amount. The specificity-control target polynucleotides may have one or more sequence mismatches compared with the corresponding polynucleotide probes. In this manner, whether only complementary target polynucleotides are hybridizing to the polynucleotide probes or whether mismatched hybrid duplexes are forming is determined.
[0138] After hybridization, the microarray is washed to remove non-hybridized nucleic acids and complex formation between the hybridizable array elements and the target polynucleotides is detected.
[0139] Methods for detecting complex formation are known in the art. In some embodiments, the target polynucleotides are labeled with a fluorescent label and
measurement of levels and patterns of fluorescence indicative of complex formation is accomplished by fluorescence microscopy, preferably confocal fluorescence microscopy. An argon ion laser excites the fluorescent label, emissions are directed to a photomultiplier and the amount of emitted light detected and quantitated. The detected signal should be proportional to the amount of probe/target polynucleotide complex at each position of the microarray. The fluorescence microscope can be associated with a computer-driven scanner device to generate a quantitative two-dimensional image of hybridization intensity. The scanned image is examined to determine the abundance/expression level of each hybridized target polynucleotide.
[0140] Typically, microarray fluorescence intensities can be normalized to take into account variations in hybridization intensities when more than one microarray is used under similar test conditions. In some embodiments, individual polynucleotide probe/target complex hybridization intensities are normalized using the intensities derived from internal normalization controls contained on each microarray, e.g., control genes.
[0141] In another aspect, the present invention further provides microfluidic device useful for detecting and quantifying genomic features of the signature genes (e.g., levels of mRNA or protein corresponding to the signature genes. The microfluidic device comprises a substrate and one or more reaction chambers comprising reagents for selective quantification of at least two signature genes. For the detection and quantification of mRNA, the microfluidic device will include a plurality of reaction chambers comprising reagents for selective quantification of the selected signature genes. For the detection and quantification of protein, the microfluidic device will include a plurality of reaction chambers comprising reagents for selective quantification of one or more proteins encoded by the signature genes, e.g., antibodies. General methods for making and using microfluidic devices are known in the art, see, e.g., U.S. Pat. Nos. 6,960,437 and 7,250,260.
[0142] In some embodiments, the microfluidic device also include a reaction chamber comprising reagents for selective quantification of an internal normalization control. In some embodiments, the microfluidic device also includes a reaction chamber comprising reagents for selective quantification of other genes.
[0143] In some embodiments, the microfluidic device also includes a reaction chamber comprising reagents for selective quantification of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3. In some embodiments, the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, R F38, TYW3, and EVIPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, EVIPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, D M3, Z HIT6, PM3. In some embodiments, the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
[0144] Screening of compounds useful for treating a disease or condition sensitive to p97 inhibition
[0145] In one aspect, the present invention provides methods for determining the liklihood of improving a disease or condition with a p97 inhibitor. In one aspect, the present invention provides methods of screening p97 inhibitors useful for treating a disease or condition sensitive to p97 inhibition. In another aspect, the present invention provides methods of screening p97 inhibitors that alter (e.g., increase or decrease) the genomic features (e.g., expression) of at least two signature genes. In another aspect, the present invention provides methods of providing personalized medicine (e.g., choice of a particular p97 inhibitor) for individuals with a given drug sensitivity profile. In some embodiments, the effect of therapeutics on the growth and/or progression of cancers with specific drug sensitivity profiles are assessed. Any suitable method for assaying tumor growth or proliferation may be utilized. In some embodiments, candidate p97 inhibitors are evaluated for their ability to alter the genomic profiles (e.g., expression) of at least two signature genes by contacting a p97 inhibitor with a cell expressing signature genes and then assaying for the effect of the candidate p97 inhibitors on genomic features.
[0146] The candidate p97 inhibitor compounds of the present invention can be obtained using any of the numerous approaches in combinatorial library methods known in the art, including spatially addressable parallel solid phase or solution phase libraries; synthetic library methods requiring deconvolution; the One-bead one-compound' library method; and synthetic library methods using affinity chromatography selection, biological libraries;
peptoid libraries (libraries of molecules having the functionalities of peptides, but with a novel, non-peptide backbone, which are resistant to enzymatic degradation but which nevertheless remain bioactive; see, e.g., Zuckennann et al., J. Med. Chem. 37:2678-85, 1994).
[0147] In some embodiments, an assay is a cell-based assay in which a cell that displays genomic features of certain signature genes is contacted with a candidate p97 inhibitor compound, and the ability of the candidate compound to the alter the genomic features of the cell is determined. [0148] This invention further pertains to novel agents identified by the above-described screening assays. Accordingly, the present invention further provides a method of determining the efficacy, toxicity, side effects, or mechanism of action, of treatment with the novel agents identified as described herein in an appropriate animal model. Furthermore, novel agents identified by the above-described screening assays can be, e.g., used for treatments of a disease or condition sensitive to p97 inhibition.
[0149] In some embodiments, the present invention provides a method of screening candidate p97 inhibitors, comprising: obtaining a candidate p97 inhibitor compound and a cell or tissue or body fluid sample from a subject, and determining the effectiveness of the candidate compound in treating a disease or condition sensitive to p97 inhibition. In some embodiments, the cell or tissue or body fluid sample has a known p97 inhibitor sensitivity profile, comprising genomic features of at least two signature genes. In some embodiments, the method further comprises the step of determining the effect of the candidate compound on the genomic features of at least two signature genes in the cell or tissue or body fluid sample. In some embodiments, the method further comprises the step of determining the effect of the candidate compound on sensitive score to p97 inhibition based on the genomic features of at least two signature genes.
[0150] In some embodiments, one or more (e.g., at least two) signature genes are selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3. In some embodiments, the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, RNF38, TYW3, and IMPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, R F38, TYW3, FMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of all 26 genes listed in Table 3.
[0151] Kits
[0152] The invention provides kits for detection and quantification of genomic features of one or more (e.g., at least two) signature genes in a cell or tissue or body fluid sample. In some embodiments, the invention provides kits for detecting and quantifying one or more selected signature genes as described herein (e.g., mRNA or protein corresponding to the signature genes) in a biological sample. In some embodiments, the kit includes a compound or agent capable of detecting mRNA or protein corresponding to the signature genes in a sample; and a standard; and optionally one or more reagents necessary for performing detection, quantification, or amplification. The compounds, agents, and/or reagents can be packaged in a suitable container. The kit can further comprise instructions for using the kit to detect and quantify signature protein or nucleic acid.
[0153] In some embodiments, the kit is an antibody-based kit. The antibody-based kit according to the present invention can include a first antibody (e.g., attached to a solid support) which binds to a polypeptide corresponding to a signature gene; and optionally a second, different antibody which binds to either the polypeptide or the first antibody and is conjugated to a detectable agent. In some embodiments, the kit can also include a buffering agent, a preservative, and/or a protein stabilizing agent. In some embodiments, the kit can also include components necessary for detecting the detectable agent (e.g., an enzyme or a substrate). In some embodiments, the kit can also contain a control sample or a series of control samples which can be assayed and compared to the test sample contained. Each component of the kit can be enclosed within an individual container and all of the various containers can be within a single package, along with instructions for interpreting the results of the assays performed using the kit.
[0154] In some embodiments, the kit is oligonucleotide-based kit, The oligonucleotide- based kit according to the present invention can include an oligonucleotide, e.g., a detectably labeled oligonucleotide, which hybridizes to a nucleic acid sequence corresponding to a signature gene; or a pair of primers useful for amplifying a nucleic acid molecule
corresponding to a signature gene.
[0155] In some embodiments, the kits include a microarray comprising a substrate and one or more individually addressable hybridizable array elements arranged thereon, wherein the individually addressable hybridizable array elements are selective for the at least two signature genes. In some embodiments, the kits include a microfluidic device comprising a substrate and one or more reaction chambers, wherein the reaction chambers comprise reagents for selective quantification of the at least two signature genes.
[0156] In some embodiments, the kits include reagents (e.g., primers or antibodies) for specific detection and quantification of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A, 2B, and 2C. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Tables 2A and 2B. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 2A. In some embodiments, the methods include determining and analyzing genomic features of one or more (e.g., at least two) signature genes selected from the group consisting of the genes listed in Table 3. In some embodiments, the methods include determining and analyzing genomic features of MUCL1 and/or BCCIP and one or more signature genes selected from TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ΖΝΉΙΤ6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCLl, BCCIP, RNF38, TYW3, and EVIPDH2, and one or more signature genes selected from SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6, BAG2, RCAM, NOC3L, Z F652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of one or more or all signature genes selected from MUCL1, BCCIP, R F38, TYW3, IMPDH2, SLC4A8, ZFP3, DACH1, UBE2G1, TTC27, MPP6 and one or more signature genes selected from BAG2, NRCAM, NOC3L, ZNF652, TNFRSFIOB, SSR3, AK2, DCLKl, RABGGTB, KLHDC9, EBNA1BP2,
MTHFD1L, DNM3, ZNHIT6, NPM3. In some embodiments, the methods include determining and analyzing genomic features of all 26 genes listed in Table 3. In some embodiments, the kits also include reagents for specific detection and quantification of a housekeeping or control gene.
[0157] Examples
[0158] Example 1 : Materials and methods [0159] Cell line sensitivity to test compounds
[0160] Panels of cancer cell lines were treated with a 10-point 2-fold serial dose titration of test compound in 384 well plates for 72 hours and viability was measured either by cell titer glo (Promega) or nuclear count. Data was fit to a four-parameter sigmoidal curve to determine compound doses that result in 50% decrease in viability (ECso).
[0161] Genomics data
[0162] Gene expression, copy number and somatic mutation data for each cell line was obtained from the cancer cell line encyclopedia (CCLE) or other databases.
[0163] Data Analysis
[0164] Normalized gene expression values collected by microarray or copy number values collected by hybrid capture were compared to test compound sensitivity across sets of cell lines and R and p values of correlation coefficients was calculated for each genomic feature. Determination of false discovery frequency was estimated by randomize test compound sensitivity data and repeating correlation analysis to provide a distribution of randomized p- values. The p-values were calculated by t-test for mutation events. Genomic features with greatest significance were used to build linear and non-linear multivariate models to predict test compound ECso values. For internal validation of multivariate model building 1/5 of cell lines from a given data set were held back as a test set and the remaining 4/5 were used to determine genomics features of greatest significance and build multivariate models. Models were then applied the hold back set to calculate predicted EC50 values. Clustering of genomics feature prior to multivariate model building was done using k-means cluster.
[0165] Example 2: Analysis conducted on 209 cancer cell lines
[0166] EC50 values for p97 inhibitor Compound 1 were determined in a panel of 209 solid tumor cancer cell lines by nuclear count (Table 1). Gene expression for these cell lines was available from microarray analysis
(www.broadinstitute.org/ccle/data/browseData?conversationPropagation=begin). When correlation of each gene to Compound 1 EC50 values was analyzed, 549 genes had significant correlation to Compound 1 sensitivity of resistance as determined by the method for determining false discovery cut off as described in Benjamini and Yekutieli (Ann. Statist 29: 1165-1188, 2001) (Table 2A and 2B). Additionally, when top correlate p-values were compared to those derived from randomized EC50 data, 86 genes (Table 2A) were more significant than any genes when EC50 data was randomized 20 times (Figure 1). These results suggest that the expression of many genes correlate with Compound 1 sensitivity or resistance in a highly significant way. When the expression of top 549 correlating genes was compared to each other using k-means clustering, there was great diversity of relative expression between cluster groups suggesting there was potential for increased predictive value of building multivariate models using several of these genes (Figure 2).
[0167] Multivariate models were built to predict EC50 of Compound 1 using linear regression. Genes to be used in multivariate model were selected in the order of significance of correlation with sensitivity or resistance to Compound 1. Example of this approach using 50 gene expression parameters and 1/5 hold back for validation is shown in Figure 3. To determine the optimal number of genes needed to build multivariate predictive models of Compound 1 sensitivity, gene numbers were varied from 5-90 of the most significant correlating genes. For each set of gene numbers, a predictive model was built randomly excluding 1/5 of cell line lines 200 times. The model was then applied to the hold back set of cell lines and the correlation between predicted EC50 and actual EC50 was calculated (Figure 4). Greater than 10 genes and less than 90 genes appeared to be optimal to build the most robust predictive models. [0168] To externally validate the linear regression model approach, a model was built using the entire set of 209 cell lines and the 26 most significant gene correlates (Table 3). The equation of this model is as follows: Predicted ECso = 0.4698 + -0.0014(GFRMUCLI) + - 0.0329(GFRBCCIP) + 0.0883(GFRRNF38) + -0.0546(GFRTYW3) + -0.0340(GFRIMPDH2) + 0.0323(GFRSLC4A8) + 0.0111(GFRZFP3) + 0.0190(GFRDACHI) + 0.0081(GFRUBE2GI) +
0.0176(GFRTTC27) + -0.0224(GFRMPP6) + -0.0028(GFRBAG2) + 0.0209(GFRNRCAM) + - 0.0200(GFRNOC3L) + -0.0076(GFRZNF652) + -0.0219(GFRTNFRSFIOB) + -0.0157(GFRSSR3) + 0.0021(GFRAK2) + -0.0052(GFRDCLKI) + 0.0255(GFRRABGGTO) + 0.0301(GFRKLHDC9) + 0.0110 (GFREBNAIBP2) + 0.0006(GFRMTHFDIL) + 0.0005(GFRDNM3) + -0.0147(GFRZNHIT6) + 0.0016(GFRNPM3), wherein GFR is the value of the readout of genomic features for each gene, and the gene expression is linear and normalized centered around zero. This model was then applied to an independent set of cell lines for which ECso values were collected by a different method (cell titer glo) (Table 5) and for which gene expression data was obtained from a different source (CCLE). The predicted ECso values for each of these cell lines correlated with the actual ECso values with statistical significance (Figure 5).
[0169] Table 1 : 72 hour viability ECso data for Compound 1
Figure imgf000059_0001
Figure imgf000059_0002
Figure imgf000059_0003
AsPCl 0.592 Hs695T 0.527 SKBR3 0.343
AU565 0.395 HS746T 0.473 SKLMS1 0.304
BE2C 0.364 Hs766T 0.625 SKMEL1 0.503
BeWo 0.473 HT1080 0.275 SKMEL28 0.817
BFTC905 0.403 HT1197 0.547 SKMEL3 0.355
BHT101 0.286 HT1376 0.649 SKMES1 0.328
BM1604 0.471 HT29 0.424 SKNAS 0.5
BPH1 0.468 HT3 0.596 SK DZ 0.914
BT20 0.551 HuCCTl 0.463 SK EP1 0.444
BT474 1.11 HUH6Clone5 0.39 SK FI 0.874
BT549 0.556 HuPT4 0.39 SKOV3 1.48
BxPC3 0.371 J82 0.521 SKUT1 0.564
C32 0.454 JAR 0.484 S B 19 0.622
C32TG 0.628 JEG3 0.477 SNU1 0.161
C33A 0.418 KATOIII 0.654 SNU16 0.345
C4I 0.306 KHOS240S 0.265 SNU423 0.55
C4II 0.38 KLE 0.715 SNU5 0.449
Cakil 0.433 KPL1 0.494 SU.86.86 0.379
Caki2 0.307 LNCaP 0.357 SW1088 0.36
Cal27 0.488 LS1034 0.965 SW13 0.656
CAL62 0.462 LS174T 0.288 SW1353 0.474
Calul 0.398 MALME3M 0.667 SW1417 1.03
Calu6 0.255 MCF7 0.606 SW1463 0.678
CAMA1 0.991 MCIXC 0.437 SW1783 0.593
CaOV3 0.285 M DA MB 23 1 0.307 SW403 0.819
Capanl 0.314 MDAMB436 0.692 SW48 0.234
Capan2 0.41 MDAMB453 0.732 SW480 0.941
CCFSTTG1 1.09 MDAMB468 0.594 SW579 0.281
CFPAC1 0.391 MEG01 0.485 SW620 0.462
CGTHW1 0.277 MESSA 0.425 SW684 0.34
ChaGoKl 0.596 MeWo 0.25 SW837 0.619
CHL1 0.41 MG63 0.282 SW872 0.366
CHP212 0.289 MiaPaCa2 0.239 SW900 0.628
Colo201 0.841 MOLT3 0.448 SW948 0.923
Colo205 0.489 MT3 0.237 SW954 0.247
Colo320DM 0.366 NCIH292 0.443 SW962 0.448
Colo320HSR 0.456 NCIH295R 1.73 SW982 0.584
COL0829 0.509 NCIH441 0.972 T24 0.317
CORL105 0.305 NCIH446 0.824 T47D 0.663
CORL23 0.414 NCIH460 0.268 T98G 0.368
D283Med 0.457 NCIH508 0.614 TCCSUP 0.312
Daoy 0.589 NCIH520 0.764 TE381.T 0.461
DBTRG05MG 0.577 NCIH596 0.634 U138MG 0.536
Detroit562 0.23 NCIH661 0.462 U20S 0.386
DKMG 0.267 NCIH69 0.958 U87MG 0.503 DLD1 0.394 NCIH747 0.761 UMUC3 0.489
DMS114 0.461 OCUG1 0.711 Wi38 0.581
DMS273 0.346 OE19 0.481 WiDr 0.343
DMS53 0.618 OE21 0.207 Y79 0.735
DoTc24510 0.558 OE33 0.348 YAPC 0.446
DU145 0.568 OVCAR3 0.691
[0170] Table 2: Top 549 gene expression correlated with Compound 1 sensitivity or resistance in solid tumor cancer cell lines.
Table 2A
Figure imgf000061_0001
unc-13 homolog B (C. elegans)
UNC13B 0.33 5E-09 0.00008257 [Source:HGNC
Hs.493791 Symbol;Acc:HGNC: 125661
Ca++-dependent secretion
CADPS 0.33 6E-09 0.000085368 activator [Source:HGNC
Hs.654933 Symbol;Acc:HGNC: 14261 erythrocyte membrane protein
EPB41L4 band 4.1 like 4B
0.33 6E-09 0.000085368
B [Source:HGNC
Hs.634067 Symbol;Acc:HGNC: 198181 apolipoprotein B mRNA editing enzyme, catalytic
APOBEC
-0.33 6E-09 0.000085368 polypeptide-like 3C 3C
[Source:HGNC
Hs.731638 Symbol;Acc:HGNC: 173531
MYB binding protein (PI 60)
MYBBP1
-0.33 9E-09 0.00012221 la [Source:HGNC
A
Hs.701718 Symbol;Acc:HGNC:75461
Endogenous Bornavirus-like
LOC1005
0.33 1E-08 0.0001245 nucleoprotein 2 pseudogene 06710
Hs.446271 (LOCI 00506710)
Rho guanine nucleotide
C9orfl00 0.33 1E-08 0.0001245 exchange factor (GEF) 39
Hs.534579 (ARHGEF39)
tRNA-yW synthesizing protein 3 homolog (S. cerevisiae)
TYW3 -0.33 1E-08 0.0001245
[Source:HGNC
Hs.348411 Symbol;Acc:HGNC:247571 dual specificity phosphatase 26
DUSP26 0.32 2E-08 0.00022252 (putative) [Source:HGNC
Hs.8719 Symbol;Acc:HGNC:281611 piggyBac transposable element
PGBD5 0.32 2E-08 0.00022252 derived 5 [Source:HGNC
Hs.520463 Symbol;Acc:HGNC: 194051 synaptotagmin XVII
SYT17 0.32 3E-08 0.00022817 [Source:HGNC
Hs.258326 Symbol;Acc:HGNC:241191
Hs.469398
C2orf55 0.32 3E-08 0.00022817
KIAA121 l-like (KIAA1211L) immunoglobulin superfamily,
IGSF9 0.32 3E-08 0.00023656 member 9 [Source:HGNC
Hs.591472 Symbol;Acc:HGNC: 181321
LOC1005
0.32 3E-08 0.00023909
05880 Hs.519873 Desmoplakin
MARVEL domain containing
MARVE
-0.32 4E-08 0.00026159 1 [Source:HGNC LD1
Hs.744073 Symbol;Acc:HGNC:286741 epithelial membrane protein 3
EMP3 -0.32 4E-08 0.00026159 [Source:HGNC
Hs.609040 Symbol;Acc:HGNC:33351 ubiquitin associated protein 2
UBAP2 0.31 4E-08 0.00026205 [Source:HGNC
Hs.493739 Symbol;Acc:HGNC: 141851 glucosidase, beta (bile acid) 2
GBA2 0.31 4E-08 0.00026205 [Source:HGNC
Hs.443134 Symbol; Acc:HGNC: 189861
NOP 16 nucleolar protein
NOP 16 -0.31 4E-08 0.00026254 [Source:HGNC
Hs.696283 Symbol;Acc:HGNC:269341 ets variant 5 [Source:HGNC
ETV5 -0.31 5E-08 0.00034723
Hs.43697 Symbol;Acc:HGNC:34941 synaptosomal-associated
SNAP23 -0.31 6E-08 0.00035906 protein, 23kDa [Source:HGNC
Hs.511149 Symbol;Acc:HGNC: 111311 spectrin repeat containing, nuclear envelope 2
SYNE2 0.31 7E-08 0.00039491
[Source:HGNC
Hs.745014 Symbol;Acc:HGNC: 170841 multiple coagulation factor
MCFD2 -0.31 7E-08 0.00039491 deficiency 2 [Source:HGNC
Hs.662152 Symbol;Acc:HGNC: 184511 oral-facial-digital syndrome 1
OFD1 0.31 7E-08 0.00039491 [Source:HGNC
Hs.6483 Symbol;Acc:HGNC:25671 neurensin 1 [Source:HGNC
NRSN1 0.31 7E-08 0.00039491
Hs.726270 Symbol;Acc:HGNC: 178811 zinc finger and BTB domain
ZBTB7C 0.31 7E-08 0.00039491 containing 7C [Source:HGNC
Hs.515388 Symbol; Acc :HGNC : 317001 zinc finger protein 554
ZNF554 0.31 8E-08 0.00039817 [Source:HGNC
Hs.307043 Symbol;Acc:HGNC:266291 sosondowah ankyrin repeat
SOWAH domain family member A
0.31 8E-08 0.00040821
A [Source:HGNC
Hs.13308 Symbol;Acc:HGNC:270331 family with sequence similarity 98, member A
FAM98A -0.31 9E-08 0.00045699
[Source:HGNC
Hs.468140 Symbol;Acc:HGNC:245201 mitogen-activated protein
MAP2K3 -0.30 1E-07 0.00048872 kinase kinase 3 [Source:HGNC
Hs.514012 Symbol;Acc:HGNC:68431
Rho GTPase activating protein
ARHGA
0.30 1E-07 0.00053767 32 [Source:HGNC P32
Hs.440379 Symbol; Acc :HGNC : 173991
FK506 binding protein 14, 22
FKBP14 -0.30 1E-07 0.00058348 kDa [Source:HGNC
Hs.603294 Symbol; Acc:HGNC: 186251 histidine triad nucleotide
binding protein 2
HINT2 0.30 1E-07 0.00058348
[Source:HGNC
Hs.70573 Symbol; Acc:HGNC: 183441 cartilage associated protein
CRTAP -0.30 2E-07 0.00067956 [Source:HGNC
Hs.517888 Symbol;Acc:HGNC:23791 fasciculation and elongation protein zeta 2 (zygin II)
FEZ2 -0.30 2E-07 0.00067956
[Source:HGNC
Hs.258563 Symbol; Acc :HGNC : 36601 nephronectin [Source:HGNC PNT 0.30 2E-07 0.00071184
Hs.623485 Symbol;Acc:HGNC:274051 enoyl-CoA delta isomerase 2
ECI2 -0.30 2E-07 0.00074303 [Source:HGNC
Hs.15250 Symbol; Acc :HGNC : 146011
abhydrolase domain containing
ABHD3 0.30 2E-07 0.00074303 3 [Source:HGNC
Hs.611824 Symbol;Acc:HGNC: 187181 zinc and ring finger 3
ZNRF3 0.30 2E-07 0.00083429 [Source:HGNC
Hs.732114 Symbol; Acc :HGNC : 181261 proline rich 15 -like
PRR15L 0.30 2E-07 0.00088199 [Source:HGNC
Hs.368260 Symbol; Acc:HGNC:281491
Table 2B pears
Gene P adjusted p
on r value value Unigene ID Description
inositol 1,4,5-trisphosphate receptor interacting protein
ITPRIP -0.30 2E-07 0.00088498
[Source:HGNC
Hs.601061 Symbol;Acc:HGNC:293701
SH2B adaptor protein 3
SH2B3 -0.30 2E-07 0.00093126 [Source:HGNC
Hs.506784 Symbol;Acc:HGNC:296051
GTPase activating
Rap/RanGAP domain-like 3
GARNL3 0.30 3E-07 0.00094065
[Source:HGNC
Hs.29304 Symbol;Acc:HGNC:254251 transforming growth factor,
TGFB 1 -0.30 3E-07 0.00094065 beta 1 [Source:HGNC
Hs.645227 Symbol; Acc :HGNC : 117661
DIRAS family, GTP-binding
DIRAS2 0.29 3E-07 0.00096451 RAS-like 2 [Source:HGNC
Hs.165636 Symbol;Acc:HGNC: 193231 hook microtubule-tethering
HOOK1 0.29 3E-07 0.00096546 protein 1 [Source:HGNC
Hs.378836 Symbol;Acc:HGNC: 198841 myosin IIIA [Source:HGNC
MY03A 0.29 3E-07 0.000968
Hs.662630 Symbol; Acc :HGNC : 76011
adenylate kinase 2
AK2 -0.29 3E-07 0.000968 [Source:HGNC
Hs.470907 Symbol;Acc:HGNC:3621 transmembrane and coiled-coil domain family 1
TMCC1 0.29 3E-07 0.0010347
[Source:HGNC
Hs.477547 Symbol;Acc:HGNC:291161 growth arrest and DNA-
GADD45 damage-inducible, gamma
0.29 3E-07 0.001099
G [Source:HGNC
Hs.9701 Symbol;Acc:HGNC:40971
LOCI 001 Importin 5 pseudogene
0.29 3E-07 0.0011171
32815 Hs.629249 (LOC100132815)
protein phosphatase 1, regulatory (inhibitor) subunit
PPP1R1B 0.29 4E-07 0.0011917
IB [Source:HGNC
Hs.286192 Symbol;Acc:HGNC:92871
DAN domain family member 5, BMP antagonist
DAND5 0.29 4E-07 0.0012612
[Source:HGNC
Hs.331981 Symbol;Acc:HGNC:267801
SPARC related modular calcium binding 2
SMOC2 0.29 4E-07 0.0012612
[Source:HGNC
Hs.487200 Symbol;Acc:HGNC:203231
CUGBP, Elav-like family
CELF4 0.29 4E-07 0.0012612 member 4 [Source:HGNC
Hs.435976 Symbol;Acc:HGNC: 140151 caldesmon 1 [Source:HGNC
CALDl -0.29 4E-07 0.0012886
Hs.490203 Symbol;Acc:HGNC: 14411 arginyl-tRNA synthetase
RARS -0.29 4E-07 0.0012886 [Source:HGNC
Hs.707944 Symbol;Acc:HGNC:98701 golgin A2 pseudogene 5
GOLGA2
0.29 5E-07 0.0013126 [Source:HGNC
P5
Hs.524660 Symbol;Acc:HGNC:253151
Uncharacterized
LOC1005
-0.29 5E-07 0.0013126 LOC100506014 06014
Hs.570180 (LOC100506014)
transmembrane protein 8B
TMEM8
0.29 5E-07 0.0013573 [Source:HGNC
B
Hs.493808 Symbol;Acc:HGNC:214271 ring finger protein 165
R F165 0.29 5E-07 0.0013573 [Source:HGNC
Hs.501114 Symbol; Acc :HGNC : 316961 ADP-dependent glucokinase
ADPGK -0.29 5E-07 0.0013786 [Source:HGNC
Hs.741024 Symbol;Acc:HGNC:252501 myosin VIIA and Rab interacting protein
MYRIP 0.29 5E-07 0.0014151
[Source:HGNC
Hs.651362 Symbol;Acc:HGNC: 191561
LOC1005 Chondroitin Sulfate
0.29 5E-07 0.001443
07372 Hs.440297 Proteoglycan 4 Pseudogene 8 interleukin 17 receptor B
IL17RB 0.29 6E-07 0.0015482 [Source:HGNC
Hs.654970 Symbol;Acc:HGNC: 180151 phosphatase, orphan 2
PHOSPH
0.29 6E-07 0.0015748 [Source:HGNC
02
Hs.745233 Symbol;Acc:HGNC:283161 dual specificity phosphatase 3
DUSP3 -0.29 7E-07 0.0016772 [Source:HGNC
Hs.181046 Symbol; Acc :HGNC : 30691
1KB KB interacting protein
IKBIP -0.29 7E-07 0.0016772 [Source:HGNC
Hs.252543 Symbol;Acc:HGNC:264301
B-box and SPRY domain
BSPRY 0.29 7E-07 0.0016772 containing [Source:HGNC
Hs.715252 Symbol; Acc:HGNC: 182321
Uncharacterized
LOCI 001
-0.29 7E-07 0.0016772 LOC100130938 30938
Hs.710069 (LOC100130938)
TSR1, 20S rRNA
accumulation, homolog (S.
TSR1 -0.29 7E-07 0.001688
cerevisiae) [Source:HGNC
Hs.388170 Symbol;Acc:HGNC:255421 sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short
SEMA4D 0.28 7E-07 0.0017071 cytoplasmic domain,
(semaphorin) 4D
[Source:HGNC
Hs.494406 Symbol;Acc:HGNC: 107321 nuclear transcription factor, X- FX1 0.28 7E-07 0.0017071 box binding 1 [Source:HGNC
Hs.413074 Symbol;Acc:HGNC:78031
TIMP metallopeptidase
TIMP1 -0.28 7E-07 0.0017185 inhibitor 1 [Source:HGNC
Hs.522632 Symbol; Acc :HGNC : 118201 solute carrier family 25
(mitochondrial iron
SLC25A
-0.28 7E-07 0.0017185 transporter), member 37 37
[Source:HGNC
Hs.122514 Symbol;Acc:HGNC:29786] Y box binding protein 2
YBX2 0.28 8E-07 0.0017965 [Source:HGNC
Hs.678212 Symbol;Acc:HGNC: 179481
Table 2C pears adjusted p
Gene P
on r value value Unigene ID Description
ubiquitin-conjugating enzyme
UBE2R2 0.28 8E-07 0.0017965 E2R 2 [Source:HGNC
Hs.643648 Symbol; Acc :HGNC : 199071 zinc finger protein 793
Z F793 0.28 8E-07 0.0018537 [Source:HGNC
Hs.627869 Symbol;Acc:HGNC:331151 v-kit Hardy -Zuckerman 4 feline sarcoma viral oncogene
KIT 0.28 9E-07 0.001889
homolog [Source:HGNC
Hs.479754 Symbol;Acc:HGNC:63421
ELAV like neuron-specific RNA binding protein 4
ELAVL4 0.28 9E-07 0.0019498
[Source:HGNC
Hs.213050 Symbol;Acc:HGNC:33151 guanine nucleotide binding protein (G protein), alpha
GNAI2 -0.28 9E-07 0.0019975 inhibiting activity polypeptide
2 [Source:HGNC
Hs.77269 Symbol;Acc:HGNC:43851 high mobility group AT -hook
HMGA2 -0.28 9E-07 0.0019975 2 [Source:HGNC
Hs.505924 Symbol;Acc:HGNC:50091
ZFP3 zinc finger protein
ZFP3 0.28 9E-07 0.0019975 [Source:HGNC
Hs.48832 Symbol;Acc:HGNC: 128611 protein C receptor, endothelial
PROCR -0.28 1E-06 0.0019975 [Source:HGNC
Hs.647450 Symbol;Acc:HGNC:94521 dCMP deaminase
DCTD -0.28 1E-06 0.0020526 [Source:HGNC
Hs.183850 Symbol;Acc:HGNC:27101 janus kinase and microtubule
JAKMIP interacting protein 1
0.28 1E-06 0.0020632
1 [Source:HGNC
Hs.479066 Symbol;Acc:HGNC:264601 glutathione peroxidase 8
GPX8 -0.28 1E-06 0.0021966 (putative) [Source:HGNC
Hs.289044 Symbol; Acc :HGNC : 331001
IMP (inosine 5'-
IMPDH2 -0.28 1E-06 0.0021966
Hs.654400 monophosphate) dehydrogenase 2
[Source:HGNC
Symbol;Acc:HGNC:60531 core-binding factor, runt domain, alpha subunit 2;
CBFA2T
0.28 1E-06 0.0023153 translocated to, 2 2
[Source:HGNC
Hs.153934 Symbol; Acc:HGNC: 15361 mitochondrial assembly of ribosomal large subunit 1
MALSU1 -0.28 1E-06 0.0025573
[Source:HGNC
Hs.87385 Symbol;Acc:HGNC:217211 dual-specificity tyrosine-(Y)- phosphorylation regulated
DYRK4 -0.28 1E-06 0.0025639
kinase 4 [Source:HGNC
Hs.439530 Symbol;Acc:HGNC:30951 maternal embryonic leucine
MELK 0.28 1E-06 0.002574 zipper kinase [Source:HGNC
Hs.184339 Symbol;Acc:HGNC: 168701 centrosomal protein 68kDa
CEP68 0.28 1E-06 0.0026071 [Source:HGNC
Hs.709257 Symbol;Acc:HGNC:290761
Chromosome 9 open reading
C9orf38 0.28 1E-06 0.0026921
Hs.676462 frame 38 (C9orf38)
relaxin/insulin-like family peptide receptor 1
RXFP 1 0.28 1E-06 0.0026921
[Source:HGNC
Hs.196119 Symbol;Acc:HGNC: 197181
LOC4403
0.28 2E-06 0.0029068
35 Hs.390599 Uncharacterized LOC440335
DDB1 and CUL4 associated
DCAFIO 0.28 2E-06 0.0029068 factor 10 [Source:HGNC
Hs.118394 Symbol;Acc:HGNC:236861 nei endonuclease VHI-like 2 EIL2 -0.28 2E-06 0.0029068 (E. coli) [Source:HGNC
Hs.293818 Symbol; Acc:HGNC: 189561 prolyl 4-hydroxylase, alpha
P4HA1 -0.28 2E-06 0.0031179 polypeptide I [Source:HGNC
Hs.500047 Symbol;Acc:HGNC:85461 hyperpolarization activated cyclic nucleoti de-gated
HCN3 0.28 2E-06 0.003121 potassium channel 3
[Source:HGNC
Hs.706960 Symbol;Acc:HGNC: 191831 thioredoxin domain containing
TXNDC1
0.27 2E-06 0.003121 16 [Source:HGNC 6
Hs.532609 Symbol;Acc:HGNC: 199651
Fanconi anemia,
FANCG 0.27 2E-06 0.0031243 complementation group G
Hs.591084 [Source:HGNC Symbol;Acc:HGNC:3588] nbosomal RNA processing 12 homolog (S. cerevisiae)
RRP12 -0.27 2E-06 0.0031546
[Source:HGNC
Hs.434251 Symbol;Acc:HGNC:291001
FYVE, RhoGEF and PH domain containing 4
FGD4 0.27 2E-06 0.0031546
[Source:HGNC
Hs.117835 Symbol;Acc:HGNC: 191251
SWI/SNF related, matrix associated, actin dependent
SMARC regulator of chromatin,
-0.27 2E-06 0.0031783
D3 subfamily d, member 3
[Source:HGNC
Hs.649478 Symbol;Acc:HGNC: 111081 metallothionein 2A
MT2A -0.27 2E-06 0.0031909 [Source:HGNC
Hs.534330 Symbol; Acc :HGNC : 74061 ataxia, cerebellar, Cayman
ATCAY 0.27 2E-06 0.0031909 type [Source:HGNC
Hs.418055 Symbol;Acc:HGNC:7791
Chromosome 7 open reading
C7orf29 0.27 2E-06 0.0031909
Hs.655915 frame 29
F-box and WD repeat domain
FBXW4P containing 4 pseudogene 1
0.27 2E-06 0.0031909
1 [Source:HGNC
Hs.729589 Symbol; Acc :HGNC : 136091 dynactin 3 (p22)
DCTN3 0.27 2E-06 0.0031909 [Source:HGNC
Hs.511768 Symbol;Acc:HGNC:27131
S-phase cyclin A-associated protein in the ER
SCAPER 0.27 2E-06 0.0031909
[Source:HGNC
Hs.458986 Symbol;Acc:HGNC: 130811
ELAV like neuron-specific RNA binding protein 3
ELAVL3 0.27 2E-06 0.003211
[Source:HGNC
Hs.1701 Symbol;Acc:HGNC:33141 rhotekin 2 [Source:HGNC
RTKN2 0.27 2E-06 0.003211
Hs.58559 Symbol; Acc :HGNC : 193641 major facilitator superfamily domain containing 6
MFSD6 0.27 2E-06 0.003211
[Source:HGNC
Hs.418581 Symbol;Acc:HGNC:247111 dual specificity phosphatase 7
DUSP7 -0.27 2E-06 0.0032791 [Source:HGNC
Hs.591664 Symbol; Acc :HGNC : 30731 family with sequence
FAM65A -0.27 2E-06 0.0032798
Hs.152717 similarity 65, member A
Figure imgf000070_0001
acetylglucosaminyltransferase, isozyme A [Source:HGNC Symbol; Acc :HGNC : 70471 premature ovarian failure, IB
POF1B 0.27 3E-06 0.00448 [Source:HGNC
Hs.605756 Symbol;Acc:HGNC: 137111 transmembrane protein 45B
TMEM45
0.27 3E-06 0.0045247 [Source:HGNC
B
Hs.504301 Symbol;Acc:HGNC:251941 microRNA 1199
LOCI 132
0.27 3E-06 0.0046077 [Source:HGNC
30
Hs.372775 Symbol;Acc:HGNC:500811
InaD-like (Drosophila)
INADL 0.27 3E-06 0.0046361 [Source:HGNC
Hs.478125 Symbol;Acc:HGNC:288811
MOK protein kinase
MOK -0.27 3E-06 0.0046361 [Source:HGNC
Hs.104119 Symbol;Acc:HGNC:98331
FK506 binding protein 10, 65
FKBP10 -0.27 4E-06 0.0046893 kDa [Source:HGNC
Hs.463035 Symbol; Acc :HGNC : 181691 dachshund family transcription
DACH1 0.27 4E-06 0.0046893 factor 1 [Source:HGNC
Hs.129452 Symbol;Acc:HGNC:26631 dopey family member 1
DOPEY1 0.27 4E-06 0.0046893 [Source:HGNC
Hs.520246 Symbol;Acc:HGNC:211941 ribosomal RNA processing 9, small subunit (SSU) processome component,
RRP9 -0.27 4E-06 0.0048853
homolog (yeast)
[Source:HGNC
Hs.153768 Symbol;Acc:HGNC: 168291 neuralized E3 ubiquitin protein
NEURL l
0.27 4E-06 0.0050628 ligase IB [Source:HGNC B
Hs.91521 Symbol;Acc:HGNC:354221 neuron navigator 3
NAV3 -0.27 4E-06 0.0051749 [Source:HGNC
Hs.655301 Symbol; Acc:HGNC: 159981 actin-like 6B [Source:HGNC
ACTL6B 0.27 4E-06 0.0053872
Hs.259831 Symbol;Acc:HGNC: 1601 chromosome 11 open reading
Cl lorf74 -0.26 4E-06 0.0055105 frame 74 [Source:HGNC
Hs.159376 Symbol; Acc:HGNC:251421 p21 protein (Cdc42/Rac)- activated kinase 7
PAK7 0.26 4E-06 0.0055399
[Source:HGNC
Hs.32539 Symbol;Acc:HGNC: 159161 ubiquitin-conjugating enzyme
UBE2G1 -0.26 4E-06 0.0055417
Hs.741319 E2G 1 [Source:HGNC Symbol;Acc:HGNC: 12482] prominin 1 [Source:HGNC
PROM1 0.26 4E-06 0.0055417
Hs.736099 Symbol;Acc:HGNC:94541 coxsackie virus and adenovirus
CXADR 0.26 4E-06 0.0055417 receptor [Source:HGNC
Hs.738988 Symbol;Acc:HGNC:25591 acyl-CoA synthetase long- chain family member 6
ACSL6 0.26 4E-06 0.0055417
[Source:HGNC
Hs.14945 Symbol; Acc :HGNC : 164961 chromosome 12 open reading
C12orf5 -0.26 5E-06 0.0055417 frame 5 [Source:HGNC
Hs.504545 Symbol;Acc:HGNC: 11851
BMS1 ribosome biogenesis
BMS1 -0.26 5E-06 0.0055417 factor [Source:HGNC
Hs.10848 Symbol;Acc:HGNC:235051
LOC1005 Small nucleolar RNA host
-0.26 5E-06 0.0055667
07246 Hs.288215 gene 16 (non-protein coding) potassium voltage-gated channel, shaker-related subfamily, member 1 (episodic
KCNA1 0.26 5E-06 0.0057321
ataxia with myokymia)
[Source:HGNC
Hs.416139 Symbol;Acc:HGNC:62181
GCFC1-
0.26 5E-06 0.0057321
AS1 Hs.657123 PAXBP1 antisense RNA 1
Membrane-spanning 4-
MS4A8B 0.26 5E-06 0.0058046 domains, subfamily A,
Hs.150878 member 8
Ral GTPase activating protein,
RALGAP alpha subunit 2 (catalytic)
0.26 5E-06 0.0058046
A2 [Source:HGNC
Hs.472285 Symbol; Acc :HGNC : 162071
SCOl cytochrome c oxidase assembly protein
SCOl -0.26 5E-06 0.0058305
[Source:HGNC
Hs.14511 Symbol; Acc :HGNC : 106031
calcium channel, voltage-
CACNA1 dependent, N type, alpha IB
0.26 5E-06 0.0058305
B subunit [Source:HGNC
Hs.495522 Symbol;Acc:HGNC: 13891 roundabout, axon guidance receptor, homolog 3
ROB03 -0.26 5E-06 0.0058807
(Drosophila) [Source:HGNC
Hs.435621 Symbol;Acc:HGNC: 134331 calcium channel, voltage-
LOC1005 dependent, P/Q type, alpha 1 A
0.26 5E-06 0.0058807
07353 subunit [Source:HGNC
Hs.501632 Symbol;Acc:HGNC: 13881
Figure imgf000073_0001
tigger transposable element
TIGD3 0.26 6E-06 0.006538 derived 3 [Source:HGNC
Hs.632121 Symbol; Acc:HGNC: 183341 chromosome 1 open reading
Clorf226 0.26 6E-06 0.006632 frame 226 [Source:HGNC
Hs.447011 Symbol;Acc:HGNC:343511 alpha-2-glycoprotein 1, zinc-
AZGP1 0.26 6E-06 0.0066408 binding [Source:HGNC
Hs.568109 Symbol;Acc:HGNC:9101
SCYl-like 3 (S. cerevisiae)
SCYL3 0.26 6E-06 0.0066915 [Source:HGNC
Hs.435560 Symbol;Acc:HGNC: 192851 leucine rich repeat containing
LRRC10
0.26 7E-06 0.0067235 10B [Source:HGNC B
Hs.441122 Symbol;Acc:HGNC:372151
Rab interacting lysosomal
RILPL2 -0.26 7E-06 0.0067865 protein-like 2 [Source:HGNC
Hs.488173 Symbol;Acc:HGNC:287871 chondrolectin [Source:HGNC
CHODL 0.26 7E-06 0.0067865
Hs.283725 Symbol;Acc:HGNC: 178071 transcription factor CP2-like 1
TFCP2L1 0.26 7E-06 0.0067865 [Source:HGNC
Hs.156471 Symbol;Acc:HGNC: 179251 solute carrier family 37
(glucose-6-phosphate
SLC37A
0.26 7E-06 0.0068766 transporter), member 1 1
[Source:HGNC
Hs.735440 Symbol; Acc :HGNC : 110241 desmoplakin [Source:HGNC
DSP 0.26 7E-06 0.0068766
Hs.519873 Symbol;Acc:HGNC:30521 catenin (cadherin-associated protein), alpha 2
CTNNA2 0.26 7E-06 0.0070127
[Source:HGNC
Hs.167368 Symbol;Acc:HGNC:25101 regulatory subunit of type II PKA R-subunit (Rlla) domain
RIIAD1 0.26 7E-06 0.0072593
containing 1 [Source:HGNC
Hs.297967 Symbol;Acc:HGNC:266861
MRG/MORF4L binding
C20orf20 -0.26 7E-06 0.0072593
Hs.590870 protein
Di George syndrome critical
DGCR2 0.26 7E-06 0.0072593 region gene 2 [Source:HGNC
Hs.517357 Symbol;Acc:HGNC:28451 lectin, galactoside-binding,
LGALS1 -0.26 7E-06 0.0072816 soluble, 1 [Source:HGNC
Hs.445351 Symbol;Acc:HGNC:65611 geranylgeranyl diphosphate
GGPS1 0.26 8E-06 0.0073652 synthase 1 [Source:HGNC
Hs.730768 Symbol;Acc:HGNC:42491 methionine sulfoxide reductase
MSRA -0.26 8E-06 0.0074691 A [Source:HGNC
Hs.490981 Symbol; Acc :HGNC : 73771 sphingomyelin
phosphodiesterase 3, neutral membrane (neutral
SMPD3 0.26 8E-06 0.0075306
sphingomyelinase II)
[Source:HGNC
Hs.368421 Symbol;Acc:HGNC: 142401 tetratncopeptide repeat domain
TTC1 -0.26 8E-06 0.0076773 1 [Source:HGNC
Hs.707461 Symbol; Acc :HGNC : 123911
HEAT repeat containing 2
HEATR2 -0.26 8E-06 0.0077073 [Source:HGNC
Hs.596432 Symbol;Acc:HGNC:260131 zinc finger protein 397
Z F397 0.26 8E-06 0.0077073 [Source:HGNC
Hs.697113 Symbol;Acc:HGNC: 188181 microtubule associated serine/threonine kinase 1
MAST1 0.26 8E-06 0.0077073
[Source:HGNC
Hs.227489 Symbol;Acc:HGNC: 190341 interferon-induced protein with tetratncopeptide repeats 5
IFIT5 -0.26 8E-06 0.0077861
[Source:HGNC
Hs.252839 Symbol;Acc:HGNC: 133281 syntaxin binding protein 5 -like
STXBP5
0.26 8E-06 0.0078956 [Source:HGNC
L
Hs.477315 Symbol;Acc:HGNC:307571
KIAA184 KIAA1841 [Source:HGNC
0.26 9E-06 0.0080616
1 Hs.468653 Symbol;Acc:HGNC:293871
SRY (sex determining region
SOX4 0.26 9E-06 0.0080845 Y)-box 4 [Source:HGNC
Hs.654258 Symbol;Acc:HGNC: 112001
WW and C2 domain
WWC2 -0.26 9E-06 0.0080845 containing 2 [Source:HGNC
Hs.333179 Symbol;Acc:HGNC:241481
N(alpha)-acetyltransferase 35, NatC auxiliary subunit
NAA35 0.26 9E-06 0.0084139
[Source:HGNC
Hs.436098 Symbol;Acc:HGNC:243401
C14orfl4 L-3 -hy droxyproline
-0.26 9E-06 0.0084844
9 Hs.729061 dehydratase (trans-)
LOCI 001 Uncharacterized
0.26 9E-06 0.0084844
34361 Hs.335413 LOC100134361
phosphatidylinositol glycan anchor biosynthesis, class X
PIGX 0.26 9E-06 0.008516
[Source:HGNC
Hs.608993 Symbol;Acc:HGNC:260461
Figure imgf000076_0001
leucine rich repeat containing
LRRC4 0.25 1E-05 0.010571 4 [Source:HGNC
Hs.655003 Symbol; Acc:HGNC: 155861 solute carrier family 35 (UDP- xylose/UDP-N- acetylglucosamine
SLC35B4 -0.25 1E-05 0.010767
transporter), member B4
[Source:HGNC
Hs.490181 Symbol;Acc:HGNC:205841 sortilin-related receptor, L(DLR class) A repeats
SORL1 0.25 1E-05 0.010767
containing [Source:HGNC
Hs.368592 Symbol; Acc:HGNC: 111851 serglycin [Source:HGNC
SRGN -0.25 1E-05 0.010767
Hs.1908 Symbol; Acc :HGNC : 93611
zinc finger protein 254
Z F254 0.25 1E-05 0.010767 [Source:HGNC
Hs.729302 Symbol; Acc :HGNC : 130471 rhomboid, veinlet-like 3
RHBDL3 0.25 1E-05 0.010814 (Drosophila) [Source:HGNC
Hs.655027 Symbol;Acc:HGNC: 165021 tachykinin 3 [Source:HGNC
TAC3 0.25 1E-05 0.010859
Hs.9730 Symbol;Acc:HGNC: 115211 exosome component 1
EXOSC1 -0.25 1E-05 0.010859 [Source:HGNC
Hs.632089 Symbol;Acc:HGNC: 172861 zinc finger protein 681
Z F681 0.25 1E-05 0.010871 [Source:HGNC
Hs.399952 Symbol;Acc:HGNC:264571 bromodomain adjacent to zinc finger domain, 2B
BAZ2B 0.25 1E-05 0.011184
[Source:HGNC
Hs.470369 Symbol;Acc:HGNC:9631 leucine rich repeat (in FLU) interacting protein 2
LRRFIP2 -0.25 1E-05 0.011229
[Source:HGNC
Hs.475319 Symbol; Acc :HGNC : 67031
translocase of outer mitochondrial membrane 7
TOMM7 -0.25 1E-05 0.011452 homolog (yeast)
[Source:HGNC
Hs.112318 Symbol;Acc:HGNC:216481 membrane associated guanylate kinase, WW and
MAGI3 0.25 1E-05 0.011555 PDZ domain containing 3
[Source:HGNC
Hs.486189 Symbol;Acc:HGNC:296471 zinc finger protein 607
Z F607 0.25 1E-05 0.011633 [Source:HGNC
Hs.116622 Symbol;Acc:HGNC:281921 potassium large conductance calcium-activated channel,
KC MB
0.25 1E-05 0.01178 subfamily M, beta member 4 4
[Source:HGNC
Hs.598920 Symbol;Acc:HGNC:62891 myotubularin related protein 7
MTMR7 0.25 1E-05 0.01178 [Source:HGNC
Hs.625674 Symbol;Acc:HGNC:74541
Fl l receptor [Source:HGNC
FUR 0.25 2E-05 0.012048
Hs.517293 Symbol;Acc:HGNC: 146851 peptidylprolyl isomerase B
PPIB -0.25 2E-05 0.012066 (cyclophilin B) [Source:HGNC
Hs.434937 Symbol;Acc:HGNC:92551 spermatogenesis associated 20
SPATA2
-0.25 2E-05 0.012085 [Source:HGNC
0
Hs.103147 Symbol;Acc:HGNC:261251 dopa decarboxylase (aromatic L-amino acid decarboxylase)
DDC 0.25 2E-05 0.012274
[Source:HGNC
Hs.359698 Symbol;Acc:HGNC:27191 derlin 2 [Source:HGNC
DERL2 -0.25 2E-05 0.012395
Hs.730726 Symbol; Acc :HGNC : 179431
phosphatidylinositol 4-kinase
PI4K2A -0.25 2E-05 0.012395 type 2 alpha [Source:HGNC
Hs.25300 Symbol; Acc :HGNC : 300311
hydroxy steroid (11 -beta)
HSD11B dehydrogenase 2
0.25 2E-05 0.012395
2 [Source:HGNC
Hs.1376 Symbol;Acc:HGNC:52091 calmodulin regulated spectrin-
CAMSA associated protein family,
0.25 2E-05 0.012395
P3 member 3 [Source:HGNC
Hs.17686 Symbol;Acc:HGNC:293071
POU class 2 homeobox 1
POU2F1 0.25 2E-05 0.012395 [Source:HGNC
Hs.283402 Symbol; Acc :HGNC : 92121 ubiquitin-like domain containing CTD phosphatase 1
UBLCP1 -0.25 2E-05 0.012395
[Source:HGNC
Hs.624592 Symbol;Acc:HGNC:281101 transmembrane protein 158
TMEM15 (gene/p seudogene)
-0.25 2E-05 0.012556
8 [Source:HGNC
Hs.740403 Symbol; Acc :HGNC : 302931
chromosome 1 open reading
Clorfl74 -0.25 2E-05 0.012693 frame 174 [Source:HGNC
Hs.103939: Symbol;Acc:HGNC:279151 shroom family member 3
SHROO
0.25 2E-05 0.012725 [Source:HGNC
M3
Hs.432504 Symbol; Acc :HGNC : 304221 phosphodiesterase 7A
PDE7A 0.25 2E-05 0.013121 [Source:HGNC
Hs.728847 Symbol;Acc:HGNC:87911 family with sequence similarity 84, member A
FAM84A 0.25 2E-05 0.013133
[Source:HGNC
Hs.260855 Symbol;Acc:HGNC:207431 sacsin molecular chaperone
SACS -0.25 2E-05 0.013139 [Source:HGNC
Hs.159492 Symbol;Acc:HGNC: 105191
E74-like factor 3 (ets domain transcription factor, epithelial-
ELF 3 0.25 2E-05 0.013142
specific ) [Source:HGNC
Hs.743671 Symbol;Acc:HGNC:33181 zinc finger protein 652
Z F652 0.25 2E-05 0.013142 [Source:HGNC
Hs.463375 Symbol;Acc:HGNC:291471 zinc finger protein 43
Z F43 0.25 2E-05 0.013142 [Source:HGNC
Hs.741831 Symbol; Acc :HGNC : 131091 nephronophthisis 1 (juvenile)
NPHP 1 -0.25 2E-05 0.013186 [Source:HGNC
Hs.280388 Symbol;Acc:HGNC:79051 ecotropic viral integration site
EVI2A -0.25 2E-05 0.013471 2A [Source:HGNC
Hs.662383 Symbol;Acc:HGNC:34991 prospero homeobox 1
PROX1 0.25 2E-05 0.013517 [Source:HGNC
Hs.744931 Symbol;Acc:HGNC:94591 phosphatidylinositol glycan anchor biosynthesis, class K
PIGK -0.25 2E-05 0.013543
[Source:HGNC
Hs.178305 Symbol;Acc:HGNC:89651
ATP -binding cassette, subfamily C (CFTR/MRP),
ABCC8 0.25 2E-05 0.01359
member 8 [Source:HGNC
Hs.54470 Symbol;Acc:HGNC:591
Myeloid/lymphoid or mixed-
MLL3 0.25 2E-05 0.013685
Hs.647120 lineage leukemia 3
RIB43 A domain with coiled-
RIBC2 0.25 2E-05 0.013685 coils 2 [Source:HGNC
Hs.475110 Symbol;Acc:HGNC: 132411 nitric oxide synthase 1 (neuronal) adaptor protein
NOSIAP 0.25 2E-05 0.013685
[Source:HGNC
Hs.731942 Symbol;Acc:HGNC: 168591 islet cell autoantigen 1, 69kDa
ICA1 0.25 2E-05 0.013732 [Source:HGNC
Hs.487561 Symbol;Acc:HGNC:53431 MIR600 host gene (non¬
MIR600 protein coding)
0.25 2E-05 0.013846
HG [Source:HGNC
Hs.708072 Symbol;Acc:HGNC:236421 centrosomal protein 350kDa
CEP350 0.25 2E-05 0.013853 [Source:HGNC
Hs.413045 Symbol;Acc:HGNC:242381 mitogen-activated protein
MAP3K1 kinase kinase kinase 13
0.25 2E-05 0.014062
3 [Source:HGNC
Hs.715977 Symbol;Acc:HGNC:68521 shisa family member 5
SHISA5 -0.25 2E-05 0.014141 [Source:HGNC
Hs.414579 Symbol; Acc :HGNC : 303761
MYLK anti sense RNA 1
MYLK-
0.25 2E-05 0.014153 [Source:HGNC AS1
Hs.556600 Symbol;Acc:HGNC:424401 inositol polyphosphate-5- phosphatase, 40kDa
INPP5A -0.25 2E-05 0.014153
[Source:HGNC
Hs.715308 Symbol; Acc :HGNC : 60761
FLJ3083
0.25 2E-05 0.014167
8 Hs.503210 Uncharacterized LOC400955 hi stone cluster 1, H2bg
HIST1H2
0.25 2E-05 0.014251 [Source:HGNC BG
Hs.591809 Symbol;Acc:HGNC:47461 insulinoma-associated 1
INSM1 0.25 2E-05 0.014251 [Source:HGNC
Hs.89584 Symbol; Acc :HGNC : 60901 potassium large conductance calcium-activated channel,
KC MB
0.25 2E-05 0.014342 subfamily M, beta member 2 2
[Source:HGNC
Hs.478368 Symbol;Acc:HGNC:62861
LOC2530
0.25 2E-05 0.014342
39 Hs.594170 Uncharacterized LOC253039 transmembrane protein 108
TMEM10
0.25 2E-05 0.014371 [Source:HGNC 8
Hs.191616 Symbol;Acc:HGNC:284511 zinc finger protein 713
Z F713 0.25 2E-05 0.014634 [Source:HGNC
Hs.737301 Symbol;Acc:HGNC:220431 protein phosphatase,
Mg2+/Mn2+ dependent, IF
PPM1F -0.25 2E-05 0.015067
[Source:HGNC
Hs.738863 Symbol;Acc:HGNC: 193881
KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein
KDELR3 -0.25 2E-05 0.015067
retention receptor 3
Hs.745072 [Source:HGNC Symbol; Acc :HGNC : 6306] integrator complex subunit 7
INTS7 0.24 2E-05 0.015131 [Source:HGNC
Hs.369285 Symbol;Acc:HGNC:244841 peripheral myelin protein 22
PMP22 -0.24 2E-05 0.015302 [Source:HGNC
Hs.372031 Symbol;Acc:HGNC:91181
C7orf42 -0.24 2E-05 0.015389 Hs.488478 Transmembrane protein 248 v-myb avian myeloblastosis viral oncogene homolog
MYB 0.24 2E-05 0.015389
[Source:HGNC
Hs.721437 Symbol;Acc:HGNC:75451 retention in endoplasmic reticulum sorting receptor 1
RER1 -0.24 2E-05 0.015414
[Source:HGNC
Hs.525527 Symbol; Acc :HGNC : 303091 deafness, autosomal dominant
DFNA5 -0.24 2E-05 0.015414 5 [Source:HGNC
Hs.599805 Symbol;Acc:HGNC:28101 claudin 3 [Source:HGNC
CLDN3 0.24 2E-05 0.015457
Hs.647023 Symbol;Acc:HGNC:20451 serine
hydroxymethyltransferase 2
SHMT2 -0.24 2E-05 0.015891 (mitochondrial)
[Source:HGNC
Hs.741179 Symbol;Acc:HGNC: 108521 myocardial infarction associated transcript (non¬
MIAT 0.24 2E-05 0.015979 protein coding)
[Source:HGNC
Hs.517502 Symbol;Acc:HGNC:334251 hook microtubule-tethering
HOOK2 0.24 2E-05 0.016056 protein 2 [Source:HGNC
Hs.30792 Symbol;Acc:HGNC: 198851
RIMS binding protein 2
RIMBP2 0.24 2E-05 0.016307 [Source:HGNC
Hs.657441 Symbol; Acc :HGNC : 303391 proline/histidine/glycine-rich 1
PHGR1 0.24 3E-05 0.01633 [Source:HGNC
Hs.447537 Symbol; Acc :HGNC : 372261 guanine nucleotide binding protein-like 3 (nucleolar)
GNL3 -0.24 3E-05 0.016531
[Source:HGNC
Hs.313544 Symbol;Acc:HGNC:299311 nudix (nucleoside diphosphate linked moiety X)-type motif 14
NUDT14 0.24 3E-05 0.016531
[Source:HGNC
Hs.526432 Symbol;Acc:HGNC:20141] potassium voltage-gated
channel, subfamily H (eag-
KC H8 0.24 3E-05 0.016531 related), member 8
[Source:HGNC
Hs.475656 Symbol; Acc:HGNC: 188641
Chromosome 1 open reading
Clorfl72 0.24 3E-05 0.016531
Hs.188881 frame 172
thiosulfate sulfurtransferase
(rhodanese)-like domain
TSTD1 0.24 3E-05 0.017165
containing 1 [Source:HGNC
Hs.720030 Symbol;Acc:HGNC:354101 headcase homolog
HECA 0.24 3E-05 0.017165 (Drosophila) [Source:HGNC
Hs.197644 Symbol;Acc:HGNC:210411 glutamate decarboxylase 2 (pancreatic islets and brain,
GAD2 0.24 3E-05 0.017227
65kDa) [Source:HGNC
Hs.231829 Symbol;Acc:HGNC:40931 zinc finger protein 140
Z F140 0.24 3E-05 0.01741 [Source:HGNC
Hs.181552 Symbol;Acc:HGNC: 129251 transmembrane 6 superfamily
TM6SF2 0.24 3E-05 0.017419 member 2 [Source:HGNC
Hs.531624 Symbol;Acc:HGNC: 118611 ubiquitin protein ligase E3C
UBE3C -0.24 3E-05 0.01757 [Source:HGNC
Hs.118351 Symbol;Acc:HGNC: 168031
PDZ and LIM domain 2
PDLIM2 -0.24 3E-05 0.017641 (mystique) [Source:HGNC
Hs.632034 Symbol; Acc :HGNC : 139921 protein unc-79 homolog
UNC79 0.24 3E-05 0.017641 [Source:RefSeq
Hs.126561 peptide;Acc: P 0658691
Dab, mitogen-responsive phosphoprotein, homolog 2
DAB2 -0.24 3E-05 0.017641
(Drosophila) [Source:HGNC
Hs.732550 Symbol;Acc:HGNC:26621
G kinase anchoring protein 1
GKAP1 0.24 3E-05 0.017711 [Source:HGNC
Hs.522255 Symbol; Acc :HGNC : 174961
LOC1005
-0.24 3E-05 0.018011
05634 Hs.562920 Uncharacterized LOC541471
GRB2 associated, regulator of
FAM59A 0.24 3E-05 0.018219
Hs.444314 MAPK1
aldehyde dehydrogenase 5
ALDH5A family, member Al
0.24 3E-05 0.018219
1 [Source:HGNC
Hs.640460 Symbol;Acc:HGNC:4081 spermidine synthase
SRM -0.24 3E-05 0.018296
Hs.76244 [Source:HGNC Symbol;Acc:HGNC: 11296] parvin, beta [Source:HGNC
PARVB -0.24 3E-05 0.018311
Hs.475074 Symbol;Acc:HGNC: 146531
CWF19-like 1, cell cycle
CWF19L control (S. pombe)
-0.24 3E-05 0.018311
1 [Source:HGNC
Hs.215502 Symbol;Acc:HGNC:256131
NFAT activating protein with FAM1 0.24 3E-05 0.018399 IT AM motif 1 [Source:HGNC
Hs.436677 Symbol;Acc:HGNC:298721
FXYD domain containing ion transport regulator 6
FXYD6 0.24 3E-05 0.018411
[Source:HGNC
Hs.744850 Symbol;Acc:HGNC:40301
MHC class I polypeptide- related sequence A
MICA -0.24 3E-05 0.018511
[Source:HGNC
Hs.130838 Symbol; Acc :HGNC : 70901 synapse defective 1, Rho GTPase, homolog 1 (C.
SYDE1 -0.24 3E-05 0.018537
elegans) [Source:HGNC
Hs.732011 Symbol;Acc:HGNC:258241
Purkinje cell protein 4
PCP4 0.24 3E-05 0.018642 [Source:HGNC
Hs.80296 Symbol;Acc:HGNC:87421
UTP15, U3 small nucleolar nbonucleoprotein, homolog (S.
UTP15 -0.24 3E-05 0.018733
cerevisiae) [Source:HGNC
Hs.406703 Symbol;Acc:HGNC:257581 cyclin-dependent kinase 19
CDK19 0.24 3E-05 0.019198 [Source:HGNC
Hs.744895 Symbol;Acc:HGNC: 193381 cysteine-rich, angiogenic
CYR61 -0.24 3E-05 0.019232 inducer, 61 [Source:HGNC
Hs.8867 Symbol;Acc:HGNC:26541
E2F transcription factor 7
E2F7 -0.24 3E-05 0.019347 [Source:HGNC
Hs.416375 Symbol;Acc:HGNC:238201 charged multivesicular body
CHMP6 -0.24 3E-05 0.019445 protein 6 [Source:HGNC
Hs.514560 Symbol;Acc:HGNC:256751 leucine rich repeat and Ig domain containing 2
LING02 0.24 3E-05 0.019445
[Source:HGNC
Hs.745029 Symbol;Acc:HGNC:212071 profilin 1 [Source:HGNC
PFN1 -0.24 3E-05 0.019445
Hs.494691 Symbol;Acc:HGNC:88811 zinc finger and BTB domain
ZBTB47 -0.24 3E-05 0.019445
Hs.409561 containing 47 [Source:HGNC Symbol;Acc:HGNC:26955]
TMEM17
0.24 3E-05 0.019445
8 Hs.40808 Transmembrane protein 178 A dishevelled associated activator of morphogenesis 1
DA AMI 0.24 3E-05 0.019445
[Source:HGNC
Hs.19156 Symbol; Acc :HGNC : 181421
RALY RNA binding protein¬
RALYL 0.24 3E-05 0.019445 like [Source:HGNC
Hs.121663 Symbol;Acc:HGNC:270361 family with sequence
FAM184 similarity 184, member A
0.24 3E-05 0.019475
A [Source:HGNC
Hs.443789 Symbol;Acc:HGNC:209911 histidyl-tRNA synthetase
HARS -0.24 3E-05 0.019822 [Source:HGNC
Hs.595156 Symbol;Acc:HGNC:48161
HOXB cluster anti sense RNA
HOXB-
0.24 3E-05 0.019822 3 [Source:HGNC
AS3
Hs.660088 Symbol;Acc:HGNC:402831 exophilin 5 [Source:HGNC
EXPH5 0.24 3E-05 0.019822
Hs.28540 Symbol;Acc:HGNC:305781 spermatid perinuclear RNA binding protein
STRBP 0.24 3E-05 0.019822
[Source:HGNC
Hs.287659 Symbol; Acc :HGNC : 164621 sorting nexin 8 [Source:HGNC
SNX8 -0.24 3E-05 0.019963
Hs.584900 Symbol;Acc:HGNC: 149721 kelch-like family member 7
KLHL7 -0.24 4E-05 0.020218 [Source:HGNC
Hs.708589 Symbol; Acc:HGNC: 156461 cytochrome P450, family 39,
CYP39A subfamily A, polypeptide 1
0.24 4E-05 0.020504
1 [Source:HGNC
Hs.387367 Symbol; Acc :HGNC : 174491 topoisom erase (DNA) II binding protein 1
TOPBP1 0.24 4E-05 0.020504
[Source:HGNC
Hs.664220 Symbol;Acc:HGNC: 170081 myelin transcription factor 1
MYT1 0.24 4E-05 0.020822 [Source:HGNC
Hs.279562 Symbol; Acc :HGNC : 76221 epithelial splicing regulatory
ESRP1 0.24 4E-05 0.021176 protein 1 [Source:HGNC
Hs.487471 Symbol;Acc:HGNC:259661 phosphatidylinositol glycan anchor biosynthesis, class O
PIGO 0.24 4E-05 0.021176
[Source:HGNC
Hs.735712 Symbol;Acc:HGNC:232151 transmembrane protein 3 OB
TMEM30
0.24 4E-05 0.021176 [Source:HGNC
B
Hs.146180 Symbol;Acc:HGNC:272541
FLJ1123
0.24 4E-05 0.021176
5 Hs.591264 Uncharacterized FLJ11235
Fas apoptotic inhibitory
FAIM2 0.24 4E-05 0.021176 molecule 2 [Source:HGNC
Hs.567424 Symbol; Acc :HGNC : 170671 calcium channel, voltage- dependent, gamma subunit 4
CACNG4 0.24 4E-05 0.021176
[Source:HGNC
Hs.514423 Symbol;Acc:HGNC: 14081 syntaxin 19 [Source:HGNC
STX19 0.24 4E-05 0.021176
Hs.679768 Symbol; Acc :HGNC : 193001 receptor accessory protein 1
REEP1 0.24 4E-05 0.021216 [Source:HGNC
Hs.368884 Symbol;Acc:HGNC:257861 leucine rich repeat containing
LRRC16
0.24 4E-05 0.021216 16A [Source:HGNC A
Hs.670507 Symbol;Acc:HGNC:215811 myosin IC [Source:HGNC
MYOIC -0.24 4E-05 0.021611
Hs.286226 Symbol;Acc:HGNC:75971
PET112 -0.24 4E-05 0.021864 Hs.119316 PET 112 homolog (yeast)
RAB3 A interacting protein
RAB3IP 0.24 4E-05 0.021971 [Source:HGNC
Hs.258209 Symbol;Acc:HGNC: 165081 sorting nexin 17
SNX17 -0.24 4E-05 0.022141 [Source:HGNC
Hs.278569 Symbol;Acc:HGNC: 149791 tetratricopeptide repeat domain
TTC39A 0.24 4E-05 0.022304 39A [Source:HGNC
Hs.112949 Symbol; Acc:HGNC: 186571 peroxiredoxin 6
PRDX6 -0.24 4E-05 0.022796 [Source:HGNC
Hs.120 Symbol;Acc:HGNC: 167531 actin binding LIM protein family, member 2
ABLIM2 0.24 4E-05 0.022796
[Source:HGNC
Hs.233404 Symbol;Acc:HGNC: 191951 erythrocyte membrane protein
EPB41L5 0.24 4E-05 0.022833 band 4.1 like 5 [Source:HGNC
Hs.369232 Symbol;Acc:HGNC: 198191 fumarylacetoacetate hydrolase (fumarylacetoacetase)
FAH -0.24 4E-05 0.022833
[Source:HGNC
Hs.73875 Symbol;Acc:HGNC:35791 regulating synaptic membrane
RIM S3 0.24 4E-05 0.022886 exocytosis 3 [Source:HGNC
Hs.654808 Symbol;Acc:HGNC:212921 trefoil factor 3 (intestinal)
TFF3 0.24 4E-05 0.022999 [Source:HGNC
Hs.612366 Symbol;Acc:HGNC: 117571 family with sequence
FAM126 similarity 126, member A
-0.24 4E-05 0.022999
A [Source:HGNC
Hs.85603 Symbol;Acc:HGNC:245871 branched chain ketoacid dehydrogenase kinase
BCKDK -0.24 4E-05 0.023014
[Source:HGNC
Hs.513520 Symbol; Acc :HGNC : 169021
WD repeat domain 43
WDR43 -0.24 4E-05 0.023014 [Source:HGNC
Hs.709228 Symbol;Acc:HGNC:289451 zinc finger protein 253
Z F253 0.24 4E-05 0.023088 [Source:HGNC
Hs.735432 Symbol;Acc:HGNC: 134971
EVIP4, U3 small nucleolar rib onucl eoprotein
IMP4 -0.24 4E-05 0.023088
[Source:HGNC
Hs.91579 Symbol;Acc:HGNC:308561
FAM211 Family with sequence
0.24 4E-05 0.023088
A Hs.25425 similarity 211, member A
NADH dehydrogenase (ubiquinone) 1 beta
NDUFB8 -0.24 4E-05 0.023088 subcomplex, 8, 19kDa
[Source:HGNC
Hs.523215 Symbol; Acc :HGNC : 77031
epithelial cell adhesion
EPCAM 0.24 5E-05 0.02412 molecule [Source:HGNC
Hs.713827 Symbol;Acc:HGNC: 115291 angiotensin I converting
ACE 0.24 5E-05 0.024328 enzyme [Source:HGNC
Hs.298469 Symbol;Acc:HGNC:27071
CKLF-like MARVEL transmembrane domain
CMTM3 -0.24 5E-05 0.024779
containing 3 [Source:HGNC
Hs.298198 Symbol; Acc :HGNC : 191741 kallikrein-related peptidase 12
KLK12 0.24 5E-05 0.024825 [Source:HGNC
Hs.411572 Symbol; Acc :HGNC : 63601 zinc finger protein 584
Z F584 -0.24 5E-05 0.024825 [Source:HGNC
Hs.439551 Symbol;Acc:HGNC:273181 pleckstrin homology domain
PLEKHA containing, family A member 6
0.24 5E-05 0.024843
6 [Source:HGNC
Hs.253146 Symbol;Acc:HGNC: 170531
RIC3 acetylcholine receptor
RIC3 0.24 5E-05 0.024862
Hs.231850 chaperone [Source:HGNC Symbol;Acc:HGNC:30338]
DnaJ (Hsp40) homolog,
41704 0.24 5E-05 0.024919
Hs.187269 subfamily C, member 24
v-maf avian
musculoaponeurotic
MAFF -0.23 5E-05 0.025325 fibrosarcoma oncogene
homolog F [Source:HGNC
Hs.517617 Symbol;Acc:HGNC:67801 suppression of tumorigenicity
ST18 0.23 5E-05 0.025737 18, zinc finger [Source:HGNC
Hs.655499 Symbol; Acc:HGNC: 186951 zinc finger protein 585B
Z F585
0.23 5E-05 0.02578 [Source:HGNC
B
Hs.390568 Symbol;Acc:HGNC:309481 aminoadipate-semi aldehyde
AASS -0.23 5E-05 0.025786 synthase [Source:HGNC
Hs.156738 Symbol; Acc :HGNC : 173661 synaptotagmin VII
SYT7 0.23 5E-05 0.026128 [Source:HGNC
Hs.684589 Symbol; Acc :HGNC : 115141 chromosome 4 open reading
C4orf29 0.23 5E-05 0.026696 frame 29 [Source:HGNC
Hs.445817 Symbol;Acc:HGNC:261111 testis specific, 10
TSGA10 0.23 5E-05 0.026833 [Source:HGNC
Hs.120267 Symbol;Acc:HGNC: 149271 gem (nuclear organelle) associated protein 5
GEMIN5 -0.23 5E-05 0.027013
[Source:HGNC
Hs.483921 Symbol;Acc:HGNC:200431 potassium channel tetramerization domain
KCTD16 0.23 5E-05 0.027119
containing 16 [Source:HGNC
Hs.7093 Symbol;Acc:HGNC:292441 interleukin 6 signal transducer
IL6ST -0.23 5E-05 0.027347 [Source:HGNC
Hs.462308 Symbol; Acc :HGNC : 60211
ashl (absent, small, or homeotic)-like (Drosophila)
ASH1L 0.23 5E-05 0.027449
[Source:HGNC
Hs.491060 Symbol;Acc:HGNC: 190881 microtubule-associated protein
MAP4 -0.23 6E-05 0.027449 4 [Source:HGNC
Hs.517949 Symbol;Acc:HGNC:68621
NOP2/Sun domain family,
NSUN7 0.23 6E-05 0.027512 member 7 [Source:HGNC
Hs.570821 Symbol;Acc:HGNC:258571
SLC35D solute carrier family 35,
0.23 6E-05 0.027644
3 Hs.369703 member D3 [Source:HGNC Symbol;Acc:HGNC: 15621]
E2F transcription factor 8
E2F8 0.23 6E-05 0.027768 [Source:HGNC
Hs.627312 Symbol;Acc:HGNC:247271 solute carrier family 44,
SLC44A
0.23 6E-05 0.028161 member 3 [Source:HGNC 3
Hs.483423 Symbol;Acc:HGNC:286891 protein phosphatase 1,
PPP1R13 regulatory subunit 13B
0.23 6E-05 0.028375
B [Source:HGNC
Hs.740057 Symbol;Acc:HGNC: 149501 interferon-related
developmental regulator 2
IFRD2 -0.23 6E-05 0.02844
[Source:HGNC
Hs.743323 Symbol;Acc:HGNC:54571 neuronal cell adhesion RCAM 0.23 6E-05 0.028523 molecule [Source:HGNC
Hs.21422 Symbol; Acc :HGNC : 79941
ER membrane protein complex
C15or£24 -0.23 6E-05 0.028632
Hs.160565 subunit 7
minichromosome maintenance complex component 9
MCM9 0.23 6E-05 0.028632
[Source:HGNC
Hs.279008 Symbol;Acc:HGNC:214841 glutathione S-transferase
GSTOl -0.23 6E-05 0.028632 omega 1 [Source :HGNC
Hs.190028 Symbol; Acc :HGNC : 133121
ArfGAP with RhoGAP domain, ankyrin repeat and PH
ARAP3 -0.23 6E-05 0.028632
domain 3 [Source:HGNC
Hs.726187 Symbol;Acc:HGNC:240971
LOC1005 Uncharacterized
0.23 6E-05 0.028779
05912 Hs.270471 LOC100505912
KIAA018
0.23 6E-05 0.028784
2 Hs.461647 Gsel coiled-coil protein
phosphoinositide-3 -kinase, regulatory subunit 3 (gamma)
PIK3R3 0.23 6E-05 0.029718
[Source:HGNC
Hs.655387 Symbol;Acc:HGNC:89811 calumenin [Source:HGNC
CALU -0.23 6E-05 0.029718
Hs.7753 Symbol;Acc:HGNC: 14581 complement component 1, q subcomponent binding protein
C1QBP -0.23 6E-05 0.029718
[Source:HGNC
Hs.555866 Symbol;Acc:HGNC: 12431
MLLT4 anti sense RNA 1
MLLT4-
0.23 6E-05 0.029718 (head to head) [Source:HGNC AS1
Hs.520556 Symbol;Acc:HGNC:212361 Jumonji C domain containing
JHDM1D 0.23 6E-05 0.029718 histone demethylase 1
Hs.308710 homolog D (S. cerevisiae) annexin A5 [Source:HGNC
ANXA5 -0.23 6E-05 0.029718
Hs.480653 Symbol;Acc:HGNC:5431 chromosome 8 open reading
C8orf58 -0.23 6E-05 0.029718 frame 58 [Source:HGNC
Hs.743508 Symbol;Acc:HGNC:322331 glycyl-tRNA synthetase
GARS -0.23 6E-05 0.029718 [Source:HGNC
Hs.404321 Symbol;Acc:HGNC:41621 cell division cycle 27
CDC27 -0.23 6E-05 0.029789 pseudogene 2 [Source:HGNC
Hs.463295 Symbol;Acc:HGNC:380921 transmembrane protein 231
TMEM23
-0.23 6E-05 0.029799 [Source:HGNC
1
Hs.156784 Symbol;Acc:HGNC:372341 large tumor suppressor kinase
LATS2 -0.23 6E-05 0.030494 2 [Source:HGNC
Hs.78960 Symbol;Acc:HGNC:65151
SC5DL 0.23 7E-05 0.030788 Hs.287749 Sterol-C5-desaturase
sphingosine- 1 -phosphate
SGPP2 0.23 7E-05 0.030828 phosphatase 2 [Source:HGNC
Hs.591604 Symbol;Acc:HGNC: 199531 lethal giant larvae homolog 2
LLGL2 0.23 7E-05 0.030938 (Drosophila) [Source:HGNC
Hs.514477 Symbol; Acc :HGNC : 66291
Myb/SANT-like DNA-binding
C9orf30 -0.23 7E-05 0.031508
Hs.530272 domain containing 3
ecdysoneless homolog
ECD -0.23 7E-05 0.031508 (Drosophila) [Source:HGNC
Hs.734299 Symbol; Acc :HGNC : 170291 importin 11 [Source:HGNC
IPOl l -0.23 7E-05 0.031714
Hs.482269 Symbol;Acc:HGNC:206281
NIN1/RPN12 binding protein 1 homolog (S. cerevisiae)
NOB1 -0.23 7E-05 0.031851
[Source:HGNC
Hs.271695 Symbol;Acc:HGNC:295401
ELK3, ETS-domain protein (SRF accessory protein 2)
ELK3 -0.23 7E-05 0.031851
[Source:HGNC
Hs.46523 Symbol;Acc:HGNC:33251 zinc finger protein 10
Z F10 0.23 7E-05 0.031851 [Source:HGNC
Hs.606293 Symbol;Acc:HGNC: 128791
WD repeat domain 36
WDR36 -0.23 7E-05 0.031851 [Source:HGNC
Hs.533237 Symbol; Acc :HGNC : 306961
SPHK1 -0.23 7E-05 0.031851 Hs.68061 sphingosine kinase 1 [Source:HGNC
Symbol;Acc:HGNC: 112401 polymerase I and transcript
PTRF -0.23 7E-05 0.031851 release factor [Source:HGNC
Hs.437191 Symbol;Acc:HGNC:96881 chromosome 12 open reading
C12orf60 -0.23 7E-05 0.032101 frame 60 [Source:HGNC
Hs.659951 Symbol;Acc:HGNC:287261 endoplasmic reticulum-golgi intermediate compartment
ERGIC1 -0.23 7E-05 0.032302
(ERGIC) 1 [Source:HGNC
Hs.604241 Symbol;Acc:HGNC:292051
G protein-coupled receptor 98
GPR98 0.23 7E-05 0.032784 [Source:HGNC
Hs.613157 Symbol; Acc :HGNC : 174161 eukaryotic translation initiation factor 3, subunit B
EIF3B -0.23 7E-05 0.032848
[Source:HGNC
Hs.730066 Symbol;Acc:HGNC:32801
A kinase (PRKA) anchor
AKAP5 0.23 7E-05 0.033161 protein 5 [Source:HGNC
Hs.659607 Symbol;Acc:HGNC:3751
V-myc myelocytomatosis viral
MYCL1 0.23 7E-05 0.033753 oncogene homolog 1, lung
Hs.437922 carcinoma derived (avian)
LOCI 001 Uncharacterized
0.23 8E-05 0.033753
34229 Hs.634333 LOCI 00134229
family with sequence
FAM105 similarity 105, member A
0.23 8E-05 0.033753
A [Source:HGNC
Hs.155085 Symbol;Acc:HGNC:256291 uroporphyrinogen
UROD -0.23 8E-05 0.033978 decarboxylase [Source:HGNC
Hs.78601 Symbol;Acc:HGNC: 125911 doublecortin [Source:HGNC
DCX 0.23 8E-05 0.034309
Hs.34780 Symbol;Acc:HGNC:27141
DNAJC3 anti sense RNA 1
DNAJC3
0.23 8E-05 0.034521 (head to head) [Source:HGNC -AS1
Hs.594844 Symbol;Acc:HGNC:398081 castor zinc finger 1
CASZ1 0.23 8E-05 0.034521 [Source:HGNC
Hs.439894 Symbol;Acc:HGNC:260021
HFM1, ATP-dependent DNA helicase homolog (S.
HFM 1 0.23 8E-05 0.034921
cerevisiae) [Source:HGNC
Hs.454818 Symbol;Acc:HGNC:201931
DEAH (Asp-Glu-Ala-Asp/His) box polypeptide 57
DHX57 -0.23 8E-05 0.035042
[Source:HGNC
Hs.468226 Symbol;Acc:HGNC:200861 ectonucleoside triphosphate diphosphohydrolase 2
ENTPD2 0.23 8E-05 0.035149
[Source:HGNC
Hs.123036 Symbol; Acc :HGNC : 33641 kinesin family member 1 A
KIF1A 0.23 8E-05 0.035149 [Source:HGNC
Hs.516802 Symbol;Acc:HGNC:8881
Calcium channel flower
C9orf7 0.23 8E-05 0.035172
Hs.62003 domain containing 1
ATR interacting protein
ATRIP -0.23 8E-05 0.035508 [Source:HGNC
Hs.694840 Symbol;Acc:HGNC:334991 myosin VB [Source:HGNC
MY05B 0.23 8E-05 0.035508
Hs.720076 Symbol; Acc :HGNC : 76031
BCL2-associated athanogene 3
BAG3 -0.23 8E-05 0.035794 [Source:HGNC
Hs.726135 Symbol;Acc:HGNC:9391
SH3 domain binding kinase 1
SBK1 0.23 8E-05 0.03598 [Source:HGNC
Hs.97837 Symbol; Acc :HGNC : 176991 multiple EGF-like-domains 9
MEGF9 0.23 8E-05 0.03598 [Source:HGNC
Hs.744903 Symbol;Acc:HGNC:32341
ADP-ribosylation factor-like 6 interacting protein 1
ARL6IP1 0.23 8E-05 0.036017
[Source:HGNC
Hs.712690 Symbol;Acc:HGNC:6971 coagulation factor II
(thrombin) receptor
F2R -0.23 8E-05 0.036119
[Source:HGNC
Hs.482562 Symbol;Acc:HGNC:35371 ring finger protein 216
R F216 -0.23 8E-05 0.036242 [Source:HGNC
Hs.487458 Symbol;Acc:HGNC:216981 coiled-coil domain containing
CCDC10
-0.23 8E-05 0.036242 102A [Source:HGNC
2A
Hs.644611 Symbol;Acc:HGNC:280971
WW domain containing transcription regulator 1
WWTR1 -0.23 8E-05 0.036242
[Source:HGNC
Hs.477921 Symbol;Acc:HGNC:240421 thromboxane A2 receptor
TBXA2R -0.23 8E-05 0.036352 [Source:HGNC
Hs.442530 Symbol;Acc:HGNC: 116081 dynein, light chain, Tctex-type
DY LT1 0.23 8E-05 0.036393 1 [Source:HGNC
Hs.445999 Symbol; Acc :HGNC : 116971 zinc finger, CCHC domain
ZCCHC2
-0.23 9E-05 0.036683 containing 24 [Source:HGNC 4
Hs.645668 Symbol;Acc:HGNC:269111 cordon-bleu WH2 repeat
COBL 0.23 9E-05 0.03676 protein [Source:HGNC
Hs.99141 Symbol;Acc:HGNC:221991 small nucleolar RNA, H/ACA
SNORA6
-0.23 9E-05 0.036849 box 64 [Source:HGNC
4
Hs.745496 Symbol; Acc :HGNC : 102211
piezo-type mechanosensitive ion channel component 1
PIEZOl -0.23 9E-05 0.037149
[Source:HGNC
Hs.377001 Symbol;Acc:HGNC:289931 connective tissue growth factor
CTGF -0.23 9E-05 0.037194 [Source:HGNC
Hs.410037 Symbol;Acc:HGNC:25001 somatostatin receptor 2
SSTR2 0.23 9E-05 0.037194 [Source:HGNC
Hs.514451 Symbol;Acc:HGNC: 113311
KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein
KDELR2 -0.23 9E-05 0.037235 retention receptor 2
[Source:HGNC
Hs.654552 Symbol;Acc:HGNC:63051 achaete-scute family bHLH transcription factor 1
ASCL1 0.23 9E-05 0.037483
[Source:HGNC
Hs.703025 Symbol;Acc:HGNC:7381
41709 0.23 9E-05 0.03803 Hs.104624 Aquaporin 9
microtubule-associated protein
MAP2 0.23 9E-05 0.038392 2 [Source:HGNC
Hs.368281 Symbol;Acc:HGNC:68391 solute carrier family 5
(sodium/glucose
SLC5A1 0.23 9E-05 0.038637 cotransporter), member 1
[Source:HGNC
Hs.1964 Symbol; Acc :HGNC : 110361
Family with sequence
C12orf34 0.23 9E-05 0.038891
Hs.661785 similarity 222, member A
translocase of outer
TOMM3 mitochondrial membrane 34
-0.23 9E-05 0.038891
4 [Source:HGNC
Hs.517066 Symbol; Acc:HGNC: 157461 seizure related 6 homolog
SEZ6 0.23 9E-05 0.038891 (mouse) [Source:HGNC
Hs.21837 Symbol; Acc:HGNC: 159551 signal peptidase complex subunit 3 homolog (S.
SPCS3 -0.23 9E-05 0.038891
cerevisiae) [Source:HGNC
Hs.42194 Symbol;Acc:HGNC:262121 suppression of tumorigenicity
ST14 0.23 9E-05 0.038935
Hs.504315 14 (colon carcinoma) [Source:HGNC
Symbol;Acc:HGNC: 113441
MARVEL domain containing
MARVE
0.23 9E-05 0.038935 2 [Source:HGNC LD2
Hs.744452 Symbol;Acc:HGNC:264011
Rho guanine nucleotide
ARHGEF exchange factor (GEF) 7
0.23 9E-05 0.038935
7 [Source:HGNC
Hs.508738 Symbol; Acc:HGNC: 156071
Treacher Collins-Franceschetti
TCOF1 -0.23 9E-05 0.038935 syndrome 1 [Source:HGNC
Hs.519672 Symbol;Acc:HGNC: 116541
N-acetylglucosamine- 1 - phosphodiester alpha-N-
NAGPA -0.23 1E-04 0.038935 acetylglucosaminidase
[Source:HGNC
Hs.21334 Symbol;Acc:HGNC: 173781
SH3 -domain GRB2-like 2
SH3GL2 0.23 1E-04 0.038935 [Source:HGNC
Hs.75149 Symbol;Acc:HGNC: 108311 forkhead box 03
FOX03 0.23 1E-04 0.038935 [Source:HGNC
Hs.220950 Symbol; Acc :HGNC : 38211
B-cell CLL/lymphoma 1 IB (zinc finger protein)
BCL11B 0.23 1E-04 0.038935
[Source:HGNC
Hs.510396 Symbol;Acc:HGNC: 132221 hi stone cluster 1, H2ae
HIST1H2
0.23 1E-04 0.039361 [Source:HGNC
AE
Hs.121017 Symbol;Acc:HGNC:47241
Fc receptor-like B
FCRLB -0.23 1E-04 0.039598 [Source:HGNC
Hs.517422 Symbol;Acc:HGNC:264311 dihydropyrimidinase-like 5
DPYSL5 0.23 1E-04 0.039778 [Source:HGNC
Hs.299315 Symbol;Acc:HGNC:206371 zinc finger protein 711
Z F711 0.23 1E-04 0.040012 [Source:HGNC
Hs.326801 Symbol; Acc:HGNC: 131281
ELOVL fatty acid elongase 6
ELOVL6 -0.23 1E-04 0.040116 [Source:HGNC
Hs.17519 Symbol; Acc :HGNC : 158291 solute carrier family 4, sodium bicarbonate cotransporter,
SLC4A7 -0.23 1E-04 0.040308
member 7 [Source:HGNC
Hs.250072 Symbol; Acc :HGNC : 110331
SECIS binding protein 2
SECISBP 0.000
0.23 0.040763 [Source:HGNC
2 1
Hs.59804 Symbol; Acc :HGNC : 309721 0.000 perilipin 3 [Source:HGNC
PLIN3 -0.22 0.041201
1 Hs.140452 Symbol;Acc:HGNC: 168931 transmembrane channel-like 5
0.000
TMC5 0.22 0.041207 [Source:HGNC
1
Hs.115838 Symbol;Acc:HGNC:229991
RAB 11 family interacting
RAB11FI 0.000 protein 4 (class II)
0.22 0.04162
P4 1 [Source:HGNC
Hs.406788 Symbol; Acc :HGNC : 302671 synaptosomal-associated
0.000
SNAP91 0.22 0.04162 protein, 91kDa [Source:HGNC
1
Hs.368046 Symbol;Acc:HGNC: 149861 zinc finger protein 593
0.000
Z F593 -0.22 0.041652 [Source:HGNC
1
Hs.477273 Symbol; Acc :HGNC : 309431
leukocyte immunoglobulin- like receptor, subfamily A
0.000
LILRA3 -0.22 0.042027 (without TM domain), member
1
3 [Source:HGNC
Hs.113277 Symbol; Acc :HGNC : 66041
GPN-loop GTPase 1
0.000
GPN1 -0.22 0.042255 [Source:HGNC
1
Hs.18259 Symbol; Acc :HGNC : 170301
PITPNM family member 3
PITP M 0.000
Ί 0.22 0.042255 [Source:HGNC
J 1
Hs.183983 Symbol;Acc:HGNC:210431
GrpE-like 1, mitochondrial (E.
0.000
GRPEL1 -0.22 0.042255 coli) [Source:HGNC
1
Hs.443723 Symbol; Acc :HGNC : 196961 protein tyrosine phosphatase,
0.000 non-receptor type 14
PTPN14 -0.22 0.042255
1 [Source:HGNC
Hs.193557 Symbol; Acc :HGNC : 96471 neurogranin (protein kinase C
0.000 substrate, RC3)
RGN -0.22 0.042255
1 [Source:HGNC
Hs.722314 Symbol;Acc:HGNC:80001
El A binding protein p300
0.000
EP300 0.22 0.042255 [Source:HGNC
1
Hs.517517 Symbol; Acc :HGNC : 33731
KIAA089 0.000 KIAA0895 [Source:HGNC
0.22 0.042255
5 1 Hs.6224 Symbol;Acc:HGNC:222061 exonuclease 3'-5' domain
0.000
EXD2 0.22 0.042255 containing 2 [Source:HGNC
1
Hs.607803 Symbol;Acc:HGNC:202171 zinc finger protein 571
0.000
Z F571 0.22 0.042255 [Source:HGNC
1
Hs.590944 Symbol;Acc:HGNC:250001
0.000 wingless-type MMTV
WNT11 0.22 0.042473
1 Hs.108219 integration site family, member 11 [Source:HGNC
Symbol;Acc:HGNC: 127761 poly (ADP-ribose) polymerase
0.000 family, member 3
PARP3 -0.22 0.042654
1 [Source:HGNC
Hs.271742 Symbol;Acc:HGNC:2731 hormonally up-regulated Neu-
0.000 associated kinase
HUNK 0.22 0.042925
1 [Source:HGNC
Hs.109437 Symbol;Acc:HGNC: 133261 apolipoprotein B mRNA editing enzyme, catalytic
APOBEC 0.000
-0.22 0.043467 polypeptide-like 3G 3G 1
[Source:HGNC
Hs.660143 Symbol;Acc:HGNC: 173571
2-oxoglutarate and iron-
0.000 dependent oxygenase domain
OGFOD1 -0.22 0.043472
1 containing 1 [Source:HGNC
Hs.231883 Symbol;Acc:HGNC:255851
0.000
FBLL1 0.22 0.043486
1 Hs.166262 Fibrillarin-like 1
protein tyrosine phosphatase,
0.000 receptor type, O
PTPRO 0.22 0.043486
1 [Source:HGNC
Hs.160871 Symbol;Acc:HGNC:96781
LOC1005 0.000
0.22 0.043486
05963 1 na na
methyltransferase like 2 IB
METTL2 0.000
-0.22 0.043486 [Source:HGNC
IB 1
Hs.632720 Symbol;Acc:HGNC:249361
Fanconi anemia,
0.000 complementation group L
FANCL 0.22 0.043486
1 [Source:HGNC
Hs.631890 Symbol;Acc:HGNC:207481 apolipoprotein B mRNA editing enzyme, catalytic
APOBEC 0.000
-0.22 0.043486 polypeptide-like 3B 3B 1
[Source:HGNC
Hs.226307 Symbol;Acc:HGNC: 173521 mastermind-like domain
MAMLD 0.000
-0.22 0.043486 containing 1 [Source:HGNC 1 1
Hs.20136 Symbol;Acc:HGNC:25681 brain-specific angiogenesis
0.000
BAD 0.22 0.043486 inhibitor 3 [Source:HGNC
1
Hs.13261 Symbol;Acc:HGNC:9451 absent in melanoma 1-like
0.000
AFM1L 0.22 0.043486 [Source:HGNC
1
Hs.128738 Symbol;Acc:HGNC: 172951
0.000 fucosyltransferase 2 (secretor
FUT2 0.22 0.043486
1 Hs.678779 status included) [Source:HGNC
Symbol;Acc:HGNC:40131
FRASl related extracellular
0.000
FREM3 0.22 0.043486 matrix 3 [Source:HGNC
1
Hs.252714 Symbol;Acc:HGNC:251721 hyaluronan and proteoglycan
0.000
HAPLN4 0.22 0.044006 link protein 4 [Source:HGNC
1
Hs.367829 Symbol;Acc:HGNC:313571 kin of IRRE like (Drosophila)
0.000
KIRREL -0.22 0.044006 [Source:HGNC
1
Hs.272234 Symbol; Acc:HGNC: 157341 serine/arginine repetitive
0.000
SRRM4 0.22 0.044109 matrix 4 [Source:HGNC
1
Hs.744964 Symbol;Acc:HGNC:293891 pentraxin 3, long
0.000
PTX3 -0.22 0.044302 [Source:HGNC
1
Hs.667287 Symbol; Acc :HGNC : 96921
MpV17 mitochondrial inner
0.000 membrane protein
MPV17 -0.22 0.044302
1 [Source:HGNC
Hs.75659 Symbol; Acc :HGNC : 72241
0.000 zyxin [Source:HGNC
ZYX -0.22 0.04443
1 Hs.490415 Symbol;Acc:HGNC: 132001
ER lipid raft associated 1
0.000
ERLIN1 -0.22 0.045088 [Source:HGNC
1
Hs.595526 Symbol; Acc :HGNC : 169471
ArfGAP with GTPase domain,
0.000 ankyrin repeat and PH domain
AGAP1 0.22 0.045088
1 1 [Source:HGNC
Hs.435039 Symbol; Acc :HGNC : 169221
HMG box domain containing 3
HMGXB 0.000
-> -0.22 0.045174 [Source:HGNC
1
Hs.736795 Symbol;Acc:HGNC:289821 metallothionein 4
0.000
MT4 -0.22 0.045232 [Source:HGNC
1
Hs.567624 Symbol; Acc:HGNC: 187051 tubulin, beta 6 class V
0.000
TUBB6 -0.22 0.046094 [Source:HGNC
1
Hs.193491 Symbol;Acc:HGNC:207761
0.000 myosin ID [Source:HGNC
MYOID 0.22 0.046337
1 Hs.740025 Symbol;Acc:HGNC:75981 neutral sphingomyelinase (N-
0.000 SMase) activation associated
NSMAF -0.22 0.046449
1 factor [Source:HGNC
Hs.372000 Symbol;Acc:HGNC:80171 tubulin polymerization-
0.000 promoting protein family
TPPP3 0.22 0.046498
1 member 3 [Source:HGNC
Hs.534458 Symbol;Acc:HGNC:241621 N-acetyltransferase 2
(arylamine N-
0.000
NAT2 0.22 0.046498 acetyltransferase)
1
[Source:HGNC
Hs.2 Symbol; Acc :HGNC : 76461 clathrin, light chain A
0.000
CLTA 0.22 0.046501 [Source:HGNC
1
Hs.735741 Symbol;Acc:HGNC:20901 nucleolar complex associated 3
0.000 homolog (S. cerevisiae)
NOC3L -0.22 0.046501
1 [Source:HGNC
Hs.74899 Symbol;Acc:HGNC:240341 mesencephalic astrocyte-
0.000 derived neurotrophic factor
MA F -0.22 0.046705
1 [Source:HGNC
Hs.436446 Symbol;Acc:HGNC: 154611
0.000 synapsin II [Source:HGNC
SYN2 0.22 0.046705
1 Hs.445503 Symbol;Acc:HGNC: 114951 glutamate receptor, ionotropic,
0.000
GRIK3 0.22 0.046783 kainate 3 [Source:HGNC
1
Hs.128848 Symbol;Acc:HGNC:45811
Josephin domain containing 2
0.000
JOSD2 -0.22 0.048519 [Source:HGNC
1
Hs.467151 Symbol;Acc:HGNC:288531 basic helix-loop-helix family,
BHLHE2 0.000
0.22 0.04881 member e22 [Source:HGNC 2 1
Hs.745052 Symbol; Acc :HGNC : 119631
translocase of inner mitochondrial membrane 22
0.000
TIMM22 -0.22 0.049318 homolog (yeast)
1
[Source:HGNC
Hs.745034 Symbol; Acc :HGNC : 173171
LOCI 002 0.000 Uncharacterized
0.22 0.049318
89019 1 Hs.693796 LOCI 00289019
sprouty-related, EVHl domain
0.000
SPRED1 -0.22 0.049325 containing 1 [Source:HGNC
1
Hs.629760 Symbol;Acc:HGNC:202491 patched domain containing 2
0.000
PTCHD2 0.22 0.049325 [Source:HGNC
1
Hs.202355 Symbol;Acc:HGNC:292511 short stature homeobox 2
0.000
SHOX2 -0.22 0.049992 [Source:HGNC
1
Hs.597944 Symbol;Acc:HGNC: 108541
[0171] Table 3 : 26 Gene Signature List
Figure imgf000097_0001
MUCL1 Hs.348419 mucin-like 1 [Source:HGNC Symbol;Acc:HGNC:305881
BRCA2 and CDKNl A interacting protein [Source:HGNC
BCCIP Hs.715543 Symbol;Acc:HGNC:9781
ring finger protein 38 [Source:HGNC
R F38 Hs.333503 Symbol; Acc:HGNC: 180521
tRNA-yW synthesizing protein 3 homolog (S. cerevisiae)
TYW3 Hs.348411 [Source:HGNC Symbol;Acc:HGNC:247571
IMPDH2 Hs.654400 IMP (inosine 5'-monophosphate) dehydrogenase 2
solute carrier family 4, sodium bicarbonate cotransporter,
SLC4A8 Hs.4749 member 8 [Source:HGNC Symbol;Acc:HGNC: 110341
ZFP3 zinc finger protein [Source:HGNC
ZFP3 Hs.48832 Symbol; Acc:HGNC: 128611
dachshund family transcription factor 1 [Source:HGNC
DACH1 Hs.129452 Symbol;Acc:HGNC:26631
ubiquitin-conjugating enzyme E2G 1 [Source:HGNC
UBE2G1 Hs.741319 Symbol; Acc:HGNC: 124821
tetratricopeptide repeat domain 27 [Source:HGNC
TTC27 Hs.468125 Symbol; Acc:HGNC:259861
membrane protein, palmitoylated 6 (MAGUK p55 subfamily
MPP6 Hs.595701 member 6) [Source:HGNC Symbol;Acc:HGNC: 181671
BCL2-associated athanogene 2 [Source:HGNC
BAG2 Hs.745046 Symbol;Acc:HGNC:9381
neuronal cell adhesion molecule [Source:HGNC
RCAM Hs.21422 Symbol;Acc:HGNC:79941
nucleolar complex associated 3 homolog (S. cerevisiae)
NOC3L Hs.74899 [Source:HGNC Symbol;Acc:HGNC:240341
zinc finger protein 652 [Source:HGNC
Z F652 Hs.463375 Symbol;Acc:HGNC:291471
tumor necrosis factor receptor superfamily, member 10b
T FRSF10B Hs.635647 [Source:HGNC Symbol;Acc:HGNC: 119051
signal sequence receptor, gamma (translocon-associated
SSR3 Hs.518346 protein gamma) [Source:HGNC Symbol; Acc:HGNC: 113251
AK2 Hs.470907 adenylate kinase 2 [Source:HGNC Symbol;Acc:HGNC:3621 doublecortin-like kinase 1 [Source:HGNC
DCLK1 Hs.507755 Symbol; Acc:HGNC:27001
Rab geranylgeranyltransferase, beta subunit [Source:HGNC
RABGGTB Hs.78948 Symbol;Acc:HGNC:97961
kelch domain containing 9 [Source:HGNC
KLHDC9 Hs.507290 Symbol; Acc:HGNC:284891
EBNA1 binding protein 2 [Source:HGNC
EBNA1BP2 Hs.673773 Symbol;Acc:HGNC: 155311
methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like [Source:HGNC
MTHFD1L Hs.591343 Symbol;Acc:HGNC:210551
D M3 Hs.685968 dynamin 3 [Source:HGNC Symbol;Acc:HGNC:291251 zinc finger, HIT -type containing 6 [Source:HGNC
Z HIT6 Hs.5111 Symbol; Acc:HGNC:260891
nucleophosmin/nucleoplasmin 3 [Source:HGNC
PM3 Hs.90691 Symbol;Acc:HGNC:7931] [0172] Table 4: Validation line Compound 1 sensitivity
Figure imgf000099_0001
Figure imgf000099_0002
Figure imgf000099_0003
GP2D 0.599 NCIH2029 0.936 SK FI 0.868
GP5D 0.911 NCIH2030 0.462 SK0V3 1.363
HCC1143 0.783 NCIH2066 0.606 SKUT1 0.691
HCC1143 0.783 NCIH2081 1.498 SNU449 0.976
HCC1187 0.684 NCIH2087 0.442 SNU475 0.617
HCC1937 0.627 NCIH209 0.681 SNUC1 1.020
HCC1954 0.728 NCIH2106 1.511 SNUC2A 0.832
HCC70 1.258 NCIH2122 0.501 SNUC2B 1.051
HCC70 1.258 NCffl2126 0.443 SNV475 0.812
HCT116 0.338 NCffl2171 0.985 SW403 0.479
HCT15 0.680 NCffl2172 1.719 SW480 0.681
HELA 0.719 NCIH2228 0.961 SW620 0.605
HEP3B2 0.708 NCIH226 0.426 SW900 0.531
HEPG2 0.645 NCffl2291 0.622 SW948 0.868
HLFa 1.130 NCffl23 0.281 Y79 0.529
[0173] Table 5: Gene list f<
Figure imgf000100_0001
Copy ADP-ribosylhydrolase like 1 [Source:HGNC Number ADPRHL1 Hs.98669 Symbol;Acc:HGNC:213031
DCN1, defective in cullin neddylation 1,
Copy domain containing 2 [Source:HGNC
Number DCUN1D2 Hs.682987 Symbol;Acc:HGNC:203281
Copy growth arrest-specific 6 [Source:HGNC Number GAS6 Hs.646346 Symbol;Acc:HGNC:41681
Copy cell division cycle 16 [Source:HGNC Number CDC 16 Hs.374127 Symbol; Acc :HGNC : 17201
UPF3 regulator of nonsense transcripts
Copy homolog A (yeast) [Source:HGNC
Number UPF3A Hs.734060 Symbol;Acc:HGNC:203321
Copy Chromosome alignment maintaining Number ZNF828 Hs.7542 phosphoprotein 1 (CHAMP 1)
Copy RAS p21 protein activator 3 [Source:HGNC Number RASA3 Hs.718751 Symbol;Acc:HGNC:203311
Copy transmembrane and coiled-coil domains 3 Number TMC03 Hs.317593 [Source:HGNC Symbol; Acc:HGNC:203291
Copy transcription factor Dp-1 [Source:HGNC Number TFDP1 Hs.79353 Symbol; Acc :HGNC : 117491
ATPase, H+/K+ exchanging, beta
Copy polypeptide [Source:HGNC
Number ATP4B Hs.434202 Symbol;Acc:HGNC:8201
Copy G protein-coupled receptor kinase 1
Number GRK1 Hs.103501 [Source:HGNC Symbol; Acc: HGNC : 100131
Copy Long intergenic non-protein coding RNA Number LOC100130386 Hs.704121 552 (LINC00552)
Copy
Number FAM70B Hs.280805 Transmembrane protein 255B (TMEM255B)
Copy Long intergenic non-protein coding RNA Number FLJ44054 Hs.740982 452 (LINC00452)
Copy ATPase, class VI, type 11A [Source:HGNC Number ATP11A Hs.29189 Symbol;Acc:HGNC: 135521
MCF.2 cell line derived transforming
Copy sequence-like [Source:HGNC
Number MCF2L Hs.135835 Symbol;Acc:HGNC: 145761
coagulation factor VII (serum prothrombin
Copy conversion accelerator) [Source:HGNC Number F7 Hs.36989 Symbol;Acc:HGNC:35441
Copy coagulation factor X [Source:HGNC
Number F10 Hs.361463 Symbol;Acc:HGNC:35281
protein Z, vitamin K-dependent plasma
Copy glycoprotein [Source:HGNC
Number PROZ Hs.1011 Symbol; Acc :HGNC : 94601
Copy insulin receptor substrate 2 [Source:HGNC Number IRS2 Hs.442344 Symbol; Acc :HGNC : 61261
Copy collagen, type IV, alpha 2 [Source:HGNC Number COL4A2 Hs.508716 Symbol;Acc:HGNC:22031 Copy RAB20, member RAS oncogene family Number RAB20 Hs.743563 [Source:HGNC Symbol; Acc:HGNC: 182601
Copy Chromosome 13 open reading frame 35 Number C13orf35 Hs.591213 (C13orf35)
Copy SRY (sex determining region Y)-box 1 Number SOX1 Hs.202526 [Source:HGNC Symbol; Acc:HGNC: 111891
Gene ring finger protein 38 [Source:HGNC
Expression RNF38 Hs.333503 Symbol; Acc:HGNC: 180521
tumor necrosis factor receptor superfamily,
Gene member 10b [Source:HGNC
Expression TNFRSF10B Hs.635647 Symbol;Acc:HGNC: 119051
Gene Rab interacting lysosomal protein-like 2
Expression RILPL2 Hs.488173 [Source:HGNC Symbol; Acc:HGNC:287871
Gene dCMP deaminase [Source:HGNC
Expression DCTD Hs.183850 Symbol;Acc:HGNC:27101
ELK3, ETS-domain protein (SRF accessory
Gene protein 2) [Source:HGNC
Expression ELK3 Hs.46523 Symbol;Acc:HGNC:33251
ArfGAP with RhoGAP domain, ankyrin
Gene repeat and PH domain 3 [Source:HGNC
Expression ARAP3 Hs.726187 Symbol;Acc:HGNC:240971
Gene KIAA1841 [Source:HGNC
Expression KIAA1841 Hs.468653 Symbol;Acc:HGNC:293871
apolipoprotein B mRNA editing enzyme,
Gene catalytic polypeptide-like 3C [Source:HGNC
Expression APOBEC3C Hs.731638 Symbol;Acc:HGNC: 173531
Gene zinc finger and BTB domain containing 5
Expression ZBTB5 Hs.161276 [Source:HGNC Symbol;Acc:HGNC:238361
Gene spermatid perinuclear RNA binding protein
Expression STRBP Hs.287659 [Source:HGNC Symbol; Acc:HGNC: 164621 solute carrier family 37 (glucose-6-phosphate
Gene transporter), member 1 [Source:HGNC
Expression SLC37A1 Hs.735440 Symbol; Acc :HGNC : 110241
regulatory subunit of type II PKA R-subunit
Gene (Rlla) domain containing 1 [Source:HGNC
Expression RIIAD1 Hs.297967 Symbol;Acc:HGNC:266861
Gene interferon, gamma-inducible protein 16
Expression IFI16 Hs.380250 [Source:HGNC Symbol;Acc:HGNC:53951
Gene dual specificity phosphatase 7
Expression DUSP7 Hs.591664 [Source:HGNC Symbol;Acc:HGNC:30731
Gene ovo-like zinc finger 2 [Source:HGNC
Expression OVOL2 Hs.710157 Symbol; Acc:HGNC: 158041
dual-specificity tyrosine-(Y)-phosphorylation
Gene regulated kinase 4 [ Source :HGNC
Expression DYRK4 Hs.439530 Symbol;Acc:HGNC:30951
Ral GEF with PH domain and SH3 binding
Gene motif 1 [Source:HGNC
Expression RALGPS1 Hs.432842 Symbol;Acc:HGNC: 16851] Gene nephronectin [Source:HGNC
Expression PNT Hs.623485 Symbol;Acc:HGNC:274051
sosondowah ankyrin repeat domain family
Gene member A [Source:HGNC
Expression SOWAHA Hs.13308 Symbol;Acc:HGNC:270331
Gene zinc finger protein 554 [Source:HGNC
Expression Z F554 Hs.307043 Symbol;Acc:HGNC:266291
Gene ZFP3 zinc finger protein [Source:HGNC
Expression ZFP3 Hs.48832 Symbol;Acc:HGNC: 128611
Gene cartilage associated protein [Source:HGNC
Expression CRTAP Hs.517888 Symbol;Acc:HGNC:23791
Gene unc-13 homolog B (C. elegans)
Expression UNC13B Hs.493791 [Source:HGNC Symbol; Acc:HGNC: 125661 v-maf avian musculoaponeurotic
Gene fibrosarcoma oncogene homolog F
Expression MAFF Hs.517617 [Source:HGNC Symbol;Acc:HGNC:67801
Gene WD repeat domain 34 [Source:HGNC
Expression WDR34 Hs.495240 Symbol;Acc:HGNC:282961
Gene Y box binding protein 2 [Source:HGNC
Expression YBX2 Hs.678212 Symbol;Acc:HGNC: 179481
Gene
Expression LOC440335 Hs.390599 Uncharacterized LOC440335 (LOC440335)
Gene immunoglobulin superfamily, member 9
Expression IGSF9 Hs.591472 [Source:HGNC Symbol;Acc:HGNC: 181321
Gene
Expression LOC100132815 Hs.629249 Importin 5 pseudogene (LOCI 00132815)
[0174] Example 3 : Correlation of sensitivity of p97 inhibitor Compound 1 to p97 inhibitor Compound 2, Compound 3, and a proteasome inhibitor bortezomib (Compound 4)
[0175] Predicted ECso values derived from a model built with p97 inhibitor Compound 1 (see Example 2) were compared to actual ECso of a structurally related inhibitor of p97, Compound 2. Statistically significant correlation was seen for Compound 2, which was not the case for the proteasome inhibitor bortezomib (Figures 6 and 7). These data suggest that the predictive model described in Example 2 can be applied to inhibitors other than
Compound 1 but is specific to p97 inhibitors.
[0176] Similarly, a correlation between sensitivity to Compound 1 and sensitivity to Compound 3 (a p97 allosteric inihibitor, NMS-873; Magnaghi et al., Nat Chem Biol, 9:548- 556, 2013) was observed (FIG. 10).
[0177] Example 4: Multivariate models that incorporate gene mutation, copy number and protein levels to predict sensitivity to p97 inhibitor Compound 1 [0178] The most significant correlates to Compound 1 sensitivity or resistance for gene expression, copy number and mutation sets obtained from the CCLE database were converted to Boolean factors (Table 5). Gene expression was divided into high and low expression with mean expression as a cutoff. These genomic features were then combined into a multivariate linear regression model and used to predicted sensitivity to Compound 1 (Figure 8). The predictive value of combining multiple types of genomic features was statistically significant with a Pearson correlation determined p-value of < 0.0001.
[0179] Example 5: Use of support vector machine classifier to develop a nonlinear model
[0180] Cell EC50 was first divided into sensitive and resistant group using a median cutoff for 209 solid tumor cancer cell lines. A classifier was then built using the top 50 significant genes as determined by a modified t-test. When the output of this model was compare to the actual classification of sensitive or resistant, the prediction was robust with a p < 0.0001 as determined by the Fisher's exact test (Figure 9).
[0181] All patent filings, other publications, accession numbers and the like cited above are incorporated by reference in their entirety for all purposes to the same extent as if each individual item were specifically and individually indicated to be so incorporated by reference. If different variants of a sequence are associated with an accession number at different times, the version associated with the accession number at the filing date of this application is meant. Any feature, step, element, embodiment, or aspect of the invention can be used in combination with any other unless specifically indicated otherwise. Although the present invention has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be apparent that certain changes and
modifications may be practiced within the scope of the appended claims.

Claims

Attwo 2016/154110fumber: CLBI-000100PC PCT/US2016/023405 WHAT IS CLAIMED IS:
1. A method of predicting sensitivity to p97 inhibition by a p97 inhibitor in a cell or tissue or body fluid sample from a subject, comprising:
assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in the cell or tissue or body fluid sample.
2. A method for selecting a subject for treatment of a disease or condition with a therapy comprising p97 inhibitor, comprising:
(a) assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject according to the method of claim 1 ; and
(b) selecting the subject for treatment with a therapy comprising a p97 inhibitor based on the assigned sensitivity score.
3. A method of prognosis of a disease or condition suitable for treatment with a therapy comprising a p97 inhibitor in a patient, comprising:
assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject according to the method of claim 1; wherein the prognosis of patient with the disease or condition is based on the assigned sensitivity score.
4. A method of predicting a response to a p97 inhibitor therapy in a patient, comprising:
assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject according to the method of claim 1; wherein the patient is predicted to respond to or not respond to a p97 inhibitor therapy based on the assigned sensitivity score.
5. A method for predicting efficacy of, or monitoring treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition, comprising:
assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes according to the method of claim 1 in a cell or tissue or body fluid sample from a subject who is or has been treated with the therapy comprising the p97 inhibitor; wherein the assigned sensitivity score indicates whether the treatment is effective or is likely to be effective, or is an indicator of the progress of treatment.
6. A method for improving clinical outcome of treatment with a therapy comprising a p97 inhibitor in a subject having a disease or condition, comprising
assigning a sensitivity score to p97 inhibition based on genomic features of at least two signature genes in a cell or tissue or body fluid sample from the subject according to the method of claim 1 ; and
developing appropriate treatment based on the assigned sensitivity score thereby improving clinical outcome.
7. The method according to any of claims 4 to 6, further comprising: altering treatment based on the assigned sensitivity score.
8. The method according to claim 1, further comprising: obtaining the cell or tissue or body fluid sample from the subject; and analyzing the cell or tissue or body fluid sample from the subject for genomic features of the at least two signature genes.
9. The method according to claim 1, wherein the assigning the sensitivity score comprises determining expression levels of at least two signature genes.
10. The method according to claim 1, wherein the assigned sensitivity score is a predicted ICso, wherein an increase in the predicted ICso indicates a decrease in sensitivity to p97 inhibition and a decrease in the predicted ICso indicates an increase in sensitivity to p97 inhibition.
1 1. The method according to claim 1-7, further comprising: comparing the assigned sensitivity score to a reference sensitivity score.
12. The method according to claim 1 1, wherein the reference sensitivity is determined from a reference sample, wherein the reference sample is a sample from a healthy subject, is a sample from an individual not having the disease or condition, is a baseline sample from the subject prior to treatment with a therapy comprising a p97 inhibitor or is a sample from a subject prior to the last dose of a therapy comprising a p97 inhibitor.
13. The method according to any of claims 2-6, wherein the disease or condition is a cancer.
14. The method according to claim 13, wherein the cancer is a solid tumor malignancy.
15. The method according to claim 13, wherein the cancer is a
hematological malignancy.
16. The method according to any of claims 2-6, wherein the therapy is a combination therapy.
17. The method according to claim 1, wherein the assigning the sensitivity score comprises applying a linear regression model to the genomic features of at least two signature genes; and optionally combining the genomic features into a predictive model using a multivariate algorithm.
18. The method of claim 17, wherein the linear regression model is a multivariate linear regression model.
19. The method of claim 17, wherein the linear regression model is represented by the following algorithm:
Predicted ECso = 0.4698 + -0.0014(GFRMUCLI) + -0.0329(GFRBCCIP) + 0.0883(GFRRNF38) + -0.0546(GFRTYW3) + -0.0340(GFRIMPDH2) + 0.0323(GFRSLC4A8) + 0.01 1 1(GFRZFP3) + 0.0190(GFRDACHI) + 0.0081(GFRUBE2GI) + 0.0176(GFRTTC27) + - 0.0224(GFRMPP6) + -0.0028(GFRBAG2) + 0.0209(GFRNRCAM) + -0.0200(GFRNOC3L) + - 0.0076(GFRZNF652) + -0.0219(GFRTNFRSFIOB) + -0.0157(GFRSSR3) + 0.0021(GFRAK2) + - 0.0052(GFRDCLKI) + 0.0255(GFRRABGGTO) + 0.0301(GFRKLHDC9) + 0.01 10 (GFREBNAIBP2) + 0.0006(GFRNOHFDIL) + 0.0005(GFRDNM3) + -0.0147(GFRZNHIT6) + 0.0016(GFRNPM3), wherein GFR is the value of the readout of genomic features for each gene.
20. The method according to claim 1, wherein the genomic features comprise a feature selected from the group consisting of gene expression (mRNA expression or protein expression), gene copy number, and activating or deactivating point mutation.
21. The method according to claim 1, wherein the sensitivity score is assigned based on genomic features of at least 5, 10, or 25 signature genes.
22. The method according to claim 1, wherein the at least two signature genes are selected from the group consisting of the genes listed in Tables 2A, 2B, 2C and 3.
23. The method according to claim 1, wherein the at least two signature genes are selected from the list consisting of MUCLl, BCCIP, R F38, TYW3, IMPDH2, SLC4A8, ZFP3, DACHl, UBE2G1, TTC27, MPP6, BAG2, NRCAM, NOC3L, ZNF652, TNFRSF10B, SSR3, AK2, DCLK1, RABGGTB, KLHDC9, EBNA1BP2, MTHFD1L, DNM3, ZNHIT6 and NPM3.
24. The method according to claim 1, wherein the p97 inhibitor is a small molecule.
25. The method according to claim 24, wherein the small molecule p97 inhibitor is a fused pyrimidine compound of Formula I or a salt or hydrate thereof
Figure imgf000108_0001
Formula I
wherein:
the A ring is fused to the pyrimidine ring and is a saturated or unsaturated five or six membered ring having zero, one, two or three heteroatoms in the ring, the remaining atoms of the ring being carbon, each heteroatom being independently selected from the group consisting of nitrogen, oxygen and sulfur;
Figure imgf000108_0002
R1 and R2 are each independently hydrogen or alkyl of one to four carbons in length;
n is zero or an integer from 1 to 4 and when G is not N or O and n is zero, G is a single covalent bond;
R3 is selected from the group consisting of hydrogen, an aliphatic component and an aromatic component, each component being substituted by zero, one or two aliphatic or aromatic components;
R4 and R5 are each independently bound to carbon or nitrogen and are each independently selected from the group consisting of hydrogen, an aliphatic component, a functional component, an aromatic component, and a combination thereof; R6 is a covalent bond joining nitrogen to Ar or is an alkyl group of 1 to 4 carbons or an alkenyl group of 2 to 4 carbons;
Ar is an aromatic component;
Het is a saturated or unsaturated 5:5 or 5:6 bicyclic ring having zero, one, two or three heteroatoms in the bicyclic ring, the remaining atoms being carbon, the bicyclic ring being substituted with zero, one, two or three substituents each independently selected from the group consisting of an aliphatic group, an aromatic group and any combination thereof;
provided that when the A ring is benzo or substituted benzo, the Het ring is not unsubstituted indolinyl, unsubstituted benzoxazol-2-one, unsubstituted 2- aminobenzimidazole, 5,6-dimethyl-2-aminobenzamidazole, unsubstituted benzimidazole or an unsubstituted 2-aminoimidazole fused to a unsubstituted cyclopentane, cyclohexane or cycloheptane ring; and when the A ring is an unsubstituted cyclobutane, cyclopentane, cyclohexane or cycloheptane ring containing a ring oxygen, a ring aminomethyl, a ring aminoethyl or a ring aminophenyl moiety, the Het ring is not a 2-aminobenzamidazole with no substituent or with a methyl, fluoro, chloro, bromo or methoxyl substituent.
26. The method according to claim 25, wherein the small molecule p97 inhibitor is a fused pyrimidine compound of Formula II or a salt or hydrate thereof
Figure imgf000109_0001
Formula II
wherein:
A is CH2, R1, O or S;
m is an integer of 1-3;
n is 0 or an integer of 1-2;
the ring containing A is a five or six member ring;
Y is selected from the group consisting of hydrogen, halogen, Rc, ORc, CN, COzH, CON(Rc)2, C( RC)N(RC)2, CH2N(RC)2, S02N(Rc)2 and S02Rc wherein each Rc is
independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl and any combination thereof;
Z is selected from the group consisting of halogen, unsubstituted alkyl of 1 to 6 carbons, substituted alkyl or alkenyl of 1 to 4 carbons, and substituted alkoxy of 1 to 4 carbons; wherein
The substituted alkyl or alkenyl group is substituted with ORa , SRa, OC(O) Ra, C(0)Ra, C(0)ORa, OC(0)N(Ra)2, C(0)N(Ra)2, N(Ra)C(0)ORa, N(Ra)C(0)Ra,
N(Ra)C(0)N(Ra)2, N(Ra)C( Ra)N(Ra)2, N(Ra)S(0)tRa, S(0)tORa, S(0)tN(Ra)2, N(Ra)2 or P03(Ra)2 wherein each Ra is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof; and,
The substituted alkoxy group is substituted with ORb , Rb, OC(0)Rb, N(Rb)2, C(0)Rb, C(0)ORb , OC(0)N(Rb)2, C(0)N(Rb)2, N(Rb)C(0)ORb, N(Rb)C(0)Rb,
N(Rb)C(0)N(Rb)2, N(Rb)C( Rb)N(Rb)2, N(Rb)S(0)tRb, S(0)tORb, S(0)tN(Rb)2, N(Rb)2 or P03(Rb)2 wherein each Rb is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof;
R1 is selected from a group consisting of hydrogen, unsubstituted alkyl of 1 to 6 carbons, substituted alkyl of 1 to 4 carbons and -C(0)Rd; wherein,
The substituted alkyl is substituted with ORd, SRd, OC(0) Rd, C(0)Rd, C(0)ORd ,-OC(0)N(Rd)2, C(0)N(Rd)2, N(Rd)C(0)ORd, N(Rd)C(0)Rd, N(Rd)C(0)N(Rd)2,
N(Rd)C( Rd)N(Rd)2, N(Rd)S(0)tR, S(0)tORd, S(0)tN(Rd)2, N(Rd)2 or P03(Rd)2; and wherein each Rd is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl alkenyl, alkynyl or any combination thereof;
Each t is independently selected from an integer of 1 or 2; and,
Ar is a phenyl, thiophenyl, pyridinyl, pyrrolyl, furanyl, or a substituted version thereof wherein the substituent is optional, independent and optionally multiple and is an aliphatic, functional or aromatic component.
27. The method according to claim 25, wherein the small molecule p97 inhibitor is a fused pyrimidine compound of Formulas Ilia or Illb or a salt or hydrate thereof
Figure imgf000111_0001
Formula IIIA Formula IIIB
wherein:
A is CH2, R1, O or S;
m is an integer of 1-3;
n is 0 or an integer of 1-2;
The sum of m+n is no more than 4 and no less than 1;
Y is selected from the group consisting of H, CN, C02H, CON(Rc)2,
C( RC)N(RC)2, CH2N(RC)2, S02N(Rc)2 and S02Rc wherein each Rc is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl and any combination thereof;
Z is selected from the group consisting of unsubstituted alkyl of 1 to 6 carbons, substituted alkyl or alkenyl of 1 to 4 carbons and substituted alkoxy of 1 to 4 carbons; wherein,
The substituted alkyl or alkenyl group is substituted with ORa , SR a, OC(O) Ra, C(0)Ra, C(0)ORa, OC(0)N(Ra)2, C(0)N(Ra)2, N(Ra)C(0)ORa, N(Ra)C(0)Ra, N(Ra)C(0)N(Ra)2, N(Ra)C( Ra)N(Ra)2, N(Ra)S(0)tRa, S(0)tORa, S(0)tN(Ra)2, N(Ra)2 or P03(Ra)2 wherein each Ra is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof; and,
The substituted alkoxy group is substituted with ORb , SRb, OC(O) Rb, N(Rb)2, C(0)Rb, C(0)ORb , OC(0)N(Rb)2, C(0)N(Rb)2, N(Rb)C(0)ORb, N(Rb)C(0)Rb, N(Rb)C(0)N(Rb)2, N(Rb)C( Rb)N(Rb)2, N(Rb)S(0)tRb, S(0)tORb, S(0)tN(Rb)2, N(Rb)2 or P03(Rb)2 wherein each Rb is independently hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl or any combination thereof;
R1 is selected from a group consisting of hydrogen, unsubstituted alkyl of 1 to 6 carbons, substituted alkyl of 1 to 4 carbons and -C(0)Rd; wherein, The substituted alkyl is substituted with 0Rd, SRd, OC(0)-Rd, C(0)Rd, C(0)ORd , OC(0)N(Rd)2, C(0)N(Rd)2, N(Rd)C(0)ORd, N(Rd)C(0)Rd, N(Rd)C(0)N(Rd)2, N(Rd)C( Rd)N(Rd)2, N(Rd)S(0)tR, S(0)tORd, S(0)tN(Rd)2, N(Rd)2 or P03(Rd)2; wherein each Rd is independently selected from the group consisting of hydrogen, alkyl, fluoroalkyl, carbocyclyl, carbocyclylalkyl, aryl, aralkyl, heterocyclyl, heterocyclylalkyl, heteroaryl, heteroarylalkyl alkenyl, alkynyl or any combination thereof;
Each t is independently selected from a group of integers consisting of 1 or 2;
B is CH2, CH, C=0, N or O;
D and E are each independently selected from C or N;
The dash line signifies a single or double bond according to the valence bond requirements of the molecular identities of the bonded atoms; and,
Ar is an unsubstituted or substituted aromatic component.
28. The method according to claim 26, wherein the small molecule p97 inhibitor is a fused pyrimidine compound having any of the following RJPAC nomenclature names, or a salt or hydrate thereof:
N-benzyl-2-(2-methyl-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine;
N-benzyl-2-(2-ethyl-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine;
2-[2-(aminomethyl)-lH-indol-l-yl]-N-benzyl-5,6,7,8-tetrahydroquinazolin-4- amine;
2-[2-(l-aminoethyl)-lH-indol-l-yl]-N-benzyl-5,6,7,8-tetrahydroquinazolin-4- amine;
2-[5-(aminomethyl)-4H-pyrrolo[2,3-d][l,3]thiazol-4-yl]-N-benzyl-5,6,7,8- tetrahydroquinazolin-4-amine;
N-benzyl-2-(2-methoxy-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; { l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2- yl} methanol;
N-benzyl-2-[2-(methoxymethyl)-lH-indol-l-yl]-5,6,7,8-tetrahydroquinazolin-
4-amine;
N-benzyl-2-{2-[(methylamino)methyl]-lH-indol-l-yl}-5,6,7,8- tetrahydroquinazolin-4-amine;
N-benzyl-2-{2-[(dimethylamino)methyl]-lH-indol-l-yl}-5,6,7,8- tetrahydroquinazolin-4-amine; N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2- yl }methyl)acetamide;
({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2- yl}methyl)urea;
methyl N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol- 2-yl }methyl)carbamate;
N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2- yl}methyl)methanesulfonamide;
4-N-benzyl-2-N-[l-(lH-indol-2-yl)ethyl]-5,6,7,8-tetrahydroquinazoline-2,4- diamine;
N-benzyl-2-[2-(morpholin-4-ylmethyl)-lH-indol-l-yl]-5,6,7,8- tetrahydroquinazolin-4-amine;
N-benzyl-2-(2-methyl-lH-indol-3-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4- carbonitrile;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-indole-
4-carbonitrile;
N-benzyl-2-(2-ethoxy-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine; N-benzyl-2-[2-(trifluoromethyl)-lH-indol-l-yl]-5,6,7,8-tetrahydroquinazolin-
4-amine;
N-benzyl-2-(2-chloro-lH-indol-l-yl)-5,6,7,8-tetrahydroquinazolin-4-amine;
N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2- yl }methyl)prop-2-ynamide;
N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2- yl}methyl)prop-2-enamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4- carboxamide;
1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-indole- 4-carboxamide;
2- (aminomethyl)-l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH- indole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4- carboxylic acid; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4- sulfonamide;
N-benzyl-2-(4-methanesulfonyl-2-methyl-lH-indol-l-yl)-5, 6,7,8- tetrahydroquinazolin-4-amine;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-ethyl-lH-indole-4- carboxamide;
N-benzyl-2-[2-methyl-4-(lH-l,2,3,4-tetrazol-5-yl)-lH-indol-l-yl]-5,6,7,8- tetrahydroquinazolin-4-amine;
1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-(2-methoxyethoxy)- lH-indole-4-carboxamide;
2- [4-(aminomethyl)-2-methyl-lH-indol-l-yl]-N-benzyl-5,6,7,8- tetrahydroquinazolin-4-amine;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-(propan-2-yl)-lH- indole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-cyclopropyl-lH- indole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-N,2-dimethyl-lH- indole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-N,N,2-trimethyl-lH- indole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-N-ethyl-2-methyl-lH- indole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-N-(2-methoxyethyl)-2- methyl-lH-indole-4-carboxamide;
N-(2-aminoethyl)-l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2- methyl-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-ethoxy-lH-indole-4- carboxamide;
1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-(2-methoxyethoxy)- lH-indole-4-carbonitrile;
2- [2-( 1 -aminoethyl)- lH-indol- 1 -yl]-N-benzyl-5H,7H, 8H-pyrano[4,3 - d]pyrimidin-4-amine;
N-benzyl-2-(2-methoxy-lH-indol-l-yl)-5H,7H,8H-pyrano[4,3-d]pyrimidin-4- amine; N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,7H,8H-pyrano[4,3-d]pyrimidin-4- amine;
2-[2-(aminomethyl)-lH-indol-l-yl]-N-benzyl-5H,7H,8H-pyrano[4,3- d]pyrimidin-4-amine;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methoxy-lH- indole-4-carbonitrile;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carbonitrile;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methoxy-lH- indole-4-carboxamide;
1- [4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxamide;
2- (aminomethyl)-l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2- yl]-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2- [(dimethylamino)methyl]-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2- (hydroxymethyl)-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N,2-dimethyl- lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N,N,2-trimethyl- lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-N- (propan-2-yl)-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N-(butan-2-yl)- 2-methyl-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N-[2- (dimethylamino)ethyl]-2-methyl-lH-indole-4-carboxamide;
N-benzyl-2-{2-methyl-4-[(morpholin-4-yl)carbonyl]-lH-indol-l-yl}- 5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine;
N-benzyl-2-{2-methyl-4-[(piperazin-l-yl)carbonyl]-lH-indol-l-yl}- 5H,7H,8H-pyrano[4,3-d]pyrimidin-4-amine;
N-(2-aminoethyl)-l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2- yl]-2-methyl-lH-indole-4-carboxamide; l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxylic acid;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-sulfonamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-ethyl-lH- indole-4-carboxamide;
N-benzyl-2-(4-methanesulfonyl-2-methyl-lH-indol-l-yl)-5H,7H,8H- pyrano[4,3-d]pyrimidin-4-amine;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboximidamide;
N-benzyl-2-[2-methyl-4-(lH-l,2,3,4-tetrazol-5-yl)-lH-indol-l-yl]-5H,7H,8H- pyrano[4,3-d]pyrimidin-4-amine;
l-(4-{[(4-fluorophenyl)methyl]amino}-5H,7H,8H-pyrano[4,3-d]pyrimidin-2- yl)-2-methyl-lH-indole-4-carboxamide;
1- (4-{[(2-fluorophenyl)methyl]amino}-5H,7H,8H-pyrano[4,3-d]pyrimidin-2- yl)-2-methyl-lH-indole-4-carboxamide;
2- [4-(aminomethyl)-2-methyl-lH-indol-l-yl]-N-benzyl-5H,7H,8H- pyrano[4,3-d]pyrimidin-4-amine;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2- [(carbamoylamino)methyl]-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-(propan-2-yl)- lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-cyclopropyl- lH-indole-4-carboxamide;
1- (4-{[(3-fluorophenyl)methyl]amino}-5H,7H,8H-pyrano[4,3-d]pyrimidin-2- yl)-2-methyl-lH-indole-4-carboxamide;
2- [2-(l-aminoethyl)-lH-indol-l-yl]-N-benzyl-5H,6H,8H-pyrano[3,4- d]pyrimidin-4-amine;
N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,7H-furo[3,4-d]pyrimidin-4-amine; N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-
4-amine;
l-[4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-6-yl]ethan-l-one; 2-[2-(l-aminoethyl)-lH-indol-l-yl]-N-benzyl-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-(2-methoxy-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
2-[2-(aminomethyl)-lH-indol-l-yl]-N-benzyl-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
2-[2-(aminomethyl)-lH-indol-l-yl]-N-[(4-fluorophenyl)methyl]- 5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4-amine;
N-benzyl-2-(2-ethoxy-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-
4-amine;
N-benzyl-2-[2-(morpholin-4-ylmethyl)-lH-indol-l-yl]-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-4-amine;
N-benzyl-2-(2-methoxy-lH-indol-l-yl)-6-methyl-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
{ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indol-2- yl} methanol;
l-{ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indol- 2-yl}ethan-l-ol;
N-benzyl-2-[2-(methoxymethyl)-lH-indol-l-yl]-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-{2-[(dimethylamino)methyl]-lH-indol-l-yl}-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-4-amine;
N-({ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH- indol-2-yl}methyl)acetamide;
({ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indol- 2-yl}methyl)urea;
methyl N-({ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]- lH-indol-2-yl}methyl)carbamate;
N-({ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH- indol-2-yl}methyl)methanesulfonamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2,3-dihydro- lH-indol-2-one;
{ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indol-2- yl}methyl carbamate; N-benzyl-2-(2,4-dimethyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-(4-fluoro-2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carbonitrile;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-6-carbonitrile;
N-benzyl-2-(4-methoxy-2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-[2-(trifluoromethyl)-lH-indol-l-yl]-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-6-methyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-[2-(propan-2-yl)-lH-indol-l-yl]-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-(2-ethyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4- amine;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indole-2- carboxamide;
N-benzyl-2-(4-chloro-2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-6-ethyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-(2-methyl-lH-indol-l-yl)-6-(propan-2-yl)-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-4-amine;
N-benzyl-2-(2-methyl-lH-indol-l-yl)-6-propyl-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methoxy- lH-indole-4-carbonitrile;
4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-6-ol; l-[4-(benzylamino)-6-methyl-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2- methyl-lH-indole-4-carbonitrile;
N-benzyl-2-[2-methyl-4-(trifluoromethyl)-lH-indol-l-yl]-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-4-amine;
N-benzyl-2-(2-chloro-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-4- amine;
l-[4-(benzylamino)-6-ethyl-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2- methyl-lH-indole-4-carbonitrile;
l-[4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-6-yl]prop-2-yn-l-one;
l-[4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-6-yl]prop-2-en- 1 -one;
4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidine-6-carbaldehyde;
N-{ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl- lH-indol-4-yl}acetamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-N,2- dimethyl-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-N,N,2- trimethyl-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-N- (propan-2-yl)-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-N-(butan-2- yl)-2-methyl-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-indole-4- carboxamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-2,3- dihydro-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-2,3- dihydro-lH-indole-4-carbonitrile;
l-[6-(2-aminoacetyl)-4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin- 2-yl]-2-methyl-lH-indole-4-carbonitrile;
l-[4-(benzylamino)-6-(2-methoxyacetyl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-2-yl]-2-methyl-lH-indole-4-carbonitrile; l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methoxy- lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-sulfonamide;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxylic acid;
N-benzyl-2-(4-methanesulfonyl-2-methyl-lH-indol-l-yl)-5H,6H,7H,8H- pyrido[4,3-d]pyrimidin-4-amine;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-(2- methoxyethoxy)-lH-indole-4-carboxamide;
N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4-d]pyrimidin-
4-amine;
tert-butyl 4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H- pyrido[3,4-d]pyrimidine-7-carboxylate;
1- [4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4- d]pyrimidin-7-yl]ethan-l-one;
tert-butyl 4-(benzylamino)-2-(2-methoxy-lH-indol-l-yl)-8-oxo- 5H,6H,7H,8H-pyrido[3,4-d]pyrimidine-7-carboxylate;
2- [2-(aminomethyl)-lH-indol-l-yl]-N-benzyl-5H,6H,7H,8H-pyrido[3,4- d]pyrimidin-4-amine;
N-benzyl-2-(2-methoxy-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4- d]pyrimidin-4-amine;
1- {2-[2-(aminomethyl)-lH-indol-l-yl]-4-(benzylamino)-5H,6H,7H,8H- pyrido[3 ,4-d]pyrimidin-7-yl } ethan- 1 -one;
2- [2-(aminomethyl)-lH-indol-l-yl]-N-benzyl-7-ethyl-5H,6H,7H,8H- pyrido[3,4-d]pyrimidin-4-amine;
methyl 2-[2-(aminomethyl)-lH-indol-l-yl]-4-(benzylamino)-5H,6H,7H,8H- pyrido[3,4-d]pyrimidine-7-carboxylate;
N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4-d]pyrimidin-
4-amine;
N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H-pyrrolo[3,4-d]pyrimidin-4- amine;
l-[4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H-pyrrolo[3,4- d]pyrimidin-6-yl]ethan-l-one; l-[4-(benzylamino)-5H,6H,7H-pyrrolo[3,4-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxamide;
l-[4-(benzylamino)-6-methyl-5H,6H,7H-pyrrolo[3,4-d]pyrimidin-2-yl]-2- methyl-lH-indole-4-carboxamide;
l-[6-acetyl-4-(benzylamino)-5H,6H,7H-pyrrolo[3,4-d]pyrimidin-2-yl]-2- methyl-lH-indole-4-carboxamide.
29. The method according to claim 27, wherein the small molecule p97 inhibitor is a fused pyrimidine compound having any of the following IUPAC names, or a salt or hydrate thereof:
1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-l,3- benzodiazole-4-carbonitrile;
N-benzyl-2-(2-methoxy-lH-l,3-benzodiazol-l-yl)-5, 6,7,8- tetrahydroquinazolin-4-amine;
2- [2-(aminomethyl)-lH-l,3-benzodiazol-l-yl]-N-benzyl-5, 6,7,8- tetrahydroquinazolin-4-amine;
2-[2-(l-aminoethyl)-lH-l,3-benzodiazol-l-yl]-N-benzyl-5, 6,7,8- tetrahydroquinazolin-4-amine;
N-benzyl-2-(2-methyl-lH-l,3-benzodiazol-l-yl)-5,6,7,8-tetrahydroquinazolin-
4-amine;
{ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-l,3-benzodiazol-
2-yl}methanol;
N-benzyl-2-{2-[(dimethylamino)methyl]-lH-l,3-benzodiazol-l-yl}-5, 6,7,8- tetrahydroquinazolin-4-amine;
N-benzyl-2-[2-(morpholin-4-ylmethyl)-lH-l,3-benzodiazol-l-yl]-5,6,7,8- tetrahydroquinazolin-4-amine;
N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-l,3- b enzodi azol -2-y 1 } methy l)acetami de ;
({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-l,3-benzodiazol- 2-yl}methyl)urea;
N-benzyl-2-[2-(morpholin-4-yl)-lH-l,3-benzodiazol-l-yl]-5, 6,7,8- tetrahydroquinazolin-4-amine;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-l,3- benzodiazole-4-carbonitrile; 1- [4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-l,3- benzodiazole-4-carboxamide;
2- (aminomethyl)-l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH- l,3-benzodiazole-4-carbonitrile;
2-(aminomethyl)-l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH- 1 , 3 -b enzodi azol e-4-carb oxami de ;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-l,3- benzodiazole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-l,3- benzodiazole-4-carboxylic acid;
N-benzyl-2-(2-ethoxy-lH-l,3-benzodiazol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-(2-methoxy-lH-l,3-benzodiazol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
N-benzyl-2-(2-methyl-lH-l,3-benzodiazol-l-yl)-5H,6H,7H,8H-pyrido[4,3- d]pyrimidin-4-amine;
{ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-l,3- b enzodi azol -2-y 1 } methanol ;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-l,3- benzodiazol-2-yl carbamate;
{ l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-lH-l,3- benzodiazol-2-yl }urea;
N-benzyl-2-[2-(trifluoromethyl)- 1H- 1 ,3 -b enzodi azol- 1 -yl]-5H,6H,7H, 8H- pyrido[4,3-d]pyrimidin-4-amine;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- l,3-benzodiazole-4-carbonitrile;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- 1 , 3 -b enzodi azol e-4-carb oxami de .
30. The method according to claim 28 or 29, wherein the small molecule p97 inhibitor is a fused pynmidine compound having the IUPAC name or a salt or hydrate thereof:
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4- carbonitrile; l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4- carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-indole- 4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-ethoxy-lH-indole-4- carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-(2-methoxyethoxy)- lH-indole-4-carbonitrile;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-cyclopropyl-lH- indole-4-carboxamide;
N-({ l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-lH-indol-2- yl }methyl)prop-2-ynamide;
N-benzyl-2-[2-methyl-4-(lH-l,2,3,4-tetrazol-5-yl)-lH-indol-l-yl]-5,6,7,8- tetrahydroquinazolin-4-amine;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methoxy-lH- indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxamide;
l-(4-{[(3-fluorophenyl)methyl]amino}-5H,7H,8H-pyrano[4,3-d]pyrimidin-2- yl)-2-methyl-lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxylic acid;
N-benzyl-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-
4-amine;
l-[4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxamide;
1- [4-(benzylamino)-5H,6H,7H,8H-pyrido[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxylic acid;
2- [4-(aminomethyl)-2-methyl-lH-indol-l-yl]-N-benzyl-5,6,7,8- tetrahydroquinazolin-4-amine;
l-[4-(benzylamino)-2-(2-methyl-lH-indol-l-yl)-5H,6H,7H,8H-pyrido[3,4- d]pyrimidin-7-yl]prop-2-yn-l-one;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4- sulfonamide; N-benzyl-2-(4-methanesulfonyl-2-methyl-lH-indol-l-yl)-5, 6,7,8- tetrahydroquinazolin-4-amine;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-N-methyl-2-methyl- lH-indole-4-carboxamide;
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-N,2-dimethyl- lH-indole-4-carboxamide;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-l,3- benzodiazole-4-carboxylic acid;
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methoxy-lH-l,3- benzodiazole-4-carboxamide;
N-benzyl-2-(2-methoxy-lH-l,3-benzodiazol-l-yl)-5, 6,7,8- tetrahydroquinazolin-4-amine.
31. The method according to claim 25, wherein the fused pyrimidine compound is a compound having the following IUPAC name, or a salt or hydrate thereof:
l-[4-(benzylamino)-5,6,7,8-tetrahydroquinazolin-2-yl]-2-methyl-lH-indole-4- carboxamide.
32. The method according to claim 25, wherein the fused pyrimidine compound is a compound having the following IUPAC name, or a salt or hydrate thereof:
l-[4-(benzylamino)-5H,7H,8H-pyrano[4,3-d]pyrimidin-2-yl]-2-methyl-lH- indole-4-carboxamide.
33. The method according to claim 1, wherein the p97 inhibitor is an antibody, a protein, a peptide, or a p97 inhibitor introduced by gene therapy.
34. The method according to claim 1, wherein the sample is a biopsy sample from a solid tumor or a bone marrow aspirate.
35. The method according to claim 1, wherein the sample is a fluid sample that is a blood, serum, plasma, ascites, urine, sweat, semen, saliva, cerebral spinal fluid, or lymph sample.
36. The method according to claim 1, wherein the sample is obtained by needle biopsy, CT-guided needle biopsy, aspiration biopsy, endoscopic biopsy,
bronchoscopic biopsy, bronchial lavage, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, skin biopsy, bone marrow biopsy, and the Loop Electrosurgical Excision Procedure (LEEP).
37. A computer-implemented method of identifying genes associated with sensitivity to p97 inhibition, the method comprising:
(a) analyzing a cell or tissue or body fluid sample from a subject for genomic features of one or more subsets of genes;
(b) assigning a sensitivity score to p97 inhibition in the cell or tissue or body fluid sample based on the genomic features of each of the one or more subsets of genes; and
(c) identifying a subset comprising at least two signature genes, the genomic features of which are correlated with the sensitivity to p97 inhibition.
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CN110564846A (en) * 2019-07-11 2019-12-13 德阳市人民医院 TYW3 for diagnosing male osteoporosis
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