WO2022082317A1 - Gene signature for predicting progression and progress of urinary cancers and methods of use thereof - Google Patents

Gene signature for predicting progression and progress of urinary cancers and methods of use thereof Download PDF

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WO2022082317A1
WO2022082317A1 PCT/CA2021/051492 CA2021051492W WO2022082317A1 WO 2022082317 A1 WO2022082317 A1 WO 2022082317A1 CA 2021051492 W CA2021051492 W CA 2021051492W WO 2022082317 A1 WO2022082317 A1 WO 2022082317A1
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iqgap1
genes
cancer
gene
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Xiaozeng LIN
Yan GU
Anil Kapoor
Damu Tang
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Mcmaster University
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    • 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
    • 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
    • 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
    • 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
    • C12Q1/6869—Methods for sequencing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01N33/57407—Specifically defined cancers
    • G01N33/57434—Specifically defined cancers of prostate
    • 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
    • C12Q2600/118—Prognosis of disease development
    • 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

Definitions

  • the present application relates to the field of cancer, and in particular, relates to a gene signature biomarker for predicting the progression and prognosis of urogenital cancers and methods of use thereof.
  • PC Prostate cancer
  • HGPIN prostatic intra-epithelial neoplasia
  • Primary PCs are managed with active surveillance, radiation, and surgery depending on disease severity, patient age and preference.
  • PCs are graded with the Gleason score (GS) and World Health Organization (WHO) PC grading system (WHO grade group 1-5) or ISUP (the International Society of Urological Pathology) grade; WHO or its equivalent ISUP is GS-based.
  • Gleason score Gleason score
  • WHO World Health Organization
  • ISUP International Society of Urological Pathology
  • IQGAP1 IQ motif GTPase-activating scaffold proteins
  • IQGAP1 stimulates ERK activation, associates with Cdc42 and Rael, and stabilizes their GTP binding; IQGAP 1, thus, induces cytoskeleton dynamics, displays oncogenic activities and is upregulated in several cancers, including thyroid cancer, breast cancer, colorectal carcinoma, esophageal squamous cell carcinoma, hepatocellular carcinoma, and ovarian cancer. While IQGAP2 shares an overall homology of 62% with IQGAP 1, and even higher levels of homology between their respective structural motifs except the WW domain, IQGAP2 surprisingly possesses tumor suppressive activities. Nonetheless, downregulation of IQGAP1 correlates with tumor progression and poor prognosis in bladder cancer and IQGAP 1 suppresses tumor metastasis in the liver via inhibition of TGFP-mediated myofibroblast activation in tumor stroma.
  • IQGAP 1 The impact of IQGAP 1 on PC has not been thoroughly investigated.
  • IQGAP 1 In vitro IQGAP 1 has been reported to bind with p21 activated 6 (PAK6) in LNCaP cells; the association decreases cell-cell adhesion in DU145 cells.
  • PAK6 p21 activated 6
  • the master oncogenic AKT phosphorylates FOXO1, leading to FOXO1 -derived tumor suppression in LNCaP and DU145 cells via binding with IQGAP 1; the association prevents IQGAP 1 to activate ERK.
  • upregulation of IQGAP 1 was reported in metastasis produced by PC3 cells.
  • evidence of IQGAP1 alterations following PC tumorigenesis is not available.
  • Kidney cancer is the 9th and 14th most common cancer in men and women, respectively. Renal cell carcinoma (RCC) accounts for 85% of kidney cancer cases; the most common subtypes are clear cell RCC (ccRCC, 80%), papillary RCC (pRCC, 15%), and chromophobe RCC (5%). Clear cell RCC is the most aggressive RCC and contributes to majority of kidney cancer deaths. The main curative treatment for primary ccRCC remains complete and partial nephrectomy; in these patients 30- 40% will experience recurrence and metastasis. Metastatic ccRCCs are currently treated with systemic therapies targeting the vascular endothelial growth factor (VEGF) and mTOR pathways.
  • VEGF vascular endothelial growth factor
  • VHL von Hippel-Lindau
  • HEF hypoxia inducible factors
  • PI3K phosphoinositide 3-kinase
  • Adrenocortical carcinoma is an orphan disease with an annual incidence of approximately 1-2 cases per million. ACC is an aggressive endocrine carcinoma; the estimated 5-year survival rate is less than 50%. A recent epidemiological study of 2014 ACC cases in USA from 1973-2014 revealed the disease mortality being 52% with a median survival time less than 2 years. ACC affects more women than men and occurs at a median age within the fifth and sixth decades of life. Surgical resection is the only curative treatment. However, the relapse rate is high with 86% recurrence being reported in 133 ACC patients. Local relapses commonly associate with metastasis, to which therapeutic options are less effective.
  • ACC has variable or heterogenous prognosis with either no recurrence or slow metastatic progression in some tumors.
  • Effective prediction of ACC fatality or its clinical behavior at the time of diagnosis is critical for patient management via individualized therapies.
  • Clinical outcomes can be estimated by the ACC staging system modified by the European Network for the Study of Adrenal tumors (ENSAT).
  • ENSAT European Network for the Study of Adrenal tumors
  • Other prognostic classifiers include Ki67 index, CpG island methylation, and transcriptome-based classification.
  • CpG island methylator phenotype (CIMP) profile has been used to cluster ACCs into either non-CIMP and high CIMP groups with the latter being divided into CIMP -high and CIMP -low, or three groups consisting of CIMP-high, CIMP-intermediate, and CIMP-low. Increases in CIMP are associated with poor prognosis.
  • ACCs can be clustered into CIA and C1B with the former being more aggressive. While CIMP-low ACCs are largely within the C1B group, both CIMP-intermediate and CIMP-high reside in the CIA group with CIMP-high being more overlapped with CIA compared to CIMP-intermediate. Thus, both methylation and transcription omics can classify low- and high-risk ACCs with an overlap manner.
  • biomarkers associated with a urogenital cancer such as PC, RCC and/or ACC, as well as other urogenital cancers, to provide methods of diagnosis and prognosis, and thereby improve patient management.
  • a method of diagnosing a urogenital cancer in a mammal comprising the steps of: i) detecting the level of IQGAP 1 in a biological sample from the mammal; ii) comparing the sample level of IQGAP 1 to a control level of IQGAP 1; and iii) diagnosing the mammal with a urogenital cancer when the sample level of IQGAP 1 is significantly reduced in comparison to the control level of IQGAP 1.
  • a method of diagnosing a urogenital cancer, progression of the cancer and/or survival following the cancer in a mammal comprising the steps of: i) optionally detecting the level of IQGAP 1 in a biological sample from the mammal, and comparing the sample level of IQGAP1 to a control level of IQGAP1; ii) detecting the level of genes in a gene signature obtained from the biological sample, wherein the gene signature comprises LINC01089, and comparing the sample level of genes in the gene signature to a control level; and iii) diagnosing the mammal with a urogenital cancer when the sample level of LINC01089 is a statistically significant different level as compared to the control level of LINC01089, and optionally, when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP 1.
  • a method of diagnosing adrenal cancer in a mammal comprising the steps of: i) optionally detecting the level of IQGAP 1 in a biological sample from the mammal, and comparing the sample level of IQGAP1 to a control level of IQGAP1; ii) detecting the level of genes in a gene signature obtained from the biological sample, wherein the gene signature comprises: a) one or more of LCN12, VGF, RGS11, MXD3, BIRC5, FPR3, RAB30, NOD2, TEFC, ZFHX4, and HDAC9; b) one or more of LCN12, VGF, RGS11, MXD3, BIRC5, RAB30, NOD2 and ZFHX4; c) one or more of SNHG10, RECQL4, MXD3 and RAB30; or d) one or more of LOC100128288, SNHG10 or HERC2P2, and comparing the sample
  • FIGURE 1 shows downregulation of IQGAP1 in advanced PCs in an exemplary embodiment of the application.
  • FIGURE 2 shows decreases in IQGAP1 mRNA expression following the course of PC in an exemplary embodiment of the application.
  • the Liu (a) and Wallace (b) datasets of PC microarray studies within OncomineTM were analyzed for IQGAP1 expression in primary PC and normal prostate tissues. **: p ⁇ 0.01 and ***: p ⁇ 0.001.
  • the indicated microarray datasets of PC from OncomineTM (c/d) were analyzed for IQGAP1 expression in primary PCs and distant metastasis PCs. **: p ⁇ 0.01 and ***: p ⁇ 0.001 by 2-tailed Student’s t-test.
  • FIGURE 3 shows downregulation of IQGAP1 associates with therapy resistance of PC in an exemplary embodiment of the application
  • IQGAP1 mRNA expression in the Grasso dataset (OncomineTM) in androgen sensitive PCs and CRPCs was analyzed. ***: p ⁇ 0.001 by 2-tailed Student’s t-test.
  • LNCaP xenografts were generated in NOD/SCID mice. Mice were either untreated or castrated when tumors were 100-200 mm 3 , followed by monitoring for PSA increases. IQGAP1 mRNA expression was quantified in each.
  • FIGURE 4 illustrates that downregulation of IQGAP1 is associated with PC recurrence.
  • the TCGA PanCancer Atlas PC dataset was divided into a high and low relapse risk group following prostatectomy using the cutoff point of -1SD (standard deviation). Kaplan Meier survival curve and lograk test were performed using tools provided by cBioPortal.
  • FIGURE 5 shows pathway enrichment of IQGAP1 DEGs in an exemplary embodiment of the application, (a) Representatives of top 20 enriched clusters of GO biological process terms and KEGG pathways are shown, (b) Network relationship of those enriched clusters. Analyses were carried out with Metascape.
  • FIGURE 6 shows geneset enrichment in an exemplary embodiment of the application.
  • IQGAP1 DEGs relative to IQGAP1 downregulation were defined at q ⁇ 0.0001 and analyzed for geneset enrichment among human hallmark gene set.
  • Three enriched genesets functioning in interferon gamma response, inflammatory response and oxidative phosphorylation are included.
  • FIGURE 7 shows Sig27gene robustly stratifies the risk of PC recurrence in an exemplary embodiment of the application, (a) HR, 95% CI, and p values for prediction of PC biochemical recurrence in the indicated populations are shown.
  • Signature scores were either from the Training group (“Training score”), Testing cohort (“Testing score”), or full TCGA cohort (“Full cohort score”), (b) Timedependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated cohorts are shown,
  • FIGURE 8 shows validation of Sig27gene with the independent MSKCC PC cohort in an exemplary embodiment of the application,
  • Sig27gene was analyzed for the stratification of PC recurrence risk in MSKCC dataset using scores defined either from the TCGA Training cohort (TCGA Training score) or from the MSKCC cohort (MSKCC score),
  • TCGA Training score TCGA Training score
  • MSKCC score MSKCC cohort
  • time-dependent AUC for the indicated score systems in prediction of PC recurrence in MSKCC cohort.
  • FIGURE 9 shows differential expression of the indicated component genes of Sig27gene.
  • the indicated gene expressions (mRNA) in PC (T) and matched normal prostate tissues (N) were analyzed using the GEPIA2 program. *p ⁇ 0.05.
  • FIGURE 10 shows prediction of ACC, poor OS and progression by Sig27gene in an exemplary embodiment of the application.
  • A Sig27gene and two subsignatures for assessing either OS (overall survival) or PFS (progression-free survival).
  • B, C Time-dependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated multigene panels and major component genes (BIRC5, MXD3, and RAB30) in assessing OS (B) and PFS (C) are shown.
  • D, E Kaplan Meier curves for Sig27gene in stratifying fatality risk (D) and progression risk (E). Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test. For assessing PFS, C3orf47 was removed, as its presence made multivariate analysis not work and thus coefficients for component genes could not be derived.
  • FIGURE 11 shows pathway enrichment of DEGs relative to IQGAP1 downregulation in an exemplary embodiment of the application.
  • A Representatives of top clusters enriched.
  • B Network presentation of those enriched clusters. Analyses were performed using Metascape.
  • FIGURE 12 shows Geneset enrichment in an exemplary embodiment of the application. IQGAP1 DEGs analyzed for geneset enrichment among human hallmark gene set. Two enriched genesets functioning in inflammatory response and oxidative phosphorylation are presented.
  • FIGURE 13 shows SigIQGAPINW robustly stratifies risks of poor prognosis of ccRCC in the Training sub-population in an exemplary embodiment of the application.
  • A HR, 95% CI, and p values.
  • OS overall survival; DSS: disease-specific survival; PFS: progression free survival.
  • B Time-dependent ROC (receiver operating characteristic) curve. Months for PFS are specifically labeled.
  • C Kaplan Meier survival curves. Statistical analyses were performed using logrank test.
  • FIGURE 14 shows SigIQGAPINW efficiently predicts the risks of poor prognosis of ccRCC in the Testing population in exemplary embodiments of the application.
  • A, B Analyses of the fatality risk of ccRCC using SigIQGAPINW scores defined from the Training population (A) or the Testing cohort (B). HR, 95% CI, and p values along with logrank p value for Kaplan Meier survival curves are provided. The respective median survival times are also indicated.
  • C Time-dependent ROC curve. Months for PFS are specifically labeled.
  • FIGURE 15 shows SigIQGAPINW classifies the risks of poor prognosis of ccRCC in the TCGA PanCancer Atlas ccRCC cohort with a high degree of certainty in an exemplary embodiment of the application.
  • A Kaplan Meier survival curve.
  • B HR, 95% CI, and p values for the indicated ccRCC events.
  • C Timedependent ROC curve. Months for PFS are specifically labeled.
  • FIGURE 16 shows the association of SigIQGAPINW with worse clinical features of ccRCC in an exemplary embodiment of the application.
  • Stage 1 and 2 are expressed as “0”, while Stage 2 and 4 are represented as “1”.
  • Tumour size T stages 1 and 2 are converted to “0”; T3 and T4 are combined to “1”.
  • SigIQGAPINW scores are used for analysis.
  • FIGURE 17 shows the association of SigIQGAPINW component genes with poor OS of ccRCC.
  • Kaplan Meier survival curves for the indicated component genes along with logrank p values, median survival month and other information are included in exemplary embodiments of the application.
  • FIGURE 18 shows Kaplan Meier survival curves for the indicated component genes of SigIQGAPINW in exemplary embodiments of the application. Cutoff points for these component genes were determined based on their mRNA expression using Maximally Selected Rank Statistics (the Maxstat package) in R. The individual survival curves were produced using the R survival package. Statistical analyses were performed with logrank test.
  • FIGURE 19 shows differential expression of the HERC2P2 and THSD7A component genes in ccRCC (Tumor/T) and matched non-tumor kidney tissues (N) in an exemplary embodiment of the application.
  • Gene expression was determined by RNA-seq (TCGA) and analyzed using the GEPIA2 program. Four mRNA clusters are indicated. TPM: transcripts per million.
  • Statistical analyses were performed by GEPIA2, *p ⁇ 0.05.
  • FIGURE 20 shows prediction of ACC poor OS and progression by SigIQGAPINW in an exemplary embodiment of the application.
  • A SigIQGAPINW and two sub-signatures for assessing either OS (overall survival) or PFS (progression- free survival).
  • B, C Time-dependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated multigene panels in assessing OS (B) and PFS (C) are shown.
  • D, E Kaplan Meier curves for SigIQGAPINW in stratifying fatality risk (D) and progression risk (E). The median months survival (19 months, D) and median months progression-free survival (E, 6.43 months) are shown. Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test.
  • FIGURE 21 shows prediction of ACC poor OS and progression by SigIQGAPINW in an exemplary embodiment of the application.
  • A SigIQGAPINW and two sub-signatures for assessing either OS (overall survival) or PFS (progression- free survival).
  • B, C Time-dependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated multigene panels in assessing OS (B) and PFS (C) are shown.
  • D, E Kaplan Meier curves for SigIQGAPINW in stratifying fatality risk (D) and progression risk (E). The median months survival (19 months, D) and median months progression-free survival (E, 6.43 months) are shown. Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test.
  • FIGURE 22 shows Kaplan Meier curves for the indicated sub-panels of SigIQGAPINW and the indicated component genes in stratifying ACC poor OS (A, C, D) and progression (B, E, F) in an exemplary embodiment of the application. Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test.
  • FIGURE 23 shows differential expression of the indicated component genes of SigIQGAPINW in exemplary embodiments of the application.
  • the indicated gene expressions in ACC (T) and normal adrenal gland tissues (N) were analyzed using the GEPIA2 program. *p ⁇ 0.05.
  • FIGURE 24 shows Prediction of ACC poor OS and progression by Sig27gene in an exemplary embodiment of the application.
  • A Sig27gene and two subsignatures for assessing either OS (overall survival) or PFS (progression-free survival).
  • B, C Time-dependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated multigene panels in assessing OS (B) and PFS (C) are shown.
  • D, E Kaplan Meier curves for Sig27gene in stratifying fatality risk (D) and progression risk (E). Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test. For assessing PFS, C3orf47 was removed, as its presence made multivariate analysis not work and thus coefficients for component genes could not be derived.
  • FIGURE 25 shows Kaplan Meier curves for the indicated sub-panels of Sig27gene and the indicated component genes in stratifying ACC poor OS (A, C, D) and progression (B, E, F, G) in exemplary embodiments of the application. Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test.
  • FIGURE 26 shows differential expression of the indicated component genes of Sig27gene in an exemplary embodiment of the application.
  • the indicated gene expressions in ACC (T) and normal adrenal gland tissues (N) were analyzed using the GEPIA2 program. *p ⁇ 0.05.
  • FIGURE 27 shows assessing ACC OS and PFS by Combosig in an exemplary embodiment of the application.
  • A HR, 95% CI, and p values for Combosig in predicting ACC OS and PFS are shown.
  • B tAUC values for Combosig in assessing ACC OS and PFS; the time points relevant to AUC values are indicated.
  • C, D Kaplan Meier curves for Combosig in stratifying ACC fatality risk (C) and progression risk (D). The median months survival (C) and median months progression-free survival (D) are included.
  • a set of stratification parameters for OS (left) and PFS (right) are indicated.
  • PPV positive prediction value
  • NPV negative prediction value.
  • FIGURE 28 shows comparisons of ACC biomarkers documented here with the best published predictor of ACC prognosis in an exemplary embodiment of the application.
  • A tAUC profiles of the indicated multigene panels in assessing ACC OS. The pair of BUB1B-PINK1 is the best predictor of ACC prognosis previously reported.
  • B tAUC profiles of RECQL4 (a component gene) with BUB IB (best prognosis predictor previously reported) in predicting OS.
  • C, D Stratification of ACC prognosis by RECQL4 (C) and BUB IB (D). Median months survival, p values, sensitivity, specificity, PPV, and NPV for the stratifications are indicated.
  • a method of diagnosing a urogenital cancer in a mammal comprising the steps of: i) detecting the level of IQGAP1 in a biological sample from the mammal; ii) comparing the sample level of IQGAP1 to a control level of IQGAP1; and iii) diagnosing the mammal with a urogenital cancer when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP1.
  • Urogenital cancer refers to cancer of the kidney (e.g. renal cell cancer (RCC), clear cell renal cell carcinoma, Wilms tumors or transitional cell carcinoma), bladder, ureter, urethra, prostate (PCC) and testicle.
  • RRC renal cell cancer
  • PCC clear cell renal cell carcinoma
  • Urogenital cancer also encompasses adrenal or adrenocortical carcinoma (ACC) (including pheochromocytoma and paraganglioma) in which malignant cells form in the outer layer of the adrenal gland which is located on top of the kidney.
  • ACC adrenal or adrenocortical carcinoma
  • a biological sample is obtained from a human subject.
  • biological sample is meant to encompass any human sample that may contain nucleic acid, including biological fluids such as, but not limited to, blood, plasma/serum, urine, sweat, saliva, sputum, and cerebrospinal fluid. Tumor biopsies from organs that may be affected may also be used, including, for example, prostate, kidney and adrenocortical tissues.
  • the sample is obtained from the subject in a manner well-established in the art.
  • a suitable biological sample is obtained, it is analyzed to determine the concentration or level of IQGAP1 therein.
  • the sample Prior to analysis, the sample may be subject to processing such as extraction, fdtration, centrifugation or other sample preparation techniques to provide a sample that is suitable for further analysis.
  • biological fluids may be fdtered or centrifuged (e.g. ultracentrifugation) to remove solids from the sample to facilitate analysis.
  • Tissue samples may be subject to extractions in order to provide an analyzable sample.
  • biomarker level may be determined using one of several techniques established in the art that would be suitable for detecting the biomarker, including chromatographic techniques such as high performance liquid chromatography and gas chromatography, immunoassay or enzyme-based assays with colorimetric, fluorescence or radiometric detection.
  • chromatographic techniques such as high performance liquid chromatography and gas chromatography, immunoassay or enzyme-based assays with colorimetric, fluorescence or radiometric detection.
  • the level of IQGAP1 in a sample may be measured by immunoassay using an antibody specific to IQGAP1.
  • the antibody binds to the IQGAP1 and bound antibody is quantified by measuring a detectable marker which may be linked to the antibody or other component of the assay, or which may be generated during the assay.
  • Detectable markers may include radioactive, fluorescent, phosphorescent and luminescent (e.g. chemiluminescent or bioluminescent) compounds, dyes, particles such as colloidal gold and enzyme labels.
  • the term “antibody” is used herein to refer to monoclonal or polyclonal antibodies, or antigenbinding fragments thereof, e.g. an antibody fragment that retains specific binding affinity for IQGAP1.
  • IQGAP1 antibodies may be commercially available, such as anti- IQGAP1 antibodies from Abeam and Invitrogen. Alternatively, antibodies may also be raised using techniques conventional in the art. For example, antibodies may be made by injecting a host animal, e.g. a mouse or rabbit, with the antigen (IQGAP1), and then isolating antibody from a biological sample taken from the host animal.
  • a host animal e.g. a mouse or rabbit
  • IQGAP1 antigen
  • Different types of immunoassay may be used to determine the level of IQGAP1 in a sample, including indirect immunoassay in which the IQGAP1 biomarker is non-specifically immobilized on a surface; sandwich immunoassay in which the biomarker is specifically immobilized on a surface by linkage to a capture antibody bound to the surface; and a competitive binding immunoassay in which a sample is first combined with a known quantity of biomarker antibody to bind biomarker in the sample, and then the sample is exposed to immobilized biomarker which competes with the sample to bind any unbound antibody.
  • Enzyme Linked ImmunoSorbent Assay ELISA
  • ELISA Enzyme Linked ImmunoSorbent Assay
  • the biomarker to be analyzed is generally immobilized on a solid support, complexed with an antibody to the biomarker which is itself linked to an enzyme indicator, such as horseradish peroxidase (HRP), alkaline phosphatase (AP), B- galactosidase, acetylcholinesterase and catalase. Detection may then be accomplished by incubating this enzyme-complex with a substrate for the enzyme that yields a detectable product.
  • HRP horseradish peroxidase
  • AP alkaline phosphatase
  • B- galactosidase acetylcholinesterase
  • catalase catalase
  • IQGAP1 Once the level of IQGAP1 is determined in the sample, its level is compared to a control level, i. e. the level in a corresponding healthy sample not afflicted with cancer, to determine the average fold-change (FC) difference and statistical significance (p-value) between the sample and control levels.
  • FC fold-change
  • p-value statistical significance
  • the mammal is determined to have cancer when the difference in the level of IQGAP1 in the biological sample is statistically significantly reduced or downregulated as compared to the control level.
  • the determination of statistical significance is well-established in the art. Statistical significance is attained when a p- value is less than the significance level. The -value is the probability of observing an effect given that the null hypothesis is true whereas the significance or alpha (a) level is the probability of rejecting the null hypothesis given that it is true. Generally, a statistically significant difference, i.e.
  • an increase or decrease, in the level of IQGAP1 in accordance with the present method is a difference in the level of the biomarker from the control level of at least about 5%, or greater, e.g. at least about 10%, 15%, 20%, 25%, 30%, 40%, 50%, or more.
  • corrected p-values are often used to correct for multiple hypothesis testing in order to reduce false discoveries, such as the use of a false discovery rate (q ⁇ 0.05) or a more conservative Bonferroni correction.
  • the present method comprises the detection of IQGAP1 downregulation in a biological sample concurrently with the detection in the biological sample of a statistically significant different level of LINC01089 as compared to the control level of LINC01089.
  • the term “statistically significant different level” may refer to either a statistically significant increased level or a statistically significant decreased level as compared to a control level depending on the disease to be diagnosed, e g. PC, RCC or ACC.
  • LINC01089 Long Intergenic Non-Protein Coding RNA1089 is an RNA gene, and is affiliated with the IncRNA class.
  • LINC01089 refers herein to mammalian LINC01089, encompassing human and other mammalian LINC01089, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LINC01089.
  • the sequence of human LINC01089 is denoted by the Ensembl gene ID, ENSG00000212694, from the Ensembl online database (https : // www. ensembl . org).
  • the present method comprises the optional detection of IQGAP1 downregulation in a biological sample with the detection in the biological sample of a statistically significant different level of one or more genes selected from the group of: LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A, as compared to the control level of the one or more genes.
  • the term ''level'' is used herein to refer to concentration, or expression level.
  • SPACA6 sperm acrosome associated 6
  • SPACA6 is protein-coding and refers herein to mammalian SPACA6, encompassing human and other mammalian SPACA6, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of SPACA6.
  • the sequence of human SPACA6 is denoted by the Ensembl gene ID, ENSG00000182310.
  • LOC 155060 (zinc finger protein pseudogene) refers herein to mammalian LOCI 55060, encompassing human and other mammalian LOC 155060, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LOC155060.
  • the sequence of human LOC155060 is denoted by the Ensembl gene ID, ENSG00000244560.
  • LOC 100128288 is an RNA gene, and is affiliated with the IncRNA class.
  • LOC100128288 refers herein to mammalian LOC100128288, encompassing human and other mammalian LOC100128288, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LOC100128288.
  • the sequence of human LOC100128288 is denoted by the NCBI accession no., 100128288 ((National Centre for Biotechnology Information).
  • SNHG10 (Small Nucleolar RNA Host Gene 10) is an RNA gene, and is affiliated with the IncRNA class. SNHG10 refers herein to mammalian SNHG10, encompassing human and other mammalian SNHG10, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of SNHG10.
  • the sequence of human SNHG10 is denoted by the Ensembl gene ID, ENSG00000247092.
  • RECQL4 (RecQ Like Helicase 4) is a protein coding gene and refers herein to mammalian RECQL4, encompassing human and other mammalian RECQL4, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RECQL4.
  • the sequence of human RECQL4 is denoted by the Ensembl gene ID, ENSG00000160957.
  • HERC2P2 Hect Domain And RED 2 Pseudogene 2 is a pseudogene and refers herein to mammalian HERC2P2, encompassing human and other mammalian HERC2P2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of HERC2P2.
  • the sequence of human HERC2P2 is denoted by the Ensembl gene ID, ENSG00000276550.
  • ATXN7L2 (Ataxin 7 Like 2) is a protein coding gene and refers herein to mammalian ATXN7L2, encompassing human and other mammalian ATXN7L2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of ATXN7L2.
  • the sequence of human ATXN7L2 is denoted by the Ensembl gene ID, ENSG00000162650.
  • THSD7A Thrombospondin Type 1 Domain Containing 7A
  • mammalian THSD7A encompassing human and other mammalian THSD7A, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of THSD7A.
  • the sequence of human THSD7A is denoted by the Ensembl gene ID, ENSG00000005108.
  • the method comprises the detection in the biological sample of a statistically significant different level of one or more genes selected from the group of: LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A. These genes are referred to herein as the 9-gene signature, SigIQGAPINW.
  • RNA sequencing is a sequencing technique which uses next-generation sequencing (NGS) of cDNA, e.g. Illumina, 454, Ion torrent and Ion proton sequencing, to reveal the presence and quantity of RNA in a biological sample.
  • NGS next-generation sequencing
  • RNA-seq also includes single cell sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing.
  • NanoString technology may also be used which utilizes fluorescent- labelled reporter probes that hybridize to the target RNA and a capture probe to immobilize the reporter-target complex(es) for detection and quantitation.
  • This permits the direct digital quantification of target nucleic acids, from various sample types such as cell, tissue, and blood lysates, as well as RNA and DNA from tissues using targetspecific, color-coded probe pairs. It does not require the conversion of mRNA to cDNA by reverse transcription or the amplification of the resulting cDNA by PCR.
  • Each target gene of interest is detected using a pair of reporter and capture probes carrying 35- to 50- base target-specific sequences.
  • each reporter probe carries a unique color code at the 5' end that enables the molecular barcoding of the genes of interest, while the capture probes all carry a biotin label at the 3' end that provides a molecular handle for attachment of target genes to facilitate downstream digital detection.
  • excess probes are removed and the probe/target complexes are aligned and immobilized for digital analysis.
  • Hundreds of thousands of color codes designating mRNA targets of interest are directly imaged on the surface of the cartridge. The expression level of a gene is measured by counting the number of times the color-coded barcode for that gene is detected, and the barcode counts are then tabulated.
  • PCR-based methods may also be used, including real-time PCR, and digital PCR, as well as microarray technologies. Probes and/or primers required in these sequences methods are designed based on the sequence information provided herein for each of the target genes.
  • Nucleic acid from the biological sample may be extracted from the sample using techniques well-known to those of skill in the art, including chemical extraction techniques utilizing phenol-chloroform (Sambrook et al., 1989), guanidine- containing solutions, or CTAB-containing buffers.
  • chemical extraction techniques utilizing phenol-chloroform (Sambrook et al., 1989), guanidine- containing solutions, or CTAB-containing buffers.
  • commercial DNA extraction kits are also widely available from laboratory reagent supply companies, including for example, the QIAamp DNA Blood Minikit available from QIAGEN®, or the Extract-N-Amp blood kit available from Sigma-Aldrich®.
  • Detection of one or more of a statistically significant increase in the level of LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2 or ATXN7L2, or a statistically significant decrease in the level of THSD7A, is indicative of a urogenital cancer selected from PC, RCC and ACC.
  • detection of one or more of a statistically significant increase in the level of LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2 or ATXN7L2, or a statistically significant decrease in the level of THSD7A is predictive of poor overall survival, metastasis and progression (recurrence) of disease in PC, RCC and ACC.
  • use of the 9-gene signature, SigIQGAPINW predicts an increasing risk of poor overall survival, metastasis and progression (recurrence) of disease with a higher gene signature score, e.g.
  • the present method comprises the detection of IQGAP1 downregulation in a biological sample concurrently with the detection in the biological sample of statistically significant different levels of one or more genes selected from the group of: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5.
  • genes selected from the group of: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP
  • HAGHL Hydrolase Like
  • HAGHL refers herein to mammalian HAGHL, encompassing human and other mammalian HAGHL, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of HAGHL.
  • the sequence of human HAGHL is denoted by the Ensembl gene ID, ENSG00000103253.
  • LCN12 (Lipocalin 12) is a protein-coding gene and refers herein to mammalian LCN12, encompassing human and other mammalian LCN12, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LCN12.
  • the sequence of human LCN12 is denoted by the Ensembl gene ID, ENSG00000184925.
  • DCST2 (DC-STAMP Domain Containing 2) is a protein coding gene and refers herein to mammalian DCST2, encompassing human and other mammalian DCST2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of DCST2.
  • the sequence of human DCST2 is denoted by the Ensembl gene ID, ENSG00000163354.
  • VGF Nerve Growth Factor Inducible
  • ENSG00000128564 The sequence of human VGF is denoted by the Ensembl gene ID, ENSG00000128564.
  • RGS11 (Regulator Of G Protein Signaling 11) is a protein coding gene and refers herein to mammalian RGS11, encompassing human and other mammalian RGS11, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RGS11.
  • the sequence of human RGS11 is denoted by the Ensembl gene ID, ENSG00000076344.
  • PRR7 Protein Rich 7, Synaptic
  • PRR7 is a protein coding gene and refers herein to mammalian PRR7, encompassing human and other mammalian PRR7, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PRR7.
  • the sequence of human PRR7 is denoted by the Ensembl gene ID, EENSGOOOOO131188.
  • MXD3 (MAX Dimerization Protein 3) is a protein coding gene and refers herein to mammalian MXD3, encompassing human and other mammalian MXD3, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of MXD3.
  • the sequence of human MXD3 is denoted by the Ensembl gene ID, ENSG00000213347.
  • BIRC5 Bacteoviral IAP Repeat Containing 5
  • mammalian BIRC5 encompassing human and other mammalian BIRC5, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of BIRC5.
  • the sequence of human BIRC5 is denoted by the Ensembl gene ID, ENSG00000089685.
  • LTC4S Leukotriene C4 Synthase
  • ENSG00000213316 The sequence of human LTC4S is denoted by the Ensembl gene ID, ENSG00000213316.
  • H1FX-AS1 (Hl-10 Antisense RNA 1) is a non-coding gene and refers herein to mammalian H1FX-AS1, encompassing human and other mammalian THSD7A, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of H1FX-AS1.
  • the sequence of human H1FX-AS1 is denoted by the Ensembl gene ID, ENSG00000206417.
  • FPR3 (Formyl Peptide Receptor 3) is a protein coding gene and refers herein to mammalian FPR3, encompassing human and other mammalian FPR3, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of FPR3.
  • the sequence of human FPR3 is denoted by the Ensembl gene ID, ENSG00000187474.
  • RAB30 (RAB30, Member RAS Oncogene Family) is a protein coding gene and refers herein to mammalian RAB30, encompassing human and other mammalian RAB30, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RAB30.
  • the sequence of human RAB30 is denoted by the Ensembl gene ID, ENSG00000137502.
  • RIPOR2 (RHO Family Interacting Cell Polarization Regulator 2) is a protein coding gene and refers herein to mammalian RIPOR2, encompassing human and other mammalian RIPOR2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RIPOR2.
  • the sequence of human RIPOR2 is denoted by the Ensembl gene ID, ENSG00000111913.
  • NOD2 Nucleotide Binding Oligomerization Domain Containing protein 2
  • ENSG00000167207 The sequence of human NOD2 is denoted by the Ensembl gene ID, ENSG00000167207.
  • PLXNA4 (Plexin A4) is a protein coding gene and refers herein to mammalian PLXNA4, encompassing human and other mammalian PLXNA4, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PLXNA4.
  • the sequence of human PLXNA4 is denoted by the Ensembl gene ID, ENSG00000221866.
  • RRAGC Ras Related GTP Binding C
  • mammalian RRAGC encompassing human and other mammalian RRAGC, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RRAGC.
  • the sequence of human RRAGC is denoted by the Ensembl gene ID, ENS G00000116954.
  • TFEC Transcription Factor EC
  • mammalian TFEC encompassing human and other mammalian TFEC, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of TFEC.
  • the sequence of human TFEC is denoted by the Ensembl gene ID, ENSG00000105967.
  • PI 15 is a protein coding gene and refers herein to mammalian PI15, encompassing human and other mammalian PI15, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PI15.
  • the sequence of human PI 15 is denoted by the Ensembl gene ID, ENSG00000137558.
  • ZFHX4 Zainc finger homeobox protein 4
  • ZFHX4 is a protein coding gene and refers herein to mammalian ZFHX4, encompassing human and other mammalian ZFHX4, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of ZFHX4.
  • the sequence of human ZFHX4 is denoted by the Ensembl gene ID, ENSG00000091656.
  • LAMP3 (Lysosomal Associated Membrane Protein 3) is a protein coding gene and refers herein to mammalian LAMP3, encompassing human and other mammalian LAMP3, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LAMP3.
  • the sequence of human LAMP3 is denoted by the Ensembl gene ID, ENSG00000078081.
  • HDAC9 Histone Deacetylase 9
  • mammalian HDAC9 encompassing human and other mammalian HDAC9, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of HDAC9.
  • the sequence of human HDAC9 is denoted by the Ensembl gene ID, ENSG00000048052.
  • MCTP1 (Multiple C2 And Transmembrane Domain Containing 1) is a protein coding gene and refers herein to mammalian MCTP1, encompassing human and other mammalian MCTP1, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of MCTP1.
  • the sequence of human MCTP1 is denoted by the Ensembl gene ID, ENSG00000175471.
  • KCNN3 (Potassium Calcium-Activated Channel Subfamily N Member 3) is a protein coding gene and refers herein to mammalian KCNN3, encompassing human and other mammalian KCNN3, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of KCNN3.
  • the sequence of human KCNN3 is denoted by the Ensembl gene ID, ENSG00000143603.
  • PCDHB8 (Protocadherin Beta 8) is a protein coding gene and refers herein to mammalian PCDHB8, encompassing human and other mammalian PCDHB8, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PCDHB8.
  • the sequence of human PCDHB8 is denoted by the Ensembl gene ID, ENSG00000120322.
  • PCDHGB2 Protocadherin Gamma Subfamily B, 2 is a protein coding gene and refers herein to mammalian PCDHGB2, encompassing human and other mammalian PCDHGB2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PCDHGB2.
  • the sequence of human PCDHGB2 is denoted by the Ensembl gene ID, ENSG00000253910.
  • PCDHGA5 Protocadherin Gamma Subfamily A, 5 is a protein coding gene and refers herein to mammalian PCDHGA5, encompassing human and other mammalian PCDHGA5, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PCDHGA5.
  • the sequence of human PCDHGA5 is denoted by the Ensembl gene ID, ENSG00000253485.
  • the detection of IQGAP1 downregulation in the biological sample is determined by the detection in the biological sample of statistically significant different levels of one or more genes selected from the group of: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5.
  • genes are referred to herein as the 27-gene signature, Sig27gene.
  • use of the 27-gene signature, Sig27gene predicts an increasing risk of poor overall survival, metastasis and progression (recurrence) of disease with a higher gene signature score, e.g. the greater the number of genes of Sig27gene detected with greater differences in level of gene expression from control levels, to be predictive of poor overall survival, metastasis, or recurrence of disease, the greater the risk of poor overall survival, metastasis, or recurrence of disease.
  • the method comprises the detection of the level of one or more genes selected from: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RIPOR2, NOD2, PLXNA4, TFEC, ZFHX4, MCTP1, PCDHGB2 and PCDHGA5.
  • genes selected from: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RIPOR2, NOD2, PLXNA4, TFEC, ZFHX4, MCTP1, PCDHGB2 and PCDHGA5.
  • a method of predicting poor ACC prognosis comprising the detection of a statistically significant difference in the level of one or more of LINC01089, SNHG10, HERC2P2 or RECQL4 in a biological sample of a mammal.
  • a method of predicting progression of ACC comprising the detection of a statistically significant difference in the level of LOC100128288, SNHG10, HERC2P2 or RECQL4 in a biological sample of a mammal.
  • a method of predicting poor ACC prognosis comprising the detection of one or more of a statistically significant difference in the level of LINC01089, MXD3, BIRC5, RGS11 or RAB30 in a biological sample of a mammal.
  • the method includes the detection of the level of one or more of MXD3, BIRC5 and RAB30 in the sample.
  • the level of each of MXD3, BIRC5 and RAB30 is detected in the sample.
  • a method of predicting poor ACC prognosis and/or disease progression comprising the detection of one or more of a statistically significant decrease in the level of LINC01089, FPR3, LCN12, RAB30, RGS11, TFEC or VGF, or a statistically significant increase in the level of BIRC5, HAGHL, MXD3 or PRR7 in a biological sample.
  • the level of each of LINC01089, FPR3, LCN12, RAB30, RGS11, TFEC, VGF, BIRC5, HAGHL, MXD3, and PRR7 is detected in the sample.
  • the method comprises the step of detecting the level of one or of each of SNHG10, RECQL4, MXD3 and RAB30 in the sample.
  • the method comprises the step of detecting the level of each of LINC01089, SNHG10, RECQL4, BIRC5, MXD3 and RAB30 in the sample.
  • the method comprises the step of detecting the level of RECQL4 in the sample.
  • a method of predicting progression of ACC comprising the detection of one or more of a statistically significant difference in the level of one or more of LCN12, VGF, RGS11, MXD3, BIRC5, FPR3, RAB30, NOD2, TEFC, ZFHX4, and HDAC9 in a biological sample of a mammal.
  • the method includes the detection of the level of one or more of LCN12, VGF, RGS11, MXD3, BIRC5, RAB30, NOD2 and ZFHX4 in the sample.
  • the level of each of LCN12, VGF, RGS11, MXD3, BIRC5, RAB30, NOD2 and ZFHX4 is detected in the sample.
  • an appropriate treatment is selected.
  • the treatment may include active surveillance, radiation therapy, hormone therapy, cryosurgery, chemotherapy and surgery (prostatectamy).
  • Use of the present method to detect disease progression or recurrence permits the application of personalized treatment. For example, where the risk of disease progression is low, then treatment is preferably surveillance, while detection of disease progression warrants more aggressive treatment including radiation, chemotherapy or surgery.
  • the treatment may include surgery (nephrectomy), medications, radiation therapy and minimally invasive procedures, such as cryoablation and radiofrequency ablation.
  • surgery nephrectomy
  • radiation therapy nephrectomy
  • minimally invasive procedures such as cryoablation and radiofrequency ablation.
  • treatment options may be based on the risk of disease progression. For example, where the risk of disease progression is low, then treatment is preferably surveillance, while detection of disease progression warrants more aggressive treatment including radiation, medications or surgery.
  • the treatment may include surgery (adrenalectomy), chemotherapy (e.g. mitotane), radiation, biologic therapy or targeted therapy.
  • surgery adrenalectomy
  • chemotherapy e.g. mitotane
  • radiation e.g., radiation, biologic therapy or targeted therapy.
  • treatment options may be based on the risk of disease progression. For example, where the risk of disease progression is low, then treatment is preferably surveillance, while detection of disease progression warrants more aggressive treatment including radiation, medications or surgery.
  • kits are provided for detection of the gene signatures disclosed herein.
  • a kit is provided for the detection of a gene signature comprising the genes: LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A.
  • kits for the detection of a gene signature comprising the genes: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5.
  • kits for the detection of a gene signature comprising the genes: LINC01089, SNHG10, RECQL4, MXD3, BIRC5, and RAB30 is provided for the detection of adrenal cancer.
  • the kits comprise specific reactants to detect the genes within the panels, for example, primers and/or probes for use to detect each of the genes in the gene signatures, or a subset thereof, in a biological sample from a mammal.
  • PC tissues were obtained from Hamilton Health Sciences, Hamilton, Ontario, Canada under approval from the local Research Ethics Board (REB# 11-3472).
  • LNCaP, PC3, and DU145 cells were purchased from American Type Culture Collection (ATCC) and cultured in RPMI1640, F12 or MEM respectively, followed with supplementation of 10% FBS (Sigma Aldrich) and 1% Penicillin-Streptomycin (Thermo Fisher Scientific). The cell lines were authenticated (Cell Line Authentication Service, ATCC), and routinely checked for Mycoplasma contamination (a PCR kit from Abm, Cat#: G238).
  • Formation of xenograft tumors Xenografts were generated as previously described (Ojo et al. Cancer letters. 2018;426: 4-13; Yan et al. Cancer research. 2016; 76: 1603-14).
  • LNCaP cells (5xl0 6 )-derived s.c. xenografts were generated in NOD/SCID mice (The Jackson Laboratory) with tumor volume determined. Tumor growth was measured by serum PSA levels (PSA kit, Abeam). Surgical castration was performed when tumor reached 100-200 mm 3 . Serum PSA was determined before and following castration. Rise in serum PSA indicates CRPC growth.
  • PTENloxp/loxp C;129S4-PtentmlHwu/J; the Jackson Laboratory
  • mice with PB-Cre4 mice B6.Cg-Tg(Pbsn-cre)4Prb, the NCI Mouse Repository) following published conditions (Wong et al. Oncotarget. 2017, 8(12): 19218-19235)
  • Surgical castration was performed when mice were 23 weeks old and subsequently monitored for 13 weeks. All animal protocols were approved by the McMaster University Animal Research Ethics Board.
  • IHC Immunohistochemistry
  • IQGAP1 mRNA expression The PC datasets were retrieved from the OncomineTM database (https://www.oncomine.org/). IQGAP1 mRNA expression data was analyzed in PC vs prostate tissues, metastasis vs local PC, and CRPCs vs non-CRPC tumors.
  • Cutoff point estimation Cutoff points to stratify patients into a high- and low-risk group were estimated by Maximally Selected Rank Statistics (the Maxstat package) in R. [00130] Regression analysis: Cox proportional hazards (Cox PH) regression analyses were carried out with the R survival package. The PH assumption was tested.
  • IQGAP2 demonstrates tumor suppressive properties
  • IQGAP2 expression is largely present in the cell membrane.
  • the membrane localization of IQGAP1 was observed in PC xenografts produced from LNCaP, PC3, and DU145 cells, primary PCs, as well as PCs developed in TRAMP transgenic mice.
  • those produced by LNCaP cells exhibit evidently more cell membrane IQGAP1.
  • LNCaP53 and PC354 cells were derived from lymph node and bone metastases respectively; PC predominantly metastasizes to the bone.
  • the scores of Sig27gene robustly predict PC recurrence risk at HR 2.72, 95% CI 2.22-3.33, and p ⁇ 2e-16 [Figure 8 (a)].
  • the score discriminates PC recurrence with time-dependent area under curve (tAUC) values ranging from 88.5% at 10.8 months (88.5%/10.8M) to 77.5%/47.7 months [Figure 8 (b)].
  • Tstage 1 tumor stage 1 (3+4) in comparison to Tstage 0 (tumor stage 1+2).
  • cancers can be classified as integrative clusters (iClusters).
  • PCs have been classified into iCluster 1, iCluster 2, and iCluster 3.
  • PCs in iCluster 1 are enriched with ETV1 and ETV4 fusion, SHOP mutations, FOXA1 mutations, and CHD1 deletion, but lack ERG fusion.
  • iCluster 2 PCs are particularly enriched with ERG fusion and PTEN deletion.
  • iCluster 3 PCs contain ERG fusion. TP53 hetero-deficiency and RBI deletion are detected more frequently in iCluster 1 and iCluster 2 PCs.
  • Sig27gene a novel multigene signature in predicting PC biochemical recurrence:
  • 8 genes have been reported in PC [Table 5], including 4 upregulated (VGF, RGS11, BIRC5 and LTC4S) and 4 downregulated (NOD2, PI15, LAMP3, and HDAC9) genes relative to IQGAP1 downregulation [Table 2; Table 5],
  • NOD2 facilitates innate immune response in prostate epithelial cells and likely plays a role in PC; NOD2 was also implicated in immunosuppression of gastric cancer. In line with this knowledge, NOD2 expression was evidently reduced in PCs compared to the matched normal prostate tissues. Methylation of CpGs of the PI 15 gene occurs in metastatic PC, which contributes to the stratification of metastatic PC from nonrecurrent PCs.
  • Blood PI15 is a biomarker of cholangiocarcinoma [Table 5]
  • LAMP3 was suggested to play a role in detoxification of cisplatin in CRPC and associate with aggressive breast cancer [Table 5], Chromosome rearrangements in HDAC9 occur more frequently in high-risk PC compared to low-risk PCs. Increases in HDAC9 were observed in basal bladder cancer.
  • LAMP3 the genes which co-downregulated with IQGAP1 negatively impact PC, which reinforces a negative correlation of IQGAP1 with PC progression.
  • the signature also affect immune reactions, particularly innate immune response.
  • NOD2 facilitates innate immune response in prostate epithelial cells, and is likely downregulated in PC, and co-reduced in PC with IQGAP1 [Table 2]
  • PLXNA4 inhibits tumor cell migration, induces innate immune responses via working with Tolllike receptor [Table 5], and is also co-downregulated with IQGQP1 in PC [Table 2],
  • Biochemical recurrence remains a critical issue in PC management; this is not only due to this progression being the initial point of therapy resistance leading to poor prognosis but also because this is conceptually the most effective point of intervention. While mechanisms underlying BCR have been extensively investigated, with numerous biomarkers and systems in place to assess BCR, the current capacity in predicting BCR is clearly not sufficient.
  • IQGAP1 regulates PC cell adhesion or the related processes indirectly via its DEGs or network and 2) the cell surface IQGAP1 is attributable to these actions.
  • the second possibility is appealing as the location is relevant to cell adhesion and IQGAP2, a PC suppressor, was largely detected on PC cell surface.
  • the high level of homology with IQGAP2 supports the membrane location of IQGAP1 and this proportion of IQGAP1 in suppression of PC. This concept is supported by IQGAP1 being more abundantly localized to xenograft PC cell membrane produced by LNCaP cell compared to those generated by PC3 cells, a more aggressive PC cell line.
  • IQGAP1 upregulations of IQGAP1 in metastasis of PC3 cell-generated tumors, which was previously reported, was largely intracellular IQGAP1.
  • pro-tumorigenic roles of IQGAP1 were observed in breast cancer; its cytosolic and nuclear expressions, where IQGAP1 was co-localized with BRCA1, were detected in triple negative breast cancer.
  • IQGAP1 promoted thyroid cancer and was largely expressed in the cytosol.
  • the cytosolic expression of IQGAP1 in colorectal cancer was associated with its pro-oncogenic functions.
  • the cytoplasmic and nuclear expressions of IQGAP1 were correlated with lymph node metastasis and poor overall survival.
  • IQGAP1 may facilitate PC, this work supports its inhibiting roles towards PC, a concept that is in accordance with molecular events affected by IQGAP1 downregulation.
  • Sig27gene modulation of multiple critical oncogenic processes is likely a major attribute to the robust efficiency of Sig27gene in predicting PC recurrence.
  • An interesting feature of Sig27gene is the clusters of 3 component genes at 5q31.3 and 3 (PRR7, MXD3, and LTC4S) at 5q35.3, and 2 (PI15 and ZFHX4) at 8q21.13 [Table 2], The importance of these clusters remains unclear.
  • HAGHL Hydrolase Like
  • LCN12 Lipocalin 12
  • DCST2 DC-STAMP Domain Containing 2
  • PRR7 Protein Rich 7, Synaptic
  • Sig27gene remains a significant risk factor of ACC poor prognosis and progression after adjusting for age at diagnosis and tumor stage (Table 6).
  • Table 7 Stratification efficiencies of ACC prognosis and progression by Sig27gene and its components
  • PPV positive prediction value
  • NPV negative prediction value
  • MMS mediate months survival
  • MMPFS mediate months progression-free survival
  • M month
  • Sig27 is a novel prognostic signature of ACC: Among the 27 component genes of Sig27gene include 20 genes with activities relevant to oncogenesis and 7 genes with unknown tumorigenic roles (Table 5). VGF facilitates resistance to tyrosine kinase inhibitors in lung cancer. 94 RGS11 is a biomarker of lung cancer. 95 Evidence supports MXD3 in promotion of medulloblastoma 96 and both LINC01089 97 and H1FX-AS1 98 display tumor suppressive functions (Table 5). BIRC5 or Survivin is a well-studied anti-apoptotic protein promoting tumorigenesis and progression.
  • LTC4S is a component gene in a immune signature associated with clinical response in breast cancer.
  • 101 Evidence supports negative impacts on tumorigenesis for FPR3, 102 RAB3O, 103 RIPOR2 (FAM65B), 104 PLXNA4, 105 106 MCTP1, 107 and KCNN3.
  • NOD2 was implicated in immunosuppression of gastric cancer.
  • Blood PI 15 is a biomarker of cholangiocarcinoma.
  • LAMP3 associates with aggressive breast cancer.
  • 111 Increases in HDAC9 were observed in basal bladder cancer.
  • 112 ZFHX4 is one of the 9 under-expressed genes and is a susceptibility locus of cutaneous basal cell carcinoma.
  • Both RRAGC 114 and TFEC regulate mTOR activation with the latter affects mTOR via lysosome biogenesis.
  • Sig27gene is a novel prognostic biomarker panel for ACC.
  • Sig27gene is novel to ACC. Unlike all biomarkers that have been specifically formulated based on specific cancer types, this ACC biomarkers is derived from studies on PC, a urinary cancer type. The central role of adrenal gland in hormone synthesis 117 might underline the relationship of PC Sig27gene to ACC prognosis. Importantly, the PC multigene panel is highly effective in assessing ACC prognosis and progression and outperforms the current biomarkers formulated on ACC.
  • cBioPortal https://www.cbioportal.org/
  • the TCGA PanCancer Atlas ccRCC dataset consists of 512 patients with primary ccRCC. All tumors were surgically removed and profiled for RNA expression using RNA sequencing. The dataset has been well-demonstrated for its suitability in ccRCC OS biomarker studies.
  • Pathway enrichment analysis Enrichment analyses were performed using Metascape (https://metascape.Org/gp/index.html#/main/stepl) and Galaxy (https://usegalaxy.org/) for geneset enrichment.
  • Six rounds of selection at the setting were performed and all unique genes obtained were combined into the final multigene panel SigIQGAPINW.
  • Cutoff point estimation Cutoff point to separate tumors with high risk of mortality was estimated using Maximally Selected Rank Statistics (the Maxstat package) in R.
  • Odds ratio determination OR analysis was performed using the glmnet package in R.
  • DEGs were thus defined at q ⁇ 0.0001 and fold change >
  • , which resulted in n 611 genes that are differentially expressed in ccRCCs with concurrent IQGAP1 downregulation (high risk group) compared to those without the downregulation (low risk group).
  • SigIQGAPINW scores discriminate ccRCC fatality with time-dependent area under curve (tAUC) values ranging from 71.8% at 14.6 months (71.8%/14.6M) to 80.2%/72.6 months (Figure 13B).
  • the tAUC values for DSS and PFS are 76.9%/14.7M to 81%/62.6M and 62.2%/8.1M to 70.7%/64.1M respectively ( Figure 13B).
  • SigIQGAPINW biomarker potential in predicting OS in the Testing group was first validated using those coefs produced from the Training group, i.e. SigIQGAPINW scores based on the setting of the Training cohort.
  • SigIQGAPINW To reveal the full potential of SigIQGAPINW in the evaluation of fatality risk, component gene coefs were rederived and recalculated SigIQGAPINW scores based on the Testing population following the system described above.
  • SigIQGAPINW potential can be significantly enhanced based on HR and its enhanced efficiency in separation of the low- and high-risk group (Figure 14B, see its comparison with the panel A).
  • the prediction rate in the high-risk group reaches to 74.1% (20/27) and patients in this group showed a substantially reduced survival time (Figure 14B).
  • the efficiency in prediction of OS, DSS, and PFS has the respective tAUC values of 70.6%/13M, 62. 1%/12.8M, and 68%/7.9M ( Figure 14C).
  • SigIQGAPINW effectively predicts OS, DSS, and PFS in the Testing population, which significantly enhances the biomarker value of SigIQGAPINW.
  • SigIQGAPINW being a novel and robust multigene panel in predicting OS of ccRCC.
  • SigIQGAPINW a novel multigene panel of ccRCC biomarker: In view of all 9 component genes being individual predictors of OS shortening (Table 10), their oncogenic potential was analyzed. It was noticed that the directionality of these genes in predicting poor OS are in accordance with their differential expression in ccRCCs with IQGAP1 downregulation. For instance, THSD7A is co-downregulated with IQGAP1 (Table 8) and its expression levels are reversely associated with poor OS (HR ⁇ 1; Table 10), which is in line with IQGAPl’s relationship with the fatality risk of ccRCC.
  • the 9 component genes identified consist of long non-coding RNA (IncRNA) LINC01089, IncRNA LOC100128288, AI894139 pseudogene LOC155060, hect domain and RLD 2 pseudogene 2 HERC2P2, a non-protein coding RNA SNHG10 and four protein coding genes (SPACA6, RecQL4, ATXN7L2, and THSD7A).
  • Evidence thus supports SigIQGAPINW affecting multiple signaling events or processes, which might underlie its effectiveness in predicting poor prognosis of ccRCC.
  • SigIQGAPINW multigene panel
  • Two approaches in the signature construction were used: random division of cohort into a Training and Testing population and the involvement of cross validation in covariate selections from the Training group.
  • cross validation can be equivalent to the conventional validation by splitting a dataset into a Training set and a validation (Testing) set, it can be trusted that the inclusion of both in the study contributed to the production of a robust gene signature. Additional favorable factors include the TCGA PanCancer Atlas ccRCC dataset being an excellent resource supporting OS biomarker studies.
  • SigIQGAPINW Another feature of SigIQGAPINW is the inclusion of 5 non-protein coding genes among its 9 component genes. With current knowledge of non-coding RNA being important in regulation of networks rather than specific genes, SigIQGAPINW likely affects complex networks, a potential underlying reason for the impressive robustness observed in this multigene panel in assessing poor OS. This is consistent with the current consensus for the importance of a multigene panel to possess multiple features or affecting multiple processes for it to be clinically useful in patient management.
  • SPACA6 Small Acrosome Associated 6
  • ATXN7L2 ATXN7L2
  • RECQL4 is well known for its impact in maintaining genome stability and its involvement in tumorigenesis. While its contribution to ccRCC has not been reported, RECQL4 is clearly important.
  • 611 DEGs RECQL4 is one of nine being selected for impact on OS and importantly, it potently predicts the fatality risk of ccRCC ( Figure 18). With respect to THSD7A, there are no reports for its oncogenic functions.
  • IQGAP1 downregulation correlates with network changes consisting of 611 DEGs; these DEGs are enriched in pathways important to ccRCC and the typical features of the disease.
  • SigIQGAPINW a novel 9-gene signature
  • all 9 component genes of SigIQGAPINW are novel to ccRCC and 5/9 are novel to oncogenic functions in general.
  • the first evidence for ccRCC-associated alterations in two component genes, HERC2P2 and THSD7A is also provided. This research may have a profound impact on ccRCC with respect to research and patient management.
  • LAML acute myeloid leukemia
  • HR 2 mesothelioma
  • ACC adrenocortical carcinoma
  • Table 13 Univariate and multivariate Cox analyses of SigIQGAPINW and its subsignatures in predicting ACC prognosis and progression
  • SigIQGAPINW is a novel prognostic signature of ACC. None of the genes have been reported in ACC. This ccRCC multigene panel is highly effective in assessing ACC prognosis and progression and outperforms the current biomarkers formulated on ACC.
  • cBioPortal database The cBioPortal (http://www.cbioportal.org/index.do) database contains the most well-organized cancer genetics for various cancer types.
  • Cutoff point estimation Cutoff points to stratify patients into a high- and low-risk group were estimated by Maximally Selected Rank Statistics (the Maxstat package) in R.
  • Table 15 Univariate and multivariate Cox analyses of SigIQGAPINW component genes in predicting ACC OS and progression
  • stage 1 In analysis with age at diagnosis and tumor stage [stage 1 (3+4) vs stage 0 (1+2)] ; 2: continuous gene expression data was used in analysis; *p ⁇ 0.05; **p ⁇ 0.01; ***p ⁇ 0.001
  • PPV positive prediction value
  • NPV negative prediction value
  • MMS median months survival
  • MMPFS median months progression-free survival
  • M month
  • SigIQGAPINW is somewhat superior over SubSigIQGAPINW-OS, RECQL4, and SNHG10 in assessing ACC prognosis (Table 16).
  • SubSigIQGAPINW-OS and both single genes are effective and appealing due to their simple composition. This set of biomarkers thus offers different combinations for rapid and effective stratification of ACC prognostic outcomes; for instance, predictions can be achieved at 92.6% sensitivity and 92.2% specificity (Table 16).
  • SubSigIQGAPINW-PFS predicts ACC progression more effectively compared to SigIQGAPINW, SNHG10, and RECQL4 (Table 16).
  • SigIQGAPINW, SubSigIQGAPINW-PFS, and RECQL4 have clinical potential in assessing ACC progression.
  • RECQL4 can provide an initial screen as a single gene; SigIQGAPINW predicts early recurrence (6.43M) at 88.5% accuracy (PPV), and SubSigIQGAPINW-PFS provides a high overall efficiency in predicting ACC progression (Table 16).
  • Both SNHG10 and RECQL4 are component genes in both sub-signatures: SubSigIQGAPINW-OS and SubSigIQGAPINW-PFS. These observations provide additional support for the prognostic potential of SigIQGAPINW in ACC.
  • Table 17 Univariate and multivariate Cox analyses of Sig27gene component genes in predicting ACC OS and progression
  • stage 1 In analysis with age at diagnosis and tumor stage [stage 1 (3+4) vs stage 0 (1+2)] ; 2: continuous gene expression data was used in analysis; *p ⁇ 0.05; **p ⁇ 0.01; ***p ⁇ 0.001
  • MXD3, BIRC5, and RAB30 estimate poor OS effectively evident by their low p values and independently of age at diagnosis and tumor stage. Both MXD3 and BIRC5 predict poor OS at tAUC values > 80% ( Figure 24B), and both effectively stratify ACCs prognostic outcomes according to their fatality risk ( Figure 25C, D).
  • Sig27gene, SubSig27gene-OS, MXD3, and BIRC5 constitute a set of effective predictors of poor prognosis.
  • MXD3, BIRC5, and RAB30 are effective in the risk assessment based on their tAUC values (Figure 24C) and their effectiveness in stratification of ACCs with high risk progression (Figure 25E-G).
  • RAB30 is unique in the risk stratification; the accuracy of positive prediction or PPV is 91.7% (11/12) and specificity is 97.4% (Figure 25G).
  • patients in the high-risk group stratified by RAB30 have the most rapid course of disease progression with the median months progression-free survival 6.23 months ( Figure 25G, Table 18).
  • Table 18 Stratification efficiencies of ACC prognosis and progression by Sig27gene and its components
  • PPV positive prediction value
  • NPV negative prediction value
  • MMS mediate months survival
  • MMPFS mediate months progression-free survival
  • M month
  • Sig27gene component genes in ACC compared to normal tissues: The expression status of all 27 component genes was analyzed in ACC vs normal adrenal gland tissues. Among the 27 genes, 11 are differentially expressed.
  • the IncRNA LINC01089 is a component gene in both SigIQGAPINW and Sig27gene; its downregulation in ACC was observed ( Figure 23).
  • Other six genes with expression reduced in ACC are FPR3, LCN12, RAB30, RGS11, TFEC, and VGF; all downregulations occur in either CIMP-high, CIMP-intermediate, or both ( Figure 26).
  • Combination signature (Combosig) with robust efficiencies in assessing ACC prognosis and progression.
  • RECQL4 constitute the core component genes of SigIQGAP 1NW, while MXD3, BIRC5, and RAB30 are individual genes within Sig27gene that are effective in assessing ACC prognosis. These six genes are combined to form Combosig.
  • the predictions are highly effective evident by the associated tAUC values of 94.6% at 18.7M and 95.1% at 71.
  • Combosig robustly stratifies ACC fatality and progression risk (Figure 27C, D). As expected, Combosig assesses ACC prognosis and progression after adjusting for age at diagnosis and tumor stage (Table 19). Collectively, Combosig is very appealing for clinical applications because of its simplicity (6 genes) and effectiveness.
  • PPV positive prediction value
  • NPV negative prediction value
  • MMS median months survival
  • M month
  • BUB1B-PINK1 is the best predictor of ACC prognosis.
  • BUB1B predicts ACC poor OS more efficiently than PINK1 (data not shown).
  • RECQL4 is the most efficient single gene in assessing ACC prognosis.
  • RECQL4 is more robust in predicting poor OS than BUB1B evident by the respective tAUC profiles ( Figure 28B) and their abilities in separation of ACCs into a high-risk and low-risk fatality group ( Figure 28C, D).
  • SigIQGAPINW and Sig27gene are novel to ACC. Their importance is further strengthened by the identification of differential expression of multiple component genes in both multigene panes in ACC compared to normal adrenal glands ( Figures 23 and 26).
  • the core components of SigIQGAPINW and Sig27gene are among these differentially expressed genes, including the 6 component genes of Combosig: LINC01089, SNHG10, RECQL4, MXD3, BIRC5, and RAB30.
  • BIRC5 the other 5 genes are novel to ACC and 4 of these 5 genes (except LINC01089) are effective in predicting ACC poor OS and progression as individual genes.
  • RECQL4 is particularly robust in assessing prognosis and RAB30 is highly effective in predicting rapid ACC progression.

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Abstract

A method of diagnosing a urogenital cancer, progression of the cancer and/or survival following the cancer in a mammal is provided. The method comprises the steps of: i) optionally detecting the level of IQGAP1 in a biological sample from the mammal, and comparing the sample level of IQGAP1 to a control level of IQGAP1; ii) detecting the level of genes in a gene signature obtained from the biological sample, wherein the gene signature comprises LINC01089, and comparing the sample level of genes in the gene signature to a control level; and iii) diagnosing the mammal with a urogenital cancer when the sample level of LINC01089 is a statistically significant different level as compared to the control level of LINC01089, and optionally, when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP1.

Description

GENE SIGNATURE FOR PREDICTING PROGRESSION AND PROGNOSIS OF URINARY CANCERS AND METHODS OF USE THEREOF
FIELD
[0001] The present application relates to the field of cancer, and in particular, relates to a gene signature biomarker for predicting the progression and prognosis of urogenital cancers and methods of use thereof.
BACKGROUND
[0002] Prostate cancer (PC) is the top ranked male malignancy in the developed world. The disease is developed from high grade prostatic intra-epithelial neoplasia (HGPIN) lesions prior to progression to PC and metastasis. Primary PCs are managed with active surveillance, radiation, and surgery depending on disease severity, patient age and preference. PCs are graded with the Gleason score (GS) and World Health Organization (WHO) PC grading system (WHO grade group 1-5) or ISUP (the International Society of Urological Pathology) grade; WHO or its equivalent ISUP is GS-based. Despite surgery being a primary curative therapy for PC, the release rate is approximately 30%; the biochemical detection of PC relapse characterized by an increase in serum prostate-specific antigen (PSA) is defined as biochemical recurrence (BCR). This recurrence is a major turning point leading to poor prognosis. A large percentage of BCR tumors will progress to metastatic PCs, with which therapeutic options remain limited and the outcome is poor. The standard of care for metastatic PCs is androgen deprivation therapy (ADT), which was developed following the seminal discovery of androgen-dependency in PC proliferation in the 1940s. However, ADT is a palliative care as castration-resistant PC (CRPC) commonly occurs. A variety of regimens are available in managing CRPCs, including taxane-based chemotherapy, androgen receptor (AR)-targeting therapy involving either abiraterone or enzalutamide, and immunotherapy. However, these treatments only produce modest survival benefits. Conceptually, BCR remains the favorite point of intervention before disease progression to metastasis and CRPC. Nonetheless, realization of this therapeutic option requires a better understanding of BCR as it is regulated by complex networks.
[0003] Small G proteins are important oncogenic factors of PC. Besides being the most-well studied, Ras, Cdc42 and Rac regulate cytoskeleton and reactive oxygen species (ROS), and activate MAP kinases and PI3K, the well-demonstrated oncogenic events and pathways. A critical mediator of Cdc42 and Rac is IQGAP1 which belongs to the IQ motif GTPase-activating scaffold proteins (IQGAPs). Both humans and mice have three IQGAP proteins, IQGAP1, 2 and 3. Except for the WW motif, all domains among the IQGAPs are highly conserved with reported homology ranging from 60% to 93%. IQGAP1 stimulates ERK activation, associates with Cdc42 and Rael, and stabilizes their GTP binding; IQGAP 1, thus, induces cytoskeleton dynamics, displays oncogenic activities and is upregulated in several cancers, including thyroid cancer, breast cancer, colorectal carcinoma, esophageal squamous cell carcinoma, hepatocellular carcinoma, and ovarian cancer. While IQGAP2 shares an overall homology of 62% with IQGAP 1, and even higher levels of homology between their respective structural motifs except the WW domain, IQGAP2 surprisingly possesses tumor suppressive activities. Nonetheless, downregulation of IQGAP1 correlates with tumor progression and poor prognosis in bladder cancer and IQGAP 1 suppresses tumor metastasis in the liver via inhibition of TGFP-mediated myofibroblast activation in tumor stroma.
[0004] The impact of IQGAP 1 on PC has not been thoroughly investigated. In vitro IQGAP 1 has been reported to bind with p21 activated 6 (PAK6) in LNCaP cells; the association decreases cell-cell adhesion in DU145 cells. Surprisingly, the master oncogenic AKT phosphorylates FOXO1, leading to FOXO1 -derived tumor suppression in LNCaP and DU145 cells via binding with IQGAP 1; the association prevents IQGAP 1 to activate ERK. Additionally, upregulation of IQGAP 1 was reported in metastasis produced by PC3 cells. However, evidence of IQGAP1 alterations following PC tumorigenesis is not available.
[0005] Kidney cancer is the 9th and 14th most common cancer in men and women, respectively. Renal cell carcinoma (RCC) accounts for 85% of kidney cancer cases; the most common subtypes are clear cell RCC (ccRCC, 80%), papillary RCC (pRCC, 15%), and chromophobe RCC (5%). Clear cell RCC is the most aggressive RCC and contributes to majority of kidney cancer deaths. The main curative treatment for primary ccRCC remains complete and partial nephrectomy; in these patients 30- 40% will experience recurrence and metastasis. Metastatic ccRCCs are currently treated with systemic therapies targeting the vascular endothelial growth factor (VEGF) and mTOR pathways. These targeted treatments are developed based on the long-term investigations demonstrating loss of the von Hippel-Lindau (VHL) tumor suppressor due to 3p loss, mutations, and promoter methylation, as the common initiating event of ccRCC. Loss of VHL leads to stabilization of hypoxia inducible factors (HIF) which upregulates VEGF. In addition to loss of VHL, other oncogenic events are required for ccRCC; one trajectory is the activation of the phosphoinositide 3-kinase (PI3K)-AKT- mTOR pathway. These newly developed agents targeting the VEGF and mTOR pathways clearly improved overall survival (OS) and progression-free survival (PFS) in patients with metastatic ccRCC over the classic interferon-a and interleukin-2 therapy. However, benefits are modest and metastatic ccRCC remains incurable. One way to improve patient management is to effectively classify primary ccRCCs with high risk of recurrence, metastasis, and death. Currently, tumor stage is used to provide the prognostic information. However, this system is far from being perfect.
[0006] A variety of serum and urine biomarkers have been actively investigated to better assess ccRCC progression, including circulating tumor cells, cell-free tumor DNA, circulating RNA, and proteins. These biomarkers, however, still require substantial validation. Most recently, the field of ccRCC biomarkers has been systemically evaluated and updated. Among the 28 biomarkers examined, ccB was the only independent prognostic biomarker after adjusting for tumor grade and stage. Effective ccRCC biomarkers, thus, have yet to be developed.
[0007] The role of IQGAP1 in ccRCC remains to be investigated, despite the identification of its role in other cancers.
[0008] Adrenocortical carcinoma (ACC) is an orphan disease with an annual incidence of approximately 1-2 cases per million. ACC is an aggressive endocrine carcinoma; the estimated 5-year survival rate is less than 50%. A recent epidemiological study of 2014 ACC cases in USA from 1973-2014 revealed the disease mortality being 52% with a median survival time less than 2 years. ACC affects more women than men and occurs at a median age within the fifth and sixth decades of life. Surgical resection is the only curative treatment. However, the relapse rate is high with 86% recurrence being reported in 133 ACC patients. Local relapses commonly associate with metastasis, to which therapeutic options are less effective.
[0009] Despite as an aggressive carcinoma, ACC has variable or heterogenous prognosis with either no recurrence or slow metastatic progression in some tumors. Effective prediction of ACC fatality or its clinical behavior at the time of diagnosis is critical for patient management via individualized therapies. Clinical outcomes can be estimated by the ACC staging system modified by the European Network for the Study of Adrenal tumors (ENSAT). Other prognostic classifiers include Ki67 index, CpG island methylation, and transcriptome-based classification. CpG island methylator phenotype (CIMP) profile has been used to cluster ACCs into either non-CIMP and high CIMP groups with the latter being divided into CIMP -high and CIMP -low, or three groups consisting of CIMP-high, CIMP-intermediate, and CIMP-low. Increases in CIMP are associated with poor prognosis. Based on gene expression profiles, ACCs can be clustered into CIA and C1B with the former being more aggressive. While CIMP-low ACCs are largely within the C1B group, both CIMP-intermediate and CIMP-high reside in the CIA group with CIMP-high being more overlapped with CIA compared to CIMP-intermediate. Thus, both methylation and transcription omics can classify low- and high-risk ACCs with an overlap manner.
[0010] While both the omics can stratify prognostic outcome, the core events from either omics need to be specified for clinical applications. Towards this goal, hypermethylation of the G0S2 gene predominantly occurs in CIMP-high ACC and significantly predicts disease free survival (DFS) and OS with the prediction of DFS being more efficient. With respect to gene expression, the disk large-associated protein 5 (DLGAP5 or DLG7) and PTEN-induced putative kinase 1 (PINK1) genes are predictive of DFS, while benzimidazoles 1 homolog beta (BUB IB) and PINK1 expressions are the best predictor of OS. Even with these tools, there are no biomarkers in the clinic to evaluate ACC progression and fatality risks.
[0011] It would be desirable, thus, to identify biomarkers associated with a urogenital cancer such as PC, RCC and/or ACC, as well as other urogenital cancers, to provide methods of diagnosis and prognosis, and thereby improve patient management.
SUMMARY
[0012] It has now surprisingly been determined that downregulation of IQGAP 1 is associated with urogenital cancers, including PC, RCC and ACC. Further, methods of detecting novel gene expression signatures in combination with IQGAP 1 downregulation has been determined to be useful in methods of diagnosis and prognosis of mammals with a urogenital cancer.
[0013] Thus, in one aspect of the invention, a method of diagnosing a urogenital cancer in a mammal is provided comprising the steps of: i) detecting the level of IQGAP 1 in a biological sample from the mammal; ii) comparing the sample level of IQGAP 1 to a control level of IQGAP 1; and iii) diagnosing the mammal with a urogenital cancer when the sample level of IQGAP 1 is significantly reduced in comparison to the control level of IQGAP 1.
[0014] In another aspect, a method of diagnosing a urogenital cancer, progression of the cancer and/or survival following the cancer in a mammal is provided, comprising the steps of: i) optionally detecting the level of IQGAP 1 in a biological sample from the mammal, and comparing the sample level of IQGAP1 to a control level of IQGAP1; ii) detecting the level of genes in a gene signature obtained from the biological sample, wherein the gene signature comprises LINC01089, and comparing the sample level of genes in the gene signature to a control level; and iii) diagnosing the mammal with a urogenital cancer when the sample level of LINC01089 is a statistically significant different level as compared to the control level of LINC01089, and optionally, when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP 1.
[0001] In another aspect, a method of diagnosing adrenal cancer in a mammal is provided comprising the steps of: i) optionally detecting the level of IQGAP 1 in a biological sample from the mammal, and comparing the sample level of IQGAP1 to a control level of IQGAP1; ii) detecting the level of genes in a gene signature obtained from the biological sample, wherein the gene signature comprises: a) one or more of LCN12, VGF, RGS11, MXD3, BIRC5, FPR3, RAB30, NOD2, TEFC, ZFHX4, and HDAC9; b) one or more of LCN12, VGF, RGS11, MXD3, BIRC5, RAB30, NOD2 and ZFHX4; c) one or more of SNHG10, RECQL4, MXD3 and RAB30; or d) one or more of LOC100128288, SNHG10 or HERC2P2, and comparing the sample level of the gene(s) in the gene signature to a control level; and iii) diagnosing the mammal with cancer when the sample level of the one or more detected gene(s) is a statistically significant different as compared to the control level of the gene, and optionally, when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP1.
[0015] Other features and advantages of the present application will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating embodiments of the application, are given by way of illustration only and the scope of the claims should not be limited by these embodiments, but should be given the broadest interpretation consistent with the description as a whole.
DRAWINGS
[0016] The embodiments of the application will now be described in greater detail with reference to the attached drawings in which:
[0017] FIGURE 1 shows downregulation of IQGAP1 in advanced PCs in an exemplary embodiment of the application. Primary PCs with low Gleason scores (GS 6-7) and high GS (9-10) were stained for IQGAP1 by IHC and quantified.
[0018] FIGURE 2 shows decreases in IQGAP1 mRNA expression following the course of PC in an exemplary embodiment of the application. The Liu (a) and Wallace (b) datasets of PC microarray studies within OncomineTM were analyzed for IQGAP1 expression in primary PC and normal prostate tissues. **: p < 0.01 and ***: p < 0.001. The indicated microarray datasets of PC from OncomineTM (c/d) were analyzed for IQGAP1 expression in primary PCs and distant metastasis PCs. **: p < 0.01 and ***: p < 0.001 by 2-tailed Student’s t-test.
[0019] FIGURE 3 shows downregulation of IQGAP1 associates with therapy resistance of PC in an exemplary embodiment of the application, (a) IQGAP1 mRNA expression in the Grasso dataset (OncomineTM) in androgen sensitive PCs and CRPCs was analyzed. ***: p < 0.001 by 2-tailed Student’s t-test. (b) LNCaP xenografts were generated in NOD/SCID mice. Mice were either untreated or castrated when tumors were 100-200 mm3, followed by monitoring for PSA increases. IQGAP1 mRNA expression was quantified in each. (c,d) IQGAP1 mRNA expression data, determined by RNA-seq, was retrieved along with the relevant clinical data from the TCGA PanCancer Atlas and MSKCC dataset within cBioPortal. Cutoff points to separate the individual cohort into a high and low recurrence risk group were defined by Maximally Selected Rank Statistics using the R Maxstat package. Kaplan Meier curves and logrank test were performed using the R Survival package. Numbers at risk are indicated.
[0020] FIGURE 4 illustrates that downregulation of IQGAP1 is associated with PC recurrence. The TCGA PanCancer Atlas PC dataset was divided into a high and low relapse risk group following prostatectomy using the cutoff point of -1SD (standard deviation). Kaplan Meier survival curve and lograk test were performed using tools provided by cBioPortal.
[0021] FIGURE 5 shows pathway enrichment of IQGAP1 DEGs in an exemplary embodiment of the application, (a) Representatives of top 20 enriched clusters of GO biological process terms and KEGG pathways are shown, (b) Network relationship of those enriched clusters. Analyses were carried out with Metascape.
[0022] FIGURE 6 shows geneset enrichment in an exemplary embodiment of the application. IQGAP1 DEGs relative to IQGAP1 downregulation were defined at q<0.0001 and analyzed for geneset enrichment among human hallmark gene set. Three enriched genesets functioning in interferon gamma response, inflammatory response and oxidative phosphorylation are included.
[0023] FIGURE 7 shows Sig27gene robustly stratifies the risk of PC recurrence in an exemplary embodiment of the application, (a) HR, 95% CI, and p values for prediction of PC biochemical recurrence in the indicated populations are shown. Signature scores were either from the Training group (“Training score”), Testing cohort (“Testing score”), or full TCGA cohort (“Full cohort score”), (b) Timedependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated cohorts are shown, (c) Kaplan Meier curve for Training cohort was produced based on the cutoff point of Sig27gene determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test, (d, e) Kaplan Meier curves for the Testing cohort were produced using the Training scores (d) or Testing scores (e) of Sig27gene. (f, g) Examination of Sig27gene in the full TCGA PanCancer Atlas PC dataset using the Training scores (f) and full cohort scores (g). All Kaplan Meier analyses and logrank tests were performed using the R Survival package.
[0024] FIGURE 8 shows validation of Sig27gene with the independent MSKCC PC cohort in an exemplary embodiment of the application, (a, b) Sig27gene was analyzed for the stratification of PC recurrence risk in MSKCC dataset using scores defined either from the TCGA Training cohort (TCGA Training score) or from the MSKCC cohort (MSKCC score), (c) Time-dependent AUC for the indicated score systems in prediction of PC recurrence in MSKCC cohort.
[0025] FIGURE 9 shows differential expression of the indicated component genes of Sig27gene. The indicated gene expressions (mRNA) in PC (T) and matched normal prostate tissues (N) were analyzed using the GEPIA2 program. *p<0.05.
[0026] FIGURE 10 shows prediction of ACC, poor OS and progression by Sig27gene in an exemplary embodiment of the application. (A) Sig27gene and two subsignatures for assessing either OS (overall survival) or PFS (progression-free survival). (B, C) Time-dependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated multigene panels and major component genes (BIRC5, MXD3, and RAB30) in assessing OS (B) and PFS (C) are shown. (D, E) Kaplan Meier curves for Sig27gene in stratifying fatality risk (D) and progression risk (E). Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test. For assessing PFS, C3orf47 was removed, as its presence made multivariate analysis not work and thus coefficients for component genes could not be derived.
[0027] FIGURE 11 shows pathway enrichment of DEGs relative to IQGAP1 downregulation in an exemplary embodiment of the application. (A) Representatives of top clusters enriched. (B) Network presentation of those enriched clusters. Analyses were performed using Metascape. [0028] FIGURE 12 shows Geneset enrichment in an exemplary embodiment of the application. IQGAP1 DEGs analyzed for geneset enrichment among human hallmark gene set. Two enriched genesets functioning in inflammatory response and oxidative phosphorylation are presented.
[0029] FIGURE 13 shows SigIQGAPINW robustly stratifies risks of poor prognosis of ccRCC in the Training sub-population in an exemplary embodiment of the application. (A) HR, 95% CI, and p values. OS: overall survival; DSS: disease-specific survival; PFS: progression free survival. (B) Time-dependent ROC (receiver operating characteristic) curve. Months for PFS are specifically labeled. (C) Kaplan Meier survival curves. Statistical analyses were performed using logrank test.
[0030] FIGURE 14 shows SigIQGAPINW efficiently predicts the risks of poor prognosis of ccRCC in the Testing population in exemplary embodiments of the application. (A, B) Analyses of the fatality risk of ccRCC using SigIQGAPINW scores defined from the Training population (A) or the Testing cohort (B). HR, 95% CI, and p values along with logrank p value for Kaplan Meier survival curves are provided. The respective median survival times are also indicated. (C) Time-dependent ROC curve. Months for PFS are specifically labeled.
[0031 ] FIGURE 15 shows SigIQGAPINW classifies the risks of poor prognosis of ccRCC in the TCGA PanCancer Atlas ccRCC cohort with a high degree of certainty in an exemplary embodiment of the application. (A) Kaplan Meier survival curve. (B) HR, 95% CI, and p values for the indicated ccRCC events. (C) Timedependent ROC curve. Months for PFS are specifically labeled.
[0032] FIGURE 16 shows the association of SigIQGAPINW with worse clinical features of ccRCC in an exemplary embodiment of the application. Stage 1 and 2 are expressed as “0”, while Stage 2 and 4 are represented as “1”. Tumour size T stages 1 and 2 are converted to “0”; T3 and T4 are combined to “1”. SigIQGAPINW scores are used for analysis.
[0033] FIGURE 17 shows the association of SigIQGAPINW component genes with poor OS of ccRCC. Kaplan Meier survival curves for the indicated component genes along with logrank p values, median survival month and other information are included in exemplary embodiments of the application.
[0034] FIGURE 18 shows Kaplan Meier survival curves for the indicated component genes of SigIQGAPINW in exemplary embodiments of the application. Cutoff points for these component genes were determined based on their mRNA expression using Maximally Selected Rank Statistics (the Maxstat package) in R. The individual survival curves were produced using the R survival package. Statistical analyses were performed with logrank test.
[0035] FIGURE 19 shows differential expression of the HERC2P2 and THSD7A component genes in ccRCC (Tumor/T) and matched non-tumor kidney tissues (N) in an exemplary embodiment of the application. Gene expression was determined by RNA-seq (TCGA) and analyzed using the GEPIA2 program. Four mRNA clusters are indicated. TPM: transcripts per million. Statistical analyses were performed by GEPIA2, *p<0.05.
[0036] FIGURE 20 shows prediction of ACC poor OS and progression by SigIQGAPINW in an exemplary embodiment of the application. (A) SigIQGAPINW and two sub-signatures for assessing either OS (overall survival) or PFS (progression- free survival). (B, C) Time-dependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated multigene panels in assessing OS (B) and PFS (C) are shown. (D, E) Kaplan Meier curves for SigIQGAPINW in stratifying fatality risk (D) and progression risk (E). The median months survival (19 months, D) and median months progression-free survival (E, 6.43 months) are shown. Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test.
[0037] FIGURE 21 shows prediction of ACC poor OS and progression by SigIQGAPINW in an exemplary embodiment of the application. (A) SigIQGAPINW and two sub-signatures for assessing either OS (overall survival) or PFS (progression- free survival). (B, C) Time-dependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated multigene panels in assessing OS (B) and PFS (C) are shown. (D, E) Kaplan Meier curves for SigIQGAPINW in stratifying fatality risk (D) and progression risk (E). The median months survival (19 months, D) and median months progression-free survival (E, 6.43 months) are shown. Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test.
[0038] FIGURE 22 shows Kaplan Meier curves for the indicated sub-panels of SigIQGAPINW and the indicated component genes in stratifying ACC poor OS (A, C, D) and progression (B, E, F) in an exemplary embodiment of the application. Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test.
[0039] FIGURE 23 shows differential expression of the indicated component genes of SigIQGAPINW in exemplary embodiments of the application. The indicated gene expressions in ACC (T) and normal adrenal gland tissues (N) were analyzed using the GEPIA2 program. *p<0.05.
[0040] FIGURE 24 shows Prediction of ACC poor OS and progression by Sig27gene in an exemplary embodiment of the application. (A) Sig27gene and two subsignatures for assessing either OS (overall survival) or PFS (progression-free survival). (B, C) Time-dependent ROC (receiver operating characteristic) curve; time-dependent area under the curve (AUC) values for the indicated multigene panels in assessing OS (B) and PFS (C) are shown. (D, E) Kaplan Meier curves for Sig27gene in stratifying fatality risk (D) and progression risk (E). Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test. For assessing PFS, C3orf47 was removed, as its presence made multivariate analysis not work and thus coefficients for component genes could not be derived.
[0041 ] FIGURE 25 shows Kaplan Meier curves for the indicated sub-panels of Sig27gene and the indicated component genes in stratifying ACC poor OS (A, C, D) and progression (B, E, F, G) in exemplary embodiments of the application. Cutoff points were determined by Maximally Selected Rank Statistics. Statistical analyses were performed using logrank test.
[0042] FIGURE 26 shows differential expression of the indicated component genes of Sig27gene in an exemplary embodiment of the application. The indicated gene expressions in ACC (T) and normal adrenal gland tissues (N) were analyzed using the GEPIA2 program. *p<0.05. [0043] FIGURE 27 shows assessing ACC OS and PFS by Combosig in an exemplary embodiment of the application. (A) HR, 95% CI, and p values for Combosig in predicting ACC OS and PFS are shown. (B) tAUC values for Combosig in assessing ACC OS and PFS; the time points relevant to AUC values are indicated. (C, D) Kaplan Meier curves for Combosig in stratifying ACC fatality risk (C) and progression risk (D). The median months survival (C) and median months progression-free survival (D) are included. A set of stratification parameters for OS (left) and PFS (right) are indicated. PPV: positive prediction value; NPV: negative prediction value.
[0044] FIGURE 28 shows comparisons of ACC biomarkers documented here with the best published predictor of ACC prognosis in an exemplary embodiment of the application. (A) tAUC profiles of the indicated multigene panels in assessing ACC OS. The pair of BUB1B-PINK1 is the best predictor of ACC prognosis previously reported. (B) tAUC profiles of RECQL4 (a component gene) with BUB IB (best prognosis predictor previously reported) in predicting OS. (C, D) Stratification of ACC prognosis by RECQL4 (C) and BUB IB (D). Median months survival, p values, sensitivity, specificity, PPV, and NPV for the stratifications are indicated.
DETAILED DESCRIPTION
[0045] A method of diagnosing a urogenital cancer in a mammal is provided comprising the steps of: i) detecting the level of IQGAP1 in a biological sample from the mammal; ii) comparing the sample level of IQGAP1 to a control level of IQGAP1; and iii) diagnosing the mammal with a urogenital cancer when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP1.
[0046] The term “urogenital cancer” as used herein refers to cancer of the kidney (e.g. renal cell cancer (RCC), clear cell renal cell carcinoma, Wilms tumors or transitional cell carcinoma), bladder, ureter, urethra, prostate (PCC) and testicle. Urogenital cancer also encompasses adrenal or adrenocortical carcinoma (ACC) (including pheochromocytoma and paraganglioma) in which malignant cells form in the outer layer of the adrenal gland which is located on top of the kidney.
[0047] To conduct the method, a biological sample is obtained from a human subject. The term “biological sample” is meant to encompass any human sample that may contain nucleic acid, including biological fluids such as, but not limited to, blood, plasma/serum, urine, sweat, saliva, sputum, and cerebrospinal fluid. Tumor biopsies from organs that may be affected may also be used, including, for example, prostate, kidney and adrenocortical tissues. The sample is obtained from the subject in a manner well-established in the art.
[0048] Once a suitable biological sample is obtained, it is analyzed to determine the concentration or level of IQGAP1 therein. Prior to analysis, the sample may be subject to processing such as extraction, fdtration, centrifugation or other sample preparation techniques to provide a sample that is suitable for further analysis. For example, biological fluids may be fdtered or centrifuged (e.g. ultracentrifugation) to remove solids from the sample to facilitate analysis. Tissue samples may be subject to extractions in order to provide an analyzable sample. As one of skill in the art will appreciate, biomarker level may be determined using one of several techniques established in the art that would be suitable for detecting the biomarker, including chromatographic techniques such as high performance liquid chromatography and gas chromatography, immunoassay or enzyme-based assays with colorimetric, fluorescence or radiometric detection.
[0049] For example, the level of IQGAP1 in a sample may be measured by immunoassay using an antibody specific to IQGAP1. The antibody binds to the IQGAP1 and bound antibody is quantified by measuring a detectable marker which may be linked to the antibody or other component of the assay, or which may be generated during the assay. Detectable markers may include radioactive, fluorescent, phosphorescent and luminescent (e.g. chemiluminescent or bioluminescent) compounds, dyes, particles such as colloidal gold and enzyme labels. The term “antibody” is used herein to refer to monoclonal or polyclonal antibodies, or antigenbinding fragments thereof, e.g. an antibody fragment that retains specific binding affinity for IQGAP1. IQGAP1 antibodies may be commercially available, such as anti- IQGAP1 antibodies from Abeam and Invitrogen. Alternatively, antibodies may also be raised using techniques conventional in the art. For example, antibodies may be made by injecting a host animal, e.g. a mouse or rabbit, with the antigen (IQGAP1), and then isolating antibody from a biological sample taken from the host animal.
[0050] Different types of immunoassay may be used to determine the level of IQGAP1 in a sample, including indirect immunoassay in which the IQGAP1 biomarker is non-specifically immobilized on a surface; sandwich immunoassay in which the biomarker is specifically immobilized on a surface by linkage to a capture antibody bound to the surface; and a competitive binding immunoassay in which a sample is first combined with a known quantity of biomarker antibody to bind biomarker in the sample, and then the sample is exposed to immobilized biomarker which competes with the sample to bind any unbound antibody. Enzyme Linked ImmunoSorbent Assay (ELISA) may also be used to determine the level of a biomarker in a sample. In this case, the biomarker to be analyzed is generally immobilized on a solid support, complexed with an antibody to the biomarker which is itself linked to an enzyme indicator, such as horseradish peroxidase (HRP), alkaline phosphatase (AP), B- galactosidase, acetylcholinesterase and catalase. Detection may then be accomplished by incubating this enzyme-complex with a substrate for the enzyme that yields a detectable product.
[0051] Once the level of IQGAP1 is determined in the sample, its level is compared to a control level, i. e. the level in a corresponding healthy sample not afflicted with cancer, to determine the average fold-change (FC) difference and statistical significance (p-value) between the sample and control levels.
[0052] The mammal is determined to have cancer when the difference in the level of IQGAP1 in the biological sample is statistically significantly reduced or downregulated as compared to the control level. The determination of statistical significance is well-established in the art. Statistical significance is attained when a p- value is less than the significance level. The -value is the probability of observing an effect given that the null hypothesis is true whereas the significance or alpha (a) level is the probability of rejecting the null hypothesis given that it is true. Generally, a statistically significant difference, i.e. an increase or decrease, in the level of IQGAP1 in accordance with the present method, is a difference in the level of the biomarker from the control level of at least about 5%, or greater, e.g. at least about 10%, 15%, 20%, 25%, 30%, 40%, 50%, or more. When performing multivariate statistical analysis during biomarker discovery, corrected p-values are often used to correct for multiple hypothesis testing in order to reduce false discoveries, such as the use of a false discovery rate (q < 0.05) or a more conservative Bonferroni correction.
[0053] In one embodiment, the present method comprises the detection of IQGAP1 downregulation in a biological sample concurrently with the detection in the biological sample of a statistically significant different level of LINC01089 as compared to the control level of LINC01089. The term “statistically significant different level” may refer to either a statistically significant increased level or a statistically significant decreased level as compared to a control level depending on the disease to be diagnosed, e g. PC, RCC or ACC.
[0054] LINC01089 (Long Intergenic Non-Protein Coding RNA1089) is an RNA gene, and is affiliated with the IncRNA class. LINC01089 refers herein to mammalian LINC01089, encompassing human and other mammalian LINC01089, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LINC01089. The sequence of human LINC01089 is denoted by the Ensembl gene ID, ENSG00000212694, from the Ensembl online database (https : // www. ensembl . org).
[0055] In another embodiment, the present method comprises the optional detection of IQGAP1 downregulation in a biological sample with the detection in the biological sample of a statistically significant different level of one or more genes selected from the group of: LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A, as compared to the control level of the one or more genes. The term ''level'' is used herein to refer to concentration, or expression level.
[0056] SPACA6 (sperm acrosome associated 6) is protein-coding and refers herein to mammalian SPACA6, encompassing human and other mammalian SPACA6, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of SPACA6. The sequence of human SPACA6 is denoted by the Ensembl gene ID, ENSG00000182310.
[0057] LOC 155060 (zinc finger protein pseudogene) refers herein to mammalian LOCI 55060, encompassing human and other mammalian LOC 155060, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LOC155060. The sequence of human LOC155060 is denoted by the Ensembl gene ID, ENSG00000244560.
[0058] LOC 100128288 is an RNA gene, and is affiliated with the IncRNA class. LOC100128288 refers herein to mammalian LOC100128288, encompassing human and other mammalian LOC100128288, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LOC100128288. The sequence of human LOC100128288 is denoted by the NCBI accession no., 100128288 ((National Centre for Biotechnology Information).
[0059] SNHG10 (Small Nucleolar RNA Host Gene 10) is an RNA gene, and is affiliated with the IncRNA class. SNHG10 refers herein to mammalian SNHG10, encompassing human and other mammalian SNHG10, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of SNHG10. The sequence of human SNHG10 is denoted by the Ensembl gene ID, ENSG00000247092.
[0060] RECQL4 (RecQ Like Helicase 4) is a protein coding gene and refers herein to mammalian RECQL4, encompassing human and other mammalian RECQL4, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RECQL4. The sequence of human RECQL4 is denoted by the Ensembl gene ID, ENSG00000160957.
[0061 ] HERC2P2 (Hect Domain And RED 2 Pseudogene 2) is a pseudogene and refers herein to mammalian HERC2P2, encompassing human and other mammalian HERC2P2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of HERC2P2. The sequence of human HERC2P2 is denoted by the Ensembl gene ID, ENSG00000276550.
[0062] ATXN7L2 (Ataxin 7 Like 2) is a protein coding gene and refers herein to mammalian ATXN7L2, encompassing human and other mammalian ATXN7L2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of ATXN7L2. The sequence of human ATXN7L2 is denoted by the Ensembl gene ID, ENSG00000162650.
[0063] THSD7A (Thrombospondin Type 1 Domain Containing 7A) is a protein coding gene and refers herein to mammalian THSD7A, encompassing human and other mammalian THSD7A, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of THSD7A. The sequence of human THSD7A is denoted by the Ensembl gene ID, ENSG00000005108. [0064] Alternatively, in another embodiment, the method comprises the detection in the biological sample of a statistically significant different level of one or more genes selected from the group of: LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A. These genes are referred to herein as the 9-gene signature, SigIQGAPINW.
[0065] The detection of the level of one or more genes of SigIQGAPINW is conducted using methods such as RNA sequencing (RNA-seq) is a sequencing technique which uses next-generation sequencing (NGS) of cDNA, e.g. Illumina, 454, Ion torrent and Ion proton sequencing, to reveal the presence and quantity of RNA in a biological sample. RNA-seq also includes single cell sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing.
[0066] NanoString technology may also be used which utilizes fluorescent- labelled reporter probes that hybridize to the target RNA and a capture probe to immobilize the reporter-target complex(es) for detection and quantitation. This permits the direct digital quantification of target nucleic acids, from various sample types such as cell, tissue, and blood lysates, as well as RNA and DNA from tissues using targetspecific, color-coded probe pairs. It does not require the conversion of mRNA to cDNA by reverse transcription or the amplification of the resulting cDNA by PCR. Each target gene of interest is detected using a pair of reporter and capture probes carrying 35- to 50- base target-specific sequences. In addition, each reporter probe carries a unique color code at the 5' end that enables the molecular barcoding of the genes of interest, while the capture probes all carry a biotin label at the 3' end that provides a molecular handle for attachment of target genes to facilitate downstream digital detection. After solution-phase hybridization between target mRNA and reporter-capture probe pairs, excess probes are removed and the probe/target complexes are aligned and immobilized for digital analysis. Hundreds of thousands of color codes designating mRNA targets of interest are directly imaged on the surface of the cartridge. The expression level of a gene is measured by counting the number of times the color-coded barcode for that gene is detected, and the barcode counts are then tabulated.
[0067] PCR-based methods may also be used, including real-time PCR, and digital PCR, as well as microarray technologies. Probes and/or primers required in these sequences methods are designed based on the sequence information provided herein for each of the target genes.
[0068] Nucleic acid from the biological sample may be extracted from the sample using techniques well-known to those of skill in the art, including chemical extraction techniques utilizing phenol-chloroform (Sambrook et al., 1989), guanidine- containing solutions, or CTAB-containing buffers. As well, as a matter of convenience, commercial DNA extraction kits are also widely available from laboratory reagent supply companies, including for example, the QIAamp DNA Blood Minikit available from QIAGEN®, or the Extract-N-Amp blood kit available from Sigma-Aldrich®.
[0069] Detection of one or more of a statistically significant increase in the level of LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2 or ATXN7L2, or a statistically significant decrease in the level of THSD7A, is indicative of a urogenital cancer selected from PC, RCC and ACC.
[0070] In embodiments of the present method, detection of one or more of a statistically significant increase in the level of LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2 or ATXN7L2, or a statistically significant decrease in the level of THSD7A, is predictive of poor overall survival, metastasis and progression (recurrence) of disease in PC, RCC and ACC. In these methods, use of the 9-gene signature, SigIQGAPINW, predicts an increasing risk of poor overall survival, metastasis and progression (recurrence) of disease with a higher gene signature score, e.g. based on the number of genes of SigIQGAPINW detected and their expression levels, For example, the greater the number of genes of the gene signature detected, and the greater the difference in their detected level from normal control levels, the greater the risk of poor overall survival, metastasis, or recurrence of disease.
[0071 ] In another embodiment, the present method comprises the detection of IQGAP1 downregulation in a biological sample concurrently with the detection in the biological sample of statistically significant different levels of one or more genes selected from the group of: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5. [0072] HAGHL (Hydroxyacylglutathione Hydrolase Like) is a protein coding gene. HAGHL refers herein to mammalian HAGHL, encompassing human and other mammalian HAGHL, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of HAGHL. The sequence of human HAGHL is denoted by the Ensembl gene ID, ENSG00000103253.
[0073] LCN12 (Lipocalin 12) is a protein-coding gene and refers herein to mammalian LCN12, encompassing human and other mammalian LCN12, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LCN12. The sequence of human LCN12 is denoted by the Ensembl gene ID, ENSG00000184925.
[0074] DCST2 (DC-STAMP Domain Containing 2) is a protein coding gene and refers herein to mammalian DCST2, encompassing human and other mammalian DCST2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of DCST2. The sequence of human DCST2 is denoted by the Ensembl gene ID, ENSG00000163354.
[0075] VGF (VGF Nerve Growth Factor Inducible) is a protein coding gene and refers herein to mammalian VGF, encompassing human and other mammalian VGF, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of VGF. The sequence of human VGF is denoted by the Ensembl gene ID, ENSG00000128564.
[0076] RGS11 (Regulator Of G Protein Signaling 11) is a protein coding gene and refers herein to mammalian RGS11, encompassing human and other mammalian RGS11, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RGS11. The sequence of human RGS11 is denoted by the Ensembl gene ID, ENSG00000076344.
[0077] PRR7 (Proline Rich 7, Synaptic) is a protein coding gene and refers herein to mammalian PRR7, encompassing human and other mammalian PRR7, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PRR7. The sequence of human PRR7 is denoted by the Ensembl gene ID, EENSGOOOOO131188.
[0078] LINC01089 is described above. [0079] MXD3 (MAX Dimerization Protein 3) is a protein coding gene and refers herein to mammalian MXD3, encompassing human and other mammalian MXD3, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of MXD3. The sequence of human MXD3 is denoted by the Ensembl gene ID, ENSG00000213347.
[0080] BIRC5 (Baculoviral IAP Repeat Containing 5) is a protein coding gene and refers herein to mammalian BIRC5, encompassing human and other mammalian BIRC5, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of BIRC5. The sequence of human BIRC5 is denoted by the Ensembl gene ID, ENSG00000089685.
[0081 ] LTC4S (Leukotriene C4 Synthase) is a protein coding gene and refers herein to mammalian LTC4S, encompassing human and other mammalian LTC4S, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LTC4S. The sequence of human LTC4S is denoted by the Ensembl gene ID, ENSG00000213316.
[0082] H1FX-AS1 (Hl-10 Antisense RNA 1) is a non-coding gene and refers herein to mammalian H1FX-AS1, encompassing human and other mammalian THSD7A, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of H1FX-AS1. The sequence of human H1FX-AS1 is denoted by the Ensembl gene ID, ENSG00000206417.
[0083] FPR3 (Formyl Peptide Receptor 3) is a protein coding gene and refers herein to mammalian FPR3, encompassing human and other mammalian FPR3, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of FPR3. The sequence of human FPR3 is denoted by the Ensembl gene ID, ENSG00000187474.
[0084] RAB30 (RAB30, Member RAS Oncogene Family) is a protein coding gene and refers herein to mammalian RAB30, encompassing human and other mammalian RAB30, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RAB30. The sequence of human RAB30 is denoted by the Ensembl gene ID, ENSG00000137502. [0085] RIPOR2 (RHO Family Interacting Cell Polarization Regulator 2) is a protein coding gene and refers herein to mammalian RIPOR2, encompassing human and other mammalian RIPOR2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RIPOR2. The sequence of human RIPOR2 is denoted by the Ensembl gene ID, ENSG00000111913.
[0086] NOD2 (Nucleotide Binding Oligomerization Domain Containing protein 2) is a protein coding gene and refers herein to mammalian NOD2, encompassing human and other mammalian NOD2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of NOD2. The sequence of human NOD2 is denoted by the Ensembl gene ID, ENSG00000167207.
[0087] PLXNA4 (Plexin A4) is a protein coding gene and refers herein to mammalian PLXNA4, encompassing human and other mammalian PLXNA4, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PLXNA4. The sequence of human PLXNA4 is denoted by the Ensembl gene ID, ENSG00000221866.
[0088] RRAGC (Ras Related GTP Binding C) is a protein coding gene and refers herein to mammalian RRAGC, encompassing human and other mammalian RRAGC, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of RRAGC. The sequence of human RRAGC is denoted by the Ensembl gene ID, ENS G00000116954.
[0089] TFEC (Transcription Factor EC) is a protein coding gene and refers herein to mammalian TFEC, encompassing human and other mammalian TFEC, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of TFEC. The sequence of human TFEC is denoted by the Ensembl gene ID, ENSG00000105967.
[0090] PI 15 (Peptidase Inhibitor 15) is a protein coding gene and refers herein to mammalian PI15, encompassing human and other mammalian PI15, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PI15. The sequence of human PI 15 is denoted by the Ensembl gene ID, ENSG00000137558. [0091 ] ZFHX4 (Zinc finger homeobox protein 4) is a protein coding gene and refers herein to mammalian ZFHX4, encompassing human and other mammalian ZFHX4, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of ZFHX4. The sequence of human ZFHX4 is denoted by the Ensembl gene ID, ENSG00000091656.
[0092] LAMP3 (Lysosomal Associated Membrane Protein 3) is a protein coding gene and refers herein to mammalian LAMP3, encompassing human and other mammalian LAMP3, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of LAMP3. The sequence of human LAMP3 is denoted by the Ensembl gene ID, ENSG00000078081.
[0093] HDAC9 (Histone Deacetylase 9) is a protein coding gene and refers herein to mammalian HDAC9, encompassing human and other mammalian HDAC9, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of HDAC9. The sequence of human HDAC9 is denoted by the Ensembl gene ID, ENSG00000048052.
[0094] MCTP1 (Multiple C2 And Transmembrane Domain Containing 1) is a protein coding gene and refers herein to mammalian MCTP1, encompassing human and other mammalian MCTP1, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of MCTP1. The sequence of human MCTP1 is denoted by the Ensembl gene ID, ENSG00000175471.
[0095] KCNN3 (Potassium Calcium-Activated Channel Subfamily N Member 3) is a protein coding gene and refers herein to mammalian KCNN3, encompassing human and other mammalian KCNN3, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of KCNN3. The sequence of human KCNN3 is denoted by the Ensembl gene ID, ENSG00000143603.
[0096] PCDHB8 (Protocadherin Beta 8) is a protein coding gene and refers herein to mammalian PCDHB8, encompassing human and other mammalian PCDHB8, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PCDHB8. The sequence of human PCDHB8 is denoted by the Ensembl gene ID, ENSG00000120322. [0097] PCDHGB2 (Protocadherin Gamma Subfamily B, 2) is a protein coding gene and refers herein to mammalian PCDHGB2, encompassing human and other mammalian PCDHGB2, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PCDHGB2. The sequence of human PCDHGB2 is denoted by the Ensembl gene ID, ENSG00000253910.
[0098] PCDHGA5 (Protocadherin Gamma Subfamily A, 5) is a protein coding gene and refers herein to mammalian PCDHGA5, encompassing human and other mammalian PCDHGA5, as well as functional variants, i.e. variants including sequence differences but which essentially retain the function of PCDHGA5. The sequence of human PCDHGA5 is denoted by the Ensembl gene ID, ENSG00000253485.
[0099] Alternatively, in another embodiment, the detection of IQGAP1 downregulation in the biological sample is determined by the detection in the biological sample of statistically significant different levels of one or more genes selected from the group of: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5. These genes are referred to herein as the 27-gene signature, Sig27gene.
[00100] Detection of one or more of a statistically significant increase in the level of HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S orHIFX-ASI, or a statistically significant decrease in the level ofFPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PH 5, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 or PCDHGA5, is indicative of a urogenital cancer, including PC and ACC.
[00101] In embodiments of the present method, detection of one or more of a statistically significant increase in the level of HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S or H1FX-AS1, or a statistically significant decrease in the level of FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 or PCDHGA5, is predictive of poor overall survival, metastasis and progression (recurrence) of disease in PC and ACC. In these methods, use of the 27-gene signature, Sig27gene, predicts an increasing risk of poor overall survival, metastasis and progression (recurrence) of disease with a higher gene signature score, e.g. the greater the number of genes of Sig27gene detected with greater differences in level of gene expression from control levels, to be predictive of poor overall survival, metastasis, or recurrence of disease, the greater the risk of poor overall survival, metastasis, or recurrence of disease. In one embodiment, the method comprises the detection of the level of one or more genes selected from: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RIPOR2, NOD2, PLXNA4, TFEC, ZFHX4, MCTP1, PCDHGB2 and PCDHGA5.
[00102] In an embodiment, a method of predicting poor ACC prognosis, e.g. poor overall survival, is provided comprising the detection of a statistically significant difference in the level of one or more of LINC01089, SNHG10, HERC2P2 or RECQL4 in a biological sample of a mammal.
[00103] In another embodiment, a method of predicting progression of ACC is provided, comprising the detection of a statistically significant difference in the level of LOC100128288, SNHG10, HERC2P2 or RECQL4 in a biological sample of a mammal.
[00104] In another embodiment, a method of predicting poor ACC prognosis is provided comprising the detection of one or more of a statistically significant difference in the level of LINC01089, MXD3, BIRC5, RGS11 or RAB30 in a biological sample of a mammal. In an embodiment, the method includes the detection of the level of one or more of MXD3, BIRC5 and RAB30 in the sample. In another embodiment, the level of each of MXD3, BIRC5 and RAB30 is detected in the sample.
[00105] In a further embodiment, a method of predicting poor ACC prognosis and/or disease progression is provided comprising the detection of one or more of a statistically significant decrease in the level of LINC01089, FPR3, LCN12, RAB30, RGS11, TFEC or VGF, or a statistically significant increase in the level of BIRC5, HAGHL, MXD3 or PRR7 in a biological sample. In an embodiment, the level of each of LINC01089, FPR3, LCN12, RAB30, RGS11, TFEC, VGF, BIRC5, HAGHL, MXD3, and PRR7 is detected in the sample.
[00106] In another embodiment, in the level of one or more of a statistically significant decrease in the level of LINC01089, RAB30 or SNHG10, or a statistically significant increase in RECQL4, BIRC5 or MXD3 is detected in a biological sample of a mammal in a method of predicting poor ACC prognosis and/or disease progression. In embodiments, the method comprises the step of detecting the level of one or of each of SNHG10, RECQL4, MXD3 and RAB30 in the sample. In another embodiment, the method comprises the step of detecting the level of each of LINC01089, SNHG10, RECQL4, BIRC5, MXD3 and RAB30 in the sample. In another embodiment, the method comprises the step of detecting the level of RECQL4 in the sample.
[00107] In another embodiment, a method of predicting progression of ACC is provided, comprising the detection of one or more of a statistically significant difference in the level of one or more of LCN12, VGF, RGS11, MXD3, BIRC5, FPR3, RAB30, NOD2, TEFC, ZFHX4, and HDAC9 in a biological sample of a mammal. In an embodiment, the method includes the detection of the level of one or more of LCN12, VGF, RGS11, MXD3, BIRC5, RAB30, NOD2 and ZFHX4 in the sample. In another embodiment the level of each of LCN12, VGF, RGS11, MXD3, BIRC5, RAB30, NOD2 and ZFHX4 is detected in the sample.
[00108] Following diagnosis of a urogenital cancer, an appropriate treatment is selected. For prostate cancer, the treatment may include active surveillance, radiation therapy, hormone therapy, cryosurgery, chemotherapy and surgery (prostatectamy). Use of the present method to detect disease progression or recurrence permits the application of personalized treatment. For example, where the risk of disease progression is low, then treatment is preferably surveillance, while detection of disease progression warrants more aggressive treatment including radiation, chemotherapy or surgery.
[00109] For renal cancer, the treatment may include surgery (nephrectomy), medications, radiation therapy and minimally invasive procedures, such as cryoablation and radiofrequency ablation. If the method relates to diagnosis of disease progression or recurrence, then treatment options may be based on the risk of disease progression. For example, where the risk of disease progression is low, then treatment is preferably surveillance, while detection of disease progression warrants more aggressive treatment including radiation, medications or surgery.
[00110] For adrenocortical cancer, the treatment may include surgery (adrenalectomy), chemotherapy (e.g. mitotane), radiation, biologic therapy or targeted therapy. If the method relates to diagnosis of disease progression or recurrence, then treatment options may be based on the risk of disease progression. For example, where the risk of disease progression is low, then treatment is preferably surveillance, while detection of disease progression warrants more aggressive treatment including radiation, medications or surgery.
[001 1 1] In other aspects of the invention, kits are provided for detection of the gene signatures disclosed herein. Thus, in one embodiment, a kit is provided for the detection of a gene signature comprising the genes: LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A. In another embodiment, a kit is provided for the detection of a gene signature comprising the genes: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5. In a further embodiment, a kit for the detection of a gene signature comprising the genes: LINC01089, SNHG10, RECQL4, MXD3, BIRC5, and RAB30 is provided for the detection of adrenal cancer. The kits comprise specific reactants to detect the genes within the panels, for example, primers and/or probes for use to detect each of the genes in the gene signatures, or a subset thereof, in a biological sample from a mammal.
I, Definitions
[001 12] Unless otherwise indicated, the definitions and embodiments described in this and other sections are intended to be applicable to all embodiments and aspects of the present application herein described for which they are suitable as would be understood by a person skilled in the art.
[001 13] In understanding the scope of the present application, the term “comprising” and its derivatives, as used herein, are intended to be open ended terms that specify the presence of the stated features, elements, components, groups, integers, and/or steps, but do not exclude the presence of other unstated features, elements, components, groups, integers and/or steps. The foregoing also applies to words having similar meanings such as the terms, “including”, “having” and their derivatives. The term “consisting” and its derivatives, as used herein, are intended to be closed terms that specify the presence of the stated features, elements, components, groups, integers, and/or steps, but exclude the presence of other unstated features, elements, components, groups, integers and/or steps. The term “consisting essentially of’, as used herein, is intended to specify the presence of the stated features, elements, components, groups, integers, and/or steps as well as those that do not materially affect the basic and novel characteristic(s) of features, elements, components, groups, integers, and/or steps.
[001 14] Terms of degree such as “substantially”, “about” and “approximately” as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree should be construed as including a deviation of at least ±5% of the modified term if this deviation would not negate the meaning of the word it modifies.
[001 15] As used in this application, the singular forms “a”, “an” and “the” include plural references unless the content clearly dictates otherwise.
[001 16] The term “and/or” as used herein means that the listed items are present, or used, individually or in combination. In effect, this term means that “at least one of’ or “one or more” of the listed items is used or present.
EXAMPLES
[001 17] The following non-limiting examples are illustrative of the present application:
Example 1 - Biomarkers associated with PC
[001 18] The following study was conducted to identify biomarkers associated with prostate cancer.
[001 19] Collection of PC tissues: PC tissues were obtained from Hamilton Health Sciences, Hamilton, Ontario, Canada under approval from the local Research Ethics Board (REB# 11-3472).
[00120] Cell culture: LNCaP, PC3, and DU145 cells were purchased from American Type Culture Collection (ATCC) and cultured in RPMI1640, F12 or MEM respectively, followed with supplementation of 10% FBS (Sigma Aldrich) and 1% Penicillin-Streptomycin (Thermo Fisher Scientific). The cell lines were authenticated (Cell Line Authentication Service, ATCC), and routinely checked for Mycoplasma contamination (a PCR kit from Abm, Cat#: G238). [00121] Formation of xenograft tumors: Xenografts were generated as previously described (Ojo et al. Cancer letters. 2018;426: 4-13; Yan et al. Cancer research. 2016; 76: 1603-14). Briefly, LNCaP, DU145 or PC3 cells (3xl06) in 0.1 ml culture media were mixed with Matrigel mixture (BD) at 1 : 1 (volume : volume), and implanted subcutaneously (s.c.) into the flank of NOD/SCID mice (6-weeks old males with 5 mice per group; The Jackson Laboratory). Tumor growth was monitored. Tumor size was weekly measured using calipers and calculated as V = L x W2 x 0.52. Endpoints were defined as tumor volume > 1000 mm3. All animal experiments were carried out based on the protocols approved by the McMaster University Animal Research Ethics Board (AUP#: 16-06-24).
[00122] Generation of CRPC in animal models: LNCaP cells (5xl06)-derived s.c. xenografts were generated in NOD/SCID mice (The Jackson Laboratory) with tumor volume determined. Tumor growth was measured by serum PSA levels (PSA kit, Abeam). Surgical castration was performed when tumor reached 100-200 mm3. Serum PSA was determined before and following castration. Rise in serum PSA indicates CRPC growth.
[00123] Prostate-specific PTEN-/- mice were generated by crossing
PTENloxp/loxp (C;129S4-PtentmlHwu/J; the Jackson Laboratory) mice with PB-Cre4 mice (B6.Cg-Tg(Pbsn-cre)4Prb, the NCI Mouse Repository) following published conditions (Wong et al. Oncotarget. 2017, 8(12): 19218-19235) Surgical castration was performed when mice were 23 weeks old and subsequently monitored for 13 weeks. All animal protocols were approved by the McMaster University Animal Research Ethics Board.
[00124] The male TRAMP animals (C57BL/6-Tg (TRAMP)8247Ng/J; the Jackson Laboratory) and the nontransgenic littermates were routinely obtained as [TRAMP C57BL/6 x C57BL/6] FL Ear clips were taken and incubated in digest buffer (IM Tris-HCl pH 8.0, 0.5M EDTA pH 8.0, 3M NaCl, 10% SDS, lOmg/mL proteinase K) overnight at 55°C. Mouse DNA was then extracted with ethanol precipitation. PCR was performed with DreamTaq Hot Start PCR Mastermix (Invitrogen) following PCR condition established by the Jackson Laboratory. Amplified PCR products were run on 2.25% agarose gel and visualized with ultraviolet transilluminator. Once genotype was confirmed, male TRAMP mice were maintained and monitored for 34 weeks or when endpoints were reached (10% reduction in weight loss, or apparent physical distress). All animal protocols were approved by the McMaster University Animal Research Ethics Board.
[00125] Immunohistochemistry (IHC): Slides were deparaffinized in xylene and cleared in an ethanol series. Antigens were retrieved through heat treatment in sodium citrate buffer (pH = 6.0), followed by blocking in PBS containing 1% BSA and 10% normal goat serum (Vector Laboratories) for 1 hour and incubation with anti-IQGAPl antibody (1 : 800, Cell Signalling) overnight at 4°C. Secondary antibody (biotinylated goat anti-,-rabbit IgG) and Vector ABC reagent (Vector Laboratories) were then applied. Secondary antibody alone was used as negative control. Chromogenic reaction was developed with diaminobenzidine (Vector Laboratories); slides were counterstained with hematoxylin (Sigma Aldrich). Images were analyzed using ImageScope software (Leica Microsystems Inc.); staining intensity was quantified as HScores using the formula [H- Score = (% Positive) x (intensity) + 1],
[00126] Analysis of IQGAP1 mRNA expression: The PC datasets were retrieved from the OncomineTM database (https://www.oncomine.org/). IQGAP1 mRNA expression data was analyzed in PC vs prostate tissues, metastasis vs local PC, and CRPCs vs non-CRPC tumors.
[00127] cBioPortal database: The cBioPortal43, 44
(http://www.cbioportal.org/index.do) database contains the most well-organized cancer genetics for various cancer types. The TCGA PanCancer Atlas PC dataset contains n=494 tumors. Tumors have been removed by prostatectomy with RNA expression profiled by RNA sequencing (RNA-seq). The suitability of the dataset for PC overall survival (OS)- related biomarker studies has been demonstrated (Liu et al. Cell. 2018; 173: 400-16 el l). The MSKCC dataset was also used (Taylor et al. Cancer cell. 2010; 18: 11-22).
[00128] Pathway enrichment analysis: Enrichment analyses were carried out using Metascape (https://metascape.Org/gp/index.html#/main/stepl); geneset enrichment was performed using fgsea in Galaxy (https://usegalaxy.org/).
[00129] Cutoff point estimation: Cutoff points to stratify patients into a high- and low-risk group were estimated by Maximally Selected Rank Statistics (the Maxstat package) in R. [00130] Regression analysis: Cox proportional hazards (Cox PH) regression analyses were carried out with the R survival package. The PH assumption was tested.
[00131 ] Establishing of a multigene panel predicting PC biochemical recurrence: IQGAPl-associated differential expressed genes (DEGs, n=598) were derived from the TCGA PanCancer Atlas PC dataset within the cBioPortal database (https://www.cbioportal.org/). The dataset was randomly divided into a Training and Testing population at the ratio of 7:3 using R. DEGs were selected for best prediction of BCR using Elastic-net logistic regression (the glmnet package in R) with 10-fold cross validation. The mixing parameter of a was used at 0.5. At a=0. Elastic-net runs as Ridge regression which shrinks the coefficients of correlated predictors without covariate selection; at a=l, it operates as Lasso which selects one co variate among a group of related variables; this will reduce signature’s biomarker potential. Thus, a=0.5 was set. Because of variation in variable selection during individual rounds of selection, 6 rounds of selection were performed and all unique genes obtained were combined into the final multigene panel Sig27gene.
[00132] Assignment of signature scores to individual PCs: Component genes (n=27) of Sig27gene were examined for associations with BCR using multivariate Cox PH regression with the R Survival package. The signature scores for individual tumors were given using the formula: Sum (coefl x genelexp + coef2 x gene2exp + + coefin x genenexp), where coefl ... coefin are the coefs of individual genes and genelexp genenexp are the expression of individual genes.
[00133] Examination of gene expression: The expression of Sig27gene component genes was determined using a newly established GEPIA2 dataset (Tang et al. Nucleic acids research. 2019; 47: W556-W60).
[00134] Statistical analysis: Kaplan-Meier survival analyses and logrank test were carried out using the R Survival package, with tools provided by cBioPortal. Univariate and multivariate Cox regression analyses were run with the R survival package. Timedependent receiver operating characteristic (tROC) analyses were performed using the R timeROC package. Two-tailed Student’s t-test and one-way ANOVA were also used. A value of p < 0.05 is considered statistically significant.
Results [00135] Downregulation of IQGAP1 following the course of PC: IQGAP1 is a pro-oncogenic protein in multiple cancer types. While there are n=4 articles in PubMed reporting IQGAP1 as a pro-PC protein, the status of IQGAP1 following PC tumorigenesis has not been determined in primary PCs.
[00136] To examine IQGAP1 expression in primary PCs, a set of primary PC tissues from Hamilton Health Sciences was obtained, consisting of advanced PCs (GS9- 10, n=14) and low grade PCs (GS6-7, n=13). IQGAP1 protein expression in these PCs was determined by IHC and quantified. In comparison to low grade PCs, significant reductions in IQGAP1 were observed in high grade PCs [Figure 1],
[00137] To further determine IQGAP1 expression in PC, the PC gene expression datasets available in the OncomineTM database (Compendia Bioscience, Ann Arbor, MI) were used. In both Liuet al. (Cancer research. 2006; 66: 4011-9) and Wallace et al. (Cancer research. 2008; 68:927-36) datasets, significant reductions of IQGAP1 mRNA in PC compared to normal prostate tissues were demonstrated [Figure 2 (a, b)]. Additionally, in the Grasso et al. (Nature. 2012; 487: 239-43) dataset and Chandran et al. (BMC Cancer. 2007; 7:64) dataset, downregulations of IQGAP1 occurred in distant PC metastases compared to organ-confined PC [Figure 2 (c, d)]. Collectively, IQGAP1 downregulation following the course of PC was demonstrated for the first time, i.e. from prostate to PC and to distant metastasis.
[00138] In addition to expression levels, protein’s cellular location also bears important functional impact. In PC for which IQGAP2 demonstrates tumor suppressive properties, IQGAP2 expression is largely present in the cell membrane. Similarly, the membrane localization of IQGAP1 was observed in PC xenografts produced from LNCaP, PC3, and DU145 cells, primary PCs, as well as PCs developed in TRAMP transgenic mice. In comparison to PC3 cell-generated xenografts, those produced by LNCaP cells exhibit evidently more cell membrane IQGAP1. LNCaP53 and PC354 cells were derived from lymph node and bone metastases respectively; PC predominantly metastasizes to the bone. While both LNCaP and PC3 cells can metastasize to the bone in mice via intracardiac injection, PC3 cells are widely regarded to be more aggressive. The observations of enhanced IQGAP1 membrane expression in LNCaP cell -produced xenograft tumors in comparison to PC3 cell-generated xenografts potentially support a functional importance of membrane IQGAP1 in inhibiting PC progression. [00139] Association of IQGAP1 downregulation with therapy resistance: Progression of ADT-resistance in the form of CRPC is a lethal progression of PC. It is thus interesting to see a significant reduction of IQGAP1 in patient-derived CRPCs compared to non-CRPCs in the Grasso dataset within the OncomineTM database [Figure 3 (a)]. This reduction was also demonstrated at protein level in PC progressed in castration-resistant PTEN-/- mice compared to PC produced in intact PTEN-/- mice.
[00140] The cell membrane location of IQGAP1 was clearly observed. Furthermore, LNCaP cell-produced xenografts are androgen-sensitive, which will progress to CRPC following surgical castration in mouse. In comparison to androgensensitive LNCaP tumors, LNCaP CRPCs were associated with a significant IQGAP1 downregulation [Figure 3b]. Taken together, this evidence supports a correlation of IQGAP1 reduction with the development of PCs resistant to ADT.
[00141] Recurrence to the major curative therapy prostatectomy is the first major therapy resistance in PC progression. To investigate a potential association of IQGAP1 reduction with PC relapse following prostatectomy, IQGAP1 expression data was downloaded along with PC biochemical recurrence (BCR) information from the MSKCC and TCGA PanCancer Atlas PC datasets from cBioPortal. With optimal cutoff points defined by Maximally Selected Rank Statistics, PCs in the group with low IQGAP1 expression were associated with a rapid course of PC recurrence in both the TCGA (p=0.001) and MSKCC (p=4e-6) cohorts [Figure 3c/d], IQGAP1 expression correlates with PC recurrence at hazard ratio (HR) 0.9996, 95% confidence interval (CI) 0.9996-1, and p=0.0154 in the TCGA cohort and HR 0.2242, 95% CI 0. 1006-0.4999, and p=0.000258 in the MSKCC cohort. Taken together, a comprehensive set of evidence for an association of IQGAP1 downregulation with therapy resistance in PC is provided.
[00142] Enrichment of oncogenic pathways within the IQGAP1 network: To characterize the association of IQGAP1 downregulation with PC progression, differentially expressed genes (DEGs) were derived relative to IQGAP1 downregulation, following an established system (Jiang et al. Therapeutic advances in medical oncology. 2019; 11: 1758835919846372; Jiang et al. Molecular oncology. 2018; 12: 1559-78). In the TCGA PanCancer Atlas PC dataset, reduction of IQGAP1 mRNA expression at -1SD (standard deviation or z-score at -1) stratifies PCs into high or low risk group of PC recurrence with the high-risk group expressing reduced IQGAP1 [Figure 4 - SI], DEGs (n=598) in the high-risk group (n=72) vs the low-risk group (n=421) were derived at q<0.001 and fold change > |2| or log2Ratio > |1|. IQGAP1 was expressed at log2Ratio = -0.94 and q = 8.24e-34 in high-risk vs low-risk PCs.
[00143] Pathway enrichment in these DEGs was subsequently analyzed using the Metascape network (https : // metascape, org/ gp/index, html#/ main/ step 1 ). Top 20 non- redundant clusters are enriched, which include terms of GO (gone oncology) biological processes (BP) and KEGG pathways. The representatives of individual clusters [Figure 5(a)], the network of these enriched clusters [Figure 5(b)] are presented. The theme of enrichment centers on cytoskeleton dynamic-based processes (chemotaxis, cell adhesion, regulation of cell adhesion, extracellular matrix organization, responses to mechanical stimulus, cellular extravasation, cell morphogenesis, and cell-substrate adhesion), signaling responses (positive regulation of response to external stimulus, positive regulation of kinase activity, transmembrane receptor protein tyrosine kinase signaling pathway, and second-messenger-mediated signaling), and immune responses (regulation of cytokine production, leukocyte migration, and cytokine-mediated signaling pathway) [Figure 5(a)], The importance of these enriched clusters in tumori genesis has been well established.
[00144] The above oncogenic contributions of the IQGAP1 network to PC is further supported by geneset enrichment analyses. Multiple immune reactions were downregulated in IQGAP1 DEGs, including interferon gamma (IFNy) response, inflammatory response, IL2-STAT5 signaling, complement, TNFa, IFNa, IL6-JAK- STAT3 signaling, and TGF signaling [Figure 6, Table 1], The enhanced processes include Myc targets, DNA damage repair, and oxidative phosphorylation [Figure 7, Table 1], Collectively, comprehensive evidence supporting the network associated with IQGAP1 downregulation in stimulating PC tumorigenesis and progression is provided.
Table 1. Human hallmark gene set enrichment of IQGAP1 DEGs. p.adj:; ES:; NES: normalized enrichment score
Figure imgf000035_0001
Figure imgf000036_0001
[00145] Construction of a multigene panel from the IQGAP1 network in predicting PC recurrence following prostatectomy: To further investigate the potential of the DEGs relative to IQGAP1 downregulation in association to PC, generating a signature or multigene panel from these DEGs to assess BCR was attempted. The TCGA PanCancer Atlas PC cohort was randomly divided into a Training (n=344) and Testing (n=148) population at the ratio of 7:3. The comparable demographics of both Training and Testing populations was demonstrated. Using the Training population, six rounds of covariable selection were carried out among the 598 DEGs for predicting PC recurrence using Elastic-net within the R glmnet package with the mixing parameter a set at 0.5 and cross-validation set at 10-fold. Twenty -seven DEGs Sig27gene were generated [Table 2], Table 2. The component genes of Sig(27genes)
Figure imgf000037_0001
***: p<0.001
[00146] Sig27gene in evaluating BCR in the Training cohort was examined.
Sig27gene scores for individual tumors were derived according to the formula: (fi)n (fi: Cox coefficient/coef of genei x genei expression, n=27). Cox coefs for individual component genes were generated by multivariate Cox analysis. The scores of Sig27gene robustly predict PC recurrence risk at HR 2.72, 95% CI 2.22-3.33, and p<2e-16 [Figure 8 (a)]. The score discriminates PC recurrence with time-dependent area under curve (tAUC) values ranging from 88.5% at 10.8 months (88.5%/10.8M) to 77.5%/47.7 months [Figure 8 (b)]. With the cutoff points determined using Maximally Selected Rank Statistics, Sig27gene efficiently stratifies patients in the Training cohort into groups with high- and low-risk for PC recurrence [Figure 8 (c)]. [00147] Testing Sig27gene: Two strategies were employed to test Sig27gene. Sig27gene in the Testing population was first validated using Training-derived coefs. This signature score is associated with PC recurrence in Testing cohort at HR 1.71, 95% CI 1.31-2.12, and p=5.88e-5 [Figure 7 (a)]; the best prediction efficiency is at tAUC value of 77.3%/47.8M. In the full TCGA PanCancer Atlas PC population, Sig27gene with the coefs derived from the Training population evaluates PC recurrence with HR 2.23, 95% CI 1.92-2.60, and p<2e-16 [Figure 7 (a)] with the associated tAUC values at 81.4%/11.5M, 74.2%/21.9M, 77.6%/31.7M, and 77.1%/47.8M. Sig27gene scores derived from Training effectively classify PCs with high-risk of recurrence from those with low-risk of recurrence in both the Testing and full TCGA cohort [Figure 7 (d, f)].
[00148] A second approach was used to reveal the full potential of Sig27gene in the prediction of PC relapse in the Testing and full TCGA cohorts via re-defining component gene coefs within each cohort using multivariate Cox analysis. The prediction efficiencies were enhanced in both cohorts based on HR values [Figure 7 (a)]. The tAUC values are from 84.2%/13.1M to 93%/47.8/M for Testing and 83%/l 1.5M to 79%/31.7M for full cohort [Figure 7 (b)]. These signature scores stratify high-risk PCs from low-risk PCs with robust efficiencies [Figure 7 (e, g)]. Collectively, evidence reveals Sig27gene possessing properties to efficiently evaluate the risk of PC relapse.
[00149] Validation of Sig27gene using an independent PC cohort: The above 2 approaches were used to validate Sig27gene using an independent cohort, the MSKCC dataset. Using the Training coefs, Sig27gene scores significantly stratify PCs into a highland low-risk group [Figure 8 (a)] with the prediction efficiency at tAUC 73.2%/18.4M [Figure 8 (c)]. The efficiency of Sig27gene scores in predicting PC relapse was robustly enhanced once the coefs for the component genes were re-derived from the MSKCC cohort [Figure 8 (b, c)] with tAUC values ranging from 87.5% to 90.5% [Figure 8 (c)], revealing the effectiveness of Sig27gene in estimating PC relapse in an independent PC cohort.
[00150] Sig27gene as an independent risk factor of PC recurrence: To examine the relationship of Sig27gene with clinical features in the estimation of PC relapse, multivariate Cox analysis was performed for Sig27gene, age at diagnosis, WHO prostate cancer grade (Grade I = GS6, Grade II = GS3+4; Grade III = GS4+3, Grade IV = GS8, and Grade V = GS9-10), margin status, and tumor stage. After adjusting for these clinical features, Sig27gene remain a strong risk factor of PC biochemical recurrence [Table 3],
Table 3. Univariate and multivariate Cox analysis of Sig(27genes) for PC DFS
Figure imgf000039_0001
1 : Age at diagnosis; 2: WHO prostate cancer grade IV and V in comparison to WHO prostate cancer grade I; 3: Surgical margin 1 compared to surgical margin 0; 4: Tstage 1 : tumor stage 1 (3+4) in comparison to Tstage 0 (tumor stage 1+2).
[00151 ] Characterization of Sig27gene: To further explore the biomarker potential of Sig27gene, it was shown that among its 27 components genes 20 possess significant biomarker value in predicting PC recurrence [Table 4], Among the remaining 7 component genes, both LAMP3 and KCNN3 are associated with PC relapse at p=0.0616 and p=0.0668 respectively. For the 20 significant component genes, they predict PC relapse with significant low p values up to 3.12e-8 [Table 4], Furthermore, 10 of these 20 component genes are independent factors of PC biochemical recurrence after adjusting for age at diagnosis, WHO prostate cancer grade, margin status, and tumor stage [Table 4]; this is impressive considering their individual status.
Table 4. Association of Sig(27genes) component genes with PC recurrence
Figure imgf000039_0002
Figure imgf000040_0001
a: determined by univariate Cox PH (proportional hazard) analysis; b: PH assumption was not confirmed; c: independent risk factors of PC relapse (p<0.05) after adjusting for age at diagnosis, WHO prostate cancer grade, surgical margin, and tumor stage.
[00152] By using a recently established database GEPIA2,48 the expression status of all component genes in PC vs matched normal prostate tissues was systemically determined. Among the 11 component genes with upregulations in relationship to IQGAP1 downregulation [Table 2], HAGHL, BIRC5, MXD3, PRR7, and RSG11 are significantly overexpressed in iCluster 1, iCluster 2, or both iClusters PCs compared to the matched prostate tissues [Figure 9], In comparison, among 16 downregulated genes relative to IQGAP1 under-expression [Table 2], FAM65B (RIPOR2), PI15, and HDAC9 are downregulated in either iCluster 1, iCluster 2, or both in comparison to the matched non-tumor tissues [Figure 9], The only exception is PCDHB8; while it is co- downregulated with IQGAP1 [Table 2], PCDHB8 is upregulated in iCluster 2 PCs compared to the matched normal controls [Figure 9], A point to take note is that none of the significant alterations occurred in iCluster 3 PCs [Figure 9], While the differences in IQGAP1 expression between PC and matched prostate tissues did not reach statistical significance, reductions of IQGAP1 in PC are apparent, particularly in iCluster 1 and 2 PCs, which provides additional support for downregulation of IQGAP1 in PCs.
[00153] With the recent advances in the landscape of cancer-associated changes in genome, methylation, and gene expression, cancers can be classified as integrative clusters (iClusters). PCs have been classified into iCluster 1, iCluster 2, and iCluster 3. PCs in iCluster 1 are enriched with ETV1 and ETV4 fusion, SHOP mutations, FOXA1 mutations, and CHD1 deletion, but lack ERG fusion. iCluster 2 PCs are particularly enriched with ERG fusion and PTEN deletion. iCluster 3 PCs contain ERG fusion. TP53 hetero-deficiency and RBI deletion are detected more frequently in iCluster 1 and iCluster 2 PCs. In line with this knowledge, advanced PCs (GS>8) are much more frequently in iCluster 1 and iCluster 2 compared to iCluster 3. Collectively, the frequent alteration of Sig27gene component genes in iCluster 1 and iCluster 2 [Figure 9] supports the robustness of Sig27gene in predicting PC relapse.
[00154] Sig27gene, a novel multigene signature in predicting PC biochemical recurrence: Among the 27 component genes of Sig27gene, 8 genes have been reported in PC [Table 5], including 4 upregulated (VGF, RGS11, BIRC5 and LTC4S) and 4 downregulated (NOD2, PI15, LAMP3, and HDAC9) genes relative to IQGAP1 downregulation [Table 2; Table 5],
Table 5. Reported oncogenic role of the Sig(27 enes) component genes
Figure imgf000041_0001
Figure imgf000042_0001
[00155] NOD2 facilitates innate immune response in prostate epithelial cells and likely plays a role in PC; NOD2 was also implicated in immunosuppression of gastric cancer. In line with this knowledge, NOD2 expression was evidently reduced in PCs compared to the matched normal prostate tissues. Methylation of CpGs of the PI 15 gene occurs in metastatic PC, which contributes to the stratification of metastatic PC from nonrecurrent PCs. Blood PI15 is a biomarker of cholangiocarcinoma [Table 5], LAMP3 was suggested to play a role in detoxification of cisplatin in CRPC and associate with aggressive breast cancer [Table 5], Chromosome rearrangements in HDAC9 occur more frequently in high-risk PC compared to low-risk PCs. Increases in HDAC9 were observed in basal bladder cancer. Taken together, with the exception of LAMP3, the genes which co-downregulated with IQGAP1 negatively impact PC, which reinforces a negative correlation of IQGAP1 with PC progression.
[00156] Nineteen Sig27gene component genes are unknown to participate in PC [Table 5] . Nonetheless, 12 of these 19 genes are reported to function in tumorigenesis in general [Table 5], which include 3 upregulated and 9 downregulated genes [Table 2; Table 5], LINC01089, MXD3, and H1FX-AS1 are upregulated component genes [Table 2], Evidence supports MXD3 in promotion of medulloblastoma and both LINC01089 and H1FX-AS1 display tumor suppressive functions [Table 5], ZFHX4 is one of the 9 under-expressed genes and is a susceptibility locus of cutaneous basal cell carcinoma [Table 5], Both RRAGC and TFEC regulate mTOR activation with the latter affects mTOR via lysosome biogenesis [Table 5], With LAMP3 also functioning in lysosome, Sig27gene likely affects lysosome biology and mTOR activation. Evidence supports negative impacts on tumorigenesis for the rest of 6 downloaded genes [Table 5], including FPR3, RAB30, RIPOR2 (FAM65B), PLXNA4, MCTP1, and KCNN3. Collectively, the positive and negative impacts of these 12 genes unknown to PC on tumorigenesis are generally in line with the notion of IQGAP1 negatively associating with PC.
[00157] In addition to Sig27gene affecting mTOR and lysosome processes as discussed above, the signature also affect immune reactions, particularly innate immune response. NOD2 facilitates innate immune response in prostate epithelial cells, and is likely downregulated in PC, and co-reduced in PC with IQGAP1 [Table 2], PLXNA4 inhibits tumor cell migration, induces innate immune responses via working with Tolllike receptor [Table 5], and is also co-downregulated with IQGQP1 in PC [Table 2],
[00158] Biochemical recurrence (BCR) remains a critical issue in PC management; this is not only due to this progression being the initial point of therapy resistance leading to poor prognosis but also because this is conceptually the most effective point of intervention. While mechanisms underlying BCR have been extensively investigated, with numerous biomarkers and systems in place to assess BCR, the current capacity in predicting BCR is clearly not sufficient.
[00159] This research represents a novel attempt in improving this capacity of
BCR assessment through the angle of IQGAP1. This research was approached because the dynamics of cytoskeleton organization is essential for tumor progression through processes of epithelial mesenchymal transition (EMT) and mesenchymal epithelial transition (MET) as well as communications with microenvironment. IQGAP1 plays an important role in cytoskeleton reorganization via stabilization of the GTP -bound Cdc42 and Rael. Most published evidence supports IQGAP1 in promoting tumorigenesis, a concept that is in agreement with the limited number (n=4 in PubMed) of PC studies. However, comprehensive evidence suggesting a potential tumor suppressive role of IQGAP1 in PC is provided herein, which includes 1) downregulation of IQGAP1 during the course of PC, 2) the association of IQGAP1 reduction with PC biochemical recurrence in two independent cohorts, MSKCC and TCGA PanCancer Atlas, and 3) the enrichment of pathways or processes underlined by cytoskeleton dynamics [Table 1], [00160] The mechanisms responsible for IQGAP1 downregulation-affected cytoskeleton dynamics remain unclear. It is unlikely that these actions are mediated through Cdc42 and Rael, as this connection would favor oncogenesis. While these mechanisms require further investigations, it is tempting to propose that 1) IQGAP1 regulates PC cell adhesion or the related processes indirectly via its DEGs or network and 2) the cell surface IQGAP1 is attributable to these actions. The second possibility is appealing as the location is relevant to cell adhesion and IQGAP2, a PC suppressor, was largely detected on PC cell surface. The high level of homology with IQGAP2 supports the membrane location of IQGAP1 and this proportion of IQGAP1 in suppression of PC. This concept is supported by IQGAP1 being more abundantly localized to xenograft PC cell membrane produced by LNCaP cell compared to those generated by PC3 cells, a more aggressive PC cell line. Additionally, upregulations of IQGAP1 in metastasis of PC3 cell-generated tumors, which was previously reported, was largely intracellular IQGAP1. Intriguingly, pro-tumorigenic roles of IQGAP1 were observed in breast cancer; its cytosolic and nuclear expressions, where IQGAP1 was co-localized with BRCA1, were detected in triple negative breast cancer. IQGAP1 promoted thyroid cancer and was largely expressed in the cytosol. Similarly, the cytosolic expression of IQGAP1 in colorectal cancer was associated with its pro-oncogenic functions. In non-small cell lung cancer, the cytoplasmic and nuclear expressions of IQGAP1 were correlated with lymph node metastasis and poor overall survival. Evidence thus suggests that different cellular expressions of IQGAP1 might in part explain the PC-facilitative function of IQGAP1 reported by others and the PC-suppressive roles of IQGAP1 observed here. While IQGAP1 may facilitate PC, this work supports its inhibiting roles towards PC, a concept that is in accordance with molecular events affected by IQGAP1 downregulation.
[00161] The maj or pathways or processes affected by the IQGAP 1 network are the reductions of a set of immune signaling [Figure 6], Appealingly, these reductions in immune signaling within the DEGs are also reflected in the signature constructed. It is intriguing to see that bothNOD2 and PLXNA4 are among the downregulated component genes in Sig27gene and both induce innate immune reactions [Table 5], Modulation of immune-profiles to set up permissive microenvironment for PC might be directly relevant to the cell membrane expression of IQGAP 1. [00162] Another novel feature of Sig27gene is modulation of mTOR activation and regulation of lysosome biology. Lysosome is well-regarded to induce mTOR activation in response to nutrient cues. Collectively, modulation of multiple critical oncogenic processes is likely a major attribute to the robust efficiency of Sig27gene in predicting PC recurrence. An intriguing feature of Sig27gene is the clusters of 3 component genes at 5q31.3 and 3 (PRR7, MXD3, and LTC4S) at 5q35.3, and 2 (PI15 and ZFHX4) at 8q21.13 [Table 2], The importance of these clusters remains unclear.
[00163] Finally, in addition to the novelties described above, Sig27gene is composed of a large proportion of component genes (n=19) novel to PC and tumorigenesis in general (n=7) [Table 5], These genes are likely relevant at least to PC. For instance, HAGHL (Hydroxyacylglutathione Hydrolase Like), LCN12 (Lipocalin 12), DCST2 (DC-STAMP Domain Containing 2), and PRR7 (Proline Rich 7, Synaptic) not only significantly predicts PC recurrence but also remain robust risk factors after adj usting for age at diagnosis, WHO prostate cancer grade, margin status, and tumor stage [Table 4],
[00164] Robust assessment of ACC progression and poor prognosis: In the initial screen of Sig27gene for its prognostic prediction values in 33 TCGA cancer types, the signature stratifies poor OS towards ACC (p=6.2e-5), LAML (p=0.034), brain lower grade Glioma (LGG, 0.0025), liver hepatocellular carcinoma (LIHC, p=0.026), and uveal melanoma (UVM, p=0.0086). Sig27gene (p=6.2e-5) was more effective in assessing poor OS in ACC.
[00165] Based on the initial screen, Sig27gene biomarker values in assessing
ACC progression and prognosis was further analyzed. The signature scores significantly predict ACC poor prognosis (HR 2.72, 95% CI 1.89-3.90, p=5.78e-8) and progression (HR 2.72, 95% CI 2.07-3.56, p=3.73e-13) (Figure 10A). The prediction is very efficient with tAUC values from 93.6% at 18.7M to 100% at 71. IM for poor OS and 86.9% at 7M to 95.6% at 30.5M for PFS (Figure 10B, C). Sig27gene robustly stratifies ACC into a high-risk and low-risk group based on fatality risk or progression risk (Figure 10D, E). Sig27gene remains a significant risk factor of ACC poor prognosis and progression after adjusting for age at diagnosis and tumor stage (Table 6). The stratification of poor OS is at 85.2% sensitivity and specificity 84.3%, PPV 74.2%, NPV 91.5%, and p=2e-15; the stratification of ACC progression risk is at 80% sensitivity, specificity 92.1%, PPV 91.4%, NPV 81.4%, and p=2e-15 (Table 7).
Table 6: Univariate and multivariate Cox analyses of Sig27gene and its sub-signatures in predicting ACC prognosis and progression
Figure imgf000046_0001
1 : Age at diagnosis; 2: Tumor stage 1 (3+4) in comparison to Tumor stage 0 (1+2). *p<0.05; **p<0.01; ***p<0.001
Table 7: Stratification efficiencies of ACC prognosis and progression by Sig27gene and its components
Figure imgf000046_0002
PPV: positive prediction value; NPV: negative prediction value; MMS: mediate months survival;
MMPFS: mediate months progression-free survival; M: month; *p<0.05; **p<0.01; ***p<0.001
[00166] Sig27 is a novel prognostic signature of ACC: Among the 27 component genes of Sig27gene include 20 genes with activities relevant to oncogenesis and 7 genes with unknown tumorigenic roles (Table 5). VGF facilitates resistance to tyrosine kinase inhibitors in lung cancer.94 RGS11 is a biomarker of lung cancer.95 Evidence supports MXD3 in promotion of medulloblastoma96 and both LINC0108997 and H1FX-AS198 display tumor suppressive functions (Table 5). BIRC5 or Survivin is a well-studied anti-apoptotic protein promoting tumorigenesis and progression.99’100 LTC4S is a component gene in a immune signature associated with clinical response in breast cancer.101 Evidence supports negative impacts on tumorigenesis for FPR3,102 RAB3O,103 RIPOR2 (FAM65B),104PLXNA4,105 106 MCTP1,107 and KCNN3.108
[00167] NOD2 was implicated in immunosuppression of gastric cancer.109 Blood PI 15 is a biomarker of cholangiocarcinoma.110 LAMP3 associates with aggressive breast cancer.111 Increases in HDAC9 were observed in basal bladder cancer.112 ZFHX4 is one of the 9 under-expressed genes and is a susceptibility locus of cutaneous basal cell carcinoma.113 Both RRAGC114 and TFEC regulate mTOR activation with the latter affects mTOR via lysosome biogenesis.115
[00168] Except BIRC5, the remaining 26 genes are novel to ACC (Table 5).
Upregulation of BIRC5 was reported in ACC compared to benign adrenocortical adenomas and normal adrenal glands and the upregulation may associate with ACC poor prognosis (p=0.053).116 Collectively, Sig27gene is a novel prognostic biomarker panel for ACC.
[00169] Sig27gene is novel to ACC. Unlike all biomarkers that have been specifically formulated based on specific cancer types, this ACC biomarkers is derived from studies on PC, a urinary cancer type. The central role of adrenal gland in hormone synthesis117 might underline the relationship of PC Sig27gene to ACC prognosis. Importantly, the PC multigene panel is highly effective in assessing ACC prognosis and progression and outperforms the current biomarkers formulated on ACC.
Discussion
[00170] A comprehensive effort in investigating IQGAP1 contributions to PC is reported. Significant reductions of IQGAP1 expression were observed following PC tumorigenesis and progression from prostate to PC, metastasis, and CRPC. Membrane presence of IQGAP1 was detected in primary PC, xenograft tumors, and mouse PC produced in PTEN-Z- and TRAMP mice. IQGAP1 reductions are shown to be associated with PC biochemical recurrence (BCR) in both MSKCC and TCGA PanCancer Atlas PC cohorts. Furthermore, the IQGAP1 network is enriched with important oncogenic processes including reductions in immune responses. The network possesses capacity to robustly predict BCR, evident by the construction of Sig27gene. Collectively, this study indicates tumor suppressive properties of IQGAP1 in PC. Furthermore, Sig27gene predicts ACC fatality risk at HR 2.72, 95% CI 1.89-3.9, and p=5.78e-8; the prediction is at tAUC values of 93.6%/18.7M, 95.7/32.5M, 99.2%/45.2M, and 100%/71.1M. Sig27gene stratifies ACCs into ahigh-risk and low-risk fatality group at 85.2% sensitivity and 84.3% specificity. Sub-multigene panels from SigIQGAPINW and Sig27gene were derived; they largely retained the respective biomarker values.
Example 2 - Biomarkers for Renal Cell Carcinoma
[00171] The present study was conducted to determine biomarkers associated with renal cell carcinoma.
[00172] Patient populations: cBioPortal (https://www.cbioportal.org/) contains the most well-organized and comprehensive cancer genetic data for different cancer types. The TCGA PanCancer Atlas ccRCC dataset consists of 512 patients with primary ccRCC. All tumors were surgically removed and profiled for RNA expression using RNA sequencing. The dataset has been well-demonstrated for its suitability in ccRCC OS biomarker studies. The TCGA PanCancer Atlas ACC dataset contains n=78 tumors. Tumors were removed by surgery with RNA expression profiled by RNA sequencing (RNA-seq). The suitability of the dataset for ACC overall survival (OS)-related biomarker studies has been demonstrated
[00173] Pathway enrichment analysis : Enrichment analyses were performed using Metascape (https://metascape.Org/gp/index.html#/main/stepl) and Galaxy (https://usegalaxy.org/) for geneset enrichment.
[00174] Regression analyses: Logistic regression was carried out with R. Cox proportional hazards (Cox PH) regression analyses were performed using the R survival package. The PH assumption was tested.
[00175] Construction of multigene signatures: The TCGA PanCancer Atlas ccRCC dataset within the cBioPortal database (https://www.cbioportal.org/) was used to derive DEGs relative to IQGAP1 downregulation (n=611). Random split of the dataset into a Training and Testing at 6:4 was performed using R. DEGs were selected for impact on OS using Elastic-net logistic regression within the glmnet package in R with 10-fold cross validation. The mixing parameter of a was used at 0.5. At a=0, Elastic-net operates as Ridge regression which shrinks the coefficients of correlated predictors without performing covariate selection; at a=l, it runs as Lasso which tends to select one covariate among a group of related variables; this will reduce signature’s biomarker potential. Six rounds of selection at the setting were performed and all unique genes obtained were combined into the final multigene panel SigIQGAPINW.
[00176] Assignment of signature scores to patients/tumors: All component genes were examined for an association with OS or PFS shortening using multivariate Cox PH regression with the R “survival” package. The signature scores for individual patients were given using Sum (coefl x gene 1 exp + coef2 x gene2exp + > + coefin x genenexp), where coefl ... coefn are the coefs of individual genes and gene 1 exp genenexp are the expression for individual genes.
[00177] Cutoff point estimation: Cutoff point to separate tumors with high risk of mortality was estimated using Maximally Selected Rank Statistics (the Maxstat package) in R.
[00178] Odds ratio determination: OR analysis was performed using the glmnet package in R.
[00179] Statistical analysis: Kaplan-Meier surviving curves and log-rank test were carried out using the R survival package, and tools provided by cBioPortal. Univariate and multivariate Cox regression analyses were run using the R survival package. Timedependent receiver operating characteristic (tROC) analysis was performed using the R time ROC package. A value of p < 0.05 is considered statistically significant.
Results
[00180] By using the GEPIA2 database (http://gepia2.cancer-pku.en/#index), an association of IQGAP1 downregulation with shortening of overall survival (OS) in ccRCC at a hazard ratio (HR) 0.68 (p<0.05) was shown. It was further observed that reductions of IQGAP1 mRNA expression at -0.6SD (standard deviation or z-scores at - 0.6), -0.8SD, and -1SD significantly stratified ccRCCs into high and low risk groups of fatality within the TCGA PanCancer Atlas ccRCC dataset. To further analyze this association, differentially expressed genes (DEGs) relative to IQGAP1 downregulation were obtained. Based on the separation of ccRCCs into high and low fatality risk groups at the cutoff points of -0.6SD, -0.8SD, and -1SD, the TCGA PanCancer Atlas ccRCC cohort was divided into a high-risk group with IQGAP1 downregulation (n=l l l) and a low-risk group without the reduction (n=399) using -0.8SD cutoff point. From these two groups a set of DEGs were obtained; at q<0.0001, n=6563 DEGs were identified. As IQGAP1 is reduced at the log2 value of -0.95 (fold 1.93 reduction) in the high risk group (q=2.54e-57), DEGs were thus defined at q<0.0001 and fold change > |1.93| or log2 value > |0.95|, which resulted in n=611 genes that are differentially expressed in ccRCCs with concurrent IQGAP1 downregulation (high risk group) compared to those without the downregulation (low risk group).
[00181] To analyze the pathway or processes affected by the IQGAP1 network (DEGs), enrichment analysis was performed using the Metascape network (https://metascape.Org/gp/index.html#/main/stepl). A set of top enriched non-redundant clusters GO (gone oncology) terms of biological processes (BP) and KEGG pathways were obtained; the top representative GO BP terms and KEGG pathways of the top 20 clusters are shown (Figure 11 A) and the networks of these enriched clusters are included (Figure 11B). The inclusion of the enriched cluster of positive regulation of cellular component movement, chemotaxis, focal adhesion, response to mechanical stimulus, regulation of cell adhesion, extracellular extravasation, and cell matrix adhesion (Figure 11 A) is in line with the classical knowledge of IQGAPs being involved in cytoskeleton organization. Other critical oncogenic processes affected by IQGAP1 DEGs include angiogenesis, response to PI3K signaling, and immune responses (including cytokine biosynthetic process and macrophage activation) (Figure 11 A). Collectively, evidence indicates alterations of multiple critical oncogenic processes in ccRCCs with downregulated IQGAP1 expression.
[00182] The above concept is further supported by geneset enrichment analyses. A robust downregulation of immune responses is observed, including downregulations of interferon y response, IL-2-STAT5 signaling, allograft rejection, TNFa signaling, complement, inflammatory response and others (Figure 12, Table 8). On the other hand, oxidative phosphorylation and DNA damage repair are enriched (Figure 12, Table 8). The enrichment of the former is in accordance with the alteration of oxidative phosphorylation cluster derived from Metascape-based enrichment analyses. Taken together, a comprehensive analysis for alterations in multiple oncogenic processes that are associated with IQGAP1 reductions in ccRCC is provided.
Table 8. Human hallmark gene set enrichment of IQGAP1 DEGs
Figure imgf000051_0001
p.adj : adjusted p value; ES: enrichment score; NES: normalized enrichment score
[00183] Construction of SigIQGAPINW to predict OS in ccRCC following nephrectomy: To further examine the oncogenic impact of IQGAP1 DEGs in ccRCC, the network’s potential in predicting OS was determined. The TCGA PanCancer ccRCC cohort was randomly divided into a Training (n=300) and Testing (n=208) population at the ratio of 6:4. The effectiveness of randomization was confirmed based on the distributions of age and other clinical features. From the Training sub-population, 6 rounds of covariate selection were performed among the 611 DEGS for impact on OS using Elastic-net within the R glmnet package with the mixing parameter a set at 0.5 and 10-fold cross-validation. All unique DEGs identified by these selections constitute the final multigene panel (SigIQGAPINW with NW representing network), which includes 9 genes (Table 9). Table 9. Composition of SigIQGAPINW
Figure imgf000052_0001
1 IQGAPl downregulation group vs the group without the downregulation
[00184] The effectiveness of SigIQGAPINW in the evaluation of mortality risk in the Training group was first confirmed. SigIQGAPINW scores for individual ccRCCs were calculated as (fi)n [fi: Cox coefficient (coef) of Genei x Genei expression, n=9]. Individual Cox coefs were obtained by multivariate Cox analysis. The scores significantly predict the fatality risk at HR=2.72, 95% CI=2. 16-3.44, and p<2e-16 (Figure 13A). Increase in SigIQGAPINW score are also associated with poor disease specific survival (DSS) and shortening of progression-free survival (PFS) (Figure 13A). SigIQGAPINW scores discriminate ccRCC fatality with time-dependent area under curve (tAUC) values ranging from 71.8% at 14.6 months (71.8%/14.6M) to 80.2%/72.6 months (Figure 13B). The tAUC values for DSS and PFS are 76.9%/14.7M to 81%/62.6M and 62.2%/8.1M to 70.7%/64.1M respectively (Figure 13B). With the respective cutoff points for OS, DSS, and PFS defined by Maximally Selected Rank Statistics, SigIQGAPINW robustly classifies high- and low-risk groups based on OS, DSS, and PFS respectively (Figure 13C).
[00185] Testing SigIQGAPINW: To mimic clinical diagnosis applications, SigIQGAPINW biomarker potential in predicting OS in the Testing group was first validated using those coefs produced from the Training group, i.e. SigIQGAPINW scores based on the setting of the Training cohort. In the Testing group, SigIQGAPINW stratifies fatality risk at HR=2, 95% CI=1.31-3.06, and p=0.00137 and effectively separates ccRCCs in the Testing cohort into a low- and high-risk mortality group (Figure 14A). To reveal the full potential of SigIQGAPINW in the evaluation of fatality risk, component gene coefs were rederived and recalculated SigIQGAPINW scores based on the Testing population following the system described above. SigIQGAPINW potential can be significantly enhanced based on HR and its enhanced efficiency in separation of the low- and high-risk group (Figure 14B, see its comparison with the panel A). Impressively, the prediction rate in the high-risk group reaches to 74.1% (20/27) and patients in this group showed a substantially reduced survival time (Figure 14B). The efficiency in prediction of OS, DSS, and PFS has the respective tAUC values of 70.6%/13M, 62. 1%/12.8M, and 68%/7.9M (Figure 14C). Collectively, SigIQGAPINW effectively predicts OS, DSS, and PFS in the Testing population, which significantly enhances the biomarker value of SigIQGAPINW.
[00186] Similar observations were also obtained on the full TCGA PanCancer Atlas ccRCC cohort. The Training group-produced SigIQGAPINW scores predict poor OS at HR=2.04, 95% CI=1.56-2.63, p=3.11e-18. When using the full cohort-derived signature scores, SigIQGAPINW robustly stratifies patients with high risk of mortality from those with a low-risk (Figure 15A). Along with predicting OS, SigIQGAPINW efficiently predict the risk of DSS and PFS (Figure 15B/C).
[00187] Association of SigIQGAPINW with worse clinical features of ccRCC: To further examine SigIQGAPINW biomarker potential, its relationship in predicting the fatality risk of ccRCC was investigated with other established clinical features. As expected, age at diagnosis, stage, tumor size (T stage), distant metastasis (M), and Winter Hypoxia Score are all clear risk factors of poor OS (Table 10). Activation of hypoxia is a major oncogenic driver in ccRCC owing to the common inactivation of the tumor suppressor VHL], The level of hypoxia can be quantified based on a 99-gene hypoxia signature defined by Winter et al. (Cancer research 2007, 67, 3441-3449). Of note, increases in Winter Hypoxia Score predict poor OS. After adjustment of these clinical factors, increases in SigIQGAPINW score remain significant in its prediction of fatality at HR=1.82, 95% CI=1.44-2.32, p=6.93e-07 in the TCGA PanCancer Atlas ccRCC cohort (Table 10). Consistent with these observations, SigIQGAPINW is significantly associated with worse clinical features of ccRCC, including high tumor stage, grade, size (T stage), and distant metastasis (Figure 16). Furthermore, all 9 component genes of SigIQGAPINW are individually associated with OS shortening (Table 11). Except THSD7A, LINC01089 (p=1.47e-8), SPACA6 (p=3.96e-6), LOC155060 (p=1.15e-5), LOC100128288 (p=1.78e-5), SNHG10 (p=4.85e-6), RECQL4 (p=0.002315), HERC2P2 (p=2.2e-6), and ATXN7L2 (p=1.53e-5) are all independent risk factors (i.e. independent of clinical feature: tumor stage, grade, size (T stage), and distant metastasis) of poor OS following the adjustment for age at diagnosis, stage, tumor size (T stage), distant metastasis (M), and Winter Hypoxia Score. Collectively, evidence supports SigIQGAPINW being a novel and robust multigene panel in predicting OS of ccRCC.
Table 10. Univariate and multivariate Cox analysis of SigIQGAPINW for poor OS of ccRCC
Figure imgf000054_0001
1 SigIQGAPINW score; 2 Age at diagnosis; 3 Compared to Stage I, Stage II is not significant at univariate Cox analysis; 4 T stage 3 and 4 were combined and compared to T stage 1 and 2; 5 Ml distant metastasis was compared to MO; 6 Winter hypoxia score; *, **, *** for p < 0.05, 0.01, and 0.001 respectively
Table 11. Association of SigIQGAPINW component genes with poor OS of ccRCC
Figure imgf000054_0002
p<0.05; *** p<0.0001
[00188] SigIQGAPINW, a novel multigene panel of ccRCC biomarker: In view of all 9 component genes being individual predictors of OS shortening (Table 10), their oncogenic potential was analyzed. It was noticed that the directionality of these genes in predicting poor OS are in accordance with their differential expression in ccRCCs with IQGAP1 downregulation. For instance, THSD7A is co-downregulated with IQGAP1 (Table 8) and its expression levels are reversely associated with poor OS (HR<1; Table 10), which is in line with IQGAPl’s relationship with the fatality risk of ccRCC. On the other hand, the rest of component genes are upregulated in ccRCC with IQGAP1 downregulation (Table 8) and elevations in their expressions predict poor OS (HR>1; Table 10). These observations further support the association between IQGAP1 downregulation with increases in mortality risk of ccRCC.
[00189] The 9 component genes identified consist of long non-coding RNA (IncRNA) LINC01089, IncRNA LOC100128288, AI894139 pseudogene LOC155060, hect domain and RLD 2 pseudogene 2 HERC2P2, a non-protein coding RNA SNHG10 and four protein coding genes (SPACA6, RecQL4, ATXN7L2, and THSD7A). Evidence thus supports SigIQGAPINW affecting multiple signaling events or processes, which might underlie its effectiveness in predicting poor prognosis of ccRCC.
[00190] None of the 9 component genes of SigIQGAPINW has been reported in ccRCC (Table 12). Besides a modest significant association of THSD7A with poor OS, the rest of component genes all robustly predict mortality risk evident by their p values (Table 11). Consistent with these observations, LINC01089, SPACA6, HERC2P2 (Figure 17) and others (Figure 18) effectively stratify ccRCCs with elevated fatality risk from those with a low fatality risk as individual genes. Furthermore, among four mRNA clusters of ccRCCs which resulted from unsupervised clustering of global mRNA expression, analyses using the recently established GEPIA2 dataset reveal a significant elevation of HERC2P2 in mRNA cluster 2 tumors and significant downregulations of THSD7A expression in all mRNA clusters of ccRCC in comparison to the matched nontumor controls (Figure 19). Taken together, evidence supports SigIQGAPINW as anovel multigene panel with a potent biomarker potential in predicting poor prognosis of ccRCC.
Table 12. Oncogenic functions of SigIQGAPINW component genes
Figure imgf000055_0001
Figure imgf000056_0001
1 functioning in ccRCC; 2 function in tumorigenesis
Discussion
[00191 ] This clinical need is approached through investigation of a novel ccRCC factor IQGAP1. It was demonstrated for the first time that downregulation of IQGAP1 is significantly associated with reductions of OS in ccRCC patients. The DEGs (n=611) relative to IQGAP1 downregulation affect pathways regulating the typical functions of IQGAP1, i.e. cytoskeleton involved cellular processes including cellular component movement, chemotaxis, focal adhesion, response to mechanical stimulus, regulation of cell adhesion, extracellular extravasation, and cell matrix adhesion. Furthermore, these DEGs affect important oncogenic pathways, including downregulation of immune responses as well as upregulations of mitochondria-related oxidative phosphorylation and DNA damage repair, all in line with ccRCC being a metabolic disease. Downregulations of the immune system and abnormalities in DNA damage repair are major contributors to oncogenesis and cancer progression. These observations thus suggest that IQGAP1 suppresses ccRCC.
[00192] To further investigate the relevance of IQGAP1 -associated network alterations (DEGs), a novel and robust multigene panel (SigIQGAPINW) in assessing poor OS of ccRCC was constructed. Two approaches in the signature construction were used: random division of cohort into a Training and Testing population and the involvement of cross validation in covariate selections from the Training group. Although cross validation can be equivalent to the conventional validation by splitting a dataset into a Training set and a validation (Testing) set, it can be trusted that the inclusion of both in the study contributed to the production of a robust gene signature. Additional favorable factors include the TCGA PanCancer Atlas ccRCC dataset being an excellent resource supporting OS biomarker studies. [00193] An attractive feature of SigIQGAPINW is its novelty in which none of the nine component genes has been previously reported in ccRCC. The importance of these genes, nonetheless, is validated by their effectiveness stratifications of poor OS individually, and their abilities to independently predict poor OS (except THSD7A) after adjusting for a set of clinical features. These properties outline their appealing clinical applications individually and as component genes of SigIQGAPINW.
[00194] Another feature of SigIQGAPINW is the inclusion of 5 non-protein coding genes among its 9 component genes. With current knowledge of non-coding RNA being important in regulation of networks rather than specific genes, SigIQGAPINW likely affects complex networks, a potential underlying reason for the impressive robustness observed in this multigene panel in assessing poor OS. This is consistent with the current consensus for the importance of a multigene panel to possess multiple features or affecting multiple processes for it to be clinically useful in patient management. Along this line, the first demonstration of a significant upregulation of the HERC2P2 pseudogene in mRNA cluster 2 (Figure 19), which is a more aggressive sub-type of ccRCCs than tumors in mRNA cluster 1, further supports this research being novel and relevant.
[00195] Among the four protein coding component genes, they likely influence different aspects of oncogenesis. SPACA6 (Sperm Acrosome Associated 6) is an oocyte factor and contributes to sperm-egg fusion with its oncogenic involvement unknown. A similar situation applies to ATXN7L2. RECQL4 is well known for its impact in maintaining genome stability and its involvement in tumorigenesis. While its contribution to ccRCC has not been reported, RECQL4 is clearly important. Among 611 DEGs, RECQL4 is one of nine being selected for impact on OS and importantly, it potently predicts the fatality risk of ccRCC (Figure 18). With respect to THSD7A, there are no reports for its oncogenic functions. Its inclusion in SigIQGAPINW among 611 genes supports its potential impact on ccRCC. Furthermore, a novel and additional support for its involvement in ccRCC is provided, i.e. the across downregulation of THSD7A in all four mRNA clusters of ccRCC (Figure 19).
[00196] The first evidence for a significant association of IQGAP1 reductions with ccRCC mortality is provided. IQGAP1 downregulation correlates with network changes consisting of 611 DEGs; these DEGs are enriched in pathways important to ccRCC and the typical features of the disease. Furthermore, from the IQGAP1 -associated network, a novel 9-gene signature (SigIQGAPINW) has been constructed; it robustly predicts ccRCC fatality and recurrence after nephrectomy with a high level of certainty. Furthermore, all 9 component genes of SigIQGAPINW are novel to ccRCC and 5/9 are novel to oncogenic functions in general. The first evidence for ccRCC-associated alterations in two component genes, HERC2P2 and THSD7A is also provided. This research may have a profound impact on ccRCC with respect to research and patient management.
[00197] Effective prediction of ACC prognosis and progression using SigIQGAPINW: By using GEPIA2, SigIQGAPINW was screened for predicting poor OS in all 33 TCGA cancer types. SigIQGAPINW shows a solid prediction of rapid prostate cancer (PC) relapse (HR 2.4, p=9.1e-5), consistent with both PC and ccRCC being urinary carcinomas; this observation provides an additional validation of SigIQGAPINW. The multigene panel predicts poor OS in acute myeloid leukemia (LAML, HR 2, p=0.015), mesothelioma (HR 1.8, p=0.021), and adrenocortical carcinoma (ACC, HR 5.9, p=1.7e-4). Although SigIQGAPINW significantly predicts poor OS in LAML and mesothelioma, the levels of prediction, evident by the p values, indicate SigIQGAPINW being unlikely to have clinical applications in these two cancer types. Nonetheless, the biomarker value of SigIQGAPINW appears sufficient for potential clinical applications in assessing ACC prognosis; this possibility is intriguing considering the physical location of adrenal glands on top of kidneys.
[00198] To further analyze SigIQGAPINW prognostic value towards ACC OS, the ACC dataset used in the TCGA Pan-Cancer study was downloaded from cBioPortal. SigIQGAPINW scores for individual tumors were derived according to the formula: (fi)n (fi: Cox coefficient/coef of Genei x Genei expression, n=9). Cox coefs for individual component genes were generated by multivariate Cox analysis. SigIQGAPINW effectively predicts poor OS (HR 2.72, 95% CI 1.98-3.74, p=7.99e-10) and PFS (progression free survival; HR 2.72, 95% CI 1.82-4.05, p=9.26e-7) (Figure 20A). The effectiveness of these predictions is reflected by the tAUC values of 90.8% at 18.7 months for ACC death and 84.5% at 7 months for ACC progression (recurrence) (Figure 20B, C). SigIQGAPINW remains robust in predicting ACC progression and fatality after adjusting for age at diagnosis and tumor stage (Table 13). With cutoff points defined by Maximally Selected Rank Statistics, SigIQGAPINW robustly stratifies ACCs with high risk of death and recurrence (Figure 20D, E). The stratification of fatality risk is at 66.7% sensitivity, 92.2% specificity, 81.8% positive predictive value, 83.9% negative predictive value, median survival 19 months, and p=8e-l l (Table 14). Collectively, SigIQGAPINW efficiently predicts ACC prognosis.
Table 13: Univariate and multivariate Cox analyses of SigIQGAPINW and its subsignatures in predicting ACC prognosis and progression
Figure imgf000059_0001
1: Age at diagnosis; 2: Tumor stage 1 (3+4) in comparison to Tumor stage 0 (1+2). *p<0.05; **p<0.01; ***p<0.001
Table 14: Stratification efficiencies of ACC prognosis and progression by SigIQGAPINW and its components
Figure imgf000059_0002
survival; MMPFS: mediate months progression-free survival; M: month; *p<0.05; **p<0.01; ***p<0.001 [00199] SigIQGAPINW is a novel prognostic signature of ACC. None of the genes have been reported in ACC. This ccRCC multigene panel is highly effective in assessing ACC prognosis and progression and outperforms the current biomarkers formulated on ACC.
Example 3 - Biomarkers associated with Adrenocortical Carcinoma
[00200] The following study was conducted to identify biomarkers associated with adrenocortical carcinoma.
[00201] cBioPortal database: The cBioPortal (http://www.cbioportal.org/index.do) database contains the most well-organized cancer genetics for various cancer types. The TCGA PanCancer Atlas ACC dataset has n=78 tumors. Tumors were removed by surgery resection with RNA expression profded by RNA sequencing (RNA-seq). The suitability of this ACC dataset for overall survival (OS)-related biomarker studies has been demonstrated.
[00202] Cutoff point estimation: Cutoff points to stratify patients into a high- and low-risk group were estimated by Maximally Selected Rank Statistics (the Maxstat package) in R.
[00203] Regression analysis: Cox proportional hazards (Cox PH) regression analyses were carried out with the R survival package. The PH assumption was tested.
[00204] Assignment of signature scores to individual PCs: Component genes (n=9 or n=27) were examined for associations with PFS or OS using multivariate Cox PH regression with the R Survival package. The signature scores for individual tumors were given using the formula: Sum (coefl x Gene 1 exp + coef2 x Gene2exp + + coefin x Genenexp), where coefl . . . coefn are the coefs of individual genes and Gene 1 exp . Genenexp are the expression of individual genes.
[00205] Examination of gene expression: The expression of component genes was determined using anewly established GEPIA2 dataset (Tang et al. Nucleic acids research. 2019; 47: W556-W60).
[00206] Statistical analysis: Kaplan-Meier survival analyses and logrank test were carried out using the R Survival package, with tools provided by cBioPortal. Univariate and multivariate Cox regression analyses were run with the R survival package. Time- dependent receiver operating characteristic (tROC) analyses were performed using the R time ROC package. A value of p < 0.05 is considered statistically significant.
ACC prognosis and progression using SigIQGAPINW
[00207] Stratification of ACC prognosis via individual component genes and SubSigIQGAPINW-OS: Three of the 9 component genes possess properties in predicting poor OS individually: LINC01089 (HR 1.002, 95% CI 1-1.003, p=0.0479), SNHG10 (HR 1.011, 95% CI 1.006-1.017, p=1.72e-5), and RECQL4 (HR 1.002, 95% CI 1.001-1.002, p=9.26e-8). The latter two remain risk factors of poor prognosis after adjusting for age at diagnosis and tumor stage (Table 15).
Table 15: Univariate and multivariate Cox analyses of SigIQGAPINW component genes in predicting ACC OS and progression
Figure imgf000061_0001
1 : In analysis with age at diagnosis and tumor stage [stage 1 (3+4) vs stage 0 (1+2)] ; 2: continuous gene expression data was used in analysis; *p<0.05; **p<0.01; ***p<0.001
[00208] The predictive values of these three genes as a sub multigene panel
(SubSigIQGAPINW-OS) in assessing OS was then analyzed. The scores of SubSigIQGAPINW-OS effectively predict ACC fatality risk at HR 2.72, 95% CI 1.93- 3.84, and p=1.22e-8 (Figure 21A); the prediction is at tAUC values ranging from 82.9% at 18.7M to 86.9% at 71. IM with predictive (Figure 21B). With a cutoff point defined by the Maximally Selected Rank Statistics, SubSigIQGAPINW-OS stratifies ACCs into a high-risk and low-risk group at a p=le-6 (Figure 22A) with 85.2% sensitivity and 66.7% specificity (Table 16). Table 16: Stratification efficiencies of ACC prognosis and progression by SigIQGAPINW and its components
Figure imgf000062_0001
PPV: positive prediction value; NPV: negative prediction value; MMS: median months survival; MMPFS: median months progression-free survival; M: month; *p<0.05; **p<0.01; ***p<0.001
[00209] Evidence supports both SNHG10 and RECQL4 being more effective than LINC01089 in predicting ACC OS (Table 15). The tAUC values for their predictions reveal that both significantly assess ACC prognostic outcome at the individual gene levels with RECQL4 being impressively effective (Figure 2 IB). Both SNHG10 and RECQL4 robustly stratify ACCs with elevated fatality risk (Figure 22C, D). RECQL4 is surprisingly effective. It predicts early ACC fatality particularly efficient evident by the tAUC values of 90.9% at 18.7 months and 90.2% at 32.5 months (Figure 21B). Its overall effectiveness in the stratification of ACC fatality risk is with 81.5% sensitivity and 80.1% specificity. SNHG10 stratifies ACC fatality risk at 92.6% sensitivity (Table 16). As a single gene, RECQL4 is very attractive as an ACC prognostic biomarker.
[00210] With all assessment parameters (sensitivity, specificity, PPV/positive prediction value, NPV/negative prediction value, median months survival/MMS, and p values) considered, SigIQGAPINW is somewhat superior over SubSigIQGAPINW-OS, RECQL4, and SNHG10 in assessing ACC prognosis (Table 16). However, SubSigIQGAPINW-OS and both single genes are effective and appealing due to their simple composition. This set of biomarkers thus offers different combinations for rapid and effective stratification of ACC prognostic outcomes; for instance, predictions can be achieved at 92.6% sensitivity and 92.2% specificity (Table 16). [0021 1] Stratification of ACC progression via individual component genes and subSigIQGAPINW-PFS: Among the 9 component genes of SigIQGAPINW, LOC100128288, SNHG10, and RECQL4 can predict ACC progression at the individual gene level and the latter two remain risk factors of ACC progression after adjusting for age at diagnosis and tumor stage (Table 15). The combination of these three genes into a sub multigene panel (SubSigIQGAPINW-PFS) effectively predicts ACC progression. The scores of SubSigIQGAPINW-PFS predicts ACC progression at HR 2.72, 95% CI 1.79-4.12, and p=2.54e-6 (Figure 21A); the prediction is at tAUC values ranging from 80.3% at 7M to 92. 1% at 64.2M with predictive (Figure 21C). SubSigIQGAPINW-PFS robustly separates ACC into a high-risk and low-risk group based on their progression risk (Figure 22B) with 82.5% sensitivity, 86.8% specificity, 86.8% PPV, 82.5% NPV, median months progression-free survival 9.1 IM, and p=7e-13 (Table 15).
[00212] Both SNHG10 and RECQL4 effectively assess ACC progression risk evident by their tAUC values (Figure 21C); both significantly stratify ACCs into a high- risk and low-risk progression group with RECQL4 being much more efficient (Figure 22E, F).
[00213] With the consideration of sensitivity, specificity, PPV, NPV, median months progression free survival, and p values, SubSigIQGAPINW-PFS predicts ACC progression more effectively compared to SigIQGAPINW, SNHG10, and RECQL4 (Table 16). Collectively, SigIQGAPINW, SubSigIQGAPINW-PFS, and RECQL4 have clinical potential in assessing ACC progression. For instance, RECQL4 can provide an initial screen as a single gene; SigIQGAPINW predicts early recurrence (6.43M) at 88.5% accuracy (PPV), and SubSigIQGAPINW-PFS provides a high overall efficiency in predicting ACC progression (Table 16).
[00214] Differential expression of SigIQGAPINW component genes in ACC compared to normal tissues: To further study SigIQGAPINW, its component gene expression in ACC in comparison to normal adrenal gland tissues was examined. Interestingly, downregulations of 3 IncRNAs LINC01089, SNHG10, and HERC2P2 in ACCs were observed compared to normal adrenal gland tissues (Figure 23). Both LINC01089 and SNHG10 are component genes in SubIQGAPINW-OS. On the other hand, RECQL4 is significantly upregulated in CIMP-high and CIMP -intermediate ACCs (Figure 23), two aggressive clusters of ACC. Both SNHG10 and RECQL4 are component genes in both sub-signatures: SubSigIQGAPINW-OS and SubSigIQGAPINW-PFS. These observations provide additional support for the prognostic potential of SigIQGAPINW in ACC.
ACC prognosis and progression using Sig27gene
[00215] Stratification of ACC prognosis via individual component genes and subSig27gene-OS: 5 genes among the 27 component genes were first identified which predicts poor OS as individual genes, including RGS11, LOC338779 (LINC01089, see Table 17), MXD3, BIRC5, and RAB30 (Table 17). These 5 genes were formulated into a sub signature. SubSig27gene-OS effectively predicts poor OS, evident by its HR value (Figure 24A) and tAUC values (Figure 24B). SubSig27gene-OS stratifies ACC prognostic outcome with a high level of certainty (Figure 25 A), 77.8% sensitivity, and 84.8% specificity.
Table 17: Univariate and multivariate Cox analyses of Sig27gene component genes in predicting ACC OS and progression
Figure imgf000064_0001
1 : In analysis with age at diagnosis and tumor stage [stage 1 (3+4) vs stage 0 (1+2)] ; 2: continuous gene expression data was used in analysis; *p<0.05; **p<0.01; ***p<0.001
[00216] MXD3, BIRC5, and RAB30 estimate poor OS effectively evident by their low p values and independently of age at diagnosis and tumor stage. Both MXD3 and BIRC5 predict poor OS at tAUC values > 80% (Figure 24B), and both effectively stratify ACCs prognostic outcomes according to their fatality risk (Figure 25C, D). Collectively, Sig27gene, SubSig27gene-OS, MXD3, and BIRC5 constitute a set of effective predictors of poor prognosis.
[00217] Stratification of ACC progression via individual component genes and subSig27gene-PFS: Following the same strategy above, those genes with predictive value of ACC progression include LCN12, VGF, RGS11, MXD3, BIRC5, FPR3, RAB30, NOD2, TEFC, ZFHX4, and HDAC9 (Table 17). Except FPR3, TEFC, and HDAC9, others individually predict ACC progression independently of age at diagnosis and tumor stage.
[00218] These 11 genes form sub signature SubSig27gene-PFS that effectively assesses ACC progression risk, evident by the HR (Figure 24A), tAUC values (Figure 24C), and separation of ACCs into groups with high and low progression risk (Figure 25B).
[00219] Among these 11 genes, MXD3, BIRC5, and RAB30 are effective in the risk assessment based on their tAUC values (Figure 24C) and their effectiveness in stratification of ACCs with high risk progression (Figure 25E-G). RAB30 is unique in the risk stratification; the accuracy of positive prediction or PPV is 91.7% (11/12) and specificity is 97.4% (Figure 25G). Importantly, patients in the high-risk group stratified by RAB30 have the most rapid course of disease progression with the median months progression-free survival 6.23 months (Figure 25G, Table 18). Both MXD3 and BIRC5 also effectively predict ACC progression; nonetheless, Sig27gene is most effective compared to SubSig27gene-PFS, MXD3, BIRC5, and RAB30 when sensitivity, specificity, PPV, NPV, MMPFS, p-value, and tAUC profiles are compared. Nonetheless, the combinations of these biomarkers are clearly superior to the individual biomarkers in assessing ACC progression.
Table 18: Stratification efficiencies of ACC prognosis and progression by Sig27gene and its components
Figure imgf000065_0001
Figure imgf000066_0001
PPV: positive prediction value; NPV: negative prediction value; MMS: mediate months survival; MMPFS: mediate months progression-free survival; M: month; *p<0.05; **p<0.01; ***p<0.001
[00220] Differential expression of Sig27gene component genes in ACC compared to normal tissues: The expression status of all 27 component genes was analyzed in ACC vs normal adrenal gland tissues. Among the 27 genes, 11 are differentially expressed. The IncRNA LINC01089 is a component gene in both SigIQGAPINW and Sig27gene; its downregulation in ACC was observed (Figure 23). Other six genes with expression reduced in ACC are FPR3, LCN12, RAB30, RGS11, TFEC, and VGF; all downregulations occur in either CIMP-high, CIMP-intermediate, or both (Figure 26). Along with confirmation of BIRC5 upregulation in CIMP-high and CIMP-intermediate ACC, HAGHL, MXD3, and PRR7 are also upregulated in either CIMP-high, CIMP- intermediate or both (Figure 26). The alterations of these component genes in aggressive CIMP-high and CIMP-intermediate ACCs support the potent biomarker potential of Sig27gene in prediction of ACC poor prognosis and progression. The importance of these genes in the prediction of ACC progression and poor OS is evident by their ability to evaluate these risks as individual genes and their core contributions to form sub signatures that largely retain Sig27gene biomarker values.
Combination signature (Combosig) with robust efficiencies in assessing ACC prognosis and progression.
[00221] In view of both SigIQGAPINW and Sig27gene being highly effective in predicting ACC prognosis and progression as well as both signatures containing sub signatures that largely retain the full signature’s biomarker values, it was assessed whether combination of both signatures enhances the risk assessments. When both gene panels are fully combined, the ability to predict poor OS was substantially compromised; as a matter of fact, the scores of the combination were unable to perform prediction as the PH (proportional hazard) assumption was violated. These observations indicate the uniqueness of the individual multigene panels.
[00222] A second approach was then undertaken. LINC01089, SNHG10, and
RECQL4 constitute the core component genes of SigIQGAP 1NW, while MXD3, BIRC5, and RAB30 are individual genes within Sig27gene that are effective in assessing ACC prognosis. These six genes are combined to form Combosig. The panel predicts ACC poor OS and progression at the maximal HR 2.72 with p=4.56e-l l and p=2.47e-9 respectively (Figure 27 A). The predictions are highly effective evident by the associated tAUC values of 94.6% at 18.7M and 95.1% at 71. IM for OS prediction and tAUC values ranging from 83.7% at 7 months to 86.5% at 64.2M for progression (Figure 27B). Combosig robustly stratifies ACC fatality and progression risk (Figure 27C, D). As expected, Combosig assesses ACC prognosis and progression after adjusting for age at diagnosis and tumor stage (Table 19). Collectively, Combosig is very appealing for clinical applications because of its simplicity (6 genes) and effectiveness.
Table 19: Univariate and multivariate Cox analyses of Combosig in predicting ACC prognosis and progression
Figure imgf000067_0001
1: Age at diagnosis; 2: Tumor stage 1 (3+4) in comparison to Tumor stage 0 (1+2). *p<0.05; **p<0.01; ***p<0.001
[00223] The gene pair of BUB1B and PINK1 has been reported to be the best predictor of ACC prognosis. Following this system, it is possible to confirm the pair is effective in predicting fatality risk with HR 2.72, 95% CI 1.91-3.88, and p=3.2e-8. In comparison to the OS panels, only SubSigIQGAPINW-OS works less efficient compared to the BUB1B-PINK1 pair (Figure 28A); Sig27gene, SubSig27gene-OS, and ComboSig are clearly superior to the BUB1B-PINK1 pair (Figure 28A). The concept is supported by the panels’ efficiencies in stratifying ACC prognostic outcomes (Table 20).
Table 20: Comparison of stratification efficiencies of multigene panels
Figure imgf000067_0002
Figure imgf000068_0001
PPV: positive prediction value; NPV: negative prediction value; MMS: median months survival; M: month; *p<0.05; **p<0.01; ***p<0.001; BUB1B-PINK1 is the best predictor of ACC prognosis.9- 11
[00224] At the individual gene level, BUB1B predicts ACC poor OS more efficiently than PINK1 (data not shown). Among the individual genes RECQL4, MXD3, BIRC5, and RAB30, RECQL4 is the most efficient single gene in assessing ACC prognosis. In comparison, RECQL4 is more robust in predicting poor OS than BUB1B evident by the respective tAUC profiles (Figure 28B) and their abilities in separation of ACCs into a high-risk and low-risk fatality group (Figure 28C, D).
[00225] Initial risk assessment is an essential step in patient management.
However, the clinical capacity in this aspect remains generally poor; this is particularly the situation for ACC, a rare and aggressive cancer with heterogenous prognosis. Despite BUB1B-PINK1 being reported to be the best predictor of ACC poor OS, there is no molecular knowledge or biomarkers in clinical risk assessment or being recommended by the European Society of Endocrinology Clinical Practice Guidelines on ACC. The set of biomarkers documented here are likely to fulfill this gap.
[00226] SigIQGAPINW and Sig27gene are novel to ACC. Their importance is further strengthened by the identification of differential expression of multiple component genes in both multigene panes in ACC compared to normal adrenal glands (Figures 23 and 26). The core components of SigIQGAPINW and Sig27gene are among these differentially expressed genes, including the 6 component genes of Combosig: LINC01089, SNHG10, RECQL4, MXD3, BIRC5, and RAB30. Besides BIRC5, the other 5 genes are novel to ACC and 4 of these 5 genes (except LINC01089) are effective in predicting ACC poor OS and progression as individual genes. RECQL4 is particularly robust in assessing prognosis and RAB30 is highly effective in predicting rapid ACC progression.
[00227] While the present application has been described with reference to examples, it is to be understood that the scope of the claims should not be limited by the embodiments set forth in the examples, but should be given the broadest interpretation consistent with the description as a whole.
[00228] All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. Where a term in the present application is found to be defined differently in a document incorporated herein by reference, the definition provided herein is to serve as the definition for the term.

Claims

1. A method of diagnosing a urogenital cancer, progression of the cancer and/or survival following the cancer in a mammal comprising the steps of: i) optionally detecting the level of IQGAP1 in a biological sample from the mammal, and comparing the sample level of IQGAP1 to a control level of IQGAP1; ii) detecting the level of genes in a gene signature obtained from the biological sample, wherein the gene signature comprises LINC01089, and comparing the sample level of genes in the gene signature to a control level; and iii) diagnosing the mammal with a urogenital cancer when the sample level of LINC01089 is a statistically significant different level as compared to the control level of LINC01089, and optionally, when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP1.
2. The method of claim 1, wherein the urogenital cancer is prostate cancer, renal cancer or adrenocortical cancer.
3. The method of claim 1, wherein the biological sample is a tumour sample.
4. The method of claim 1, wherein the level of LINC01089 is significantly increased as compared to the control level.
5. The method of claim 1, wherein the gene signature additionally comprises one or more genes selected from the group consisting of SPACA6, LOC155060, LOCI 00128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A, and the level of the one or more genes is detected to be significantly different in comparison to a corresponding control level of each gene.
6. The method of claim 5, wherein the gene signature comprises each of SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A.
7. The method of claim 5, wherein the level of any one or more of SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2 or ATXN7L2 is significantly increased in comparison to the corresponding control level, and/or the level of THSD7A is significantly decreased in the sample as compared to the corresponding control level.
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8. The method of claim 1, wherein the gene signature additionally comprises one or more genes selected from the group consisting of HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5, and the level of the one or more genes is detected to be significantly different in comparison to a corresponding control level of each gene.
9. The method of claim 8, wherein the gene signature comprises each of HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX- AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5, and the urogenital cancer is prostate cancer or adrenocortical cancer.
10. The method of claim 8, wherein the level of any one or more of HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S or H1FX- AS1 is significantly increased in comparison to the corresponding control level, and/or the level of any one or more of FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 or PCDHGA5 is significantly decreased in comparison to the corresponding control level.
11. The method of claim 8, wherein the gene signature comprises one or more of genes selected from: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX-AS1, FPR3, RIPOR2, NOD2, PLXNA4, TFEC, ZFHX4, MCTP1, PCDHGB2 and PCDHGA5.
12. The method of claim 1, wherein the gene signature comprises: i) one or more of SNHG10, HERC2P2 or RECQL4; ii) one or more of MXD3, BIRC5 and RAB30; iii) one or more of FPR3, LCN12, RAB30, RGS11, TFEC, VGF, BIRC5, HAGHL, MXD3 or PRR7; or iv) one or more of SNHG10, RECQL4, BIRC5, MXD3 and RAB30, and the urogenital cancer is adrenocortical cancer.
13. The method of claim 1, wherein the method additionally comprises treatment of the patient with one more of radiation, chemotherapy, surgery or active surveillance.
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14. The method of claim 1, wherein the level of the genes in the gene signature is detected using RNA sequencing based on next-generation sequencing of cDNA.
15. A method of diagnosing adrenal cancer in a mammal, comprising the steps of: i) optionally detecting the level of IQGAP1 in a biological sample from the mammal, and comparing the sample level of IQGAP1 to a control level of IQGAP1; ii) detecting the level of genes in a gene signature obtained from the biological sample, wherein the gene signature comprises: a) one or more of LCN12, VGF, RGS11, MXD3, BIRC5, FPR3, RAB30, NOD2, TEFC, ZFHX4, and HDAC9; b) one or more of LCN12, VGF, RGS11, MXD3, BIRC5, RAB30, NOD2 and ZFHX4; c) one or more of SNHG10, RECQL4, MXD3 and RAB30; or d) one or more of LOC100128288, SNHG10 or HERC2P2, and comparing the sample level of the gene(s) in the gene signature to a control level; and iii) diagnosing the mammal with cancer when the sample level of the one or more detected gene(s) is a statistically significant different as compared to the control level of the gene, and optionally, when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP1.
16. A method of diagnosing prostate cancer, grade of prostate cancer or metastasis of prostate cancer in a mammal comprising in a mammal comprising the steps of: i) detecting the level of IQGAP1 in a biological sample from the mammal, and comparing the sample level of IQGAP1 to a control level of IQGAP1; and iii) diagnosing the mammal with prostate cancer, advanced prostate cancer or metastasized prostate cancer when the sample level of IQGAP1 is significantly reduced in comparison to the control level of IQGAP1.
17. A kit for detection of a 27-gene signature for diagnosing urogenital cancer comprising primers and/or probes specific for the detection of the genes: HAGHL, LCN12, DCST2, VGF, RGS11, PRR7, LINC01089, MXD3, BIRC5, LTC4S, H1FX- AS1, FPR3, RAB30, RIPOR2, NOD2, PLXNA4, RRAGC, TFEC, PI15, ZFHX4, LAMP3, HDAC9, MCTP1, KCNN3, PCDHB8, PCDHGB2 and PCDHGA5.
70
18. A kit for the detection of a gene signature comprising primers and/or probes specific for the detection of the genes: LINC01089, SPACA6, LOC155060, LOC100128288, SNHG10, RECQL4, HERC2P2, ATXN7L2 and THSD7A.
19. A kit for the detection of a gene signature comprising primers and/or probes specific for the detection of the genes: LINC01089, SNHG10, RECQL4, MXD3, BIRC5, and RAB30.
71
PCT/CA2021/051492 2020-10-23 2021-10-22 Gene signature for predicting progression and progress of urinary cancers and methods of use thereof WO2022082317A1 (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119905138A (en) * 2025-03-27 2025-04-29 福建医科大学附属第一医院 A method, device, medium and program product for constructing a model reflecting the degree of immunotherapy response

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
GU ET AL.: "Long Non-Coding RNA 001089 is a Prognostic Marker and Inhibits Glioma Cells Proliferation and Invasion", CLINICAL LABORATORY, vol. 65, 2019, pages 369 - 377, ISSN: 1433-6510, DOI: 10.7754/Clin.Lab.2018.180817 *
GU YAN, LIN XIAOZENG, KAPOOR ANIL, LI TAOSHA, MAJOR PIERRE, TANG DAMU: "Effective Prediction of Prostate Cancer Recurrence through the IQGAP1 Network", CANCERS, vol. 13, no. 3, 23 January 2021 (2021-01-23), pages 430, XP055926245, DOI: 10.3390/cancers13030430 *
LIN XIAOZENG, KAPOOR ANIL, GU YAN, CHOW MATHILDA JING, PENG JINGYI, MAJOR PIERRE, TANG DAMU: "Construction of a Novel Multigene Panel Potently Predicting Poor Prognosis in Patients with Clear Cell Renal Cell Carcinoma", CANCERS, vol. 12, no. 11, 22 November 2020 (2020-11-22), pages 3471, XP055926242, DOI: 10.3390/cancers12113471 *
YUAN HONGFAN, QIN YI, ZENG BEILEI, FENG YIXIAO, LI YUNHAI, XIANG TINGXIU, REN GUOSHENG: "Long noncoding RNA LINC01089 predicts clinical prognosis and inhibits cell proliferation and invasion through the Wnt/β-catenin signaling pathway in breast cancer", ONCOTARGETS AND THERAPY, vol. Volume 12, 21 June 2019 (2019-06-21), pages 4883 - 4895, XP055926247, DOI: 10.2147/OTT.S208830 *
ZHANG HUO, ZHU MINGXIA, DU YIPING, ZHANG HUI, ZHANG QING, LIU QINGXIE, HUANG ZEBO, ZHANG LAN, LI HAI, XU LEI, ZHOU XIN, ZHU WEI, S: "A Panel of 12-lncRNA Signature Predicts Survival of Pancreatic Adenocarcinoma", JOURNAL OF CANCER, IVYSPRING INTERNATIONAL PUBLISHER, AU, vol. 10, no. 6, 26 February 2019 (2019-02-26), AU , pages 1550 - 1559, XP055926250, ISSN: 1837-9664, DOI: 10.7150/jca.27823 *
ZHANG YANG, XIAO YING, LI GUI-CHEN, GONG FENG-YAN, ZHANG XIAO-NA, HOU KUN: "Long non-coding RNAs as epigenetic mediator and predictor of glioma progression, invasiveness, and prognosis", SEMINARS IN CANCER BIOLOGY., SAUNDERS SCIENTIFIC PUBLICATIONS, PHILADELPHIA, PA., US, 10 September 2020 (2020-09-10), US , pages 1 - 7, XP055926252, ISSN: 1044-579X, DOI: 10.1016/j.semcancer.2020.08.016 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119905138A (en) * 2025-03-27 2025-04-29 福建医科大学附属第一医院 A method, device, medium and program product for constructing a model reflecting the degree of immunotherapy response

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