WO2014080060A1 - Méthode destinée à prédire la réponse au traitement chimiothérapeutique de patients atteints de cancer - Google Patents

Méthode destinée à prédire la réponse au traitement chimiothérapeutique de patients atteints de cancer Download PDF

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WO2014080060A1
WO2014080060A1 PCT/ES2013/070808 ES2013070808W WO2014080060A1 WO 2014080060 A1 WO2014080060 A1 WO 2014080060A1 ES 2013070808 W ES2013070808 W ES 2013070808W WO 2014080060 A1 WO2014080060 A1 WO 2014080060A1
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chemotherapy
genes
response
kit
treatment
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PCT/ES2013/070808
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English (en)
Spanish (es)
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Laura VERA RAMÍREZ
Pedro SÁNCHEZ ROVIRA
César Luis RAMÍREZ TORTOSA
José Luis QUILES MORALES
María del Carmen RAMÍREZ TORTOSA
José Antonio LORENTE ACOSTA
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Servicio Andaluz De Salud
Fundación Pública Andaluza Progreso Y Salud
Fundación Pública Andaluza Para La Investigación Biosanitaria De Andalucía Oriental Alejandro Otero (Fibao)
Universidad De Granada
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Publication of WO2014080060A1 publication Critical patent/WO2014080060A1/fr

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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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention is within the field of biomedicine and medical diagnosis and prognosis, more specifically in the field of cancer prognosis, in particular, breast cancer.
  • the invention relates to a method of obtaining useful data to predict the response to the treatment of breast cancer, as well as to evaluate the response to the treatment of said disease, which allows the establishment of an individual pattern of recognition (qualitative). and quantitative) specific, which is modified after treatment, allowing the establishment of patient groups.
  • Cancer is a disease characterized by uncontrolled cell growth, as it increases its proliferation and / or processes of cell death or apoptosis are inhibited; and subsequently, by the acquisition of invasive and metastatic capacity that allows cells to colonize and proliferate in other tissues.
  • the tumor cells acquire the ability to degrade the ECM (extracellular matrix) and invade the tissues and finally the blood or lymphatic vessels.
  • ECM extracellular matrix
  • This invasive process involves an interactive series of events. between malignant cells and many components of the MEC. Subsequently, circulating cells can leave the vessels and colonize other organs, what we know as metastases.
  • breast cancer treatment has undergone significant changes characterized by less aggressive strategies for both diagnosis and treatment. Mammography and ultrasound or stereotactic biopsies have replaced surgical biopsy, and in many cases, conservative surgery of the breast and sentinel lymph node biopsy have successfully replaced the more aggressive radical mastectomy and axillary lymph node dissection .
  • the authors of the present invention have detected a series of genes that act as biomarkers for predicting the response to breast cancer treatment. They have developed a method of obtaining useful data for the prediction of response to treatment in breast cancer, as well as for monitoring the response to treatment of said disease, allowing the establishment of groups of patients.
  • a first aspect of the invention relates to the use of at least one gene that is selected from the list consisting of AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6 , HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4 or any of their combinations to predict Response to chemotherapy treatment in individuals diagnosed with breast cancer.
  • Another aspect of the invention relates to the simultaneous use of the genes AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OG, 1 PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and ZFHX4 to predict or predict the response to chemotherapy treatment in individuals diagnosed with breast cancer
  • the independent use of any of them or any of their combinations could be sufficient for the prediction of response or follow-up of said disease.
  • Another aspect of the invention relates to a method of obtaining useful data to predict or predict the response to chemotherapy treatment in individuals diagnosed with breast cancer, hereinafter the first method of the invention, comprising: a) obtaining a isolated biological sample comprising cells of an individual diagnosed with breast cancer, and
  • the first method of the invention further comprises: c) Comparing the expression levels of the AP1M2, CCDC80, CDC42 genes,
  • Another aspect of the invention relates to a method of predicting or predicting the response to chemotherapy treatment in individuals diagnosed with breast cancer, hereafter referred to as the second method of the invention, comprising steps (a) - (c) of the first method of the invention, and further comprises including the individual in the positive response group (GR) when the expression levels of the AP1M2, CCDC80, CDC42, COL1A1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6 genes, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4 respectively expressed in comparison respectively a reference sample (sample from a patient who registered a good response to chemotherapy, GR-good respond).
  • GR positive response group
  • steps (b) and / or (c) of the methods described above can be totally or partially automated, for example, by means of a robotic sensor device for the detection of the quantity in step (b) or the computerized comparison in step (c).
  • an "isolated biological sample” includes, but is not limited to, cells, tissues and / or biological fluids of an organism, obtained by any method known to a person skilled in the art.
  • the isolated biological sample comprises biopsy cells. More preferably, the biological sample is a recent tissue, paraffin embedded tissue or RNA extracted from a tissue of a breast cancer patient.
  • the isolated biological sample of an individual from step (a) is obtained from breast tissue.
  • Another aspect of the invention relates to a method for predicting or forecasting the response to chemotherapy treatment in individuals diagnosed with breast cancer, hereafter referred to as the second method of the invention, comprising steps (a) - (c) according to The first method of the invention, and further comprises including the individual in the group of bad response or negative response (BR-bad responding) when the expression of AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12, ELN, FBLN genes 1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN or ZAKX on ZAK4 on ZAK4 on ZAK4 comparison with a reference sample (sample from a patient who registered a good response to chemotherapy, GR-good respond).
  • a reference sample sample from a patient who registered
  • overexpression is defined as an expression level greater than or equal to 3.16 times the maximum score achieved by each of the genes in tumor breast biopsies of patients responding to the treatment (sample of reference) before chemotherapy.
  • overexpression is defined as a level of expression greater than or equal to the maximum score achieved in tumor breast biopsies of patients responding to the treatment (positive control) as defined in Table 1.
  • FC fold change
  • FDR false discovery rate
  • the detection of the amount of the gene expression product can be performed by any means known in the state of the art.
  • the authors of the present invention have shown that the detection of the quantity or concentration of these expression products in a semi-quantitative or quantitative manner allows differentiating between the patient with breast cancer responding to the treatment of a non-respondent.
  • the amount of gene expression product detected at different stages of the disease could establish a differential diagnosis in individuals affected by breast cancer, which allows them to subclassify them.
  • the measurement of the amount or concentration preferably semiquantitatively or quantitatively, can be carried out directly or indirectly.
  • Direct measurement refers to the measure of the quantity or concentration of the gene expression product, based on a signal that is obtained directly from the transcripts of said genes, or from the proteins to which they are translated, and which is directly correlated with the number of molecules of RNA or protein produced by genes.
  • Said signal - which we can also refer to as an intensity signal - can be obtained, for example, by measuring an intensity value of a chemical or physical property of said products.
  • the indirect measurement includes the measurement obtained from a secondary component or a biological measurement system (for example the measurement of cellular responses, ligands, "tags” or enzymatic reaction products).
  • quantity refers to, but is not limited to, the absolute or relative quantity of gene expression products, as well as any other value or parameter related thereto or that can be derived from these.
  • Said values or parameters comprise signal intensity values obtained from any of the physical or chemical properties of said expression products obtained by direct measurement. Additionally, said values or parameters include all those obtained by indirect measurement, for example, any of the measurement systems described elsewhere in this document.
  • comparison refers to, but is not limited to, the comparison of the amount of expression products of the AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12, ELN genes.
  • Gene expression profile means the gene profile obtained after quantification of the mRNA and / or protein produced by the genes of interest or biomarkers, that is, by the genes AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12 , ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4, in an isolated biological sample.
  • the expression profile of the genes is preferably performed by determining the level of mRNA derived from its transcription, after extracting the total RNA present in the isolated biological sample, which can be performed by protocols known in the state of the art.
  • the determination of the level of mRNA derived from the transcription of the AP1M2, CCDC80, CDC42, COL1A1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIPG, NDFIPG , PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4, can be performed, for example, but not limited to, by amplification by polymerase chain reaction (PCR), back transcription combination with polymerase chain reaction (RT-PCR), quantitative RT-PCR, back transcription in combination with ligase chain reaction (RT-LCR), or any
  • the gene expression profile could also be obtained by the detection and / or quantification of the proteins resulting from the translation of the mRNA derived from the transcription of the AP1M2, CCDC80, CDC42, COL1A1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1 genes , GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4 but not limited, for example Western blot immunodetection.
  • Quantitative detection of the expression of AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OGN, PDG, PDG, PDG PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4 can be performed more preferably by real-time PCR (RT-PCR or RTqPCR). Real-time detection of amplified products can be carried out through the use of molecules fluorescents that are embedded in the double stranded DNA or by hybridization with different types of probes.
  • the detection of the amount of expression product is performed by: a.
  • a genetic profile procedure such as a microarray, and / or b.
  • a microarray method comprising the isolation of RNA and / or
  • a method comprising PCR, such as a real-time PCR, and / or
  • the detection of the expression product of the AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIPG, OGN, PDGF , PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4 is performed by quantitative real-time PCR.
  • the detection of the expression product of the AP1M2, CCDC80, CDC42, COL1A1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3 genes , NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4 are performed using microarrays.
  • the detection of the expression product of the AP1M2, CCDC80, CDC42, COL1A1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3 genes , NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4 is performed by a massive gene sequencing procedure.
  • the procedure is carried out in vitro using a sample originating from the human subject, where said sample is obtained after chemotherapy treatment and the reference sample is taken before chemotherapy treatment.
  • Another aspect of the invention relates to a method for monitoring the evolution of breast cancer in an individual, hereinafter third method of the invention, which comprises performing at least twice the sequence of steps (a) - ( b) according to the first or second method of the invention, in biological samples from the same individual, and isolated at different times.
  • the standard chemotherapy treatment comprises the administration of anthracyclines and taxanes.
  • other subclassifications could be established within this principal, thus facilitating the choice and establishment of appropriate therapeutic regimens.
  • This discrimination is not intended to be correct in 100% of the samples analyzed. However, it requires that a statistically significant amount of the analyzed samples be classified correctly. The amount that is statistically significant can be established by a person skilled in the art by using different statistical tools, for example, but not limited, by determining confidence intervals, determining the significance value P, Student test or discriminant functions. Fisher, non-parametric measurements of Mann Whitney, Spearman correlation, logistic regression, linear regression, area under the ROC curve (AUC).
  • the confidence intervals are at least 90%, at least 95%, at least 97%, at least 98% or at least 99%.
  • the value of p is less than 0.1, 0.05, 0.01, 0.005 or 0.0001.
  • the present invention makes it possible to correctly detect the disease differentially by at least 60%, more preferably at least 70%, much more preferably at least 80%, or even much more preferably at least 90%. of the subjects of a certain group or population analyzed.
  • the reference amount is obtained from the constitutive expression values of the genes, in a group of individuals before being subjected to treatment.
  • the reference amount will be, for example, in the case of differentiation between patients affected by breast cancer responders to the treatment of non-responders, the constitutive expression of the gene in a control group of responding individuals.
  • the control group will consist of a group of patients with breast cancer who did not respond to treatment.
  • the reference samples are obtained in tumor breast biopsies of patients responding to the treatment (reference sample) before chemotherapy.
  • the sample or reference samples can be, for example, obtained from the biopsy cells of a patient with breast cancer, in a certain clinical phase.
  • the reference amount is obtained from a reference sample.
  • the reference amount can also be obtained, for example, from the limits of normal distribution of an amount found in samples obtained from a population of individuals with breast cancer at different stages, and from responding and non-responding patients, by well-known statistical techniques .
  • the reference sample is obtained from patients before and after treatment.
  • the expression levels of one or more of these genes may be indicative of a lack of response, or of a subject's response to treatment.
  • the response is established based on histopathological parameters, fundamentally based on the percentage reduction in the number of tumor cells after chemotherapy.
  • the response is the absence of change in the histopathological characteristics and cellularity of the tumor.
  • the response is a clinical outcome, such as progression-free survival or overall survival.
  • the biological sample is a recent tissue, which is embedded in paraffin or from which the RNA is attracted. Recent tissue is obtained during diagnosis and / or surgical remains that are obtained after mastectomies or surgical resection.
  • the evaluation of the complete response and / or the partial response is an important factor in determining the status of an individual patient. In this way, it is necessary to estimate the total tumor load at the initial value and use it as a comparator for the subsequent measurements that are carried out according to the Miller and Payne gradation system (Ogston et al., Breast 2003 Oct; 12 (5): 320-7), which resulted in a good response group -GR (Miller & Payne grades 4 and 5) -, a medium response group -MR- (Miller & Payne grade 3) - and a bad response group -BR (Miller & Payne grades 1 and 2) -.
  • GR Miller et al., Breast 2003 Oct; 12 (5): 320-7
  • Another aspect of the invention relates to a method for assigning an individual suffering from breast cancer to one of two groups, in which group 1 comprises individuals who develop chemotherapy resistance identifiable by the above method, characterized by presenting an amount of expression product greater than the reference amount; and in which group 2 represents the rest of the subjects.
  • Another aspect of the invention relates to a pharmaceutical composition
  • a modulating agent of at least one of the genes that are selected from AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6 , HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4, to be developed by a group 1 individual Chemotherapy resistance, as defined in the methods of the invention.
  • Another aspect of the invention relates to an antibody for treating a group 1 individual who develops resistance to chemotherapy, identifiable by a method of the invention, wherein the antibody is selected from among anti-AP1 M2, anti-CCDC80, anti- CDC42, anti-COL1A1, anti-CTNNB1, anti-CXCL12, anti-ELN, anti-FBLNI, anti-FLRT2, anti-FLT1, anti-GAS6, anti-HIF1A, anti-HMCN1, anti-KIT, anti-MAPK1, anti- NAP1 L3, anti-NDFIP1, anti-OGN, anti-PDGFD, anti-PER1, anti-PRKG1, anti-RASGRF2, anti-SFRP4, anti-SMAD9, anti-SOCS5, anti-SPARC, anti-SPON1, anti-SSPN , anti-ZAK and / or anti-ZFHX4.
  • the antibody is selected from among anti-AP1 M2, anti-CCDC80, anti- CDC42, anti-COL1A1, anti-CTNNB1, anti-CX
  • kit or device of the invention comprising the elements necessary to analyze the amount of the expression product of the AP1M2, CCDC80, CDC42, COL1A1, CTNNB1 genes , CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1, NAP1L3, NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARCN1, SPARCN1, SPARCN1, SPARCN1, SPARCN1 or ZFHX4.
  • the kit may include a sample or reference that is a breast biopsy sample of a patient responding to the treatment that is obtained before treatment.
  • Said kit may contain all those reagents necessary to analyze the amount of the expression product of the AP1M2, CCDC80, CDC42, COL1A 1, CTNNB1, CXCL12, ELN, FBLN1, FLRT2, FLT1, GAS6, HIF1A, HMCN1, KIT, MAPK1 genes. NAP1L3, NDFIP1, OGN, PDGFD, PER1, PRKG1, RASGRF2, SFRP4, SMAD9, SOCS5, SPARC, SPON1, SSPN, ZAK and / or ZFHX4 by any of the methods described earlier in this document.
  • the kit can also include, without any limitation, buffers, agents to prevent contamination, inhibitors of protein degradation, etc.
  • the kit can include all the supports and containers necessary for commissioning and optimization.
  • the kit further comprises instructions for carrying out any of the methods of the invention.
  • kits or devices of the invention for obtaining useful data for predicting the response to treatment and / or monitoring of breast cancer.
  • the kit can contain all the reagents and elements necessary to analyze the expression of the target genes, as well as the tools necessary to establish a useful clinical prediction, for example, a mathematical algorithm that integrates clinical information and gene information to establish the prediction of a patient's response to treatment or detect the development of chemoresistance throughout the treatment.
  • AP1 M2 (NM_005498) encodes a subunit of heterotetrameric adapter-related protein comlex 1 (AP-1), which belongs to the family of adapter complexes medium subunits and is capable of interacting with tyrosine-based classification signals.
  • AP1 M2 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 1.
  • CCDC80 (NM_199512.1) whose decrease in expression is regulated by oncogenic protein 1 and whose increase is regulated in homologous mice deficient in BRS-3.
  • CCDC80 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 2.
  • CDC42 or "cell division cycle 42" encodes a small GTPase of the Rho subfamily, which regulates transductional signaling that controls various cellular functions including cell morphology, endocytosis and cell cycle progression.
  • This protein is very similar to Saccharomyces cerevisiae Cdc 42, and is capable of complementing the yeast mutant cdc42-1 and it is believed that it could regulate actin polymerization by binding to the N-WASP protein.
  • CDC42 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 3.
  • COL1A1 (NM_000088.3) encodes the major component of type one collagen, the fibrillar collagen found in most connective tissues, including cartilage as well as bone, cornea or tendom. Mutations in this gene are associated with osteogenesis imperfecta type l-IV or osteoporosis among others.
  • COL1A1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 4.
  • CTNNB1 (NM_001098209.1) encodes a protein that is part of the group of proteins that encode the junctions of cadherins, which are necessary for the maintenance of epithelial cell layers by regulating cell growth and adhesion between cells. Mutations in this gene cause colorectal cancer, pilomatrixoma, medulloblastoma and ovarian cancer.
  • CTNNB1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 5.
  • CXCL12 (NM_000609.5) is a gene that encodes an alpha chemokine derived from stromal cells that belongs to the intercrine family.
  • CXCL12 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 6.
  • ELN (NM_000501.2) encodes a protein that is one of the two components in elastic fibers. Mutations of this gene are also related to aortic supravalvular stenosis (SVAS) and autosomal dominant cutis laxa.
  • ELN is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 7.
  • FBLN 1 (NM_001996.3) encodes a protein that is incorporated into the fibrillar extracellular matrix. Calcium binding could mediate its binding with laminin and entactin that are part of the basal laminae. This protein also mediates platelet adhesion through its binding with fibrinogen.
  • FBLN1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 8.
  • FLRT2 (NM_013231.4) encodes a member of the transmembrane protein family rich in fibronectin and leucine, which are related to cell adhesion and receptor signaling.
  • FLRT2 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 9.
  • FLT1 (NM_001 159920.1) codes for a member of the family of vascular endothelial growth factor (VEGFR) receptors. This protein binds to VEGFR-A and VEGFR-B and to placental growth factors and has an important role in angiogenesis and vasculogenesis.
  • FLT1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 10.
  • GAS6 (NM_000820.2) encodes the protein containing gamma carboxyglutamic acid that is believed to be involved in stimulating cell proliferation and may have a role in thrombosis.
  • GAS6 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 1 1.
  • HI F1A (NM_001243084.1) is a gene that codes for hypoxia-inducible transcription factor H IF1, which is composed of an alpha and beta subunit. HIF1 regulates the cellular and homeostatic response to hypoxia by activating many genes involved in energy metabolism, angiogenesis, apoptosis etc, and also has an important role in vascularization.
  • HIF1A is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 12.
  • HMCN1 (NM_031935.2), which encodes a large extracellular member of the immunoglobulin family. Mutations of this gene are related to macular degeneration that occurs at advanced ages.
  • HMCN1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 13.
  • KIT (NM_000222.2) encodes the transmembrane receptor protein type 3 of mast cell growth factors (FGM). Mutations in this gene are associated with gastrointestinal stromal tumors, mast cell diseases, acute myelogenous leukemia and piebaldism.
  • KIT is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 14.
  • MAPK1 (NM_002745.4) encodes a member protein of the MAP kinase family. This family acts as a point of integration to various biochemical signals and is involved in a wide variety of cellular processes such as proliferation, differentiation, transcription regulation, and development.
  • MAPK1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 15.
  • NAP1 L3 (NM_004538.5) encodes a protein that is a member of the family of nucleosome assembly proteins and is linked to a region of genes that is responsible for several X-fragile mental retardation syndromes.
  • NAP1 L3 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 16.
  • NDFIP1 (NM_030571.3), which encodes a protein that belongs to a small group of conserved proteins in evolution with three transmembrane domains. This protein is also a target of the Nedd4 family of proteins and is also thought to be part of the family of integral Golgi membrane proteins.
  • NDFI P1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 17.
  • OGN (NM_014057.3) encodes a protein that induces ectopic bone formation and studies have been found that relate the expression of this gene in the cochlea and the auditory phenotype of OGN-deficient mice.
  • OGN is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 18.
  • PDGFD (NM_025208.4) encodes a protein that is a member of the family of platelet-derived growth factors, which are mitogenic factors for cells of mesenchymal origin.
  • PDGFD is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 19.
  • PER1 (NM_002616.2) is a gene of the PER gene family that is expressed in a pattern circadian in the suprachiasmatic nucleus, which is the primary center of regulation of circadian rhythms in the brain in mammals.
  • PER1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 20.
  • PRKG1 (NM_001098512.2) encodes PRKG I alpha and I beta isoforms that act as mediators of oxide / cGMP transductional signaling and are important components for many such processes in many cell types.
  • PRKG1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 21.
  • RASGRF2 (NM_006909.2) that belongs to the RASGTP family that are GDP / GTP exchange factors for the Ras GTPases family that perform functions in the development of T lymphocytes.
  • RASGRF2 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 22.
  • SFRP4 (NM_003014.3) is a member gene of the SPRF family that contains a cysteine-rich domain homologous to that of the WNT gene family. The SPRF family acts as soluble modulators of WNT signaling, involved in embryological development, cell differentiation and cancer.
  • the expression of SFRP4 in the ventricular micardium is related to apoptosis derived from gene expression.
  • SFRP4 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 23.
  • SMAD9 (NM_001 127217.2) encodes a protein that is a member of the SMAD family, which transduces signals from members of the TGF-beta family. This protein is activated by bone morphogenetic proteins and interacts with SMAD4.
  • SMAD9 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 24.
  • SOCS5 (NM_014011.4) encodes a protein that contains an SH2 domain and one SOCS BOX. Although its specific function is not known, this protein belongs to the family of suppressors of cytokine-dependent signaling SOCs.
  • SOCS5 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 25.
  • SPARC (NM_0031 18.3) encodes a protein associated with the cysteine-rich matrix. This protein is required by bone collagen for calcification, but it is also involved in the synthesis of the extracellular matrix and in the promotion of changes in the cellular form. This protein is also associated with tumor suppression and also related to metastasis based on such changes in cell form that can promote cell invasion of tumors.
  • SPARC is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 26.
  • SPON1 (NM_006108.3) encodes a protein that is secreted by soil plate cells or " floor-plate "that could be involved in the axon guide.
  • SPON1 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 27.
  • SSPN (NM_001 135823.1) encodes a member of the dystrophin-glycoprotein dystrophin-glycoprotein (DGC) complex that provides a structural union between the subsarcolemmal cytoskeleton and extracellular matrix of muscle cells.
  • DGC dystrophin-glycoprotein dystrophin-glycoprotein
  • ZAK NM_016653.2 This gene is a member of the MAPKK family. This protein acts as a control point for cell cycle regulation.
  • ZAK is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 29.
  • ZFHX4 (NM_024721.4) also called zinc finger homeodomain protein 4, which has been found expressed in neuronal differentiation and contained in muscle differentiation.
  • ZFHX4 is also defined by a nucleotide or polynucleotide sequence collected in SEQ ID No. 30.
  • polynucleotide and “nucleic acid” are used interchangeably herein, referring to polymeric forms of nucleotides of any length, both ribonucleotides (RNA or RNA) and deoxyribonucleotides (DNA or DNA).
  • amino acid sequence referring to polymeric forms of amino acids of any length, which may be coding or non-coding, Chemically or biochemically modified.
  • FIG. 1 Experimental design and main results of the "Discovery Assay”
  • Figure 2 Main results derived from the comparison Post-QT vs Pre-QT in the validation test.
  • A) Representation of Log 10 of the order of magnitude change parameter of the relative abundance of mRNA of each of the differentially overexpressed genes after chemotherapy, considering all experimental groups (Post-QT vs. Pre-QT) and each of the Experimental groups individually -GR (Post-QT vs. Pre-QT), Her2G (Post-QT vs. Pre-QT), MRH (Post-QT vs. Pre-QT) and MRL (Post-QT vs. Pre-QT) - .
  • B) Venn diagram highlighting differentially expressed genes after chemotherapy in each experimental group in relation to differentially expressed genes after chemotherapy considering all experimental groups.
  • FIG. 3 Chemoresistance genes.
  • A) Log 10 of the order of magnitude change parameter of the relative abundance of mRNA of each of the differentially overexpressed genes after chemotherapy, considering the GR and BR groups before chemotherapy.
  • B) Log 10 Log 10 of the order of magnitude change parameter of the relative abundance of mRNA of each of the differentially expressed genes after chemotherapy in the GR group - GR comparison (Post-QT vs. Pre-QT) - and those differentially expressed before chemotherapy between groups GR and BR -Pre-QT (GR vs BR) comparison-
  • breast cancer patients were distributed in experimental groups, according to the pathological response to the treatment with chemotherapy based on anthracyclines and taxanes they presented, and according to the grading system of Miller and Payne (Ogston et al., Breast 2003 Oct; 12 (5): 320-7), which resulted in a good response group -GR (Miller & Payne grades 4 and 5) -, a medium response group -MR- (Miller & Payne grade 3) - and a bad response group -BR (Miller & Payne grades 1 and 2) -.
  • the validation test was performed on a set of 170 paired samples (pre and post treatment) corresponding to 85 cases of breast cancer.
  • the pairs of pre and post treatment samples were examined by a pathologist who classified the response to chemotherapy according to the Miller and Payne classification.
  • the specimens were classified into 4 groups according to their pathological response to chemotherapy and their Her2 receptor expression.
  • Samples that did not express Her2 were distributed in BR ("Bad answer” - bad responder), MR ("Mild respond” - middle responder) and GR ("Good Answer” - good responder).
  • the MR group was subdivided into 2 more groups according to the percentage of tumor cell removal after treatment and they were called MRH ("Mild respond high" - MRL responder medium and "MRL” - Mild responder low). an additional group that expressed Her2.
  • RNA isolation and microarray analysis Sample processing, microarray hybridization and gene expression analysis was carried out with the Affymetrix Genechip System (Affymetrix, Santa Clara, CAUSE). Briefly, the total RNA was extracted and purified for microarray analysis using the QIAshredder columns and the RNeasy Mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. From ⁇ g of total RNA, complementary DNA (cDNA) was synthesized using the One-Cycle cDNA Synthesis kit (Affymetrix, Santa Clara, CA, USA). The biotinylated complementary RNA was synthesized by YVT labeling kit (Affymetrix) and purified with the GeneChip Sample Cleanup Module (Affymetrix).
  • Affymetrix GeneChip Sample Cleanup Module
  • the biotinylated cRNA was fragmented and hybridized to the Genechip Human Genome U133 Plus 2.0 microarray (Affymetrix). After hybridization, washes and staining protocol were performed according to GeneChip Scanner 2000 (Affymetrix). The fluorescent signal corresponding to the hybridization intensity of each transcript was quantified and normalized by the Gene Chip Operating Software (GCOS 1.4, Affymetrix). Finally, a set of measurements was generated from the quantified image files sAffymetrix mediant the Robust Multichip Average method (RMA) of the Affy Bioconductor package (http: / www.bioconductor.org).
  • RMA Robust Multichip Average method
  • the Bonferroni test was used to correct multiple trials, being considered significant when P ⁇ 0.05 and calculating the FC (Fold-change "- change of order of magnitude) values for all comparisons.
  • Genes differentially expressed between the different conditions analyzed were identified using a linear regression model that included the pathological response to chemotherapy as variables, if the sample was obtained before or after systemic treatment and the pairing of pre and post-treatment samples were derived from the same patient.
  • the supervised PCA analysis and the clustering were performed with processed data
  • the Partek Genomics Suite v7.3.1 software Partek, St.
  • the set of genes which contain the control genes, GADPH, HPRT1, MRPL19, RPLP0, TBP and TFRC, were selected according to the literature (Drury et al. Diagn Mol Pathol. 2009 Jun; 18 (2): 103-7). The six genes were considered for normalization. Threshold cycle values (Ct values) were determined using SDS software 2.2.2 software (Applied Biosystems). DCt values were used as independent variables in the statistical analysis. A linear regression model (LIMMA) was used to detect changes between differentially expressed groups and the method of Benjamini and Hochberg's was used to control the rate of false discoveries.
  • Ct values Threshold cycle values
  • Pairs of pre-chemotherapeutic and post-chemotherapy tissue samples were obtained from each patient and we performed a gene expression analysis of the entire genome using an oligonucleotide microarray platform. After comparing the samples before and after chemotherapy, 47 overexpressed genes were identified after chemotherapy, considering all the experimental groups (Figure 1 B).
  • a functional analysis using ontological databases revealed that the proteins encoded by these genes are involved in extracellular matrix metabolism, cell proliferation and adhesion, the response to oxidative stress, angiogenesis and the processes involved in development. All these are key for the development of chemoresistance and for the progression of the disease, since they play a central role during cell invasion and its connection with cell de-differentiation (Figure 1 C).
  • Tables 2-7 included at the end of this document show the different genes differentially expressed within each experimental pre-chemotherapy and post-chemotherapy group, as detailed in the results presented below.
  • Table 2 Selected genes of interest from validation tests. The genes were selected according to the result of the discovery test and the bibliographic criteria focused on the functional relationships between genes significantly overexpressed after chemotherapy.
  • CDC42 binding protein kinase alpha DMPK-like
  • CDC42BPA coiled-coil domain containing 80 CCDC80 collagen.
  • type I alpha 1 COL1A1 collagen.
  • RAB11 family interacting protein 2 (class I)
  • RAB11 FIP2 RAB11 FIP2
  • ATP-binding cassette sub-family G (WHITE). member 2 ABCG2 catenin (cadherin-associated protein). beta 1. 88kDa CTNNB1
  • CDP-diacylglycerol synthase (phosphatidate CDS1 cytidylyltransferase) 1
  • beta 1 fibronectin receptor, beta polypeptide. antigen
  • CD29 includes MDF2. MSK12)
  • beta 3 platelet glycoprotein Illa, antigen CD61 ITGB3 integrin.
  • NOTCH1 Notchl nuclear factor of kappa light polypeptide gene enhancer in B-
  • VEGF-A165 vascular endothelial growth factor C VEGF-C
  • VEGFR1 vascular endothelial growth factor receptor 2
  • VEGFR2 vascular endothelial growth factor receptor 3
  • VEGFR3 v-rel reticuloendotheliosis viral oncogene homolog A (avian) REL A
  • RQ describes the magnitude of change of each target gene after chemotherapy with respect to its expression before chemotherapy.
  • BR bad response group
  • GR good response group
  • Her2G Her2-positive group
  • Her2-positive group Her2-positive group
  • MRH (mid-response high group) medium high response group
  • MRL (mid-response low group) medium low response group
  • Post-QT (after chemotherapy) after chemotherapy
  • Pre-QT (before chemotherapy) before chemotherapy
  • RQ (relative quantity) relative quantity.
  • Post-QT vs. Pre-GR gene Post-QT vs. Pre-Her2G (Post-QT vs. MRH (Post-QT vs. MRL (Post-QT vs.
  • Post-QT vs. Pre-GR gene Post-QT vs. Pre-Her2G (Post-QT vs. MRH (Post-QT vs. MRL (Post-QT vs.
  • GALNTL2 4.2099271 1 8.90195973 - - - - -
  • Post-QT vs Pre-GR gene Post-QT s Pre-Her2G (Post-QT vs. MRH (Post-QT vs MRL (Post-QT vs
  • the relative-relative quantity - (RQ) describes the magnitude of change of each target gene after chemotherapy with respect to its expression after chemotherapy.
  • GR (good response group) post-QT good response group, (after chemotherapy) after chemotherapy; Pre-QT, (before chemotherapy) before chemotherapy; RQ, (relative quantity) relative quantity.
  • Table 5 Comparison of genes differentially on expressed chemotherapy between the GR and BR groups -Pre-QT (GR vs BR) comparison-.
  • the RQ, (relative quantity) relative quantity describes the magnitude of change of each gene targeting chemotherapy in the GR group with respect to the bad response group BR (bad response group) group; GR, (good response group) good response group; Pre-QT, (before chemotherapy) before chemotherapy; RQ, (relative quantity) relative quantity.
  • RQ describes the magnitude of change of each of the Diana genes with respect to their expression in the experimental group that corresponds to the second comparison term.
  • BR bad response group
  • GR good response group
  • Her2G Her2-positive group
  • Her2-positive group Her2-positive group
  • MRH (mid-response high group) medium high response group
  • MRL (mid-response low group) medium low response group
  • Post-QT (after chemotherapy) after chemotherapy
  • Pre-QT (before chemotherapy) before chemotherapy
  • RQ (relative quantity) relative quantity.
  • RQ describes the magnitude of change of each of the target genes with respect to their expression in the experimental group that corresponds to the second comparison term.
  • BR bad response group
  • GR good response group
  • Her2G Her2-positive group
  • Her2-positive group Her2-positive group
  • MRH (mid-response high group) medium high response group
  • MRL (mid-response low group) medium low response group
  • Post-QT (after chemotherapy) after chemotherapy
  • Pre-QT (before chemotherapy) before chemotherapy
  • RQ (relative quantity) relative quantity.

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Abstract

L'invention concerne une méthode d'obtention de données utiles pour prédire la réponse au traitement du cancer du sein, ainsi que pour évaluer la réponse au traitement de cette maladie, par analyse de l'expression d'un ensemble de gènes. Cette méthode permet d'établir un modèle individuel de reconnaissance (qualitatif et quantitatif) spécifique qui est modifié post-traitement, ce qui permet d'établir des groupes de patients.
PCT/ES2013/070808 2012-11-23 2013-11-22 Méthode destinée à prédire la réponse au traitement chimiothérapeutique de patients atteints de cancer WO2014080060A1 (fr)

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Publication number Priority date Publication date Assignee Title
CN106676183A (zh) * 2017-02-09 2017-05-17 复旦大学 Zfhx4作为食管癌预后诊断的生物标志物
CN107179685A (zh) * 2016-03-09 2017-09-19 中国科学院沈阳自动化研究所 一种适用于多变量模型预测控制的分程控制实现方法
CN109097462A (zh) * 2018-09-10 2018-12-28 青岛市海慈医疗集团 Ap1m2基因作为诊断青光眼的分子标志物的应用

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WO2006052862A1 (fr) * 2004-11-05 2006-05-18 Genomic Health, Inc. Prediction de reaction a la chimiotherapie au moyen de marqueurs d'expression genique
WO2007107254A1 (fr) * 2006-03-22 2007-09-27 Siemens Medical Solutions Diagnostics Gmbh Prévision de la réponse d'un cancer du sein à une chimiothérapie
WO2010003773A1 (fr) * 2008-06-16 2010-01-14 Siemens Medical Solutions Diagnostics Gmbh Algorithmes de prédiction de résultat pour des patientes atteintes de cancer du sein traité par chimiothérapie avec atteinte ganglionnaire
WO2011080373A1 (fr) * 2009-12-31 2011-07-07 Centro De Investigaciones Energéticas, Medioambientales Y Tecnológicas (Ciemat) Empreinte génomique utilisée en tant que prédicteur de réponse à un traitement

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WO2006052862A1 (fr) * 2004-11-05 2006-05-18 Genomic Health, Inc. Prediction de reaction a la chimiotherapie au moyen de marqueurs d'expression genique
WO2007107254A1 (fr) * 2006-03-22 2007-09-27 Siemens Medical Solutions Diagnostics Gmbh Prévision de la réponse d'un cancer du sein à une chimiothérapie
WO2010003773A1 (fr) * 2008-06-16 2010-01-14 Siemens Medical Solutions Diagnostics Gmbh Algorithmes de prédiction de résultat pour des patientes atteintes de cancer du sein traité par chimiothérapie avec atteinte ganglionnaire
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107179685A (zh) * 2016-03-09 2017-09-19 中国科学院沈阳自动化研究所 一种适用于多变量模型预测控制的分程控制实现方法
CN107179685B (zh) * 2016-03-09 2019-12-10 中国科学院沈阳自动化研究所 一种适用于多变量模型预测控制的分程控制实现方法
CN106676183A (zh) * 2017-02-09 2017-05-17 复旦大学 Zfhx4作为食管癌预后诊断的生物标志物
CN109097462A (zh) * 2018-09-10 2018-12-28 青岛市海慈医疗集团 Ap1m2基因作为诊断青光眼的分子标志物的应用
CN109097462B (zh) * 2018-09-10 2022-01-11 青岛市海慈医疗集团 Ap1m2基因作为诊断青光眼的分子标志物的应用

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