WO2023099890A1 - Procédé de pronostic - Google Patents

Procédé de pronostic Download PDF

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Publication number
WO2023099890A1
WO2023099890A1 PCT/GB2022/053035 GB2022053035W WO2023099890A1 WO 2023099890 A1 WO2023099890 A1 WO 2023099890A1 GB 2022053035 W GB2022053035 W GB 2022053035W WO 2023099890 A1 WO2023099890 A1 WO 2023099890A1
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genes
gene panel
c9orf3
gene
acta2
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PCT/GB2022/053035
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English (en)
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Mohammed INAYATULLAH
Vijay TIWARI
Ryan LUSBY
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The Queen's University Of Belfast
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Publication of WO2023099890A1 publication Critical patent/WO2023099890A1/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 relates to methods of characterizing breast cancer.
  • the invention relates to methods of predicting resistance to chemotherapy in subjects with triple negative breast cancer.
  • TNBC Triple-negative breast cancer
  • TNBC lacks expression of estrogen and progesterone receptors and HER2 protein and accounts for ⁇ 20% of all breast cancer cases.
  • TNBC is highly aggressive, and is associated with a poor prognosis with 40% mortality within the first 5 years after diagnosis 1 ’ 3 .
  • Chemotherapy is the first line of treatment option for patients with TNBC in neoadjuvant, adjuvant, or metastatic settings. While chemotherapy is effective in some TNBC patients, nearly half of the patients develop resistance to chemotherapy, which results in poor overall survival 45 .
  • eliminating the majority of the bulk population of cancer cells has relatively little impact on the clinical outcomes for TNBC 45 . This suggests that the minor, escaping cell subpopulations may underlie TNBC aggressiveness including chemoresistance.
  • EMT epithelial to mesenchymal transition
  • the inventors performed an integrated analysis of gene expression profiles derived from scRNA-seq, bulk RNA-seq, and Microarray data from TNBC tumors treated with chemotherapy and identified subpopulations of cells that associate with key aspects of TNBC aggressiveness such as chemoresistance.
  • the inventors show that the identified signature genes can accurately predict response to NAC in primary as well as advanced stage TNBC (lymph-node positive), with the prediction accuracy greatly improved over that of known gene expression signatures for TNBC.
  • the present invention provides a method of predicting resistance to chemotherapy in a subject having triple negative breast cancer (TNBC), wherein said method comprises: a) providing a biological sample from said subject; and b) determining the expression levels of each member of a gene panel in said biological sample, said gene panel comprising at least 10 genes selected from the group consisting of ITGB1, RBFOX2, DST, RCAN1, c9orf3/AOPEP, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, and AMIGO2; wherein the determined expression levels of said at genes of said gene panel is used to determine the likelihood of resistance to said chemotherapy.
  • TNBC triple negative breast cancer
  • the gene panel comprises the following 20 genes: ITGB1 , RBFOX2, DST, RCAN1, c9orf3/AOPEP, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1, STOM, COL6A1, DSC3, and AMIGO2.
  • the gene panel consists of the following 20 genes: ITGB1 , RBFOX2, DST, RCAN1, c9orf3/AOPEP, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1, STOM, COL6A1, DSC3, and AMIGO2.
  • the inventors have shown that such a gene panel (gene signature) can predict response to chemotherapy in TNBC significantly more accurately than known gene expression signatures for TNBC. Moreover, the inventors have further shown that smaller gene panels comprising fewer of the 20 genes still outperform known gene signatures. For example, as shown in the Examples, even when the five genes having greatest effect on the performance of the gene panel are not included, the overall performance of the gene signature is still greater than that of known gene signatures.
  • the invention extends to gene panels which do not include all 20 of ITGB1 , RBFOX2, DST, RCAN1, c9orf3/AOPEP, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, and AMIGO2.
  • said gene panel comprises at least 10 genes selected from the group consisting of ITGB1, RBFOX2, DST, RCAN1 , c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, and ACTN1, wherein said gene panel comprises one, two, three, four, five or six of ITGB1, RBFOX2, DST, RCAN1 , c9orf3 and ACTA2.
  • the gene panel comprises at least 15 genes selected from the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, and AMIGO2, wherein said gene panel comprises one, two, three, four, five or six of ITGB1, RBFOX2, DST, RCAN1 , c9orf3 and ACTA2
  • the gene panel comprises at least the fifteen genes of the group consisting of ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, and ACTN1.
  • said gene panel may comprise (i) at least the fifteen genes of the group consisting of ITGB1, RBFOX2, DST, RCAN1 , c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, and ACTNI ; and optionally (ii) one or more of the genes of the group consisting of SFRP1 , STOM, COL6A1 , DSC3, and AMIGO2.
  • the likelihood of resistance to chemotherapy may be determined by comparing the expression levels of said genes of the gene panel relative to reference amounts of said genes.
  • Such control cells may chemosensitive TNBC cells, for example chemosensitive TNBC cells from patients shown to have pathological complete response (pCR) to said chemotherapy.
  • each gene of the gene panel has significantly different level of expression in TNBC cells from patients with residual disease vs TNBC cells from patients with pCR (e.g. the difference having a p value of less than 0.05).
  • Some genes of the gene panel may have significantly higher expression in TNBC cells from patients with residual disease compared to the expression of these genes in TNBC cells from patients with pCR.
  • Other genes may have significantly lower expression in TNBC cells from patients with residual disease compared to the expression of these genes in TNBC cells from patients with pCR.
  • the likelihood of resistance to said chemotherapy may be determined by applying the expression levels to a predictive model which relates expression levels of said genes of the gene panel with resistance to chemotherapy against triple negative breast cancer.
  • Applying the expression levels to such a predictive model may comprise weighting the expression levels of said genes of the gene panel according to a predetermined ranking of said genes of the gene panel.
  • the weighting of the expression levels of the genes may be determined by the coefficient value derived for each gene using LASSO regression.
  • the genes may be ranked in order of greatest to least predictive power, for example as shown in Figure 4C.
  • references to “an active agent” or “a pharmacologically active agent” includes a single active agent as well as two or more different active agents in combination, while references to “a carrier” includes mixtures of two or more carriers as well as a single carrier, and the like.
  • the present invention is based on the identification of a specific gene signature which the inventors have shown can be used to predict with high accuracy 5 resistance against chemotherapy in TNBC patients.
  • Genes which may be used in the gene signatures of the invention may comprise the following 20 genes: ITGB1, RBFOX2, DST, RCAN1, c9orf3/AOPEP, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1 , DSC3, and AMIGO2.
  • nucleic acid sequences for these genes are shown in the sequence 10 listing with sequences for each of the genes as follows: ITGB1 (SEQ ID NO: 1) RBFOX2 (SEQ ID NO: 4), DST (SEQ ID NO: 5), RCAN1 (SEQ ID NO: 10), c9orf3/AOPEP (SEQ ID NO: 17), ACTA2 (SEQ ID NO: 9), S100B (SEQ ID NO: 8), LY6E (SEQ ID NO: 19), CTNNAL1 (SEQ ID NO: 18), PRNP (SEQ ID NO: 2), TIMP3 (SEQ ID NO: 16), CD63 (SEQ ID NO: 3), IFI16 (SEQ ID NO: 12), NFIB (SEQ ID NO: 15 13), ACTN1 (SEQ ID NO: 6), SFRP1 (SEQ ID NO: 11), STOM (SEQ ID NO: 15),
  • COL6A1 SEQ ID NO: 14
  • DSC3 SEQ ID NO: 20
  • AMIGO2 SEQ ID NO: 7
  • target sequences may be used.
  • the skilled person will readily be able to identify suitable target sequences for each gene and likewise will readily be able to design suitable probes/primers based on the sequences of the genes and/or of individual
  • the gene panel may comprise c9orf3. In some embodiments of the invention, the gene panel may comprise CD63. In some embodiments of the invention, the gene panel may comprise STOM.
  • the gene panel comprises c9orf3 and at least 9, for example, 10, 11, 12, 13, 14, 15, 16, 17 , 18, or all 19 of the genes selected from the group consisting of ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1, STOM, COL6A1, DSC3, and AMIGO2.
  • the gene panel comprises CD63 and at least 9, for example, 10, 11, 12, 13, 14, 15, 16, 17 , 18, or all 19 of the genes selected from the group consisting of ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, and AMIGO2.
  • the gene panel comprises STOM and at least 9, for example, 10, 11, 12, 13, 14, 15, 16, 17 , 18, or all 19 of the genes selected from the group consisting of ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1 , COL6A1 , DSC3, and AMIGO2.
  • the gene panel comprises at least two or three genes selected from the group consisting of: c9orf3, CD63, and STOM.
  • the gene panel may comprise c9orf3 and CD63.
  • the gene panel may comprise c9orf3 and STOM.
  • the gene panel may comprise CD63 and STOM.
  • the gene panel comprises c9orf3, CD63, and STOM.
  • the gene panel comprises two or three of the genes selected from c9orf3, CD63 and STOM and at least 8, for example, 9, 10, 11 , 12, 13, 14, 15, 16, or 17 of the genes selected from the group consisting of ITGB1, RBFOX2, DST, RCAN1 , ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, IFI16, NFIB, ACTN1, SFRP1, COL6A1, DSC3, AMIGO2, KRT17, ACTG2, MYLK, ANXA1, CNN3, CAV2, MSRB3, and TNG
  • the gene panel may comprise one, two, three, four, five or six of ITGB1, RBFOX2, DST, RCAN1 , c9orf3 and ACTA2. In some such embodiments, the gene panel may comprise four, five or six of ITGB1, RBFOX2, DST, RCAN1 , c9orf3 and ACTA2 together with one, two or three genes selected from the group consisting of: c9orf3, CD63, and STOM.
  • the gene panel may comprise, or consist of, at least 12, 13, 14, 15, 16, 17, 18, or 19 genes of the group consisting of ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel comprises (i) at least the twelve genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, and CD63; and optionally (ii) one or more of the genes of the group consisting of IFI16, NFIB, ACTN1, SFRP1 , STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel comprises (i) at least the thirteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, and IFI16; and optionally (ii) one or more of the genes of the group consisting of NFIB, ACTN1, SFRP1, STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel comprises (i) at least the fourteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1 , c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16,and NFIB; and optionally (ii) one or more of the genes of the group consisting of ACTN 1 , SFRP1 , STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel comprises (i) at least the fifteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, and ACTN1 ; and optionally (ii) one or more of the genes of the group consisting of SFRP1 , STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel comprises (i) at least the sixteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1,and SFRP1; and optionally (ii) one or more of the genes of the group consisting of STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel comprises (i) at least the seventeen genes of the group consisting of ITGB1, RBFOX2, DST, RCAN1 , c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, and STOM; and optionally (ii) one or more of the genes of the group consisting of COL6A1 , DSC3, and AMIGO2.
  • the gene panel comprises (i) at least the eighteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1 , c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1 , STOM, and COL6A1 ; and optionally (ii) one or more of the genes of the group consisting of DSC3, and AMIGO2.
  • the gene panel comprises (i) at least the nineteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1 , c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1 , STOM, COL6A1, and DSC3; and optionally (ii) AMIGO2.
  • the gene panel comprises the twenty genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1 , STOM, COL6A1, DSC3, and AMIGO2.
  • the gene panel is limited to genes selected from the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1 , DSC3, and AMIGO2, i.e.
  • the genes of which expression is determined are limited to only ten or more of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1, STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel consists of the twelve genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, and CD63.
  • the gene panel consists of the thirteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, and IFI16.
  • the gene panel consists of the fourteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, and NFIB.
  • the gene panel consists of the fifteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, and ACTN1.
  • the gene panel consists of the sixteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, and SFRP1.
  • the gene panel consists of the seventeen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1 , and STOM.
  • the gene panel consists of the eighteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1 , STOM, and COL6A1.
  • the gene panel consists of the nineteen genes of the group consisting of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1 , PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, and DSC3.
  • the gene panel consists of the following genes: ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, and AMIGO2.
  • the gene panel consists of the following genes: RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1 , STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel consists of the following genes: DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1, STOM, COL6A1, DSC3, and AMIGO2.
  • the gene panel consists of the following genes: RCAN1 , c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1 , DSC3, and AMIGO2.
  • the gene panel consists of the following genes: c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1 , STOM, COL6A1, DSC3, and AMIGO2.
  • the gene panel consists of the following genes: ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1 , STOM, COL6A1, DSC3, and AMIGO2.
  • the gene panel consists of the following genes: ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1 , STOM, COL6A1, and DSC3,.
  • the gene panel consists of the following genes: ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1 , STOM, and COL6A1.
  • the gene panel consists of the following genes: ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1, and STOM.
  • the gene panel consists of the following genes: ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , and SFRP1.
  • the gene panel may comprise, in addition to genes selected from the group ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, and AMIGO2, other biomarkers associated with TNBC.
  • the gene panel may additionally comprise one or more of the genes selected from KRT17, ACTG2, MYLK, ANXA1 , CNN3, CAV2, MSRB3, and TNC.
  • the gene panel is limited to genes selected from the group consisting of ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, AMIGO2, KRT17, ACTG2, MYLK, ANXA1 , CNN3, CAV2, MSRB3, and TNG i.e., the genes of which expression is determined are limited to only ten or more of ITGB1 , RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1, STOM, COL6A1, DSC3, and AMIGO2 and optionally one or more of the genes selected from KRT17, ACTG2, MYLK,
  • the inventors have also identified the following genes as being particularly powerful in the TNBC gene signatures of the invention: MSH3, TSHZ2, SLC11A2, CTDSPL, and NDUFA6.
  • the gene panel may comprise one, two, three, four, or five of MSH3, TSHZ2, SLC11A2, CTDSPL, and NDUFA6. Examples of nucleic acid sequences for these genes are shown in the sequence listing as SEQ ID NOS 21, 22, 23, 24 and 25 respectively.
  • the gene panel comprises no more than 20 genes.
  • the gene panel consists of the following 20 genes: ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, and AMIGO2.
  • the expression level of genes of the gene panel may be identified using any suitable method. Expression of the genes may be measured directly by measurement of mRNA expression via a microarray, Northern blotting, in situ RNA detection, RNA sequencing, PCR methods or any other suitable nucleic acid amplification technique.
  • the methods may involve any suitable primers and/or probes, the design of which is within the routine knowledge of the skilled person. Primer design tools, such as the NCBI Primer-BLAST tool may be used. Typically, primers and/or probes will be approximately 15-25 nucleotides in length.
  • next generation sequencing methods employing, for example, pyrosequencing, Ion Torrent semiconductor sequencing, Illumina sequencing methods, single-molecule real-time sequencing or DNA nanoball sequencing.
  • expression of the genes in the gene samples may be determined by in situ RNA detection using, for example, in situ hybridisation techniques to localise specific RNA sequences in a cell or section of tissue.
  • the expression levels of one or more of the genes of the gene panel may be determined by measurement of protein products of the genes in cells of a tissue sample.
  • Such methods of determining expression of the genes at the protein level may include commonly known techniques such as Western blot, immunocytochemistry, immunoprecipitation, mass spectrometry, ELISA, etc.
  • immunohistochemistry may be used to determine the level of proteins from particular genes of the gene panel in cells of a tissue sample of interest.
  • antibodies or aptamers directed to the protein of interest may be used to determine its level. Methods for generating such antibodies or aptamers are well known to the person skilled in the art.
  • the antibody or aptamers used in such methods may be conjugated to a label, the label forming part of a detection agent. Such methods are well known in the art.
  • the expression level of each member of the gene panel may be determined with reference to a reference or control from e.g. chemosensitive TNBC cell(s).
  • a gene may be considered to be differentially expressed in a sample from a subject as compared to a control reference value e.g. from a non-tumour sample from a subject or group of subjects where the gene is expressed at a level which is significantly increased or significantly decreased compared to the control cell/reference value.
  • a reference level of a particular gene may be an absolute or relative amount or concentration of the gene, a presence or absence of the gene product, a range or amount of concentration of the gene product, a mean amount of the gene or gene product, and/or a median amount of or concentration of the gene or gene product.
  • a “reference level” can also be a “standard curve reference level” based on a level of one or more of the genes determined from a population and plotted on appropriate axes to produce a reference curve. The reference curve may be tailored to particular populations of subjects, with reference levels varying with, for example, age group.
  • a standard curve reference level may be determined from a group of reference levels from a group of subjects having a particular disease using statistical analysis, such as univariate or multivariate regression analysis, logistic regression analysis, linear regression analysis, etc. of the levels of such genes/biomarkers in samples from the group.
  • Such reference levels may be adjusted to specific techniques used to measure levels of gene expression, where the gene expression levels may differ based on the specific technique that is used.
  • the sample may be defined as positive for the gene signature, i.e.
  • the demonstration that at least 10 genes of the gene panel have significantly different levels of expression relative to reference levels in control cells for the corresponding genes is indicative that the sample is from TNBC cells which are resistant to chemotherapy.
  • the presence of 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, or 19 or more of the genes of the gene panel being expressed at an significantly different level compared to the reference levels in control cells is indicative of the sample being a sample of TNBC cells which are chemotherapy resistant.
  • genes of the gene panel may have significantly higher expression in TNBC cells from patients with residual disease compared to the expression of these genes in TNBC cells from patients with pCR.
  • Other genes may have significantly lower expression in TNBC cells from patients with residual disease compared to the expression of these genes in TNBC cells from patients with pCR.
  • the gene panel comprises one or more, for example four, or more of the genes of the group consisting of RBFOX2, DST, RCAN1, c9orf3, ACTA2, LY6E, PRNP, TIMP3, CD63, ACTN1, STOM, COL6A1, DSC3, and AMIGO2
  • expression of said one or more of said genes at a significantly higher level than in control cells is indicative of the sample being a sample of TNBC cells which are chemotherapy resistant.
  • the gene panel comprises one or more of the genes of the group consisting of ITGB1 , S100B, CTNNAL1, IFI16, NFIB, and SFRP1
  • expression of said one or more of said genes at a significantly lower level than in control cells is indicative of the sample being a sample of TNBC cells which are chemotherapy resistant.
  • the relative expression of each of the genes of the gene panel compared to reference expression levels of these genes from a control is combined to produce a compound gene signature score
  • each of the genes which may be used in the gene panels in the methods of the invention have been assessed for predictive power in relation to the overall gene signature, with the genes ranked in order of influence on the gene signature performance (see Figure 4C).
  • Each of the genes of the 20 gene panel comprising ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1 , SFRP1, STOM, COL6A1, DSC3, and AMIGO2 has thus been assigned a Predictive Score indicative of its influence on the performance of the 20 gene signature, with a higher value indicative of greater reduction in performance of the gene signature upon the exclusion of that gene from the gene signature.
  • the ranking of each gene in the gene panel may be taken into account in determining the overall compound expression score for the gene panel to determine the likelihood of resistance to chemotherapy in TNBC cells.
  • the compound expression score is calculated employing a weighting of the expression levels of the genes of the gene panel according to the ranking of the genes of the gene panel.
  • the ranking may be the rank position, in which a ranking of “1” is indicative that the gene has the most negative impact on performance when removed with rankings of higher numbers indicating less impact on performance when removed from the gene signature.
  • the genes of a gene signature of 20 genes may be ranked from 1 to 20, with the gene having the greatest positive effect on the predictive power of the gene signature having rank position 1 , the gene with the second highest positive effect on the predictive power of the gene signature having rank position 2, etc.
  • any formula used to determine the compound expression score may take into account the ranking score, such as the ranking scores as listed for the 20 gene signature in Figure 4C.
  • the higher the ranking score of a gene the greater the impact on performance on the gene signature when that gene is removed from the gene signature
  • the ranking score may change depending on the total number of genes in a gene panel.
  • the combined expression level as appropriately weighted according to ranking score, may thus be used to determine the final gene signature score.
  • genes included in the gene signature may thus carry unequal weight in the determination of the likelihood of resistance to chemotherapy in TNBC cells. Accordingly, the weighting/rank of each gene in the score may be taken into account in the calculation of the overall gene signature score.
  • the gene signature score may be determined using any suitable weighting formula.
  • a suitable weighting formula may make use of regression analysis.
  • Any suitable predictive package may be used.
  • one such predictive package which may be used is the caret R package. In summary it takes the absolute value of the final coefficients and ranks them based on their importance in the model.
  • the main function is varlmp() which is described as a generic method for calculating variable importance for objects produced by “train” and method specific methods (https77www.rdocumentation.Org/packages/caret/versions/6.0-90/topics/varlmp) . It can be applied to multiple models including linear, random forrest and glmnet where it calculates the importance of the outputs and ranks them. In the case of glmnet the outputs are coefficients.
  • the varlmp function in the caret package may be used to rank the importance of genes of the gene signature.
  • the compound gene signature score may be compared to a reference value which may be derived from, for example, a training set of patent data in which, for example, the threshold is established to indicate a total score which is indicative of whether or not the cells of the sample are chemotherapy resistant.
  • the performance of a particular gene signature may be determined in any suitable way.
  • the performance is assessed using Area Under the Curve (AUC).
  • AUC refers to the area under the curve of a Receiver Operating Characteristic (ROC) curve.
  • ROC Receiver Operating Characteristic
  • AUC is typically measured as a value between 0 and 1 where an AUC of 0 is indicative that the model’s predictions are 100% wrong and an AUC of 1 is indicative that the model’s predictions are 100% correct.
  • AUC may also be presented on a scale of 0% to 100%, with an AUC of 0% is indicative that the model’s predictions are 100% wrong and an AUC of 100% is indicative that the model’s predictions are 100%
  • the AUC associated with a gene panel of the invention is greater than 0.800.
  • the AUC associated with a gene panel of the invention is greater than 0.830, for example greater than 0.840, such as greater than 0.850, greater than 0.860, greater than 0.870, such as greater than 0.880, greater than 0.890 or greater than 0.900.
  • chemoresistant TNBC category other than by determining AUC and use of Logistic LASSO regression.
  • Other methods which could be used include classification and regression trees, Random Forests, Multivariate Adaptive Regression Splines, Support vector machines and Decision Tree etc.
  • the methods of the invention enable the determination as to whether a patient’s TNBC is likely to be resistant to a chemotherapy treatment.
  • the chemotherapy comprises one, two, three or more of the group consisting of anthracyclines (for example Adriamycin (doxorubicin)), alkylating agents (for example Cyclophosphamide), taxanes (for example taxol (Docetaxel)), or antimetabolites (such as fluorouracil (5-Fll).
  • anthracyclines for example Adriamycin (doxorubicin)
  • alkylating agents for example Cyclophosphamide
  • taxanes for example taxol (Docetaxel)
  • antimetabolites such as fluorouracil (5-Fll).
  • the chemotherapy treatment is a combined chemotherapy treatment regimen which comprises or consists of an anthracycline and an alkylating agent.
  • the combined chemotherapy treatment regimen consists of Adriamycin (doxorubicin) and cyclophosphamide.
  • the combined chemotherapy treatment regimen consists of Adriamycin (doxorubicin), cyclophosphamide, and paclitaxel.
  • the chemotherapy treatment is a combined chemotherapy treatment regimen which comprises or consists of an anthracycline, an alkylating agent, and an antimetabolite.
  • the combined chemotherapy treatment regimen comprises or consists of 5-Fll, Adriamycin, and Cyclophosphamide.
  • the combined chemotherapy treatment regimen consists of paclitaxel, 5-Fll, Adriamycin, and Cyclophosphamide,
  • Figure 1 Single-cell transcriptomic analysis reveals cell populations associated with TNBC aggressiveness.
  • FIG. 1 Distinct EMT-related pathways pre-exist to confer chemoresistance in TNBC.
  • the dot plot shows the enrichment score (NES) of the top transcription factor binding motifs identified by iRegulon from the promoter regions of the EMT defining signatures genes of the aggressive subpopulations.
  • the criteria set for motif enrichment analysis were as follows: identity between orthologous genes > 0.0, FDR on motif similarity ⁇ 0.001, and TF motifs with normalized enrichment score (NES) > 3.
  • the ranking option for Motif collection was set to 10 K (9,713 PWMs) and a putative regulatory region of 20 kb centered around TSS was selected for the analysis.
  • FIG. 3 Chemoresistance signature genes are enriched for EMT-related processes and tumors of mesenchymal features.
  • TGF-b treated mammary epithelial cells
  • the time point labeled with dO are untreated and d1-d20 are different EMT timepoints treated with TGF-b at day 1 to day20.
  • a cluster of genes represented by C is on the right side of the heatmap representing EMT induction time-specific genes.
  • FIG. 4 A 20-gene pane! can accurately predict chemotherapy response in TNBC patients.
  • the plots are showing a cross-validation curve (red dotted line) along with mean binomial deviance against a range of Log(A).
  • the vertical dotted lines represent lambda, min and lambda.1se. This panel shows the changes of partial likelihood deviance with A values.
  • the 20 genes were selected according to the most regularized model such that the error is within one standard error of the minimum.
  • Panel I shows ROC curves were generated for gene panels after removal of one to five of the top ranked genes (top panel); and after removal of one to five of the lower ranked genes (bottom panel).
  • each ROC curve is/are the gene(s) removed from the gene panel for the assessment; e.g, in the top panel, the first ROC curve is for a 19 gene gene panel consisting of the genes shown in the list on the left excluding ITGB1, the second ROC curve is for an 18 gene gene panel consisting of the genes shown in the list on the left excluding ITGB1 and RBFOX2, etc.
  • FIG. 5 A) scRNA-seq data analysis of pre-treated TNBC patients identified a similar subpopulation in two independent TNBC datasets. The genes defining each cluster were annotated against the cellmarker database and cell-type identities were assigned to each cluster. B) Dot plot showing top 5 ranked genes defining each cluster of each TNBC scRNA-seq dataset. C) Expression of metastasis-associated genes in pre-treated TNBC patients scRNA-seq datasets.
  • Metastasis signature of 49 genes was used by Lawson et al, 2015 and their average expression was plotted across each cluster in both datasets, D) Chemoresistance signature of 143 genes was used from Balko et al, 2012 and their average expression was plotted across each cell types in both datasets.
  • FIG. 6 A) Violin plot showing average expression of signature genes across clusters of both TNBC datasets. The average expression of all 101 signature genes was plotted in all three primary TNBC scRNA-seq datasets and confirmed their activation in similar subpopulations of basal epithelial cells.
  • the regulatory networks were generated by the iRegulon (version 1.3) package and the TF-gene network was plotted using Cytoscape (version: 3.8).
  • the genes in the hexagonal box indicate transcription factors (node in the network), and their regulatory targets are shown in the oval shape.
  • the line shows the connection of each TF (node) with their target genes (edges) in the network.
  • Figure 7 lists genes for use in the gene signatures of the invention.
  • the inventors first goal was to uncover cellular diversity in TNBC and assess the presence of cells exhibiting chemoresistance and metastasis-like features (Fig.lA). For this, the inventors analyzed the single-cell RNA-seq profile of more than 10000 cells (10,779 cells) derived from 33 TNBC patients, including patients treated with neoadjuvant chemotherapy (NAC) from four different studies 14115131132 . All four datasets were analyzed with the uniform parameters in the Seurat package (version 4.0.1), where cell pre-processing was done, and cells were removed having unique feature counts >2,500 or ⁇ 200 and had mitochondrial reads >5%.
  • Seurat package version 4.0.1
  • the inventors analysed cell subpopulations for the enrichment of metastasis and treatment response associated with distinct gene expression signatures. For this, the inventors used 49 metastasis signature genes identified in patient-derived murine xenograft models of TNBC and which stratify high versus low metastatic burden tumors 34 .
  • metastasis signature genes identified in patient-derived murine xenograft models of TNBC and which stratify high versus low metastatic burden tumors 34 .
  • therapy resistance signature genes the inventors used 143 genes (from Cluster AHI) which were earlier found to be highly correlated with high Ki-67 expression and achieved the highest chemotherapy resistance scores in basal-like breast cancer subtypes 35 .
  • the set of genes that shows distinct expression in the identified cluster was compared between all three scRNA-seq datasets to identify reproducible signature genes across the same cell types in different studies. These reproducible genes were future analyses for biological functional analysis using the ShinyGO gene ontology enrichment analysis tool.
  • the expression profiling of signature genes was investigated across TCGA breast cancer subtypes to ensure these signature genes are TNBC specific. Additionally, these genes were also screened in large breast cancer cohorts in EMT-High and EMT-Low groups.
  • the inventors first retrieved the TCGA mRNA expression z-scores of breast cancer tumors from cBioPortal 36 using the ‘cgdsr’ R package (version 1.3.0) and extracted TNBC tumors based on the ER, PR and Her2 status.
  • the EMT scores were calculated for each sample by subtracting the average expression z-scores of the 5 ‘epithelial’ markers (ESRP1 , ESRP2, OVOL1 , OVOL2, and CLDN3) from the average expression z-scores of the 7 mesenchymal’ markers (ZEB1 , ZEB2, SNAI2, TWIST 1 , TWIST2, VIM and FN1).
  • the tumor samples were then classified as EMT-High (defined by EMT scores > highest 1/3) and EMT-Low (defined by EMT scores ⁇ lowest 1/3) based on the calculated EMT scores 37 .
  • the expression levels of these markers were utilized and spearman correlation between seven mesenchymal and five epithelial marker genes was calculated using the ‘corrplot’ R package (version 0.84) with a significant coefficient (95% confidence level; P-value ⁇ 0.05), to select markers which separated patients with greater distinction.
  • the ‘Pheatmap’ R package (version 1.0.12) was used to construct the heatmap of expression levels of the twelve markers in the EMT-High and EMT-Low groups defined above. Finally, the average expression profile of identified TFs in these EMT group's breast cancer cohorts was plotted using the ggplot2 package.
  • RNA classifiers for predicting pathological complete response (pCR) or residual disease (RD)
  • the inventors For developing predictive models for stratifying TNBC tumours into pathological complete response (pCR) or residual disease (RD) groups, the inventors used four publicly available microarray-based gene expression datasets GSE25055 38 , GSE25065 38 , GSE20194 39 , and GSE20271 40 which includes TNBC tumours.
  • the inventors extracted normalised expression levels of 101 EMT signature genes from two studies of 180 patients to train the model.
  • the inventors used 130 TNBC patients from the remaining two datasets. The treatment details of all the patients were available and hence used as a class for binary classification.
  • the inventors used Lasso and Elastic-Net Regularized Generalized Linear Models in glmnet for best fitting the model with 10-fold cross- validation to remove bias. In this, the inventors first ranked the features to select the minimum set of features with maximum predictive power for pCR and RD. The inventors next tested the model performance on independent validation datasets of 130 TNBC patients.
  • Fig. 1A Single-cell expression profiles of more than 10000 cells from 33 tumors of four independent TNBC studies. Further analysis of these datasets showed heterogeneous populations of immune, luminal, progenitor, and basal epithelial as highly enriched cell types which are defined by consistent expression of cell-type markers (Fig. 1 B-C; Fig. 6A). The inventors next investigated whether any of the identified subpopulations have enrichment of signature genes associated with aggressive clinical behaviour 34 ’ 35 .
  • Fig. 2B profiling of signature genes in these patients groups revealed a significantly higher expression (p ⁇ 2.22e-16) in clusters from chemoresistant patients.
  • Fig. 2C Given the higher expression of signature genes in chemoresistance patients, the inventors further identified genes that showed transcriptional reprogramming upon chemotherapy treatment. This results in a total of 24 genes which were showing upregulation upon chemotherapy treatment in chemoresistance patients and have no expression levels in chemosensitive patients (Fig. 2C).
  • the inventors were next interested in uncovering the transcription factor (TF) network potentially controlling the expression of signature genes underlying aggressive cell subpopulations.
  • the iRegulon tool was used to examine the enrichment of TF binding motifs at genomic loci encoding of the inventors’ signature genes.
  • the analysis identified a strong enrichment of 26 transcription factors (Fig 2D) with SRF, MYLK and EP300 being the top enriched TFs that potentially regulate 64, 59 and 51 targets respectively among the signature genes (Fig 6D).
  • the top 5 TFs SRF, MYLK, EP300, ELF1 and NFIC
  • RNA- seq 75 TNBC patients before (pre), during (mid) and after treatment with NAC (AC Adriamycin (Doxorubicin) + Cyclophosphamide, T Taxol (Docetaxel), H Herceptin (Trastuzumab) with known treatment outcome, namely pCR and RD status 42 .
  • NAC AC Adriamycin (Doxorubicin) + Cyclophosphamide, T Taxol (Docetaxel), H Herceptin (Trastuzumab)
  • the inventors’ signature genes showed significantly higher expression levels in patients with residual disease (RD) (Fig. 2F).
  • the cell subpopulations associated with TNBC aggressiveness are enriched with EMT features
  • a gene ontology (GO) analysis shows enrichment for EMT-like processes such as wound healing, extracellular matrix organization and cell migration (Fig. 3A). This is in line with previous observations where EMT was proposed to underlie metastasis and therapy resistance in TNBC. Furthermore, these genes were significantly highly expressed only in EMT-like subpopulations in all primary TNBC scRNA-seq datasets (Fig. 6A-B). Further analysis of a large TCGA breast cancer cohort showed that these genes are expressed at significantly higher levels in TNBC tumours compared to Luminal and Her2 breast cancers (Fig. 3B).
  • the inventors next classified TCGA TNBC cancer samples into EMT-low and EMT- High groups based on the expression of 12 established markers (Fig. 3C) and then investigated the expression of the inventors’ signature genes. Interestingly, the inventors’ signature genes were observed to be significantly much higher expressed in patients of EMT-High groups compared to EMT-Low groups (Fig. 3D).
  • HMLEs immortalized human mammary epithelial cells
  • transcriptome sequencing performed at different EMT time points representing early, mid and late EMT.
  • expression analysis showed transcriptional induction of almost all signature genes (96 out of 101) during EMT, confirming that these genes are truly associated with mammary EMT (Fig. 3E).
  • distinct subsets of these genes showed induction at different time points during EMT, suggesting a potential division of labour during the cascade of EMT Progression.
  • the pathological complete response (pCR) has been reported to be the key surrogate marker for long-term prognoses such as disease-free survival and overall survival in patients with triple-negative breast cancer 5 ’ 43 ’ 44 .
  • Previous research attempting to reveal predictors of pCR lack in achieving necessary predictive values for clinical utility for TNBC due to multiple factors such as small sample sizes, insufficient validation data or inapplicability to TNBC 38 ' 40 ' 45 - 52 .
  • ER estrogen receptor
  • HER2+ tumors no such tests exist in clinic to stratify pCR and residual disease (RD) in response to chemotherapy in TNBC.
  • the inventors attempted to develop a predictive model which can predict pCR or RD to standard Neoadjuvant chemotherapy (NAC) in TNBC using expression of the inventors’ 101 signature genes in 307 TNBC patients from four breast cancer datasets (GSE25055, GSE25065, GSE20271 , GSE20194). These patients were treated with standard NAC incorporating taxane, anthracycline, and cyclophosphamide (AC-T), or additionally 5-fluorouracil (T-FAC), and their treatment outcome details (pCR and RD) were known.
  • NAC Neoadjuvant chemotherapy
  • ROC curves were generated for gene panels after removal of one to five of the top ranked genes (top panel); and after removal of one to five of the lower ranked genes (bottom panel).
  • each ROC curve is/are the gene(s) removed from the gene panel for the assessment; e.g, in the top panel, the first ROC curve is for a 19 gene gene panel consisting of the genes shown in the list on the left excluding ITGB1 , the second ROC curve is for an 18 gene gene panel consisting of the genes shown in the list on the left excluding ITGB1 and RBFOX2, etc.
  • the results show that gene panels in which one to five of the of the five genes having greatest effect on the performance of the gene panel (i.e. one to five of ITGB1 , RBFOX2, DST, RCAN1 , c9orf3) are not included, the overall performance of the gene signature is still significantly superior to that of known gene signatures.
  • the lower panel demonstrates that removal of the five genes having the least effect on the performance of the gene panel does not significantly reduce the performance of the gene panel.
  • the aggressiveness in Triple-negative breast cancer arises due to the extensive intra-tumoral heterogeneity and presence of small subpopulations associated with chemoresistance and metastasis features.
  • the inventors provided new insights into the contribution of cellular diversity in disease aggressiveness using integrated single-cell transcriptome analysis of more than 10000 cells from 33 TNBC patients.
  • the inventors’ analysis of treatment-naive primary TNBC tumours 14 ’ 31 32 revealed the existence of subpopulations associated with chemoresistance and metastasis.
  • the inventors further showed that these subpopulations exhibit shared transcriptional profiles across all primary tumors of pre-treated TNBC patients (named as signature genes).
  • signature genes differential levels of the same signature genes mark chemotherapy resistance TNBC tumors 15 .
  • the inventors findings imply that the pre-existing chemoresistance phenotype is defined by a set of EMT-associated genes, which undergo transcriptional reprogramming in response to chemotherapy treatment and confer resistance in TNBC. This raises the possibility that targeting EMT signaling may re-sensitize the tumour cells to chemotherapy and will open new avenues in designing therapeutic strategies to overcome chemoresistance in TNBC 69 .
  • the discovered signature genes defining chemoresistance phenotype are highly robust and detected in bulk RNA-seq data of patients with residual disease and show greater differences in levels in TNBC tumors with high EMT status. In addition, with very few exceptions, almost all of these genes are upregulated at different time points during TGF ⁇ -induced EMT in human mammary epithelial cells, suggesting a division of labour and the critical role of these genes in this process. These results are consistent with previous findings where expression of mesenchymal markers was highly evident in TNBC 70 ' 77 .
  • Chemoresistance is a major challenge in TNBC management and no clinical signatures are available for this in clinical settings 24 ' 26 ' 78 - 80 .
  • the inventors’ 20 gene signature that outperformed published signatures for chemotherapy prediction in TNBC 24 ’ 26 ’ 78 ’ 80 ' 82 , holds strong clinical potential, notably, as they are derived from chemoresistant cell populations, highly reproducible across various single-cell and bulk transcriptomes, validated in more than 300 TNBC patients and strongly associated with poor survival. Predicting which patients will have pCR or RD using the inventors’ gene panel will provide an immense opportunity for clinicians to improve or consider alternative treatment plans and prevent loss of time due to unnecessary treatments and toxicity in TNBC.
  • the inventors’ study demonstrated the power of the single-cell analysis methods in uncovering mechanisms underlying aggressive clinical behaviour in TNBC particularly chemoresistance.
  • the inventors’ study shows that chemoresistance cells pre-exit in the treatment of naive TNBC tumors and confer resistance phenotype via activated EMT programs when exposed to chemotherapy.
  • the inventors’ highly accurate 20 gene signature model will impact the early evaluation of the effectiveness of systemic therapy and ensure long-term benefits in TNBC including later-stage tumors.
  • a set of these signature genes show better sensitivity against a list of anticancer drugs, opening possibilities of alternate therapies.
  • TGF-beta plays a vital role in triple-negative breast cancer
  • EMT Epithelial-to-mesenchymal transition

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Abstract

La présente invention concerne un procédé de prédiction de la résistance à la chimiothérapie chez un sujet souffrant un cancer du sein triple négatif (CSTN), ledit procédé comprenant les étapes suivantes : (a) prélèvement d'un échantillon biologique sur ledit sujet ; et (b) établissement des niveaux d'expression de chaque membre d'un panel génétique, ledit panel génétique comprenant au moins 10 gènes choisis dans le groupe constitué de ITGB1, RBFOX2, DST, RCAN1, c9orf3, ACTA2, S100B, LY6E, CTNNAL1, PRNP, TIMP3, CD63, IFI16, NFIB, ACTN1, SFRP1, STOM, COL6A1, DSC3, et AMIGO2, dans ledit échantillon biologique ; les niveaux d'expression établis de ces gènes dudit panel génétique sont utilisés pour déterminer la probabilité de résistance à ladite chimiothérapie, ledit panel génétique comprenant un, deux, trois, quatre, cinq ou six des gènes ITGB1, RBFOX2, DST, RCAN1, c9orf3 et ACTA2.
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