TEAD family transcription factors

Abstract

The present disclosure relates to a transcriptional signature obtained on a gene set including any of gene sets 220-249 from the TEAD-500 signature and any of gene sets 210-233 from gene set (2) and its use for determining TEAD activity in cancer. The transcriptional signature is useful for a variety of applications, including predicting the likelihood that a subject will respond to TEAD pathway inhibitor treatment, selecting patients for clinical trials, assessing the efficacy of TEAD pathway inhibitor molecules, and predicting survival, response and benefit of anti-TEAD pathway treatment.

Description

[Background technology]

[0001] In normal tissues, the transcription cofactors YAP1 and WWTR1 bind to transcription factors of the TEAD family (TEAD1, TEAD2, TEAD3, and TEAD4), collectively referred to as "TEADs," to form an active protein complex. This complex recognizes and binds to specific sequence motifs in the promoters of target genes, initiating or inhibiting their transcription. Thus, cell proliferation, survival, plasticity, and migration, which are necessary for normal biological processes such as organ growth and wound healing, are regulated by TEADs (see Totaro A, et al. Nat Cell Biol. 2018;20(8):888-899. doi:10.1038 / s41556-018-0142-z). In intact adult mammalian tissues, YAP1 and WWWTR1 are generally phosphorylated by kinases of the Hippo pathway, which result in their retention in the cytoplasm and degradation. In this case, there are no active transcription complexes in the nucleus (Totaro A, et al. Nat Cell Biol 2018. Aug;20(8):888-899).

[0002] Over the past few years, YAP1, WWTR1, their TEAD partners, and their upstream regulators have received increasing interest in cancer research, especially the tumor suppressor pathway known as the Hippo pathway (Zhang N et al. Dev Cell. 2010 Jul 20;19(1):27-38. doi:10.1016 / j.devcel.2010.06.015; Lu L,et al. Proc Natl Acad Sci USA. 2010;107(4):1437-42. doi:10.1073 / pnas.0911427107; Nishio M,et al. Proc Natl Acad Sci USA. 2016;113(1):E71-80. doi:10.1073 / pnas.1517188113; Liu-Chittenden Y,et al., Genes Dev. 2012;26(12):1300-5.doi:10.1101 / gad.192856.112). Mutations or physiological dysregulation of YAP1, WWWTR1, or any of their upstream regulators can result in insufficient phosphorylation and degradation of these proteins. In the absence of phosphorylation and degradation, transcriptional cofactors enter the nucleus, bind to TEADs, and initiate oncogenic transcription. Increased activity of any component of the complex can increase or decrease transcription of downstream effectors (TEAD-dependent transcription). Some of the regulated genes are direct targets of TEADs that have TEAD-binding motifs (Zanconato, F. et al. Nat Cell Biol 2015:17:1218-1227). Others are indirectly regulated. Thus, the Hippo-YAP1 / WWWTR1-TEAD pathway (hereinafter, the "TEAD pathway") can directly induce tumor formation or render existing tumors resistant to targeted therapies (Reggiani Fet al. Biochim Biophys Acta Rev Cancer. 2020:1873(1):188341. doi:10.1016 / j.bbcan.2020.188341; Kurppa KJ et al. Cancer Cell. 2020;37(1):104-122.e12. doi:10.1016 / j.ccell.2019.12.006).

[0003] For example, mutations and deletions of Hippo genes such as LATS2 and NF2 account for the majority of malignant mesothelioma tumors (Sekido Y et al. Cancers (Basel). 2018; 10(4): 90. doi: 10.3390 / cancers10040090). YAP1 is found to be focally amplified and overexpressed at both RNA and protein levels in several types of tumors, particularly cervical cancer (Zanconato F et al. Cancer Cell. 2016; 29(6): 783-803. doi: 10.1016 / j.ccell.2016.05.005). Activation of TEAD-dependent transcription can confer a mesenchymal stem cell-like phenotype to tumor cells, as notorious stem cell transcription factors such as SOX2, OCT3 / 4, NANOG and MYc are also regulated by TEAD (Bora-Singhal N et al. Stem Cells. 2015;33(6):1705-18. doi:10.1002 / stem.1993). Summary of the Invention [Means for solving the problem]

[0004] To measure the transcriptional activity of such complexes, estimate the proportion of cases in which TEAD-dependent transcription may be involved in tumor development or evolution, and evaluate the target binding and pharmacodynamics of novel TEAD pathway inhibitors, we identified common and several tissue-specific transcriptional signatures that contain genes that are upregulated (positive effectors) or downregulated (negative effectors) when the YAP1 / WWTR1-TEAD complex ("TEAD complex") is activated. Conversely, when the complex is inhibited, positive effectors are downregulated and negative effectors are upregulated.

[0005] The general signature, referred to herein as the "TEAD-500" gene list, is derived from the top 500 differentially expressed genes with approximately equal proportions of positive and negative downstream effectors. More precisely, the TEAD-500 gene list includes 482 genes, up to about 90% of these genes. The general signature is robustly applicable to all types of cancer. It robustly measures TEAD activity, classifies samples, and predicts their response to TEAD pathway modulation. The TEAD-500 can be used to calculate a TEAD score, as described in detail below.

[0006] Additional signatures generated by discriminant function analysis (SPSS Statistics, IBM, Armonk, New York) are identified that are applicable to specific types of cancer (indications) and serve to classify TEAD-active vs. TEAD-inactive tumors as defined by TEAD-500, with accuracy approaching or reaching 100% very often. Specific short signatures are associated with specific types of cancer. The shorter signatures include optimal and possibly minimal discriminant functions. The discriminant function includes a gene set and constant coefficients of the discriminant function. Each gene of the gene set is associated with a coefficient. For a given signature, the minimal discriminant function includes genes that are essential for 100% accurate classification. Missing data portions introduce some degree of inaccuracy. For a given signature, the optimal discriminant function uses more predictors but achieves 100% accurate classification of patients even when some data are missing. The additional signatures, the minimum and the optimal discriminant function can be used to calculate the discriminant score (S) according to one method, as detailed below. Alternatively, the optimal discriminant function can be used to calculate the discriminant score (DS) according to another method, as detailed below.

[0007] The identified signatures can be utilized in a variety of clinical and research-related utilities, including, but not limited to, monitoring the activity of TEAD complexes and evaluating the pharmacodynamics of novel inhibitors of the TEAD pathway in vitro or in vivo, diagnosing tumors whose development or evolution is due to activation of the TEAD complex, recruiting patients with active TEAD into clinical trials designed to evaluate TEAD pathway inhibitors, predicting survival, response, and benefit of anti-TEAD pathway treatments alone or in combination with other therapies, estimating the proportion of cases within a cohort for each cancer indication or subtype in which TEAD-dependent transcription may be involved in tumor development or evolution to treatment resistance, and measuring TEAD-dependent transcription in any tissue, under any condition, whether pathological or not, in the laboratory or clinic.

[0008] [Abbreviation] ACC Adrenal cortical carcinoma BID 2 times a day BLCA Bladder urothelial carcinoma BRCA breast invasive cancer CCLE Cancer Cell Line Encyclopedia CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangiocellular carcinoma CMS1 Consensus molecular subtype 1 of colorectal cancer CMS2 Consensus molecular subtype 2 of colorectal cancer CMS3 Consensus molecular subtype 3 of colorectal cancer CMS4 Consensus molecular subtype 4 of colorectal cancer COAD colon adenocarcinoma Relative TEAD activity score based on ddeR effector rank (difference from control) deR Absolute TEAD activity score based on effector rank dgR absolute TEAD activity score based on whole transcriptome rank DLBC Lymphoid neoplasm Diffuse large B-cell lymphoma DMSO Dimethyl sulfoxide DN dominant negative ESCA Esophageal cancer GBM Glioblastoma multiforme GEO Gene Expression Omnibus GTEx Genotype Tissue Expression Project HNSC head and neck squamous cell carcinoma HuGO Human Genome Organization KICH Kidney chromophobe KIRC Renal clear cell carcinoma KIRP Kidney Papillary Cell Carcinoma LGG Brain low-grade glioma LIHC Liver Hepatocellular Carcinoma LUAD lung adenocarcinoma LUSC Lung Squamous Cell Carcinoma Meso Mesothelioma NA Not applicable NCBI National Center for Biotechnology Information OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and Paraganglioma PRAD Prostate adenocarcinoma QD Once a day READ Rectal Adenocarcinoma RT-qPCR Reverse transcription quantitative polymerase chain reaction SARC sarcoma SCID Severe Combined Immunodeficiency SKCM cutaneous melanoma STAD gastric adenocarcinoma TCGA The Cancer Genome Atlas TGCT Testicular Germ Cell Tumor THCA Thyroid cancer THYM Thymoma UCEC endometrial cancer UCS uterine carcinosarcoma [Brief description of the drawings]

[0009] [Figure 1] Comparison of TEAD-500 deR and dgR scores in experimental samples (N=1176). [Diagram 2]Robustness of TEAD-500 scores. Two TEAD-500 sublists, each with 10% of genes randomly removed, yielded very similar TEAD activity scores for the 1112 experimental samples from our collection. [Diagram 3] Figure 3A-C: Dose-dependent regression of TEAD-500 score (A) and inhibition of CCN1 marker of TEAD activity at RNA (B) and protein (C) levels by a benchmark chemical inhibitor of TEAD (K-975) in mesothelioma NCI-H226 cells grown in vitro. [Figure 4] Figure 4A-B: Validation of the TEAD-500 signature in vivo. MSTO-211H cells transfected with doxycycline-inducible TEAD1-DN construct were transplanted into SCID mice. Upon induction of TEAD1-DN transcription, xenografts began to regress (A), while a significant decrease in TEAD-500 score was observed within 96 hours (B). [Diagram 5] Figure 5A-B shows that SCID mice implanted with malignant mesothelioma parental NCI-H226 cells were treated for 17 consecutive days with 200 mg / kg BID of the benchmark TEAD-inhibitor K-975 (lower grey line-square dots). The 100 mg / kg BID dose remained active and tumors nearly stagnated; dotted line), whereas the 30 mg / kg BID dose was inactive (upper light grey line, black dots). (A). Approximately 30-40% inhibition of YAP1 / TEAD-dependent transcription at 24 h (dark line) is sufficient for tumor regression in this model (B). [Figure 6] The TEAD-500 score distribution shifts towards higher values ​​in tumors (TCGA primary tumors) compared to normal tissues (GTEx or TCGA tumor adjacent normal tissues). Primary tumors tend to score higher than adjacent normal tissues. Recurrent and metastatic tumors score higher than primary tumors. Thus, YAP1 / TEAD-dependent transcription increases with tumorigenesis and tumor progression. Cell lines (CCLE) achieve much higher scores, with mesothelioma cell lines being among the top scorers. [Figure 7]Figure 7A-B. TEAD-500 score distribution means cervical cancers with YAP1 amplification compared to homologous tumors with diploid YAP1 (A) (TCGA CESC cohort) and malignant mesothelioma tumors compared to all other TCGA primary tumors (TCGA MESO cohort) (B). [Figure 8] Overall survival of malignant mesothelioma patients (TCGA MESO cohort) whose tumors scored high or low for TEAD-dependent transcription as measured by TEAD-500. [Figure 9] Overall survival of patients with all pooled TCGA cancer types in association with the level of TEAD-dependent transcription as scored by TEAD-500. [Figure 10] Optimal discrimination of mesothelioma samples with active or inactive TEAD pathway. [Figure 11] Minimal discrimination of mesothelioma samples with active or inactive TEAD pathway. [Figure 12] Comparison of optimal and minimal discriminant scores. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] overview Provided herein are gene signatures (e.g., panels of biomarkers) that are useful in a variety of clinical and diagnostic applications, including, but not limited to, determining whether a subject will respond to a particular treatment, monitoring the activity of the TEAD complex, evaluating the pharmacodynamics of inhibitors of the TEAD pathway in vitro or in vivo, diagnosing tumors whose development or evolution is due to activation of the TEAD complex, recruiting patients with active TEAD into clinical trials designed to evaluate TEAD pathway inhibitors, predicting survival, response, and benefit of anti-TEAD pathway treatments, alone or in combination with other treatments, estimating the proportion of cases within a cohort in which TEAD-dependent transcription may be involved in tumor development or evolution to treatment resistance, and measuring TEAD-dependent transcription in any tissue, under any conditions, whether pathological or not, in the laboratory or clinic.

[0011] To arrive at the gene signature, the activity of TEAD complexes in various types of single tissue samples was assessed by observing the relative expression of the top 500 most significant differentially expressed genes affected by reported experimental modulation of the TEAD complexes. The TEAD-500 signature was derived from the top 500 most significant differentially expressed genes by dropping out redundant or irrelevant genes.

[0012] We refer to this panel as "TEAD-500" herein, which actually consists of about 482 genes. Up to 10% of the genes can be randomly removed from TEAD-500 without significantly compromising the accuracy of the results.

[0013] The present disclosure relates to a panel of nucleic acid biomarkers having N biomarkers, where N is an integer of at least 160 and no greater than about 482, and the N biomarkers include nucleic acids capable of detecting the expression levels of any of about 80 to about 249 genes in gene set (1) (positive effector genes) and any of 80 to 233 genes in gene set (2) (negative effector genes).

[0014] The transcriptional signatures described herein can be obtained by measuring the expression levels of any of 220-249 genes in gene set (1) (positive effector genes) and any of 210-233 genes in gene set (2) (negative effector genes), as listed below. Such transcriptional signatures or related gene sets are herein termed "TEAD-signatures."

[0015] Embodiment 1. 249 positive effector genes (or gene set (1)) in TEAD-500: <h2 style=";text-align:left;direction:ltr">AASS, ABAT, ACAT2, ADAMTS1, ADM, ADRB2, AMOT, ANXA3, ARHGAP11A, ARHGDIB, AURKB, AVPI1, AXL, AZIN1, B4GALT4, BCAT1, BIRC5, BTG3, C4BPB, CAP2, CAV1 、CAVIN1、CCBE1、CCDC80、CCN1、CCN2、CDC25A、CDC6、CDCA3、CDCA4、CDCA5、C DCA8、CDH4、CDK2、CDK6、CDV3、CENPA、CENPI、CENPM、CENPN、CHRNB1、CHST13、 CKS2、CLDN1、CLIC3、CNN3、COBL、COL8A1、COTL1、CPA4、CRIM1、CRY1、CTH、CX CL1、CYTH3、DAPK1、DCLRE1B、DDAH1、DHCR7、DHFR、DIAPH3、DKK1、DLL1、DONSO N、DUSP14、DUT、EBP、EIF2AK3、EMG1、EPHA2、EPS8L2、ESM1、ETS1、EXO1、EXOS C2、F3、FAHD2A、FAM83D、FANCA、FAT4、FDPS、FEN1、FMR1、FST、FSTL1、FSTL3、G ADD45A、GADD45B、GINS1、GPC6、GPR176、GPRC5A、GPRC5B、GRAMD2B、HASPIN、 HEG1、HEXB、HPS5、HSPB11、IDI1、IGFBP7、IKBIP、IL6、ITGB2、JDP2、JPH2、KPN A2, KRT8, KRT80, LCA5, LHFPL6, LMCD1, LMNB2, LRP8, LRRFIP2, LSM5, LYPD6, LYRM1, MAD2L1, MAP6D1, MATN2, MATN3, MCM10, MCM2, MCM5, MDC1, METRNL, MIC B, MID1, MRPL33, MSRB3, MVD, MXRA7, NCAPD3, NEDD4, NEDD4L, NEK2, NEXN, NFIB, NNMT, NOC3L, NTN4, NUAK1, NUAK2, NUDCD1, NUP107, NUP37, OGFRL1, OLFML 3、OLR1、OXCT1、PAK2、PCBD1、PCNA、PDLIM2、PDZD2、PEPD、PHLPP1、PKMYT1、P KP2、PKP4、PLCE1、PLEKHA7、PLK2、PLOD2、PPIH、PRPS1、PRPS2、PRSS23、PSG2、PSG6, PSG7, PSG9, PVR, PXMP2, QDPR, QKI, RAB11FIP1, RAB32, RACGAP1, RBM24, RBMS2, RCN2, RFC4, RND3, RNF144B, ROR1, RPS24, SCD5, SCML1, SDC2, SEC 14L1, SGK1, SGMS2, ​​SGTB, SH3RF1, SHCBP1, SKP2, SLC25A23, SLC25A3, SLC38A5, SLC3A2, SLC7A1, SLC7A5, SMPD4, SNAPC1, SNX24, SORT1, SPAG1, SPATA5, STK3, STX11, STXBP6, SUSD2, SUV39H1, SYDE2, TACC3, TAGLN, TEAD1, TEAD4, TENT5B, TGM2, THBS1, TK1, TMEM139, TMEM160, TNFAIP3, TNFRSF12A, TNNC1 , TPM1, TPX2, TRIP13, TSPAN2, TTF2, TUBB6, TUFT1, TYMS, UAP1, UBE2C, UGCG, UHRF1, VKORC1L1, WWC1, WWC2, YAP1, ZBED2, ZDHHC18, ZNF488, ZNF704, and

[0016] Embodiment 2. 233 negative effector genes in TEAD-500 (Gene Set (2)): <h2 style=";text-align:left;direction:ltr">AASDH、ABCA1、ABCC5、ABI3BP、ABLIM3、ACADVL、ACOT11、ACOX2、ACSL5、ADAM 28、AGL、AGPAT4、ALDH3A2、ANKRD12、ANKRD22、ANKRD29、ANKRD42、ANTXR2、A PBB3、ARAP3、ARHGEF2、ASF1A、ATP7A、ATXN1、BCL11B、BHLHE41、BMF、CA2、CA SP1、CBR3、CCNG2、CDC42EP4、CDK1、CEBPB、CELSR3、CLCN3、CLDN4、COL6A1、CO L6A2、CPE、CRABP2、CROT、CSRNP2、CSTA、CTNNBIP1、CTSB、CTSK、CXXC5、CYP1 B1、CYP27C1、DDR1、DEDD2、DHX32、DIAPH2、DSC2、DSG3、DUSP6、DYNC2LI1、ELN 、EPS8L3、ERAP2、FAM102A、FAM117B、FAM83B、FAM89B、FERMT1、FKBP2、FOS、F TH1、FXYD3、GDPD1、GOLGA5、GOLPH3L、GPNMB、GPRC5C、GRB10、GSN、HAS3、HBP1 、HDAC1、HDHD2、HEY1、HOXA5、IFI44、IGSF3、IGSF9、INTS3、IRAK2、IRF9、IRX 5、ITGA2、KCNMA1、KCNMB3、KCNN4、KIFAP3、KLF10、KLF13、KLHL3、KLK11、KRCC 1、KRIT1、KRTDAP、LMTK3、LRP10、LTBP4、LXN、LYPD3、MALL、MANSC1、MAPK13、 MARCKSL1、MFSD1、MFSD5、MGST2、MGST3、MLLT11、MLPH、MMP13、MSX2、MTMR11、 MTMR9、MTSS1、MYO1A、NAGK、NAPELD、NCOA3、NFIL3、NPAS2、NRIP1、OAS1、OA S2、OASL、OFD1、OSBPL7、OTUB2、OVOL1、PAG1、PAK1、PCDHB2、PCDHB9、PCGF3、P CMTD2、PERP、PHF21A、PIK3C2B、PIK3R1、PIK3R2、PIK3R3、PIP4P2、PJA2、PKI A、PLA2G4C、PNRC1、PPP1R11、PRRX2、PTPRE、PYGB、RAC2、RALGPS1、RAPGEFL1、RBM23, RBM45, RBM47, RBP1, REEP6, RGL2, RGS17, RHOC, S100A14, SAMD9, SEC14L2, SECISBP2, SH3PXD2A, SH3TC1, SHROOM2, SLC14A1, SLC1A2, SLC 30A9, SLC35C1, SLC37A2, SLC39A11, SLFN5, SLITRK6, SLK, SMOC1, SNCG, SP1, SPIRE2, SPRY4, SQSTM1, SRD5A3, SSPN, STMN3, STX1A, TBX3, TCF25, TDO2, TET2, TFF1, TLR3, TMC4, TMC7, TMEM140, TMEM144, TMEM45B, TP53INP1, TP63, TPD52L1, TRAPPC6B, TRIB1, TRIB2, TRIM13, TRIM31, TRIM38, TRIOBP, TRIP11, TSC22D1, TSPAN1, TTC17, TTLL3, TUBB3, UBC, ULK1, VAMP8, VGF, VPS52, VSNL1, WDR13, ZCWPW1, ZNF292, ZNF467, ZNF75D, ZSWIM7. ,

[0017] It should be noted that several HuGO-approved gene symbols, including the classical YAP1 activity markers CCN1 (formerly CYR61) and CCN2 (formerly CTGF), have not yet been implemented in The Cancer Genome Atlas (TCGA) but have already been adopted in other datasets.

[0018] In some embodiments, the panel includes nucleic acids capable of detecting N genes, where N is an integer of at least 160 and no more than about 482, and the N genes are genes in the following gene set (1) (positive effector genes): AASS, ABAT, ACAT2, ADAMTS1, ADM, ADRB2, AMOT, ANXA3, ARHGAP11A, ARHGDIB, AURKB, AVPI1, AXL, AZIN1, B4GALT4, BCAT1, BIRC5, BTG3, C4BPB, CAP2, CAV1, CAVIN1, CCBE1, CCDC 80, CCN1, CCN2, CDC25A, CDC6, CDCA3, CDCA4, CDCA5, CDCA8, CDH4, CDK2, CDK6, CDV3, CENPA, CENPI, CENPM, CENPN, CHRNB1, CHST13, CKS2, CLDN1, CLIC3, C NN3, COBL, COL8A1, COTL1, CPA4, CRIM1, CRY1, CTH, CXCL1, CYTH3, DAPK1, DCLRE1B, DDAH1, DHCR7, DHFR, DIAPH3, DKK1, DLL1, DONSON, DUSP14, DUT, EBP, E IF2AK3, EMG1, EPHA2, EPS8L2, ESM1, ETS1, EXO1, EXOSC2, F3, FAHD2A, FAM83D, FANCA, FAT4, FDPS, FEN1, FMR1, FST, FSTL1, FSTL3, GADD45A, GADD45B, GIN S1, GPC6, GPR176, GPRC5A, GPRC5B, GRAMD2B, HASPIN, HEG1, HEXB, HPS5, HSPB11, IDI1, IGFBP7, IKBIP, IL6, ITGB2, JDP2, JPH2, KPNA2, KRT8, KRT80, LCA5 , LHFPL6, LMCD1, LMNB2, LRP8, LRRFIP2, LSM5, LYPD6, LYRM1, MAD2L1, MAP6D1, MATN2, MATN3, MCM10, MCM2, MCM5, MDC1, METRNL, MICB, MID1, MRPL33, MSRB 3, MVD, MXRA7, NCAPD3, NEDD4, NEDD4L, NEK2, NEXN, NFIB, NNMT, NOC3L, NTN4, NUAK1, NUAK2, NUDCD1, NUP107, NUP37, OGFRL1, OLFML3, OLR1, OXCT1, PAK2,PCBD1, PCNA, PDLIM2, PDZD2, PEPD, PHLPP1, PKMYT1, PKP2, PKP4, PLCE1, PLEKHA7, PLK2, PLOD2, PPIH, PRPS1, PRPS2, PRSS23, PSG2, PSG6, PSG7, PSG9, PVR, PXMP2, QDPR, QKI, RAB11FIP1, RAB 32, RACGAP1, RBM24, RBMS2, RCN2, RFC4, RND3, RNF144B, ROR1, RPS24, SCD5, SCML1, SDC2, SEC1 4L1, SGK1, SGMS2, ​​SGTB, SH3RF1, SHCBP1, SKP2, SLC25A23, SLC25A3, SLC38A5, SLC3A2, SLC7A1 , SLC7A5, SMPD4, SNAPC1, SNX24, SORT1, SPAG1, SPATA5, STK3, STX11, STXBP6, SUSD2, SUV39H1, SYDE2, TACC3, TAGLN, TEAD1, TEAD4, TENT5B, TGM2, THBS1, TK1, TMEM139, TMEM160, TNFAIP3, TNFRSF12A, TNNC1, TPM1, TPX2, TRIP13, SPAN2, TTF2, TUBB6, TUFT1, TYMS, UAP1, UBE2C, UGCG, UHRF1, VKORC1L1, WWC1, WWC2, YAP1, ZBED2, ZDHHC18, ZNF488, and ZNF704; and N genes are genes (negative effector genes) from the following gene set (2): AASDH, ABCA1, ABCC5, ABI3BP, ABLIM3, ACADVL, ACOT11, ACOX2, ACSL5, ADAM28, AGL, AGPAT4, ALDH3A2, ANKRD12, ANKRD22, ANKRD29, ANKRD42, ANTXR2, APBB3, ARAP3, ARHGEF2, ASF1A, ATP7A, ATXN1, BCL11B, BHLHE41, BMF, CA2, CASP1, CBR3, CCNG2, CDC42EP4, CRP4, and CR4. DK1, CEBPB, CELSR3, CLCN3, CLDN4, COL6A1, COL6A2, CPE, CRABP2, CROT, CSRNP2, CSTA, CTNNBIP1, CTSB, CTSK, CXXC5, CYP1B1, CYP27C1, DDR1, DEDD2, DHX 32, DIAPH2, DSC2, DSG3, DUSP6, DYNC2LI1, ELN, EPS8L3, ERAP2, FAM102A, FAM117B, FAM83B, FAM89B, FERMT1, FKBP2, FOS, FTH1, FXYD3, GDPD1, GOLGA5, GO LPH3L, GPNMB, GPRC5C, GRB10, GSN, HAS3, HBP1, HDAC1, HDHD2, HEY1, HOXA5, IFI44, IGSF3, IGSF9, INTS3, IRAK2, IRF9, IRX5, ITGA2, KCNMA1, KCNMB3, KC NN4, KIFAP3, KLF10, KLF13, KLHL3, KLK11, KRCC1, KRIT1, KRTDAP, LMTK3, LRP10, LTBP4, LXN, LYPD3, MALL, MANSC1, MAPK13, MARCKSL1, MFSD1, MFSD5, MGS T2, MGST3, MLLT11, MLPH, MMP13, MSX2, MTMR11, MTMR9, MTSS1, MYO1A, NAGK, NAPEPLD, NCOA3, NFIL3, NPAS2, NRIP1, OAS1, OAS2, OASL, OFD1, OSBPL7, OTUB 2, OVOL1, PAG1, PAK1, PCDHB2, PCDHB9, PCGF3, PCMTD2, PERP, PHF21A, PIK3C2B, PIK3R1, PIK3R2, PIK3R3, PIP4P2, PJA2, PKIA, PLA2G4C, PNRC1, PPP1R11,PRRX2, PTPRE, PYGB, RAC2, RALGPS1, RAPGEFL1, RBM23, RBM45, RBM47, RBP1, REEP6, RGL2, RGS17, RHOC, S100A14, SAMD9, SEC14L2, SECISBP2, SH3PXD2A, SH3TC1, S HROOM2, SLC14A1, SLC1A2, SLC30A9, SLC35C1, SLC37A2, SLC39A11, SLFN5, SLITRK6, SLK, SMOC1, SNCG, SP1, SPIRE2, SPRY4, SQSTM1, SRD5A3, SSPN, STMN3, STX1A, Any of 80 to 233 of TBX3, TCF25, TDO2, TET2, TFF1, TLR3, TMC4, TMC7, TMEM140, TMEM144, TMEM45B, TP53INP1, TP63, TPD52L1, TRAPPC6B, TRIB1, TRIB2, TRIM13, TRIM31, TRIM38, TRIOBP, TRIP11, TSC22D1, TSPAN1, TTC17, TTLL3, TUBB3, UBC, ULK1, VAMP8, VGF, VPS52, VSNL1, WDR13, ZCWPW1, ZNF292, ZNF467, ZNF75D, and ZSWIM7.

[0019] In some embodiments, N is an integer of 482, and the 482 genes include the following 249 positive effector genes: AASS, ABAT, ACAT2, ADAMTS1, ADM, ADRB2, AMOT, ANXA3, ARHGAP11A, ARHGDIB, AURKB, AVPI1, AXL, AZIN1, B4GALT4, BCAT1, BIRC5, BTG3, C4BPB, CAP2, CAV1, CAVIN1, CCBE1, CCDC80, CCN1, CCN2, CDC25A, CDC6, CDCA3, CDCA4, CDCA5, CDCA8, CDH4, CDK2, CDK6, CDV3, CENPA, CENPI, CENPM, CENPN, CHRNB1, CHST13, CKS2, CLDN1, CLIC3, CNN3, COBL, COL8A1, COTL1, CPA4, CRIM1, CRY1, CTH, CXCL1, C YTH3, DAPK1, DCLRE1B, DDAH1, DHCR7, DHFR, DIAPH3, DKK1, DLL1, DONSON, DUSP14, DUT, EBP, EIF2AK3, EMG1, EPHA2, EPS8L2, ESM1, ETS1, EXO1, EXOSC2, F3 , FAHD2A, FAM83D, FANCA, FAT4, FDPS, FEN1, FMR1, FST, FSTL1, FSTL3, GADD45A, GADD45B, GINS1, GPC6, GPR176, GPRC5A, GPRC5B, GRAMD2B, HASPIN, HEG1 , HEXB, HPS5, HSPB11, IDI1, IGFBP7, IKBIP, IL6, ITGB2, JDP2, JPH2, KPNA2, KRT8, KRT80, LCA5, LHFPL6, LMCD1, LMNB2, LRP8, LRRFIP2, LSM5, LYPD6, LYRM 1, MAD2L1, MAP6D1, MATN2, MATN3, MCM10, MCM2, MCM5, MDC1, METRNL, MICB, MID1, MRPL33, MSRB3, MVD, MXRA7, NCAPD3, NEDD4, NEDD4L, NEK2, NEXN, NFIB, N NMT, NOC3L, NTN4, NUAK1, NUAK2, NUDCD1, NUP107, NUP37, OGFRL1, OLFML3, OLR1, OXCT1, PAK2, PCBD1, PCNA, PDLIM2, PDZD2, PEPD, PHLPP1, PKMYT1, PKP2,PKP4, PLCE1, PLEKHA7, PLK2, PLOD2, PPIH, PRPS1, PRPS2, PRSS23, PSG2, PSG6, PSG7, PSG9, PVR, PXMP2, QDPR, QKI, RAB11FIP1, RAB32, RACGAP1, RBM24, RBMS2, RCN2, RFC4, RND3, RNF144B, ROR1, RPS24, SCD5, SCML1, SDC2, SEC14L1, SGK1, SGMS2, SGTB, SH3RF1, SHCBP1, SKP2, SLC25A23, SLC25A3, SLC38A5, SLC3A2, SLC7A1, SLC7A5, SMPD4, SNAPC1, SNX24, SORT1, SPAG1, SPATA5, STK3, STX11, STXBP6, SUSD2, SUV39H1, SYDE2, TACC3, TAGLN, TEAD1, TEAD4, TENT5B, TGM2, THBS1, TK1, TMEM139, TMEM160, TNFAIP3, TNFRSF12A, TNNC1, TPM1, TPX2, TRIP13, TSPAN2, TTF2, TUBB6, TUFT1, TYMS, UAP1, UBE2C, UGCG, UHRF1, VKORC1L1, WWC1, WWC2, YAP1, ZBED2, ZDHHC18, ZNF488, and ZNF704.

[0020] In some embodiments, N is an integer of 482, and the 482 genes include the following 233 negative effector genes: AASDH, ABCA1, ABCC5, ABI3BP, ABLIM3, ACADVL, ACOT11, ACOX2, ACSL5, ADAM28, AGL, AGPAT4, ALDH3A2, ANKRD12, ANKRD22, ANKRD29, ANKRD42, ANTXR2, APBB3, ARAP3, ARHGEF2, ASF1A, ATP7A, ATXN1, BCL11B, BHLHE41, BMF, CA2, CASP1, CASP2, CASP3, CASP4, CASP5, CASP6, CASP7, CASP8, CASP9, CASP10, CASP11, CASP12, CASP13, CASP14, CASP15, CASP16, CASP17, CASP18, CASP19, CASP20, CASP21, CASP22, CASP23, CASP24, CASP25, CASP26, CASP27, CASP28, CASP29, CASP29, CASP30, CASP31, CASP32, CASP33, CASP34, CASP35, CASP36, CASP37, CASP38, CASP39, CASP39, CASP39, CASP40, CASP41, CASP42 ... BR3, CCNG2, CDC42EP4, CDK1, CEBPB, CELSR3, CLCN3, CLDN4, COL6A1, COL6A2, CPE, CRABP2, CROT, CSRNP2, CSTA, CTNNBIP1, CTSB, CTSK, CXXC5, CYP1B1, C YP27C1, DDR1, DEDD2, DHX32, DIAPH2, DSC2, DSG3, DUSP6, DYNC2LI1, ELN, EPS8L3, ERAP2, FAM102A, FAM117B, FAM83B, FAM89B, FERMT1, FKBP2, FOS, FTH1, FXYD3, GDPD1, GOLGA5, GOLPH3L, GPNMB, GPRC5C, GRB10, GSN, HAS3, HBP1, HDAC1, HDHD2, HEY1, HOXA5, IFI44, IGSF3, IGSF9, INTS3, IRAK2, IRF9, IRX5, I TGA2, KCNMA1, KCNMB3, KCNN4, KIFAP3, KLF10, KLF13, KLHL3, KLK11, KRCC1, KRIT1, KRTDAP, LMTK3, LRP10, LTBP4, LXN, LYPD3, MALL, MANSC1, MAPK13, MAR CKSL1, MFSD1, MFSD5, MGST2, MGST3, MLLT11, MLPH, MMP13, MSX2, MTMR11, MTMR9, MTSS1, MYO1A, NAGK, NAPEPLD, NCOA3, NFIL3, NPAS2, NRIP1, OAS1, OAS2 , OASL, OFD1, OSBPL7, OTUB2, OVOL1, PAG1, PAK1, PCDHB2, PCDHB9, PCGF3, PCMTD2, PERP, PHF21A, PIK3C2B, PIK3R1, PIK3R2, PIK3R3, PIP4P2, PJA2, PKIA,PLA2G4C, PNRC1, PPP1R11, PRRX2, PTPRE, PYGB, RAC2, RALGPS1, RAPGEFL1, RBM23, RBM45, RBM47, RBP1, REEP6, RGL2, RGS17, RHOC, S100A14, SAMD9, SEC14L2, SECISB P2, SH3PXD2A, SH3TC1, SHROOM2, SLC14A1, SLC1A2, SLC30A9, SLC35C1, SLC37A2, SLC39A11, SLFN5, SLITRK6, SLK, SMOC1, SNCG, SP1, SPIRE2, SPRY4, SQSTM1, SRD5A3 , SSPN, STMN3, STX1A, TBX3, TCF25, TDO2, TET2, TFF1, TLR3, TMC4, TMC7, TMEM140, TMEM144, TMEM45B, TP53INP1, TP63, TPD52L1, TRAPPC6B, TRIB1, TRIB2, TRIM13, T RIM31, TRIM38, TRIOBP, TRIP11, TSC22D1, TSPAN1, TTC17, TTLL3, TUBB3, UBC, ULK1, VAMP8, VGF, VPS52, VSNL1, WDR13, ZCWPW1, ZNF292, ZNF467, ZNF75D, and ZSWIM7. ,

[0021] In some embodiments, a panel of the present disclosure can be used to measure TEAD-dependent transcription levels of genes in a sample from a subject having or suspected of having cancer, which comprises: isolating a biological sample from the subject and detecting the expression level of each gene of the panel.

[0022] In some embodiments, the panels of the present disclosure can be used to characterize a subject as having a TEAD-active or TEAD-inactive cancer, which includes: isolating a biological sample from a subject having or suspected of having cancer and detecting the expression level of each gene of the panel, wherein the subject has a TEAD-active cancer if the dgR or deR is greater than about 0.055.

[0023] In some embodiments, the panels of the present disclosure can be used to predict whether a subject will respond positively to a TEAD pathway inhibitor, which prediction includes: isolating a biological sample from a subject having or suspected of having cancer and detecting the expression level of each gene of the panel, wherein the subject is likely to respond positively to a TEAD pathway inhibitor if the dgR or deR is greater than about 0.055.

[0024] In some embodiments, the disclosure relates to a transcriptional signature ("TEAD-signature") obtained by measuring the expression levels of genes of a gene set that includes any of 220-249 genes of gene set (1) (positive effector genes) and any of 210-233 genes of gene set (2) (negative effector genes).

[0025] Transcriptional signatures ("TEAD-signatures") can be used to determine TEAD activity in cancer. Transcriptional signatures can be useful for a variety of applications, including predicting the likelihood that a subject will respond to TEAD pathway inhibitor therapy, selecting patients for clinical trials, assessing the efficacy of TEAD pathway inhibitor molecules, and predicting survival, response, and benefit of anti-TEAD pathway therapy.

[0026] In some embodiments, the disclosure relates to the use of a transcriptional signature to measure TEAD activity of a cancer in a subject in need thereof, wherein the transcriptional signature is obtained by measuring the expression levels of genes of a gene set in a biological sample of the tumor, the gene set including any of 220-249 genes of gene set (1) (positive effector genes) and any of 210-233 genes of gene set (2) (negative effector genes).

[0027] In some embodiments, the disclosure relates to the use of a transcriptional signature to characterize TEAD activity of a cancer in a subject in need thereof, wherein the transcriptional signature is obtained by measuring the expression levels of genes of a gene set in a biological sample of the tumor, the gene set including any of 220-249 genes of gene set (1) (positive effector genes) and any of 210-233 genes of gene set (2) (negative effector genes).

[0028] A transcriptional signature according to the present disclosure (a "TEAD-signature") may be used to determine a TEAD score.

[0029] In some embodiments, the TEAD score is calculated according to any of the following methods: Method 1: Calculate the dgR score as follows: 1. For each gene tested (e.g., complete transcriptome), convert the expression level of the gene to a fractional rank, which, as defined herein, is the rank of the gene divided by the number of genes tested (i.e., within the list of all genes whose expression levels are tested, the gene with the lowest expression level is given a rank of 1, the next highest expression level is given a rank of 2, and so on until the most highly expressed gene is given the highest rank; equal expression levels are given an average rank, e.g., two genes with an expression of 0 are given 1.5). 2. Isolate the genes (positive effector genes) of gene set (1) and calculate their mean fractional rank (MFR positive). 3. Isolate the genes (negative effector genes) of gene set (2) and calculate their average fractional rank (MFR negative). 4. Calculate the dgR score as MFR positive - MFR negative Method 2: Calculate the deR score as follows: 1. In the TEAD-500 panel, isolate genes from gene sets (1) and (2) (positive and negative effector genes). 2. For each gene tested, convert the expression level of the gene to a fractional rank, which, as defined herein, is the rank of the gene divided by the number of genes tested (i.e., within the list of all genes whose expression levels are tested, the gene with the lowest expression level is given a rank of 1, the next highest expression level is given a rank of 2, and so on until the most highly expressed gene is given the highest rank; equal expression levels are given an average rank, e.g., two genes with an expression of 0 are given 1.5). 3. Isolate positive effectors and calculate their mean fractional rank (MFR positive) 4. Isolate negative effectors and calculate their mean fractional rank (MFR negative) 5. Calculate the deR score as MFR positive - MFR negative

[0030] The dgR and deR scores can be used to determine whether a subject has a TEAD-active or TEAD-inactive cancer. If the dgR or deR score is greater than about 0.055, the cancer is TEAD-active. If the dgR or deR score is less than or equal to about 0.055, the cancer is TEAD-inactive.

[0031] In some embodiments, a gene set for a transcriptional signature ("TEAD-signature") may include between 430 and 482 genes.

[0032] In some embodiments, the gene set of the transcriptional signature ("TEAD-signature") includes 430, 431, 432, 433, 434, 436, 437, or 482 genes.

[0033] In some embodiments, the cancer may be a cancer involving a solid tumor.

[0034] In some embodiments, the solid tumor can be in the lung, colon, ovary, cervix, uterus, peritoneum, testis, penis, tongue, lymph node, pancreas bone, breast, prostate, soft tissue, connective tissue, kidney, liver, brain, thyroid, or skin.

[0035] In some embodiments, the cancer comprising a solid tumor is selected from the group consisting of adrenocortical carcinoma, bladder urothelial carcinoma, invasive breast carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, consensus molecular subtype 1 of colorectal cancer, consensus molecular subtype 2 of colorectal cancer, consensus molecular subtype 3 of colorectal cancer, consensus molecular subtype 4 of colorectal cancer, colon adenocarcinoma, lymphoid neoplasms diffuse large B-cell lymphoma, and / or lymphomas. The tumor may be selected from the group consisting of lymphoma, esophageal carcinoma, glioblastoma multiforme, squamous cell carcinoma of the head and neck, renal clear cell carcinoma, renal papillary cell carcinoma, brain low-grade glioma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid carcinoma, thymoma, uterine endometrial carcinoma, and uterine carcinosarcoma.

[0036] In some embodiments, the use of transcriptional signatures ("TEAD signatures") includes: a) measuring the expression levels of genes in a transcriptome obtained from said biological sample; b) for each gene of said gene set, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes from said transcriptome; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (1) and calculating their average fractional rank (MFR positive); d) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR negative); and e) Calculating the dgR score as MFR positive minus MFR negative; Or, a) determining in said biological sample the expression level of each gene of said set of genes; b) for each gene of said gene set, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes from said gene set; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (1) and calculating their average fractional rank (MFR positive); d) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR negative); and e) Calculating the deR score as MFR positive - MFR negative; may include If the dgR or deR score is greater than about 0.055, the cancer is TEAD active, and if the dgR or deR score is about 0.055 or less, the cancer is TEAD inactive.

[0037] In some embodiments, the transcriptional signatures can be reduced to shorter signatures, hereafter referred to as "short TEAD signatures."

[0038] A "short TEAD signature" is obtained by measuring the expression levels of a reduced number of genes and is specific to a given cancer.

[0039] In some embodiments, the present disclosure relates to the use of a transcriptional signature for measuring TEAD activity of a cancer in a subject in need thereof, wherein the transcriptional signature is obtained by measuring the expression levels of genes of a gene set in a biological sample of a tumor of said cancer, the gene set being selected from the group consisting of sets a) to mmmm) in Table 1 below ("short TEAD signature").

[0040] In some embodiments, the present disclosure relates to the use of a transcriptional signature for characterizing TEAD activity of a cancer in a subject in need thereof, wherein the transcriptional signature is obtained by measuring the expression levels of genes of a gene set in a biological sample of a tumor of said cancer, the gene set being selected from the group consisting of sets a) to mmmm) in Table 1 below ("short TEAD signature").

[0041] In some embodiments, the disclosure relates to a panel of nucleic acid biomarkers comprising nucleic acids capable of detecting the expression level of each gene in any one of the following gene sets a) through mmmm):

[0042] [Table 1]

[0043] [Table 2]

[0044] [Table 3]

[0045] [Table 4]

[0046] [Table 5]

[0047] [Table 6]

[0048] [Table 7]

[0049]

Table 8

[0050]

Table 9

[0051]

Table 10

[0052]

Table 11

[0053]

Table 12

[0054]

Table 13

[0055]

Table 14

[0056] In some embodiments, the gene set for the "short TEAD signature" is selected from the group consisting of: adrenocortical carcinoma (ACC) tumors; bladder urothelial carcinoma (BLCA) tumors; breast invasive carcinoma (BRCA); BRCA basal tumors; BRCA non-basal tumors; cervical squamous cell carcinoma or endocervical adenocarcinoma (CESC) tumors; cholangiocarcinoma (CHOL) tumors; colon adenocarcinoma (COAD) tumors; COAD CMS1 tumors; COAD CMS2 tumors; COAD CMS4 tumors; lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumors; esophageal carcinoma (ESCA) tumors; glioblastoma multiforme (GBM) tumors; head or neck squamous cell carcinoma (HNSC) tumors; kidney chromophobe (KICH) tumors; kidney renal clear cell carcinoma (KIRC) tumors; kidney renal papillary cell carcinoma (KIRP) tumors; brain low grade glioma (LGG) tumors; liver hepatocellular carcinoma (LIHC) tumors; LIHC S1 tumors; LIHC S2 tumors;LIHC S3 tumors;Lung adenocarcinoma (LUAD) tumors;LUAD proximal inflammatory tumors;LUAD proximal proliferative tumors;Lung squamous cell carcinoma (LUSC) tumors;LUSC basal tumors;LUSC classical tumors;LUSC primitive tumors;LUSC secretory tumors;Malignant mesothelioma (MESO) tumors;Ovarian serous cystadenocarcinoma (OV) tumors;OV differentiated tumors;OV immunoreactive tumors;OV mesenchymal tumors;OV proliferative tumors;Pancreatic adenocarcinoma (PAAD) tumors;Rectal adenocarcinoma (READ) tumors;READ CMS1 tumors;READ CMS2 tumors;READ CMS4 tumors;READ unclassified tumors;Sarcoma (SARC) tumors;Cutaneous melanoma (SKCM) tumors;SKCM immune tumors;SKCM keratin tumors;SKCM low MITF tumors;Gastric adenocarcinoma (STAD) tumors;STAD MSI tumors;STAD MSS_EMT tumors;STAD MSS_TP53 tumors;STAD The cancer may be associated with a cancer selected from: MSS_TP53+ tumor; testicular germ cell tumor (TGCT) tumor; thyroid gland cancer (THYM) tumor; uterine corpus endometrial cancer (UCEC) tumor; and uterine carcinosarcoma (UCS) tumor.

[0057] The "short TEAD signature" can be used in a method for calculating a discriminant score (S) (Method 3) or a method for calculating a discriminant score (DS) (Method 4).

[0058] Method 3: Determine the discriminant score (S). To reduce the dimensionality of the test, a subset of the most beneficial genes within the TEAD-500 panel applicable to a specific cancer type was determined. These are panels of fewer genes that predict the state (active or inactive) of the TEAD complex in samples from a given cancer indication or subtype.

[0059] To determine the TEAD active or TEAD inactive state of a sample, multiply the fractional rank of the genes in the optimal or minimal set by the corresponding coefficient in parentheses. Sum the products to obtain a discrimination score (S). Compare the discrimination score (S) to the central points of the TEAD active (A) and TEAD inactive (I) groups provided for each corresponding cancer indication or subtype. If (S) is closer to (A) than to (I), the sample is classified as TEAD active. Otherwise, the sample is classified as TEAD inactive.

[0060] For example, the expression levels are determined, the fractional ranks of the genes being tested are calculated, and multiplied by the corresponding coefficient in parentheses. Sum the products to obtain a discrimination score (S). Then, basically apply the following calculation: as max(S,A) - min(S,A) < max(S,I) - min(S,I) as follows: compare (S) to the central point (A) of TEAD activity and select the maximum value. Thus, if (S) is 5 and (A) is -268.9, select 5. Next, compare (S) and (A) and select the minimum value. Thus, if (S) is 5 and (A) is -268.9, select -268.9. Next, compare (S) and (I) and select the maximum value. Thus, if (S) is 5 and (I) is 22.1, select 22.1. Next, compare (S) and (I) and select the minimum value. Thus, if (S) is 5 and (I) is 22.1, select 5. Next, ask whether 5 - (-268.9) is less than 22.1 - 5 (i.e., 5 - (-268.9) < 22.1 - 5). If yes, the cancer is TEAD active. If no, the cancer is TEAD inactive. In this hypothetical example, since 273.9 is greater than or equal to 17.1, the cancer is TEAD inactive.

[0061] As an alternative way to score the discriminant function, one can consider the average between the midpoints provided for TEAD activity (A) and TEAD inactivity (I), e.g. (0.5 to -5.7) = -2.6. If the calculated score is above -2.6, the sample can be considered as TEAD inactive. If not, it can be considered as TEAD active.

[0062] The following embodiments 3-59 provide alternative panels that can be used in the methods and uses described herein, where the methods / uses are specific to a particular cancer type. In most embodiments, there are two useful panels: the "optimal discriminant function" and the "minimum discriminant function." The term "optimal" should not be construed as better than minimum.

[0063] [Table 15]

[0064] [Table 16]

[0065] [Table 17]

[0066] [Table 18]

[0067] [Table 19]

[0068] [Table 20]

[0069] [Table 21]

[0070]

Table 22

[0071]

Table 23

[0072]

Table 24

[0073]

Table 25

[0074]

Table 26

[0075]

Table 27

[0076]

Table 28

[0077]

Table 29

[0078]

Table 30

[0079]

Table 31

[0080]

Table 32

[0081]

Table 33

[0082]

Table 34

[0083]

Table 35

[0084]

Table 36

[0085]

Table 37

[0086]

Table 38

[0087]

Table 39

[0088]

Table 40

[0089]

Table 41

[0090]

Table 42

[0091]

Table 43

[0092]

Table 44

[0093]

Table 45

[0094]

Table 46

[0095]

Table 47

[0096]

Table 48

[0097]

Table 49

[0098]

Table 50

[0099]

Table 51

[0100]

Table 52

[0101] In some embodiments, the panel can be used to characterize a subject as having TEAD-active or TEAD-inactive cancer, or to predict whether a subject will respond positively to a TEAD pathway inhibitor, and the use comprises: - determining the expression level of each gene tested; - determining the fractional rank of each gene; - multiplying the fractional rank of each gene by the corresponding coefficient shown in the parentheses below; - summing the products to obtain a discrimination score (S); - comparing the discrimination score (S) to the center points of the TEAD-active (A) and TEAD-inactive (I) groups; - determining whether (S) is closer to (A) than to (I) according to the formula max(S,A)-min(S,A)<max(S,I)-min(S,I)-min(S,I); and - if (S) is closer to (A) than to (I), characterizing the sample as TEAD-active (the above embodiments (or sets) show the coefficients and center points for each set a) to mmmm)).

[0102] In some embodiments, the use of the “short TEAD signature” according to the present disclosure is such that the cancer is - when an adrenocortical carcinoma (ACC) tumor, the gene set of the transcriptional signature is gene set a) or b); - when a bladder urothelial carcinoma (BLCA) tumor, the gene set of the transcriptional signature is gene set c); - when a breast invasive carcinoma (BRCA) tumor, the gene set of the transcriptional signature is gene set d); - when a BRCA basal tumor, the gene set of the transcriptional signature is gene set e) or f); - when a BRCA non-basal tumor, the gene set of the transcriptional signature is gene set g) or h); - if the tumor is a cervical squamous cell carcinoma or cervical adenocarcinoma (CESC), the gene set of the transcriptional signature is gene set i); - if the tumor is a cholangiocarcinoma (CHOL), the gene set of the transcriptional signature is gene set j) or k); - if the tumor is a colon adenocarcinoma (COAD), the gene set of the transcriptional signature is gene set l); - if the tumor is a COAD CMS1 tumor, the gene set of the transcriptional signature is gene set m) or n); - if the tumor is a COAD CMS2 tumor, the gene set of the transcriptional signature is gene set o) or p); - if the tumor is a COAD CMS4 tumor, the gene set of the transcriptional signature is gene set q) or q1); - if the tumor is a lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), the gene set of the transcriptional signature is gene set r) or s); - if the tumor is an esophageal cancer (ESCA) tumor, the gene set of the transcriptional signature is gene set t) or u); - if the tumor is a glioblastoma multiforme (GBM), the gene set of the transcriptional signature is gene set v) or w); - if the tumor is a head or neck squamous cell carcinoma (HNSC), the gene set of the transcriptional signature is gene set x); - if a kidney chromophobe (KICH) tumor, the gene set of the transcriptional signature is gene set y) or z); - if the tumor is a kidney renal clear cell carcinoma (KIRC), the gene set of the transcriptional signature is gene set aa); - if the tumor is a kidney papillary renal cell carcinoma (KIRP), the gene set of the transcriptional signature is gene set bb) or cc); - if the tumor is a brain low-grade glioma (LGG), the gene set of the transcriptional signature is gene set dd) or ee); - if the tumor is a liver hepatocellular carcinoma (LIHC), the gene set of the transcriptional signature is gene set ff); - if the tumor is a LIHC S1 tumor, the gene set of the transcriptional signature is gene set gg) or hh); - if the tumor is a LIHC S2 tumor, the set of genes in the transcriptional signature are genes ii) or jj); - if the tumor is a LIHC S3 tumor, the set of genes of the transcriptional signature are genes kk) or ll); - if the tumor is a lung adenocarcinoma (LUAD), the gene set of the transcriptional signature is gene set mm); - if LUAD proximal inflammatory tumor, the gene set of the transcriptional signature is gene set nn) or oo); - if the tumor is a LUAD proximal proliferative tumor, the gene set of the transcriptional signature is gene set pp) or qq); - if the tumor is a lung squamous cell carcinoma (LUSC), the gene set of the transcriptional signature is gene set rr); - if LUSC basal tumor, the gene set of the transcriptional signature is gene set ss) or tt); - If the tumor is a LUSC classical tumor, then the gene set of the transcriptional signature is gene set uu); - if the tumor is a LUSC primitive tumor, the gene set of the transcriptional signature is gene set vv) or ww); - if a LUSC secretory tumor, the gene set of the transcriptional signature is gene set xx) or yy); - if the tumor is a malignant mesothelioma (MESO) tumor, the gene set of the transcriptional signature is gene set zz) or aaa); - If the tumor is an ovarian serous cystadenocarcinoma (OV), the gene set of the transcriptional signature is gene set bbb); - if the tumor is OV differentiated, the gene set of the transcriptional signature is a combination of gene sets ccc) or ddd); - if the tumor is OV-immunoreactive, the gene set of the transcriptional signature is gene set eee) or fff); - if OV is a mesenchymal tumor, the gene set of the transcriptional signature is gene set ggg) or hhh); - if the tumor is an OV proliferative tumor, the gene set of the transcriptional signature is gene set iii) or jjj); - if the tumor is a pancreatic adenocarcinoma (PAAD), the gene set of the transcriptional signature is gene set kkk) or kkk1); - if the tumor is a rectal adenocarcinoma (READ), the gene set of the transcriptional signature is gene set lll); - READ CMS1 tumor, then the gene set of the transcriptional signature is gene set mmm); - if the tumor is a READ CMS2 tumor, then the gene set of the transcriptional signature is gene set nnn) or ooo); - if the tumor is a READ CMS4 tumor, the gene set of the transcriptional signature is gene set ppp) or ppp1); - if the tumor is unclassifiable, the gene set of the transcriptional signature is gene set qqq) or qqq1); - if the tumor is a sarcoma (SARC), the gene set of the transcriptional signature is gene set rrr); - if the tumor is a cutaneous melanoma (SKCM), the gene set of the transcriptional signature is gene set sss); - if SKCM is an immune tumor, the gene set of the transcriptional signature is gene set ttt); - if SKCM keratin tumor, the gene set of the transcriptional signature is gene set uuu) or vvv); - if the tumor is a SKCM MITF low tumor, the gene set of the transcriptional signature is gene set www) or xxx); - if the tumor is a gastric adenocarcinoma (STAD), then the gene set of the transcriptional signature is gene set yyy); - if the tumor is a STAD MSI tumor, the gene set of the transcriptional signature is gene set zzz) or aaaa); - if the tumor is a STAD MSS_EMT tumor, the gene set of the transcriptional signature is gene set bbbb) or cccc); - if STAD MSS_TP53-tumor, the gene set of the transcriptional signature is gene set dddd) or eeee); - if the tumor is a STAD MSS_TP53+ tumor, then the gene set of the transcriptional signature is gene set ffff) or gggg); - if the tumor is a testicular germ cell tumor (TGCT), the gene set of the transcriptional signature is gene set hhhh) or iiii); - if the tumor is a thyroid cancer (THYM), the gene set of the transcriptional signature is gene set jjjj); - if the tumor is a uterine endometrial carcinoma (UCEC), the gene set of the transcriptional signature is gene set kkkk); - In the case of a uterine carcinosarcoma (UCS) tumor, the gene set of the transcriptional signature may be such that it is gene set llll) or mmmm).

[0103] In some embodiments, use of a "short TEAD signature" does not include use of gene sets q1), kkk1), ppp1), and qqq1).

[0104] In some embodiments, the uses disclosed herein include: a) measuring the expression level of each gene of the "TEAD-signature" gene set, i.e., any of 220 to 249 genes of gene set (1) (positive effector genes) and any of 210 to 233 genes of gene set (2) (negative effector genes) in a biological sample of a cancer tumor; b) For each gene in the gene set shown in Table 1, convert the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of the gene by the number of genes in the gene set of the "TEAD signature"; c) Multiply each fractional rank obtained in step b) by the coefficient associated with each gene in the gene set of Table 1 (the coefficients of the genes and gene sets are shown in Table 2) to obtain the product of each gene; d) Sum the products obtained in step c) to obtain a discrimination score (S); e) Compare the discrimination score (S) obtained in step d) with the central points of the TEAD active (A) and TEAD inactive (I) groups shown in Table 2 for the gene set of Table 1; f) Determine whether (S) is closer to (A) than to (I) according to the formula max(S,A) - min(S,A) < max(S,I) - min(S,I); and g) If (S) is closer to (A) than to (I), characterizing the sample as TEAD active may be included.

[0105] In some embodiments, the uses disclosed herein are The cancer is an adrenocortical carcinoma (ACC) tumor; a bladder urothelial carcinoma (BLCA) tumor; a breast invasive carcinoma (BRCA) tumor; a BRCA basal tumor; a BRCA non-basal tumor; a cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumor; a cholangiocarcinoma (CHOL) tumor; a colon adenocarcinoma (COAD) tumor; a COAD CMS1 tumor; a COAD CMS4 tumor; a lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumor; an esophageal carcinoma (ESCA) tumor; a glioblastoma multiforme (GBM) tumor; a head and neck squamous cell carcinoma (HNSC) tumor; a kidney chromophobe (KICH) tumor; a kidney renal clear cell carcinoma (KIRC) tumor; a kidney renal papillary cell carcinoma (KIRP) tumor; a brain low-grade glioma (LGG) tumor; a liver hepatocellular carcinoma (LIHC) tumor; a LIHC S1 tumor; a LIHC S2 tumor; a LIHC S3 tumor; lung adenocarcinoma (LUAD) tumor; lung squamous cell carcinoma (LUSC) tumor; LUSC basal tumor; LUSC classical tumor; LUSC primitive tumor; LUSC secretory tumor; malignant mesothelioma (MESO) tumor; pancreatic adenocarcinoma (PAAD) tumor; rectal adenocarcinoma (READ) tumor; READ CMS1 tumor; READ CMS2 tumor; READ CMS4 tumor; sarcoma (SARC) tumor; cutaneous melanoma (SKCM) tumor; SKCM immune tumor; SKCM keratin tumor; STAD MSI tumor; STAD MSS_EMT tumor; testicular germ cell tumor (TGCT) tumor; uterine endometrial carcinoma (UCEC) tumor; uterine carcinosarcoma (UCS) tumor, Use a) measuring in a biological sample of a tumor of said cancer an expression level of each gene of a set of genes associated with said tumor, wherein the gene sets are selected from the group consisting of Set a), Set c), Set d), Set e), Set g), Set i), Set j), Set l), Set m), Set q), Set r), t), Set v), Set x), Set y), Set aa), Set bb), Set dd), Set ff), Set gg), Set ii), Set kk), Set mm), Set rr), Set ss), Set uu), Set vv), Set xx), Set zz), Set kkk), Set lll), Set mmm), Set nnn), Set ppp), Set rrr), Set sss), Set ttt), Set uuu), Set zzz), Set bbbb), Set hhhh), Set kkkk), and Set llll) of Table 1; b) for each gene of said gene set, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes in said gene set; c) multiplying each fractional rank obtained in step b) by a coefficient associated with each gene of the gene set (the coefficients for the genes and gene sets are shown in Table 2) to obtain a product (Pgene i ) to obtain; d) determining a discriminant score (DS) for the gene set, the discriminant score (DS) being the product Pgene obtained in step c) i plus a constant coefficient for said gene set as shown in Table 2; e) for said gene set, comparing the discriminant score (DS) obtained in step d) with a threshold associated with said gene set (thresholds are shown in Table 3) to determine whether said cancer is TEAD active or TEAD inactive.

[0106] [Table 53]

[0107] [Table 54]

[0108] [Table 55]

[0109] [Table 56]

[0110] The use set forth above may be referred to as Method 4.

[0111] In some embodiments, the biological sample may be blood, plasma, serum, cerebrospinal fluid (CSF), tissue, cells, or a tumor biopsy.

[0112] In some embodiments, the use may be to characterize a subject as having a TEAD-active or TEAD-inactive cancer.

[0113] In some embodiments, the use may be for predicting whether a subject will respond positively to a TEAD pathway inhibitor;

[0114] In some embodiments, a subject may be administered a TEAD pathway inhibitor if the subject is characterized as having a TEAD-active cancer or if the subject is predicted to respond positively to a TEAD pathway inhibitor.

[0115] In some embodiments, the present disclosure relates to a method of characterizing TEAD activity of a cancer in a subject in need thereof, the method comprising using a biological sample of a tumor of the cancer, the method comprising: a) measuring the expression levels of genes in a transcriptome obtained from said biological sample; b) for each gene of the gene set of the "TEAD-signature", i.e. for any of genes 220 to 249 of gene set (1) (positive effector genes) and any of genes 210 to 233 of gene set (2) (negative effector genes), converting the expression level of the gene obtained in step a) into a fractional rank by dividing it by the number of genes from the transcriptome; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (1) and calculating their average fractional rank (MFR positive); d) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR negative); e) calculating the dgR score as MFR positive-MFR negative; Or, a) measuring the expression level of each gene of the "TEAD-signature" gene set in the biological sample, i.e., any one of 220 to 249 genes of gene set (1) (positive effector genes) and any one of 210 to 233 genes of gene set (2) (negative effector genes); b) for each gene of said gene set of a "TEAD signature", converting the expression level of the gene obtained in step a) into a fractional rank by dividing said rank of said gene by the number of genes from said gene set; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (1) and calculating their average fractional rank (MFR positive); d) from the fractional ranks obtained in step b), isolating the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR negative); e) calculating the deR score as MFR positive-MFR negative; If the dgR or deR score exceeds about 0.055, the cancer is TEAD-active, and if the dgR or deR score is about 0.055 or less, the cancer is TEAD-inactive.

[0116] In some embodiments, the present disclosure relates to a method of characterizing TEAD activity of cancer in a subject in need thereof, the method using a biological sample of the tumor of the cancer and at least a) measuring, in the biological sample, the expression level of each gene of the gene set of "TEAD-signature", i.e., any one of genes 220-249 of the genes of gene set (1) (positive effector genes) and any one of genes 210-233 of the genes of gene set (2) (negative effector genes); b) for each gene of the gene set shown in Table 1, converting the expression level of the gene obtained in step a) to a fractional rank by dividing the rank of the gene by the number of genes of the gene set of the "TEAD-signature"; c) multiplying each fractional rank obtained in step b) by the coefficient associated with each gene of the gene set of Table 1 (the coefficients of the genes and gene sets are shown in Table 2) to obtain the product of each gene; d) summing the products obtained in step c) to obtain a discrimination score (S); e) comparing the discrimination score (S) obtained in step d) with the center points of the TEAD-active (A) and TEAD-inactive (I) groups shown in Table 2 for the gene set of Table 1; f) determining whether (S) is closer to (A) than to (I) according to the formula max(S,A)-min(S,A)<max(S,I)-min(S,I); and g) characterizing the cancer as TEAD-active if (S) is closer to (A) than to (I).

[0117] In some embodiments, the present disclosure relates to a method for characterizing TEAD activity of cancer in a subject in need thereof, Cancers include adrenocortical carcinoma (ACC) tumors; bladder urothelial carcinoma (BLCA) tumors; breast invasive carcinoma (BRCA) tumors; BRCA basal tumors; BRCA non-basal tumors; cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors; cholangiocarcinoma (CHOL) tumors; colon adenocarcinoma (COAD) tumors; COAD CMS1 tumors; COAD CMS4 tumors; lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumors; esophageal carcinoma (ESCA) tumors; glioblastoma multiforme (GBM) tumors; head and neck squamous cell carcinoma (HNSC) tumors; kidney chromophobe (KICH) tumors; kidney renal clear cell carcinoma (KIRC) tumors; kidney renal papillary cell carcinoma (KIRP) tumors; brain low-grade glioma (LGG) tumors; liver hepatocellular carcinoma (LIHC) tumors; LIHC S1 tumors; LIHC S2 tumors; LIHC S3 tumor; lung adenocarcinoma (LUAD) tumor; lung squamous cell carcinoma (LUSC) tumor; LUSC basal tumor; LUSC classical tumor; LUSC primitive tumor; LUSC secretory tumor; malignant mesothelioma (MESO) tumor; pancreatic adenocarcinoma (PAAD) tumor; rectal adenocarcinoma (READ) tumor; READ CMS1 tumor; READ CMS2 tumor; READ CMS4 tumor; sarcoma (SARC) tumor; cutaneous melanoma (SKCM) tumor; SKCM immune tumor; SKCM keratin tumor; STAD MSI tumor; STAD MSS_EMT tumor; testicular germ cell tumor (TGCT) tumor; uterine endometrial carcinoma (UCEC) tumor; uterine carcinosarcoma (UCS) tumor, The method uses a biological sample of a tumor of said cancer, This method requires at least: a) measuring in a biological sample from said tumor an expression level of each gene of a set of genes associated with said tumor, wherein the gene sets are selected from the group consisting of Set a), Set c), Set d), Set e), Set g), Set i), Set j), Set l), Set m), Set q), Set r), t), Set v), Set x), Set y), Set aa), Set bb), Set dd), Set ff), Set gg), Set ii), Set kk), Set mm), Set rr), Set ss), Set uu), Set vv), Set xx), Set zz), Set kkk), Set lll), Set mmm), Set nnn), Set ppp), Set rrr), Set sss), Set ttt), Set uuu), Set zzz), Set bbbb), Set hhhh), Set kkkk), and Set llll) of Table 1; b) for each gene of said gene set, converting the expression level of the gene into a fractional rank by dividing the rank of the gene by the number of genes in said gene set; c) multiplying each fractional rank obtained in step b) by a coefficient associated with each gene of the gene set (the coefficients for the genes and gene sets are shown in Table 2) to obtain a product (Pgene i ) to obtain; d) determining a discriminant score (DS) for the gene set, the discriminant score (DS) being determined by multiplying the product Pgene obtained in step c) by i plus a constant coefficient for the gene set shown in Table 2; e) for said gene set, comparing the discriminant score (DS) obtained in step d) with a threshold associated with said gene set (thresholds are shown in Table 3) to determine whether said cancer is TEAD active or TEAD inactive.

[0118] A kit comprising a solid support comprising a panel of nucleic acids for determining the transcription of a set of genes according to the present disclosure.

[0119] In some embodiments the use or method may comprise detecting the RNA expression level of each gene in the panel.

[0120] In some embodiments the use or method may comprise RNA sequencing.

[0121] In some embodiments, the disclosure relates to a kit comprising a solid support comprising a panel of nucleic acid biomarkers disclosed herein.

[0122] In some embodiments, the present disclosure relates to a kit comprising a solid support comprising a panel of nucleic acids for determining the transcription of a set of genes disclosed herein.

[0123] Detailed Description definition Signature, gene signature and transcription signature. These terms and expressions as used herein are intended to refer to the pattern of transcription of a gene set that may be associated with TEAD-active cancer or tumor. A transcription signature is obtained by measuring the expression level of the signature genes (or the signature gene set) in a biological sample. In the biological sample, the expression level of all or a part of the genes likely to be expressed in the biological sample may be measured. When only a part of the genes is measured, the measurement is necessarily performed for at least all the genes of the gene set of interest.

[0124] Transcriptome. The term "transcriptome" is intended to refer to the set of all transcriptional reads (RNA), including coding and non-coding, in an individual or population of cells. Within this disclosure, transcriptome may be used in whole or in part. When used in part, it necessarily includes at least all genes of the genes of the gene set of interest.

[0125] As used herein, "transcription" and "gene expression" are used interchangeably to refer to the production of RNA. Gene transcription or expression can be increased or decreased, resulting in increased or decreased production of RNA.

[0126] Positive effectors. Positive effectors are genes whose transcript levels positively correlate with TEAD activity. They increase when the TEAD pathway is somehow activated, or decrease when the TEAD pathway is inhibited. Positive effectors are not expected to necessarily respond to both directions of TEAD regulation. For example, a gene that is deficient in normal expression in a tissue may be seen to increase with TEAD activation, but would not be expected to decrease with TEAD inhibition. Even with higher normal levels of expression, a positive effector gene may not necessarily be sensitive to both TEAD activation and inhibition.

[0127] Negative effectors. Negative effectors are genes whose transcript levels are negatively correlated with TEAD activity. They are either decreased by TEAD activation or increased by TEAD inhibition. As with positive effectors, negative effectors are not expected to necessarily respond in both directions of TEAD regulation. For example, a gene that is deficient in normal expression in a tissue may be seen to increase with TEAD pathway inhibition but would not be expected to decrease with TEAD activation. Even with higher normal levels of expression, a negative effector gene may be sensitive to TEAD activation but unaffected by TEAD inhibition, or vice versa.

[0128] TEAD-500 Gene List. A list of 500 or less most significant positive or negative effectors of TEAD regulation. These genes were selected by differential expression analysis of 18 public expression sequence datasets. Some of the 500 genes are direct targets of TEAD transcription factors, i.e., they present TEAD recognition motifs in their promoters, but most of these 500 genes are subject to secondary or indirect regulation by the TEAD pathway. Due to technological evolution (from various commercial expression arrays to RNA sequencing technologies) as well as inconsistencies in gene nomenclature, the panel does not include practically 500 genes, but now includes 482 genes. Thus, in this disclosure, the panel is referred to as "TEAD-500", but this TEAD-500 panel consists of about 90% of the 482 genes to 482 genes. Dropouts of at least up to 10% have negligible impact on its score. This is one of the reasons why TEAD-500 is so robust.

[0129] TEAD Signature. As used herein, "TEAD signature" is intended to refer to a transcriptional signature obtained by measuring the expression levels of genes of a gene set that includes any of 220-249 positive effector genes and any of 210-233 negative effector genes, as listed herein. By extension, depending on the context, "TEAD signature" can refer to a related gene set.

[0130] Short TEAD signature. As used herein, a "short TEAD signature" is intended to refer to a transcriptional signature obtained by measuring the expression levels of genes of a set of genes selected from the group consisting of sets a) to mmmm) of Table 1. By extension, depending on the context, a "short TEAD signature" can refer to a related set of genes.

[0131] Scoring. TEAD-500 scoring (herein referred to as "TEAD score") is a procedure that calculates the activity of the TEAD pathway in a sample on a continuous scale of values. The score is based on the average fractional rank of the transcript levels of positive and negative effectors. Depending on the available data set, two types of scores can be calculated, which are called dgR or deR. The two scores are not identical, but are similar. They both reach the same conclusion, but their values ​​are not equivalent. They both represent the difference Rp-Rn, where Rp is the average fractional rank of positive effectors and Rn is the average fractional rank of negative effectors. They differ in the way the fractional rank is defined and calculated.

[0132] Fractional rank. In an array of values, such as gene levels in a transcriptome, each value is replaced by its rank, with the minimum value having a rank of 1. The fractional rank of each gene is equal to its rank divided by the number of genes in the array (the maximum rank).

[0133] Genome-wide rank difference (dgR). TEAD pathway activity score calculated from the whole transcriptome. When the entire transcriptome is available, all transcript levels can be converted to fractional rank (the level of the transcript divided by the level of the most abundant transcript observed in the sample), and the average rank of positive and negative effectors is calculated from the thus converted transcript levels.

[0134] Difference in effector rank (deR). For very large cohorts or when complete transcriptome data are not available, it may be convenient to look at only genes in the TEAD-500 list. For dgR above, we convert the levels of 500 transcripts into fractional ranks, but a rank of 500 level-values ​​is obviously not the same as a rank of, say, 20,000 values ​​in the whole transcriptome. Thus, deR is different from dgR, but both scores can be used interchangeably.

[0135] Calling TEAD activity status (binning). This is a binary conversion of the continuous deR or dgR scores of TEAD activity into two distinct state values: "active" or "inactive". In some embodiments, we empirically set the deR threshold to 0.055. This value was selected from experiments with human mesothelioma cell lines. We observed that cell lines with a deR score below 0.055 do not respond to YAP1-siRNA treatment, while cell lines with higher scores stop proliferating when YAP1 is so knocked out. The response threshold may vary for different tissues or treatments. A score value of 0.055 corresponds approximately to the 88th percentile of the scores observed in tumors from the TCGA cohort (all indications pooled). If the deR score of TEAD-500 is above 0.055, TEAD is called active, otherwise it is called inactive.

[0136] Discriminant function. In general, this is a linear combination of predictors (in our case effector genes) (each with a coefficient) that separates two (in this case) or more classes of objects. Our objects are tumor samples, and the two classes are "active" or "inactive" TEAD phenotypes, as they are determined by TEAD activity calls based on the TEAD-500 transcriptome signature. The resulting non-redundant gene combinations can be used as linear classifiers or for dimensionality reduction before subsequent classification.

[0137] Discriminant score (S). The score (typically fractional rank or other normalized metric) of a sample on a discriminant function calculated as the sum of the products of predictor transcript levels multiplied by the corresponding predictor coefficients. For example, if a discriminant function contains genes X, Y, and Z with coefficients x, y, and z, respectively, the discriminant score (S) of sample i for that function is Si=(x*Xi)+(y*Yi)+(z*Zi), where Xi, Yi, and Zi are the levels of transcripts X, Y, and Z, respectively, in sample i.

[0138] Discriminant score (DS): The score of a sample with an optimal discriminant function calculated as the sum of the products of all discriminant genes of the optimal discriminant function and the constant coefficient of the optimal discriminant function. For example, if the optimal discriminant function for the indication to which the sample belongs has 10 genes, we multiply the fractional rank of each gene by the gene's corresponding discriminant coefficient (known as the loading) and add the resulting product to the constant coefficient of the optimal discriminant function.

[0139] Discriminant score (DS) = Σ(Gene i Fractional rank of *Gene i (coefficient of gene expression) + discrimination constant (where i are the genes listed in the optimal discriminant function).

[0140] An optimal discriminant function is given for each of the gene sets a) through mmmm), each of which is associated with a particular cancer type.

[0141] Group midpoints. Simply called midpoints, these are the average scores of the discriminant functions for a group of samples. In some embodiments, the midpoint of each group is calculated as the average discriminant score within that group to classify a sample as TEAD active or TEAD inactive. The further apart the averages are, the less error there will be in the classification. Every new sample is classified according to the distance of its discriminant score from the two midpoints. If its distance from the TEAD active midpoint is less than its distance from the TEAD inactive midpoint, the new sample is classified as TEAD active, otherwise it is classified as TEAD inactive.

[0142] Optimal Discriminant Function. By default, all significant predictors are used in the discriminant function. Using multiple alternative predictors improves the robustness of the model by allowing dropout without compromising the accuracy of the prediction. Unless specifically indicated as minimal, the general term discriminant function refers herein to the optimal solution.

[0143] Minimal Discriminant Function. Considering the economics of the assay, it may be desirable to retain only essential predictors to achieve 100% accuracy in classification. Once the optimal discriminant function reaches 100% correct classification, it is possible to manually remove predictors, stopping just before the accuracy falls below 100%. Minimal discriminant functions are only relevant if the optimal solution achieves 100% correct classification, and are reported herein.

[0144] TEAD Pathway Inhibitors. As used herein, a TEAD pathway inhibitor is any small or large molecule compound that inhibits the HIPPO-YAP / WWTR1 / TEAD pathway.

[0145] Expression Level. As used herein, the "expression level" of a gene refers to the level of RNA expressed by any given gene.

[0146] Gene panels and transcriptional signatures In some embodiments, a panel of N nucleic acid biomarkers is included, where N is an integer of at least 160 and no more than 482, and the N biomarkers are selected from the group consisting of genes from gene set (1) (positive effector genes): AASS, ABAT, ACAT2, ADAMTS1, ADM, ADRB2, AMOT, ANXA3, ARHGAP11A, ARHGDIB, AURKB, AVPI1, AXL, AZIN1, B4GALT4, BCAT1, BIRC5, BTG3, C4BPB, CAP2, CAV1, CAVIN1, CCBE1, CCDC80, CCN1, CCN2, CDC25A, CDC6, CDCA3, CDCA4, CDCA5, CDCA8, CDH4, CDK2, CDK6, CDV3, CENPA, CENPI, CENPM, CENPN, CHRNB1, CHST13, CKS2, CLDN1, CLIC3, CNN3, COB L, COL8A1, COTL1, CPA4, CRIM1, CRY1, CTH, CXCL1, CYTH3, DAPK1, DCLRE1B, DDAH1, DHCR7, DHFR, DIAPH3, DKK1, DLL1, DONSON, DUSP14, DUT, EBP, EIF2AK3, EMG1, EPHA2, EPS8L2, ESM1, ETS1, EXO1, EXOSC2, F3, FAHD2A, FAM83D, FANCA, FAT4, FDPS, FEN1, FMR1, FST, FSTL1, FSTL3, GADD45A, GADD45B, GINS1, GPC 6, GPR176, GPRC5A, GPRC5B, GRAMD2B, HASPIN, HEG1, HEXB, HPS5, HSPB11, IDI1, IGFBP7, IKBIP, IL6, ITGB2, JDP2, JPH2, KPNA2, KRT8, KRT80, LCA5, LHFPL 6, LMCD1, LMNB2, LRP8, LRRFIP2, LSM5, LYPD6, LYRM1, MAD2L1, MAP6D1, MATN2, MATN3, MCM10, MCM2, MCM5, MDC1, METRNL, MICB, MID1, MRPL33, MSRB3, MVD, MXRA7, NCAPD3, NEDD4, NEDD4L, NEK2, NEXN, NFIB, NNMT, NOC3L, NTN4, NUAK1, NUAK2, NUDCD1, NUP107, NUP37, OGFRL1, OLFML3, OLR1, OXCT1, PAK2, PCBD1,PCNA, PDLIM2, PDZD2, PEPD, PHLPP1, PKMYT1, PKP2, PKP4, PLCE1, PLEKHA7, PLK2, PLOD2, PPIH, PRPS1, PRPS2, PRSS23, PSG2, PSG6, PSG7, PSG9, PVR, PXMP2, QDPR, QKI, RAB11FIP1, RAB32, RACGA P1, RBM24, RBMS2, RCN2, RFC4, RND3, RNF144B, ROR1, RPS24, SCD5, SCML1, SDC2, SEC14L1, SGK1, SGMS2, ​​SGTB, SH3RF1, SHCBP1, SKP2, SLC25A23, SLC25A3, SLC38A5, SLC3A2, SLC7A1, SLC7A5, SM any of 80 to 249 of PD4, SNAPC1, SNX24, SORT1, SPAG1, SPATA5, STK3, STX11, STXBP6, SUSD2, SUV39H1, SYDE2, TACC3, TAGLN, TEAD1, TEAD4, TENT5B, TGM2, THBS1, TK1, TMEM139, TMEM160, TNFAIP3, TNFRSF12A, TNNC1, TPM1, TPX2, TRIP13, TSPAN2, TTF2, TUBB6, TUFT1, TYMS, UAP1, UBE2C, UGCG, UHRF1, VKORC1L1, WWC1, WWC2, YAP1, ZBED2, ZDHHC18, ZNF488, and ZNF704 ("positive effector genes"); and Genes in gene set (2) (negative effector genes): AASDH, ABCA1, ABCC5, ABI3BP, ABLIM3, ACADVL, ACOT11, ACOX2, ACSL5, ADAM28, AGL, AGPAT4, ALDH3A2, ANKRD12, ANKRD22, ANKRD29, ANKRD42, ANTXR2, APBB3, ARAP3, ARHGEF2, ASF1A, ATP7A, ATXN1, BCL11B, BHLHE41, BMF, CA2, CASP1, CBR3, CCNG2, CDC42EP4, CDK1, CEBPB, CRP4, CRP5, CRP6, CRP7, CRP8, CRP9, CRP10, CRP11, CRP12, CRP13, CRP14, CRP15, CRP16, CRP17, CRP18, CRP19, CRP20, CRP21, CRP22, CRP23, CRP24, CRP25, CRP26, CRP27, CRP28, CRP29, CRP30, CRP31, CRP32, CRP33, CRP34, CRP35, CRP36, CRP37, CRP38, CRP39, CRP40, CRP41, CRP42, CRP43, CRP44, CRP45, CRP46, CRP47, CRP48, CRP49, CRP50, CRP51, CRP52, CRP53, CRP54, CRP55, CRP56, CRP57, CRP58, CRP59, CRP59, CRP60, CRP61, CRP62, CRP63, CRP64, CRP65, CRP66, CRP67, CRP68, CRP69 ELSR3, CLCN3, CLDN4, COL6A1, COL6A2, CPE, CRABP2, CROT, CSRNP2, CSTA, CTNNBIP1, CTSB, CTSK, CXXC5, CYP1B1, CYP27C1, DDR1, DEDD2, DHX32, DIAPH2, D SC2, DSG3, DUSP6, DYNC2LI1, ELN, EPS8L3, ERAP2, FAM102A, FAM117B, FAM83B, FAM89B, FERMT1, FKBP2, FOS, FTH1, FXYD3, GDPD1, GOLGA5, GOLPH3L, GPNMB , GPRC5C, GRB10, GSN, HAS3, HBP1, HDAC1, HDHD2, HEY1, HOXA5, IFI44, IGSF3, IGSF9, INTS3, IRAK2, IRF9, IRX5, ITGA2, KCNMA1, KCNMB3, KCNN4, KIFAP3, K LF10, KLF13, KLHL3, KLK11, KRCC1, KRIT1, KRTDAP, LMTK3, LRP10, LTBP4, LXN, LYPD3, MALL, MANSC1, MAPK13, MARCKSL1, MFSD1, MFSD5, MGST2, MGST3, MLL T11, MLPH, MMP13, MSX2, MTMR11, MTMR9, MTSS1, MYO1A, NAGK, NAPEPLD, NCOA3, NFIL3, NPAS2, NRIP1, OAS1, OAS2, OASL, OFD1, OSBPL7, OTUB2, OVOL1, PAG1 , PAK1, PCDHB2, PCDHB9, PCGF3, PCMTD2, PERP, PHF21A, PIK3C2B, PIK3R1, PIK3R2, PIK3R3, PIP4P2, PJA2, PKIA, PLA2G4C, PNRC1, PPP1R11, PRRX2, PTPRE,PYGB, RAC2, RALGPS1, RAPGEFL1, RBM23, RBM45, RBM47, RBP1, REEP6, RGL2, RGS17, RHOC, S100A14, SAMD9, SEC14L2, SECISBP2, SH3PXD2A, SH3TC1, SHROOM2, SLC14A1, S LC1A2, SLC30A9, SLC35C1, SLC37A2, SLC39A11, SLFN5, SLITRK6, SLK, SMOC1, SNCG, SP1, SPIRE2, SPRY4, SQSTM1, SRD5A3, SSPN, STMN3, STX1A, TBX3, TCF25, TDO2, TET2 , TFF1, TLR3, TMC4, TMC7, TMEM140, TMEM144, TMEM45B, TP53INP1, TP63, TPD52L1, TRAPPC6B, TRIB1, TRIB2, TRIM13, TRIM31, TRIM38, TRIOBP, TRIP11, TSC22D1, TSPAN1, TTC17, TTLL3, TUBB3, UBC, ULK1, VAMP8, VGF, VPS52, VSNL1, WDR13, ZCWPW1, ZNF292, ZNF467, ZNF75D, and ZSWIM7 ("negative effector genes").

[0147] In some embodiments, a panel of N nucleic acid biomarkers is included, where the N biomarkers are at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least Also 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121,at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 1 49, at least 150, at least 151, at least 152, at least 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, at least 197, at least 198, at least 199, at least 200, at least 201, at least 202, at least 203, at least 204, at least at least 205, at least 206, at least 207, at least 208, at least 209, at least 210, at least 211, at least 212, at least 213, at least 214, at least 215, at least 216, at least 217, at least 218, at least 219, at least 220, at least 221, at least 222, at least 223, at least 224, at least 225, at least 226, at least 227, at least 228, at least 229, at least 230, at least 231, at least 232,At least 233, at least 234, at least 235, at least 236, at least 237, at least 238, at least 239, at least 240, at least 241, at least 242, at least 243, at least 244, at least 245, at least 246, at least 247, at least 248, or all 249 genes of gene set (1) (positive effector genes), and at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, 6, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126,at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150, at least 151, at least 152, at least Also 153, at least 154, at least 155, at least 156, at least 157, at least 158, at least 159, at least 160, at least 161, at least 162, at least 163, at least 164, at least 165, at least 166, at least 167, at least 168, at least 169, at least 170, at least 171, at least 172, at least 173, at least 174, at least 175, at least 176, at least 177, at least 178, at least 179, at least 180, at least 181, at least 182, at least 183, at least 184, at least 185, at least 186, at least 187, at least 188, at least 189, at least 190, at least 191, at least 192, at least 193, at least 194, at least 195, at least 196, at least 197, at least 198, at least 199, at least 200, at least 201, at least 202, at least 203, at least 204, at least 205, at least Also 206, at least 207, at least 208, at least 209, at least 210, at least 211, at least 212, at least 213, at least 214, at least 215, at least 216, at least 217, at least 218, at least 219, at least 220, at least 221, at least 222, at least 223, at least 224, at least 225, at least 226, at least 227, at least 228, at least 229, at least 230, at least 231, at least 232,Or, it contains a nucleic acid capable of detecting genes (negative effector genes) of all 233 gene sets (2).

[0148] In some embodiments, a panel of nucleic acid biomarkers is included, where N is an integer of at least 10 and no more than 482, and the N biomarkers are any of 10 to 249, 20 to 249, 30 to 249, 40 to 249, 50 to 249, 60 to 249, 70 to 249, 80 to 249, 90 to 249, 100 to 249, 110 to 249, 120 to 249, 130 to 249, 140 to 249, 150 to 249, 160 to 249, 170 to 249, 180 to 249, 190 to 249, 200 to 249, 210 to 249, 220 to 249, 230 to 249, or 240 to 249 genes (positive effector genes) of gene set (1); and The nucleic acid includes a nucleic acid capable of detecting a gene (negative effector gene) of any one of gene sets (2) of 10 to 233, 20 to 233, 30 to 233, 40 to 233, 50 to 233, 60 to 233, 70 to 233, 80 to 233, 90 to 233, 100 to 233, 110 to 233, 120 to 233, 130 to 233, 140 to 233, 150 to 233, 160 to 233, 170 to 233, 180 to 233, 190 to 233, 200 to 233, 210 to 233, 220 to 233, or 230 to 233.

[0149] In some embodiments, the panel includes nucleic acid biomarkers capable of detecting about 90% of 482 of the genes (positive and negative effector genes) of gene sets (1) and (2). In some embodiments, the panel includes nucleic acid biomarkers capable of detecting 430, 431, 432, 433, 434, 436, or 437 genes.

[0150] In some embodiments, the panel comprises nucleic acid biomarkers capable of detecting the expression level of each gene in any one of the following gene sets a) through mmmm):

[0151] In some embodiments, the panel comprises nucleic acid biomarkers capable of detecting the expression level of each gene in any one of the following gene sets a) through mmmm), except for gene sets q1), kkk1), ppp1), and qqq1).

[0152] A transcriptional signature ("TEAD signature") can be obtained by measuring the expression levels of any of 220-249 genes in gene set (1) (positive effector genes) and any of 210-233 genes in gene set (2) (negative effector genes).

[0153] A transcriptional signature ("TEAD signature") can be obtained by a measure of the expression levels of genes of a gene set that includes any of 220-249 of genes (positive effector genes) of gene set (1), or any of 225-249, or any of 230-249, or any of 235-249, or any of 240-249, or any of 245-249 of genes (positive effector genes) of gene set (1).

[0154] A transcriptional signature ("TEAD signature") can be obtained by a measure of the expression levels of genes of a gene set that includes any of 210-233 of genes (negative effector genes) of gene set (2), or any of 215-233 of genes (negative effector genes) of gene set (2), or any of 220-233, or any of 225-233, or any of 230-233 of genes (negative effector genes) of gene set (2).

[0155] A transcriptional signature ("TEAD signature") can be obtained by a measure of the expression levels of genes of a gene set that includes any of the 430-482 genes set forth above, or any of the 435-482 genes set forth above, or any of the 440-482 genes, or any of the 445-482 genes, or any of the 450-482 genes, or any of the 455-482 genes, or any of the 460-482 genes, or any of the 465-482 genes, or any of the 470-482 genes, or any of the 475-482 genes, or any of the 480-482 genes.

[0156] A transcriptional signature ("TEAD signature") can be obtained by a measure of the expression levels of genes of a gene set that includes any of the 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481 or 482 genes set forth above.

[0157] The transcriptional signature ("TEAD signature") may be used to calculate a TEAD score according to method 1 or method 2 disclosed above. Depending on the method used, the TEAD score may be a dgR or deR score. A dgR or deR score greater than about 0.055 indicates a TEAD-active cancer. A dgR or deR score less than about 0.055 indicates a TEAD-inactive cancer.

[0158] In some embodiments, the transcriptional signatures can be reduced to shorter signatures, hereafter referred to as "short TEAD signatures."

[0159] A "short TEAD signature" is obtained by measuring the expression levels of a reduced number of genes and is specific to a given cancer type.

[0160] In some embodiments, the present disclosure relates to the use of a transcriptional signature ("short TEAD signature") for measuring TEAD activity of a cancer in a subject in need thereof, wherein the transcriptional signature is obtained by measuring the expression levels of genes of a gene set in a biological sample of a tumor of said cancer, the gene set being selected from the group of sets a) to mmmm) disclosed in Tables 1 or 2 above.

[0161] Each gene set is selective for analyzing a particular cancer type / subtype, as described in embodiments 3-59 of Tables 1 and 2.

[0162] The short TEAD signature can be used to calculate a discriminant score (S) or (DS) according to method 3 or method 4.

[0163] In method 3, the discriminant score (S) is compared to the midpoint of the TEAD active (A) and TEAD inactive (I) groups provided for each cancer indication or subtype associated with a gene set from sets a)-mmmm), as shown in Tables 1 and 2. If (S) is closer to (A) than to (I), the sample is classified as TEAD active. Otherwise, the sample is classified as TEAD inactive.

[0164] In some embodiments, in such methods, use of a "short TEAD signature" does not include use of gene sets q1), kkk1), ppp1), and qqq1).

[0165] In method 4, the discriminant score (DS) is compared to a threshold from those shown in Table 6 and associated with the cancer indication or subtype associated with the gene set in the group of sets a) to mmmm) to determine whether the cancer is TEAD active or TEAD inactive.

[0166] Methods and Uses Methods and uses utilizing the panel are provided, which include measuring TEAD-dependent transcription levels, characterizing subjects as having TEAD-active or TEAD-inactive cancers, and predicting whether the subject is likely to respond positively to a TEAD pathway inhibitor. The methods include isolating a biological sample from a subject having or suspected of having cancer (solid or blood), detecting the expression level of each gene, and scoring the panel according to the methods described herein, which differ by the use of the optimal / minimal discriminant function of the TEAD-500 panel or TEAD-500 subset panel. In some embodiments, the method / use further includes an active step of administering a TEAD pathway inhibitor treatment to subjects identified using the disclosed panels. In some embodiments, the step of isolating the biological sample is not part of the method, but is performed prior to the implementation of the method.

[0167] In some embodiments, the transcriptional signature ("TEAD signature" or "short TEAD signature") can be for determining, measuring, or characterizing TEAD activity in cancer tumors. The transcriptional signature can be useful for a variety of applications, including predicting the likelihood that a subject will respond to TEAD pathway inhibitor treatment, selecting patients for clinical trials, assessing the efficacy of TEAD pathway inhibitor molecules, and predicting survival, response, and benefit of anti-TEAD pathway treatments.

[0168] In some embodiments, a transcriptional signature ("TEAD signature" or "short TEAD signature") can be obtained by measuring TEAD-dependent transcription levels of genes in a biological sample from a subject having or suspected of having cancer. The genes can be derived from a gene set of the present disclosure.

[0169] The use comprises detecting or measuring the expression level of each gene of the signature gene set in a biological sample obtained from a subject in need thereof, the biological sample being isolated from a tumor present in said subject.

[0170] In some embodiments, the transcriptional signature ("TEAD signature" or "short TEAD signature") can be for characterizing a subject as having a TEAD-active or TEAD-inactive cancer.

[0171] The use involves detecting or measuring the expression level of each gene of the signature gene set in a biological sample obtained from a subject having or suspected of having cancer.

[0172] The transcriptional signatures can be used to calculate the TEAD score dgR (Method 1) or deR (Method 2) or the discriminant score (S) (Method 3) or (DS) (Method 4), depending on the case.

[0173] If the dgR or deR obtained with the "TEAD signature" is greater than about 0.055, the subject is considered to have a TEAD-active cancer.

[0174] If the discriminant score (DS) obtained with the "short TEAD signature" is closer to the midpoint of the TEAD active (A) group than to the midpoint of the TEAD inactive (I) group, the subject is considered to have a TEAD active cancer.

[0175] If the discriminant score (S) obtained with the "short TEAD signature" is above or below the thresholds shown in Table 3, depending on the case, the subject is considered to have a TEAD-active cancer.

[0176] In some embodiments, the transcriptional signature (the "TEAD signature" or "short TEAD signature") can be to predict whether a subject will respond positively to a TEAD pathway inhibitor.

[0177] If the dgR or deR obtained with the "TEAD signature" is greater than about 0.055, the subject is considered likely to respond positively to a TEAD pathway inhibitor.

[0178] If the discriminant score (DS) obtained with the "short TEAD signature" is closer to the midpoint of the TEAD active (A) group than to the midpoint of the TEAD inactive (I) group, the subject is considered to be more likely to respond positively to a TEAD pathway inhibitor.

[0179] If the discriminant score (S) obtained with the "short TEAD signature" is above or below the thresholds shown in Table 3, depending on the case, the subject is considered likely to respond positively to a TEAD pathway inhibitor.

[0180] In some embodiments involving the use of a TEAD-500 panel, once the genes on the TEAD-500 panel are detected and gene expression is calculated, a TEAD score is provided as described above in method 1 or 2. A subject is said to have a TEAD-active cancer if the dgR or deR is greater than about 0.055, and a TEAD-inactive cancer if the dgR or deR is about 0.055 or less. Similarly, a subject is likely to respond positively to a TEAD pathway inhibitor if the dgR or deR is greater than about 0.055, and is likely not to respond positively to a TEAD pathway inhibitor if the dgR or deR is about 0.055 or less.

[0181] In some embodiments, a panel of gene sets a)-mmmm) is provided for use in any of the methods 3 or 4 described herein, comprising measuring TEAD-dependent transcription levels, characterizing a subject as having a TEAD-active or TEAD-inactive cancer, or predicting whether a subject will respond positively to a TEAD pathway inhibitor, wherein the panel comprises any one of the gene subsets of embodiments 3-59 above.

[0182] Such a method 3 includes: i) determining the expression level of each gene in the set; ii) determining the fractional rank of each gene; iii) multiplying the fractional rank of each gene by the corresponding coefficient shown in parentheses for each gene within each panel; iv) summing the products to obtain a discrimination score (S); v) comparing the discrimination score (S) with the central points of the TEAD-active (A) and TEAD-inactive (I) groups; vi) determining whether S is closer to (A) than to (I) according to the formula max(S,A)-min(S,A)<max(S,I)-min(S,I); and vii) characterizing the sample as TEAD-active if (S) is closer to (A) than to (I), and characterizing the sample as TEAD-inactive if (S) is closer to (I) than to (A).

[0183] In some embodiments, method 3 includes: i) measuring, in a biological sample, the expression level of each gene of a gene set of a "TEAD signature" (i.e., any one of genes 220 - 249 of the genes of gene set (1) (positive effector genes) and any one of genes 210 - 233 of the genes of gene set (2) (negative effector genes)); ii) for each gene of the gene set shown in Table 1, converting the expression level of the gene obtained in step i) into a fractional rank by dividing the rank of the gene by the number of genes of the gene set of the "TEAD signature"; iii) multiplying each fractional rank obtained in step iii) by the coefficient associated with each gene of the gene set of Table 1 (the coefficients for the genes and gene sets are shown in Table 2) to obtain the product for each gene; iv) summing the products to obtain a discrimination score (S); v) comparing the discrimination score (S) with the central points of the TEAD-active (A) and TEAD-inactive (I) groups shown in Table 2 for the gene set of Table 1; vi) determining whether (S) is closer to (A) than to (I) according to the formula max(S,A)-min(S,A)<max(S,I)-min(S,I)-min(S,I); and vii) optionally including characterizing the sample as TEAD-active if (S) is closer to (A) than to (I), and characterizing the sample as TEAD-inactive if (S) is closer to (I) than to (A).

[0184] In some embodiments, method 3 does not include the use of gene sets q1), kkk1), ppp1), and qqq1).

[0185] Method 4: (i) determining the expression levels of the genes listed in the optimal discriminant functions of Set a), Set c), Set d), Set e), Set g), Set i), Set j), Set l), Set m), Set q), Set r), Set t), Set v), Set x), Set y), Set aa), Set bb), Set dd), Set ff), Set gg), Set ii), Set kk), Set mm), Set rr), Set ss), Set uu), Set vv), Set xx), Set zz), Set kkk), Set lll), Set mmm), Set nnn), Set ppp), Set rrr), Set sss), Set ttt), Set uuu), Set zzz), Set bbbb), Set hhhh), Set kkkk), and Set llll) of Table 1 for the indication to which the tumor belongs (shown in Table 2); The expression level may be determined by RNA sequencing, RT-qPCR, or any other reliable method; (ii) for each gene of said gene set, converting the expression level of the gene into a fractional rank (wherein the fractional rank is as defined herein the rank of the gene divided by the number of genes in said gene set (i.e., the expression of a gene relative to all genes whose expression levels are tested, the gene with the lowest expression level is given a rank of 1, the next highest expression level is given a rank of 2, and so on until the most highly expressed gene is given the highest rank; equal expression levels are given an average rank, e.g., two genes with an expression of 0 are given 1.5)); (iii) multiplying each fractional rank obtained in step (ii) by a coefficient associated with each gene of the gene set (the coefficients for the genes and gene sets are shown in Table 2) to obtain a product (Pgene i ) to obtain (iv) Calculating the discriminant score (DS) of said gene set for the tumor sample as the sum of the products of all discriminant genes and the constant coefficients of the discriminant function (i.e., the constant coefficients of said gene set shown in Table 2). For example, if the optimal discriminant function for the indication to which the sample belongs has 10 genes, we multiply the fractional rank of each gene by the gene's corresponding discriminant coefficient (known as the loading) and add the resulting product to the constant coefficient of the optimal discriminant function. Discrimination score = Σ(Gene i Fractional rank of *Gene i (coefficient of gene expression) + discrimination constant (where i are the genes listed in the discriminant function). and (v) determining the TEAD activity status of the sample using the thresholds for each indication listed in Table 3.

[0186] In some embodiments, method 4 comprises: The cancer is an adrenocortical carcinoma (ACC) tumor; a bladder urothelial carcinoma (BLCA) tumor; a breast invasive carcinoma (BRCA) tumor; a BRCA basal tumor; a BRCA non-basal tumor; a cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumor; a cholangiocarcinoma (CHOL) tumor; a colon adenocarcinoma (COAD) tumor; a COAD CMS1 tumor; a COAD CMS4 tumor; a lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumor; an esophageal carcinoma (ESCA) tumor; a glioblastoma multiforme (GBM) tumor; a head and neck squamous cell carcinoma (HNSC) tumor; a kidney chromophobe (KICH) tumor; a kidney renal clear cell carcinoma (KIRC) tumor; a kidney renal papillary cell carcinoma (KIRP) tumor; a brain low-grade glioma (LGG) tumor; a liver hepatocellular carcinoma (LIHC) tumor; a LIHC S1 tumor; a LIHC S2 tumor; a LIHC S3 tumor; lung adenocarcinoma (LUAD) tumor; lung squamous cell carcinoma (LUSC) tumor; LUSC basal tumor; LUSC classical tumor; LUSC primitive tumor; LUSC secretory tumor; malignant mesothelioma (MESO) tumor; pancreatic adenocarcinoma (PAAD) tumor; rectal adenocarcinoma (READ) tumor; READ CMS1 tumor; READ CMS2 tumor; READ CMS4 tumor; sarcoma (SARC) tumor; cutaneous melanoma (SKCM) tumor; SKCM immune tumor; SKCM keratin tumor; STAD MSI tumor; STAD MSS_EMT tumor; testicular germ cell tumor (TGCT) tumor; uterine endometrial carcinoma (UCEC) tumor; uterine carcinosarcoma (UCS) tumor, a) measuring in a biological sample of a tumor of said cancer an expression level of each gene of a set of genes associated with said tumor, wherein the gene sets are selected from the group consisting of Set a), Set c), Set d), Set e), Set g), Set i), Set j), Set l), Set m), Set q), Set r), t), Set v), Set x), Set y), Set aa), Set bb), Set dd), Set ff), Set gg), Set ii), Set kk), Set mm), Set rr), Set ss), Set uu), Set vv), Set xx), Set zz), Set kkk), Set lll), Set mmm), Set nnn), Set ppp), Set rrr), Set sss), Set ttt), Set uuu), Set zzz), Set bbbb), Set hhhh), Set kkkk), and Set llll) of Table 1; b) for each gene of said gene set, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes in said gene set; c) multiplying each fractional rank obtained in step b) by a coefficient associated with each gene of the gene set (the coefficients for the genes and gene sets are shown in Table 2) to obtain a product (Pgene i ) to obtain; d) determining a discriminant score (DS) for the gene set, the discriminant score (DS) being the product Pgene obtained in step c) i plus a constant coefficient for said gene set as shown in Table 2; e) for said gene set, comparing the discriminant score (DS) obtained in step d) with a threshold associated with said gene set (thresholds are shown in Table 3) to determine whether said cancer is TEAD active or TEAD inactive.

[0187] In some embodiments, methods and uses utilizing TEAD-500 or any of the subset panels of embodiments 3-59 (disclosed in Tables 1 and 2) are provided for monitoring activity of a TEAD complex, assessing the pharmacodynamics of inhibitors of the TEAD pathway in vitro or in vivo, diagnosing tumors whose development or evolution is due to activation of the TEAD complex, recruiting patients with active TEAD into clinical trials designed to evaluate TEAD pathway inhibitors, predicting survival, response, and benefit of anti-TEAD pathway treatment (e.g., treatment with a TEAD pathway inhibitor) alone or in combination with other therapies, estimating the proportion of cases within a cohort in which TEAD-dependent transcription may be involved in tumor development or evolution to treatment resistance, or measuring TEAD-dependent transcription in any tissue, under any conditions, whether pathological or not, in the laboratory or clinic.

[0188] In some embodiments, the methods and uses of the present disclosure do not include the use of gene sets q1), kkk1), ppp1), and qqq1).

[0189] In some embodiments, the present disclosure relates to a method of measuring a transcriptional signature ("TEAD signature" or "short TEAD signature") in a biological sample from a patient having or suspected of having cancer, the method comprising detecting or measuring the expression level of each gene of a gene set of the transcriptional signature ("TEAD signature" or "short TEAD signature") in the biological sample.

[0190] In some embodiments, the present disclosure relates to a method of characterizing TEAD activity of a cancer in a subject in need thereof, the method comprising using a biological sample of a tumor of the cancer, the method comprising: a) measuring the expression levels of genes in a transcriptome obtained from said biological sample; b) for each gene of the gene set of the "TEAD-signature", i.e. for any of genes 220 to 249 of gene set (1) (positive effector genes) and any of genes 210 to 233 of gene set (2) (negative effector genes), converting the expression level of the gene obtained in step a) into a fractional rank by dividing it by the number of genes from the transcriptome; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (1) and calculating their average fractional rank (MFR positive); d) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR negative); and e) calculating the dgR score as MFR positive-MFR negative; Or, a) measuring the expression level of each gene of the "TEAD-signature" gene set in the biological sample, i.e., any one of 220 to 249 genes of gene set (1) (positive effector genes) and any one of 210 to 233 genes of gene set (2) (negative effector genes); b) for each gene of said gene set of a "TEAD-signature", i.e. for any of genes 220 to 249 of gene set (1) (positive effector genes) and any of genes 210 to 233 of gene set (2) (negative effector genes), converting the expression level of the gene obtained in step a) into a fractional rank by dividing it by the number of genes from said gene set; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (1) and calculating their average fractional rank (MFR positive); d) from the fractional ranks obtained in step b), isolating the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR negative); e) calculating the deR score as MFR positive-MFR negative; When the dgR or deR score exceeds about 0.055, the cancer is TEAD-active, and when the dgR or deR score is about 0.055 or less, the cancer is TEAD-inactive.

[0191] In some embodiments, the present disclosure relates to a method of characterizing TEAD activity of cancer in a subject in need thereof, the method using a biological sample of the tumor of the cancer and at least a) measuring, in the biological sample, the expression level of each gene of a gene set of "TEAD-signature", i.e., any one of 220 to 249 of the genes of gene set (1) (positive effector genes) and any one of 210 to 233 of the genes of gene set (2) (negative effector genes); b) for each gene of the gene set shown in Table 1, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of the gene by the number of genes of the gene set of "TEAD-signature"; c) multiplying each fractional rank obtained in step b) by the coefficient associated with each gene of the gene set of Table 1 (the coefficients of the genes and gene sets are shown in Table 2) to obtain the product of each gene; d) summing the products obtained in step c) to obtain a discrimination score (S); e) comparing the discrimination score (S) obtained in step d) with the central points of the TEAD-active (A) and TEAD-inactive (I) groups shown in Table 2 for the gene set of Table 1; f) determining whether (S) is closer to (A) than to (I) according to the formula max(S,A) - min(S,A) < max(S,I) - min(S,I); and g) characterizing the cancer as TEAD-active when (S) is closer to (A) than to (I).

[0192] In some embodiments, the present disclosure relates to a method for measuring TEAD activity of cancer in a subject in need thereof, Cancers include adrenocortical carcinoma (ACC) tumors; bladder urothelial carcinoma (BLCA) tumors; breast invasive carcinoma (BRCA) tumors; BRCA basal tumors; BRCA non-basal tumors; cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors; cholangiocarcinoma (CHOL) tumors; colon adenocarcinoma (COAD) tumors; COAD CMS1 tumors; COAD CMS4 tumors; lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumors; esophageal carcinoma (ESCA) tumors; glioblastoma multiforme (GBM) tumors; head and neck squamous cell carcinoma (HNSC) tumors; kidney chromophobe (KICH) tumors; kidney renal clear cell carcinoma (KIRC) tumors; kidney renal papillary cell carcinoma (KIRP) tumors; brain low-grade glioma (LGG) tumors; liver hepatocellular carcinoma (LIHC) tumors; LIHC S1 tumors; LIHC S2 tumors; LIHC a tumor selected from the group consisting of: S3 tumor; lung adenocarcinoma (LUAD) tumor; lung squamous cell carcinoma (LUSC) tumor; LUSC basal tumor; LUSC classical tumor; LUSC primitive tumor; LUSC secretory tumor; malignant mesothelioma (MESO) tumor; pancreatic adenocarcinoma (PAAD) tumor; rectal adenocarcinoma (READ) tumor; READ CMS1 tumor; READ CMS2 tumor; READ CMS4 tumor; sarcoma (SARC) tumor; cutaneous melanoma (SKCM) tumor; SKCM immune tumor; SKCM keratin tumor; STAD MSI tumor; STAD MSS_EMT tumor; testicular germ cell tumor (TGCT) tumor; uterine endometrial carcinoma (UCEC) tumor; uterine carcinosarcoma (UCS) tumor, The method uses a biological sample of a tumor of said cancer, This method requires at least: a) measuring in the biological sample the expression level of each gene of the set of genes associated with the tumor, wherein the set of genes is selected from the group consisting of Set a), Set c), Set d), Set e), Set g), Set i), Set j), Set l), Set m), Set q), Set r), t), Set v), Set x), Set y), Set aa), Set bb), Set dd), Set ff), Set gg), Set ii), Set kk), Set mm), Set rr), Set ss), Set uu), Set vv), Set xx), Set zz), Set kkk), Set lll), Set mmm), Set nnn), Set ppp), Set rrr), Set sss), Set ttt), Set uuu), Set zzz), Set bbbb), Set hhhh), Set kkkk), and Set llll) of Table 1; b) for each gene of said gene set, converting the expression level of the gene into a fractional rank by dividing the rank of the gene by the number of genes in said gene set; c) multiplying each fractional rank obtained in step b) by a coefficient associated with each gene of the gene set (the coefficients for the genes and gene sets are shown in Table 2) to obtain a product (Pgene i ) to obtain; d) determining a discriminant score (DS) for the gene set, the discriminant score (DS) being determined by multiplying the product Pgene obtained in step c) by i plus a constant coefficient for the gene set shown in Table 2; e) for said gene set, comparing the discriminant score (DS) obtained in step d) with a threshold associated with said gene set (thresholds are shown in Table 3) to determine whether said cancer is TEAD active or TEAD inactive.

[0193] The methods of the disclosure can be for characterizing a subject as having a TEAD-active or TEAD-inactive cancer.

[0194] In some embodiments, the cancer may comprise a solid tumor. In some embodiments, the solid tumor is in the lung, colon, ovary, cervix, uterus, peritoneum, testis, penis, tongue, lymph node, pancreatic bone, breast, prostate, soft tissue, connective tissue, kidney, liver, brain, thyroid, or skin. In some embodiments, the cancer is adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, consensus molecular subtype 1 of colorectal cancer, consensus molecular subtype 2 of colorectal cancer, consensus molecular subtype 3 of colorectal cancer, consensus molecular subtype 4 of colorectal cancer, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymph ... The tumor may be a tumor of the esophagus, glioblastoma multiforme, squamous cell carcinoma of the head and neck, renal clear cell carcinoma, renal papillary cell carcinoma, brain low-grade glioma, hepatocellular carcinoma of the liver, adenocarcinoma of the lung, squamous cell carcinoma of the lung, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, melanoma of the skin, gastric adenocarcinoma, testicular germ cell tumor, thyroid carcinoma, thymoma, uterine endometrial carcinoma, and uterine carcinosarcoma.

[0195] In some embodiments, the cancer is a hematological cancer, hi some embodiments, the hematological cancer is leukemia, lymphoma, and myeloma.

[0196] The cancer may be selected from the group consisting of: adrenocortical carcinoma (ACC) tumor; bladder urothelial carcinoma (BLCA) tumor; breast invasive carcinoma (BRCA); BRCA basal tumor; BRCA non-basal tumor; cervical squamous cell carcinoma or endocervical adenocarcinoma (CESC) tumor; cholangiocarcinoma (CHOL) tumor; colon adenocarcinoma (COAD) tumor; COAD CMS1 tumor; COAD CMS2 tumor; COAD CMS4 tumor; lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumor; esophageal carcinoma (ESCA) tumor; glioblastoma multiforme (GBM) tumor; head or neck squamous cell carcinoma (HNSC) tumor; kidney chromophobe (KICH) tumor; kidney renal clear cell carcinoma (KIRC) tumor; kidney renal papillary cell carcinoma (KIRP) tumor; brain low grade glioma (LGG) tumor; liver hepatocellular carcinoma (LIHC) tumor; LIHC S1 tumor; LIHC S2 tumor; LIHC S3 tumors;Lung adenocarcinoma (LUAD) tumors;LUAD proximal inflammatory tumors;LUAD proximal proliferative tumors;Lung squamous cell carcinoma (LUSC) tumors;LUSC basal tumors;LUSC classical tumors;LUSC primitive tumors;LUSC secretory tumors;Malignant mesothelioma (MESO) tumors;Ovarian serous cystadenocarcinoma (OV) tumors;OV differentiated tumors;OV immunoreactive tumors;OV mesenchymal tumors;OV proliferative tumors;Pancreatic adenocarcinoma (PAAD) tumors;Rectal adenocarcinoma (READ) tumors;READ CMS1 tumors;READ CMS2 tumors;READ CMS4 tumors;READ unclassifiable tumors;Sarcoma (SARC) tumors;Cutaneous melanoma (SKCM) tumors;SKCM immune tumors;SKCM keratin tumors;SKCM low MITF tumors;Gastric adenocarcinoma (STAD) tumors;STAD MSI tumors;STAD MSS_EMT tumors;STAD MSS_TP53 tumors;STAD MSS_TP53+ tumors; testicular germ cell tumor (TGCT) tumors; thyroid gland carcinoma (THYM) tumors; uterine corpus endometrial carcinoma (UCEC) tumors; and uterine carcinosarcoma (UCS) tumors.

[0197] In some embodiments, the cancer is: - if the tumor is an adrenocortical carcinoma (ACC), the gene set of the transcriptional signature is gene set a) or b); - if the tumor is a bladder urothelial carcinoma (BLCA), the gene set of the transcriptional signature is gene set c); - if the tumor is a breast invasive carcinoma (BRCA) tumor, the gene set of the transcriptional signature is gene set d); - if the tumor is a BRCA basal tumor, the gene set of the transcriptional signature is gene set e) or f); - if the tumor is a BRCA non-basal tumor, the set of genes of the transcriptional signature are genes g) or h); - if the tumor is a cervical squamous cell carcinoma or cervical adenocarcinoma (CESC), the set of genes of the transcriptional signature are genes i); - if the tumor is a cholangiocarcinoma (CHOL), the set of genes of the transcriptional signature is gene j) or k); - if the tumor is a colon adenocarcinoma (COAD), the gene set of the transcriptional signature is gene set l); - if the tumor is a COAD CMS1 tumor, the gene set of the transcriptional signature is gene set m) or n); - if the tumor is a COAD CMS2 tumor, the gene set of the transcriptional signature is gene set o) or p); - if the tumor is a COAD CMS4 tumor, the gene set of the transcriptional signature is gene set q) or q1); - if the tumor is a lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), the gene set of the transcriptional signature is gene set r) or s); - if the tumor is an esophageal cancer (ESCA) tumor, the gene set of the transcriptional signature is gene set t) or u); - if the tumor is a glioblastoma multiforme (GBM), the gene set of the transcriptional signature is gene set v) or w); - if the tumor is a head or neck squamous cell carcinoma (HNSC), the gene set of the transcriptional signature is gene set x); - if a kidney chromophobe (KICH) tumor, the gene set of the transcriptional signature is gene set y) or z); - if the tumor is a kidney renal clear cell carcinoma (KIRC), the gene set of the transcriptional signature is gene set aa); - if the tumor is a kidney papillary renal cell carcinoma (KIRP), the gene set of the transcriptional signature is gene set bb) or cc); - if the tumor is a brain low-grade glioma (LGG), the gene set of the transcriptional signature is gene set dd) or ee); - if the tumor is a liver hepatocellular carcinoma (LIHC), the gene set of the transcriptional signature is gene set ff); - if the tumor is a LIHC S1 tumor, the gene set of the transcriptional signature is gene set gg) or hh); - if the tumor is a LIHC S2 tumor, the set of genes in the transcriptional signature are genes ii) or jj); - if the tumor is a LIHC S3 tumor, the set of genes of the transcriptional signature are genes kk) or ll); - if the tumor is a lung adenocarcinoma (LUAD), the gene set of the transcriptional signature is gene set mm); - if LUAD proximal inflammatory tumor, the gene set of the transcriptional signature is gene set nn) or oo); - if the tumor is a LUAD proximal proliferative tumor, the set of genes in the transcriptional signature are genes pp) or qq); - if the tumor is a lung squamous cell carcinoma (LUSC), the gene set of the transcriptional signature is gene rr); - if LUSC basal tumor, the set of genes of the transcriptional signature is genes ss) or tt); - If LUSC classical tumor, the gene set of the transcriptional signature is gene uu); - if the tumor is a LUSC primitive tumor, the gene set of the transcriptional signature is gene set vv) or ww); - if a LUSC secretory tumor, the gene set of the transcriptional signature is gene xx) or yy); - if the tumor is a malignant mesothelioma (MESO) tumor, the set of genes of the transcriptional signature are genes zz) or aaa); - If the tumor is an ovarian serous cystadenocarcinoma (OV), the gene set of the transcriptional signature is gene set bbb); - if the tumor is OV differentiated, the gene set of the transcriptional signature is gene set ccc) or ddd); - if the tumor is OV-immunoreactive, the gene set of the transcriptional signature is gene set eee) or fff); - if OV mesenchymal tumor, the gene set of the transcriptional signature is gene set gggg) or hhh); - if the tumor is an OV proliferative tumor, the gene set of the transcriptional signature is gene set iii) or jjj); - if the tumor is a pancreatic adenocarcinoma (PAAD), the gene set of the transcriptional signature is gene set kkk) or kkk1); - if the tumor is a rectal adenocarcinoma (READ), the gene set of the transcriptional signature is gene set lll); - READ CMS1 tumor, then the gene set of the transcriptional signature is gene set mmm); - if the tumor is a READ CMS2 tumor, then the gene set of the transcriptional signature is gene set nnn) or ooo); - if the tumor is a READ CMS4 tumor, the gene set of the transcriptional signature is gene set ppp) or ppp1); - if the tumor is READ unclassifiable, the gene set of the transcriptional signature is gene set qqq) or qqq1); - if the tumor is a sarcoma (SARC), the gene set of the transcriptional signature is gene set rrr); - if the tumor is a cutaneous melanoma (SKCM), the gene set of the transcriptional signature is gene set sss); - if SKCM is an immune tumor, the gene set of the transcriptional signature is gene set ttt); - if SKCM keratin tumor, the gene set of the transcriptional signature is gene set uuu) or vvv); - if the tumor is a SKCM MITF low tumor, the gene set of the transcriptional signature is gene set www) or xxx); - if the tumor is a gastric adenocarcinoma (STAD), then the gene set of the transcriptional signature is gene set yyy); - if the tumor is a STAD MSI tumor, the gene set of the transcriptional signature is gene set zzz) or aaaa); - if the tumor is a STAD MSS_EMT tumor, the gene set of the transcriptional signature is gene set bbbb) or cccc); - if STAD MSS_TP53-tumor, the gene set of the transcriptional signature is gene set dddd) or eeee); - if the tumor is a STAD MSS_TP53+ tumor, then the gene set of the transcriptional signature is gene set ffff) or gggg); - if the tumor is a testicular germ cell tumor (TGCT), the gene set of the transcriptional signature is gene set hhhh) or iiii); - if the tumor is a thyroid cancer (THYM), the gene set of the transcriptional signature is gene set jjjj); - if the tumor is a uterine endometrial carcinoma (UCEC), the gene set of the transcriptional signature is gene set kkkk); - If the tumor is a uterine carcinosarcoma (UCS), the gene set of the transcriptional signature is genes llll) or mmmm).

[0198] In some embodiments, use of a "short TEAD signature" does not include use of gene sets q1), kkk1), ppp1), and qqq1).

[0199] In some embodiments, the cancer is: Adrenocortical carcinoma (ACC) tumors;Urothelial carcinoma of the bladder (BLCA) tumors;Invasive carcinoma of the breast (BRCA) tumors;BRCA basal tumors;BRCA non-basal tumors;Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors;Choledochocellular carcinoma (CHOL) tumors;Colon adenocarcinoma (COAD) tumors;COAD CMS1 tumors;COAD CMS4 tumors;Lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumors;Esophageal carcinoma (ESCA) tumors;Glioblastoma multiforme (GBM) tumors;Head and neck squamous cell carcinoma (HNSC) tumors;Kidney chromophobe (KICH) tumors;Kidney renal clear cell carcinoma (KIRC) tumors;Kidney renal papillary cell carcinoma (KIRP) tumors;Brain low-grade glioma (LGG) tumors;Liver hepatocellular carcinoma (LIHC) tumors;LIHC S1 tumors;LIHC S2 tumors;LIHC selected from S3 tumors; lung adenocarcinoma (LUAD) tumors; lung squamous cell carcinoma (LUSC) tumors; LUSC basal tumors; LUSC classical tumors; LUSC primitive tumors; LUSC secretory tumors; malignant mesothelioma (MESO) tumors; pancreatic adenocarcinoma (PAAD) tumors; rectal adenocarcinoma (READ) tumors; READ CMS1 tumors; READ CMS2 tumors; READ CMS4 tumors; sarcoma (SARC) tumors; cutaneous melanoma (SKCM) tumors; SKCM immune tumors; SKCM keratin tumors; STAD MSI tumors; STAD MSS_EMT tumors; testicular germ cell tumor (TGCT) tumors; uterine endometrial carcinoma (UCEC) tumors; uterine carcinosarcoma (UCS) tumors, The gene sets of the cancer associated transcriptional signature are Set a), Set c), Set d), Set e), Set g), Set i), Set j), Set l), Set m), Set q), Set r), t), Set v), Set x), Set y), Set aa), Set bb), Set dd), Set ff), Set gg), Set ii), Set kk), Set mm), Set rr), Set ss), Set uu), Set vv), Set xx), Set zz), Set kkk), Set lll), Set mmm), Set nnn), Set ppp), Set rrr), Set sss), Set ttt), Set uuu), Set zzz), Set bbbb), Set hhhh), Set kkkk), and Set llll).

[0200] Assays / RNA Sequencing In some embodiments, the expression level of a gene can be a nucleic acid expression level, such as an RNA expression level, an mRNA expression level, or a DNA expression level. Any suitable method for determining a nucleic acid expression level can be used. In some embodiments, the nucleic acid expression level is determined using RNA-seq. For example, the nucleic acid expression level is determined using RNA access protocol or TRUSEQ RIBO-ZERO00 protocol (ILLUMINA), RT-qPCR, qPCR, multiplexed qPCR or RT-qPCR, microarray analysis, SAGE, mass array technology, or a combination thereof.

[0201] Methods for evaluating mRNA in cells are well known and include, for example, RNA sequencing (RNA-seq), whole genome sequencing (WGS), serial analysis of gene expression (SAGE), and various nucleic acid amplification assays such as RT-PCR using complementary primers specific for a given set of genes. In some embodiments, qRT-PCR is used. In addition, such methods may include one or more steps that allow for determining the level of a target mRNA in a biological sample by simultaneously examining the level of a comparative control mRNA sequence of a "housekeeping" gene, such as an actin family member. In some embodiments, the sequence of the amplified target cDNA can be determined. Optional methods include protocols for examining or detecting mRNA, such as target mRNA, in tissue or cell samples by microarray technology. Using nucleic acid microarrays, test and control mRNA samples from test and control tissue samples are reverse transcribed and labeled to generate cDNA probes. The probes are then hybridized to an array of nucleic acids immobilized on a solid support. The array is configured such that the sequence and location of each member of the array is known. For example, any of the TEAD-500 genes or subsets of the embodiments described herein can be arrayed on a solid support, and hybridization of a labeled probe to a particular array member indicates that the sample from which the probe was derived expresses that gene.

[0202] Biological samples In some embodiments, the biological sample is a sample of blood, plasma, serum, cerebrospinal fluid (CSF), nasal aspirate, throat swab, vaginal or cervical swab, sputum, tissue, cells, or a tumor biopsy.

[0203] In some embodiments, any biological sample containing one or more tumor cells can be used in the methods disclosed herein. In some embodiments, the sample is selected from a tumor biopsy. In some embodiments, the sample obtained from the subject is a formalin-fixed tumor biopsy. In some embodiments, the sample obtained from the subject is a formalin-fixed paraffin-embedded tumor (FFPE) biopsy or a paraffin-embedded tumor biopsy. In some embodiments, the sample obtained from the subject is a fresh-frozen (FF) tumor biopsy. In some embodiments, the sample is a fresh tumor biopsy. In some embodiments, the sample is an archived tumor biopsy. In some embodiments, the sample is a frozen tumor biopsy.

[0204] In some examples of any of the methods and uses, a sample is obtained from an individual prior to administration of a TEAD pathway inhibitor. In some embodiments, a sample is obtained minutes, hours, days, weeks, months, or years prior to administration of a TEAD pathway inhibitor described herein. In other words, the sample can be a baseline sample. In some embodiments, a sample is obtained from an individual after administration of a TEAD pathway inhibitor described herein. In some examples, a sample from an individual is obtained within 30 hours after administration of an endocrine therapy. In some embodiments, a sample is obtained minutes, hours, or days after administration of a TEAD pathway inhibitor described herein. In some embodiments, a sample is obtained from the same individual at different time points, for example, before and after administration of a TEAD pathway inhibitor described herein.

[0205] TEAD pathway inhibitors In some embodiments, a TEAD pathway inhibitor is administered to a subject identified using the methods and panels described herein as having a TEAD-active cancer or likely to respond to a TEAD pathway inhibitor. Any TEAD pathway inhibitor known in the art may be administered. For example, transcription enhancer binding domain (TEAD) proteins are transcription factors consisting of four family members (TEAD1-4) that function in regulating gene expression in response to the Hippo pathway. TEAD proteins preferentially bind to the transcriptional coactivator yes-associated protein 1 (YAP1) or transcriptional coactivator with a PDZ-binding motif (WWTR1, also known as TAZ). YAP1-TEAD or WWTR1-TEAD binds to DNA and initiates transcription of multiple different genes involved in cell proliferation, survival, migration, stemness and differentiation (Holden and Cunningham, Cancer 2018). YAP1 / WWTR1-TEAD activity is tightly controlled by the Hippo pathway.

[0206] The HIPPO pathway was first discovered in Drosophila melanogaster as a key regulator of tissue growth. It is an evolutionarily conserved signaling pathway that regulates numerous biological processes, including cell proliferation and fate determination, organ size control, and regeneration. The core of the Hippo pathway in mammals consists of a cascade of kinases including MST1 / 2 and LATS1 / 2, their associated adaptor proteins SAV1 and MOB1, and upstream regulators such as NF2, SCRIBBLE, CRUMBS, and multiple G protein-coupled receptors. The Hippo pathway is tightly regulated by both intrinsic and extrinsic signals, such as mechanical forces, cell-cell contacts, polarity, energy status, stress, and many diffusible hormonal factors (Ma S et al. Annu Rev Biochem. 2019;20;88:577-604. Doi:10.1146 / annurev-biochem-013118-111829). Upon activation of Hippo pathway kinases (i.e., Hippo "on" state), cytoplasmic YAP1 and WWTR1 proteins are phosphorylated and therefore remain inactive through sequestration in the cytoplasm and / or degradation by the proteasome machinery. Upon inactivation of Hippo pathway kinases (i.e., Hippo "off" state), cytoplasmic YAP1 and WWTR1 are no longer phosphorylated and are therefore free to translocate to the cell nucleus, where they bind to TEAD transcription factors, bind to DNA and regulate gene expression. A decrease in pYAP1 / YAP1 levels, as well as an increase in the expression of genes regulated by YAP1 / WWTR1-TEAD activity and an increase in promoter activity in TEAD-regulated genes, are common indicators of YAP1 activation (reviewed in Totaro A, et al. Nat Cell Biol. 2018; 20(8): 888-899. Doi: 10.1038 / s41556-018-0142-z).

[0207] Hippo-YAP1 / WWTR1 / TEAD pathway and human cancer: Recently, studies have demonstrated that deregulation of Hippo-YAP1 / WWTR1-TEAD activity is at the origin of tumor progression and resistance to therapy in many different cancer indications and settings. In mice, systematic genetic studies have clearly demonstrated that either knocking out Hippo pathway components (which are YAP1 inhibitors) or overexpressing YAP1 activators such as YAP1, WWTR1, and TEADs results in YAP1 activation and YAP1-TEAD-dependent tumor initiation and progression (Zhang N et al. Dev Cell. 2010 Jul 2;19(1:27-38. Doi:10.1016 / j.devcel.2010.06.01; Lu L,et al. Proc Natl Acad Sci USA. 2010;107(4):1437-42. Doi:10.1073 / pnas.0911427107 ; Nishio M,et al. Proc Natl Acad Sci USA. 2010;107(4):1437-42. Doi:10.1073 / pnas.0911427107 ; USA.2016;113(1):E71-80.Do:10.1073 / pnas.151718811; Liu-Chittenden Y,et al..Genes Dev.201 2;26(12):1300-5.Do i:10.1101 / gad.192856.112). In humans, genetic alterations in the pathway are most commonly found in NF2 (neurofibrillin), an upstream regulator of the core Hippo pathway that has been associated with inherited cancer syndromes and classified as a tumor suppressor gene. Hundreds of somatically acquired mutations have been reported in NF2, primarily in meningiomas, mesotheliomas, and peripheral nerve sheath tumors, but also in other cancer types (Schroeder RD et al. al.Oncotarget.2014;5(1):67-77.Doi:10.18632 / oncotarget.1557). Genetic alterations directly within the core Hippo pathway beyond NF2 are observed less frequently in patients and are found with high prevalence only in specific indications, such as malignant mesothelioma, a highly lethal cancer of the serum membrane that is almost exclusively associated with asbestos exposure.This is a therapeutic indication that shows widespread alterations in the HIPPO signaling pathway as well as a high dependency on high YAP1 activation and YAP1-TEAD activity (Sekido Y. Cancers (Basel). 2018 Mar 22; 10(4): 90. Doi: 10.3390 / cancers10040090).

[0208] Increased YAP1 or YAP1-TEAD activity is not limited to genetic alterations in the Hippo pathway, but can also be the result of upregulation by multiple interconnected signals. Numerous pathways with important roles in tumorigenesis feed into the Hippo-YAP1 / WWWTR1 / TEAD1 cascade, well-described examples include the RTK-RAS-RAF-MEK-ERK, WNT, TGF-beta, and AMPK pathways (reviewed in Han YJ Transl Med. 2019; 17(1): 116. Doi: 10.1186 / s12967-019-1869-4). Thus, the number of tumor types that are at least partially dependent on YAP1-TEAD activation is very large, ranging from breast, ovarian, uterine, and prostate cancers to lung, gastric, colorectal, bladder, pancreatic, and liver cancers, as well as sarcomas, esophageal, head and neck cancers, uveal melanoma, and gliomas (Zanconato F et al. Cancer Cell. 2016; 29(6): 783-803. Doi: 10.1016 / j.ccell.2016.05.005). Recent studies have revealed an interplay between the HIPPO-YAP / WWTR1 / TEAD pathway and the human immune response (Yamauchi T, Moroishi T. Cells 2019; 8(5): 398. Doi: 10.3390 / cells8050398).

[0209] YAP1 activation has been observed in the context of resistance to therapy and is recognized as a major mechanism of resistance and survival to anticancer therapy. In esophageal cancer, YAP1 is a positive regulator of EGFR (epidermal growth factor receptor), and induction of YAP1 is associated with resistance to 5-FU and docetaxel. In the context of targeted therapy, YAP1 in BRAF-mutated tumors acts as a parallel survival input to promote resistance to RAF and MEK inhibitor therapy in melanoma. Similarly, YAP1 activation is a mechanism of survival to EGFR and MEK inhibitor therapy in the context of EGFR-mutated lung cancer, and multiple studies have identified YAP1 activation as one of the major bypass mechanisms for KRAS inhibition. In the context of hormone-dependent tumors, WWTR1 inhibition has been shown to restore sensitivity to tamoxifen in breast cancer. In prostate cancer cells, androgen deprivation therapy resistance was associated with increased YAP1 nuclear localization and activity (Reggiani F et al. Biochim Biophys Acta Rev Cancer. 2020;1873(1):188341. Doi:10.1016 / j.bbcan.2020.188341; Kurppa KJ et al. Cancer Cell. 2020;37(1):104-122.e12. Doi:10.1016 / j.ccell.2019.12.006).

[0210] Thus, the HIPPO-YAP / WWTR1 / TEAD pathway plays a critical role in cancer development and tumor maintenance, and targeting this pathway is important for cancer therapy both in the first-line setting and in the context of overcoming drug resistance in multiple cancer indications.

[0211] As used herein, a "TEAD pathway inhibitor" can be a small molecule or a large molecule that inhibits the HIPPO-YAP / WWTR1 / TEAD pathway.

[0212] Additional anticancer therapy In some embodiments, the methods disclosed herein further comprise administration of a TEAD pathway inhibitor and an additional anti-cancer therapy. The additional anti-cancer therapy may comprise any therapy and / or any standard of care known in the art for treating a tumor in a subject. In some embodiments, the additional anti-cancer therapy comprises surgery, radiation therapy, chemotherapy, immunotherapy, hormonal therapy, or any combination thereof. In some embodiments, the additional anti-cancer therapy comprises chemotherapy. In some embodiments, the additional anti-cancer therapy comprises immunotherapy. In some embodiments, the additional anti-cancer therapy comprises hormonal therapy.

[0213] Kits and Products In some embodiments, provided herein is a kit or article of manufacture comprising any one of the panels described herein, or materials useful for preparing such a panel.

[0214] In some embodiments, the kit or article of manufacture may include one or more reagents for preparing a sample for RNA sequencing analysis.

[0215] In some embodiments, the kit or article of manufacture further comprises one or more reagents for determining a TEAD inactivity or TEAD activity score from a sample.

[0216] In some embodiments, the kit or article of manufacture may include instructions for using the kit for any of the methods / uses described herein.

[0217] In some embodiments, the kit or article of manufacture may include a container, a label on the container, and a composition contained within the container, where the composition comprises one or more polynucleotides that hybridize under stringent conditions to the complement of a gene listed herein, the label on the container indicates that the composition can be used to assess activity of a set of genes described herein, and the kit includes instructions for using the polynucleotides to assess the presence of RNA or DNA of the genes in a particular sample type.

[0218] In some embodiments, the kit or product is oligonucleotide-based and may include, for example, (1) an oligonucleotide, e.g., a detectably labeled oligonucleotide that hybridizes to a nucleic acid sequence encoding a protein, or (2) a pair of primers useful for amplifying a nucleic acid molecule. In some embodiments, the kit or product may also include a buffer, a preservative, or a protein stabilizing agent. In some embodiments, the kit or product may further include components necessary for detecting the detectable label, e.g., an enzyme or a substrate. In some embodiments, the kit or product may also include a control sample or a series of control samples that may be assayed and compared to the test sample. In some embodiments, each component of the kit or product may be enclosed within an individual container, and all of the various containers may be placed in a single package, along with instructions for interpreting the results of the assay performed using the kit or product. EXAMPLES

[0219] Example 1 Materials and Methods Major details of the materials and methods used are provided below.

[0220] Compilation of the TEAD-500 gene list. We considered the public dataset and selected genes that were differentially expressed with p-value < 0.01 by ANOVA comparing treated samples with corresponding controls. The resulting list of 37 genes was compiled into one. Genes that were found to be regulated only once and genes that were inconsistently regulated between experiments were removed from the compilation. The remaining approximately 700 genes that were consistently regulated in at least two experiments were sorted by ascending p-value, and finally the top 500 effectors were selected to be regularly examined in large cohorts.

[0221] Calculation of TEAD-500 signature score. TEAD-dependent transcription was scored in single samples as the difference Rp-Rn, where Rp is the average fractional rank of positive TEAD-effectors and Rn is the average fractional rank of negative effectors.

[0222] We considered the most abundant transcript of a gene as representative of the expression of the gene. Therefore, we kept the expression level of the main transcript (maximum level value per gene and sample, where level value can be expression array signal, RNA sequencing count, fragments per kilobase per million counts (FPKM), transcripts per million counts (TPM), or any other metric used for convenience) and discarded the values ​​of all other transcripts. The maximum expression level of a gene was converted to a fractional rank by dividing it by the maximum expression value of all genes (most abundant genes) observed in the sample. No other normalization is required.

[0223] Due to technological evolution (from various commercial expression arrays to RNA sequencing technologies) but also inconsistencies in gene nomenclature, practical panels typically do not include all 500 genes reported in the training dataset from TEAD-500. However, dropouts of at least up to 10% have negligible impact on the score. This is one of the reasons why TEAD-500 is so robust.

[0224] Epidemiological and experimental testing datasets. We scored and binned TEAD-dependent transcription into "TEAD active" and "TEAD inactive" categories in primary, recurrent, or metastatic tumors from The Cancer Genome Atlas (TCGA; ~11000 samples; https: / / www.cancer.gov / tcga), in ~9000 normal tissue samples from the Genotype-Tissue Expression Project (GTEx Portal on 09 / 24 / 21; https: / / gtexportal.org / home / ), and in laboratory cell lines from the Cancer Cell Line Encyclopedia (CCLE; ~1000 samples; https: / / sites.broadinstitute.org / ccle / ) to select experimental models. We further applied the signature to follow the degree of TEAD inhibition in cell and tumor models after genetic knockdown of YAP1 or TEAD1 or by pharmacological inhibition using the recently published (Kaneda A, et al. Am J Cancer Res. 2020;10(12):4399-4415) TEAD palmitoylation inhibitor K-975 (Kyowa Kirin Co., Ltd., Shizuoka, Japan).

[0225] Cells and culture conditions. All cell lines were grown at 37°C in 5% CO. MSTO-211 (#CRL-2081), NCI-H226 (#CRL-5826), NCI-H2052 (#CRL-5915), NCI-H28 (#CRL-5820) and HCT116 (#CCL-247) were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA) and cultured according to the supplier's recommendations. ZL5 (no. 11120715), ZL34 (no. 11120713), SPC212 (no. 11120717), Mero-14 (no. 09100101), Mero-48a (no. 09100104), Mero-95 (no. 09100108), JU77 (no. 10092309), LO68 (no. 10092311) and ONE58 (no. 10092313) were purchased from the European Collection of Authenticated Cell Cultures (ECACC, Public Health England, Salisbury, UK) and cultured according to the supplier's recommendations. Short tandem repeat assays authenticated all cell lines at Microsynth AG (Balgach, Switzerland). Mycoplasma infection was excluded by PCR using the Venor™ GEM Mycoplasma Detection Kit (Biovalley, Nanterre, France).

[0226] Cell line transfections and constructs. We generated cell lines stably expressing sh-YAP1, sh-null, or TEAD2-DN by transducing MSTO-211H and HCT116 cells with PiggyBac transposon vectors using the Lipofectamine 3000 reagent protocol from Thermo Fisher™ (Wilmington, DE). Transduced cells were selected in medium supplemented with 1 mg / ml puromycin. For the generation of PiggyBac constructs, the DNA sequences of sh-YAP1 and sh-null were cloned into the pPiggyBac-KO-Tet-one-Puro dest plasmid. Human TEAD2-DN with an N-terminal FLAG tag (Ser112-Asp447; Liu-Chittenden, Y., et al., Genes Dev, 2012.26(12):1300-5) was cloned into the pPiggyBac-A-Tet-One-puro DEST vector.

[0227] Cell proliferation and apoptosis assays. Cells were seeded in medium supplemented or not with doxycycline (1 μg / ml) and incubated at 37° C. and 5% CO2 for 96 h. Cell proliferation was measured by trypan blue exclusion using a Vi-CELL-XR Cell Viability Analyzer (Beckman Coulter). Caspase-3 / 7 activity was detected using Cell-Event Caspase-3 / 7 Green Ready-Probes reagent (Molecular Probes) and measured using an IncuCyte™ ZOOM live cell analysis system (Essen Bioscience), with scans every 2 h for 72 h. Acquired fluorescent signals of activated caspase-3 / 7 were normalized to well confluency at each time point (=normalized apoptosis). Peak apoptosis was determined as the highest normalized caspase-3 / 7 activity value during the assay.

[0228] animal Female CB17 / lcr-Prkdc scid / lcrIcoCrl mice (6-8 weeks old) were bred by Charles River (Les Oncins, France) and housed in a Sanofi AAALAC-accredited animal facility and fed irradiated food and filtered water ad libitum.

[0229] In vivo xenograft studies. Thirteen malignant mesothelioma cell lines (MSTO-211H; ZL5; NCI-H226; JU77; MERO 48A; MERO 95; H2052; ZL-34; SCP-212; MERO-14; Lo68; One58; H28) were inoculated subcutaneously into the right flank of SCID mice at a concentration of 3 × 10 6 Cells and 10 x 10 6 The cells were evaluated using 3 × 10 6 In vivo target validation studies were performed using MSTO-211H-TEAD2-DN, MSTO-211H-shYAP1 and HCT116-shYAP1 cell lines inoculated subcutaneously with cells. Immediately after cell implantation, mice were divided into two groups. Each group received drinking water supplemented with doxycycline and 5% glucose or 5% glucose alone.

[0230] In vivo efficacy testing of the K-975 compound was performed using 3×10 6 The study was carried out in SCID mice subcutaneously inoculated with MSTO-211H or NCI-H226 cells on the right flank. Mice bearing subcutaneous MSTO-211H tumors of approximately 200 mm3 or subcutaneous NCI-H226 tumors of 150 mm3 were randomly assigned to 5 groups of 8 mice / group and treated with either vehicle or K-975 compound at 30, 100 and 200 mg / kg twice daily for 18 consecutive days. The perpendicular diameter of the tumor was measured twice weekly with a caliper and the tumor volume (V) was calculated according to the following formula: V(mm3)=(d2(mm2)×D(mm)) / 2, where d is the smallest and D is the largest perpendicular tumor diameter.

[0231] In vitro validation. The effect of YAP1 downregulation on tumor growth was first tested in vitro using a genetic approach based on the TEAD dominant negative (TEAD2-DN) construct. This construct was previously reported as an efficient inhibitor of YAP1 activity (Liu-Chittenden, Y., et al., Genes Dev, 2012.26(12):1300-5), and is based on a truncated version of TEAD2, which lacks the DNA binding domain but retains the ability to bind to YAP1. It thereby acts as a non-functional competitor of endogenous TEAD to bind to YAP1.

[0232] We engineered this construct behind a doxycycline-inducible promoter and used it for stable transfection of MSTO-211H cells. Upon doxycycline addition, TEAD2-DN expression led to a 66% inhibition of tumor cell growth in vitro 96 hours after doxycycline introduction (Figure 3A-B). This was accompanied by a significant reduction in the TEAD-500 score and the classical TEAD activity protein markers CYR61 and CTGF.

[0233] The latter protein markers were measured by ELISA assay. Cell lysates were diluted 1 / 500 in cell lysis buffer. At least four biological replicates were analyzed for each model. Standards and samples were run in duplicate on the readout plate. hCYR61 was assessed using the Human Cyr61 Quantikine™ colorimetric sandwich ELISA kit (R&D Systems, Minneapolis, MN). Diluted lysates were processed and analyzed as described in the manufacturer's instructions. Absorbance values ​​were read using a microplate reader set at 450 nm.

[0234] In vivo validation. We generated cell lines stably expressing sh-YAP1, sh-null, or TEAD2-DN by transducing MSTO-211H and HCT116 cells with piggyBac transposon vectors using Thermo Fisher's Lipofectamine 3000 reagent protocol. Transduced cells were selected in medium supplemented with 1 mg / ml puromycin. For the generation of piggyBac constructs, the DNA sequences of sh-Yap1 and sh-null were cloned into pPiggyBac-KO-Tet-one-Puro DEST plasmid. Human TEAD2-DN with an N-terminal FLAG tag (Ser112-Asp447; Liu-Chittenden, Y., et al., Genes Dev, 2012.26(12):1300-5) was cloned into pPiggyBac-A-Tet-One-puro DEST vector. NCI-MSTO-211H malignant mesothelioma cells were transfected with a doxycycline-dependent dominant-negative TEAD1 shRNA (TEAD DN), implanted subcutaneously in SCID mice, and grown for 26 days. YAP1 shRNA was induced in established tumors and led to tumor regression in all mice tested.

[0235] RNA sequencing. Fresh tumors were preserved in RNAlater (Temo Fisher) and frozen at -200°C. Co-purification of miRNA and total RNA was performed using the miRNeasy mini kit (QIAGEN, 217004). RNA concentration and purity were assessed with an ND-1000 spectrophotometer (NanoDrop, Thermo Fisher). Quality metrics were RNA integrity number (RIN) and DV200 value (% of RNA fragments ≥ 200 nt in length). 100 ng of total RNA per sample was used as input material for library preparation. Libraries were prepared using the KAPA HyperCap V3.0 kit for Illumina with the KAPA HyperPrep Kit and KAPA Target Enrichment according to the manufacturer's recommendations (all kits purchased from Kapa Biosystems, Wilmington, MA). Libraries were sequenced using a NextSeq 500 paired-end sequencing platform (Illumina Inc., San Diego, CA).

[0236] Discriminant function. To calculate tissue-specific combinations of predictors of TEAD activity, a classical statistical method known as Fisher's stepwise linear discriminant function analysis was used. This algorithm builds a linear model stepwise. The most important predictor (in this case, the most important effector genes) enters the model first. The data is normalized for that predictor. At each step, the predictor that minimizes the overall Wilks' lambda is entered into the equation. All predictors in the model are reevaluated. Those that are no longer significant are removed from the model. Predictor selection stops when all predictors in the model are significant and all remaining predictors are non-significant. SPSS statistical software (IBM, Armonk, NY) was used for these calculations.

[0237] result Robustness of the TEAD-500 score. Due to the large number of genes in the TEAD-500 list and the high redundancy (correlation between genes) of this signature, its score is largely insensitive to gene dropout. We simulated dropout by generating two randomized sublists, each containing only 90% of the genes. A different 10% of the genes were excluded from each sublist. Using these different sublists, we calculated dgR scores for 1112 experimental samples. These samples were cell lines with TEAD-500 scores at the upper end of the scale (TEAD-activated), but grown under various TEAD-inhibited conditions. As shown in Figure 2, the correlation between the two randomly corrupted scores was nearly perfect (R 2 =0.997).

[0238] In vitro validation of the TEAD-500 score. When the malignant mesothelioma cell line NCI-H226 was treated for 24 h with the benchmark TEAD-inhibitor (K-975; see, e.g., Kaneda A et al. Am J Cancer Res. 2020;10(12):4399-4415), the TEAD-500 score was regressed in a dose-dependent manner (Figure 3A-B), as were the RNA and protein levels of the classical secreted marker of Hippo pathway activity, CYR61 (Figure 3C).

[0239] In vivo validation of the TEAD-500 score. NCI-MSTO-211H malignant mesothelioma cells were transfected with doxycycline-dependent dominant-negative TEAD1 shRNA, implanted subcutaneously in SCID mice, and grown for 26 days. YAP1 shRNA was induced in established tumors and resulted in tumor regression in all mice tested (Figure 4A). Tumor regression was accompanied by a significant decrease in TEAD-dependent transcription (TEAD-500 score) (Figure 4B). Thus, TEAD-dependent transcription was essential for tumor maintenance in malignant mesothelioma in vivo.

[0240] TEAD-500 as a pharmacodynamic marker. The same type of events was observed when SCID mice implanted with malignant mesothelioma parental NCI-H226 cells were treated with 200 mg / kg BID of the benchmark TEAD-inhibitor K-975 for 17 consecutive days (Kaneda A et al. Am J Cancer Res. 2020;10(12):4399-4415) (Figure 5). Approximately 30-40% inhibition of TEAD-dependent transcription seems sufficient for tumor regression in this model. A dose of 100 mg / kg BID was still active with ΔT / ΔC=12 (near tumor stasis), whereas a dose of 30 mg / kg BID was inactive. Thus, the TEAD-500 score correlates with tumor behavior under treatment and can be used as a marker of pharmacodynamics or drug efficacy.

[0241] Epidemiology of TEAD-500 scores. Figure 6 shows that the TEAD-500 deR score distribution shifts toward higher values ​​in tumors (TCGA) compared to normal tissues (GTEx). Primary tumors tend to score higher than adjacent normal tissues. Recurrent and metastatic tumors score higher than primary tumors. Thus, TEAD-dependent transcription increases with tumor formation and tumor progression, at least in some cases. This result is consistent with numerous observations that the TEAD pathway confers a mechanism for therapeutic escape (Coggins GE et al. Cancer Res. 2019;79(24):6204-6214. Doi:10.1158 / 0008-5472. CAN-19-1415; Ansari D et al. Anticancer Res. 2019;39(7):3317-3321. Doi:10.21873 / anticanres.13474; Kim CG et al. Cancer Immunol Res. 2018;6(3):255-266. Doi:10.1158 / 2326-6066. CIR-17-0320).

[0242] Primary malignant mesothelioma is the indication with very high TEAD activity among all TCGA cohorts tested. Its mean TEAD-500 score is even higher than that in metastatic tumors. This supports published observations that TEAD-dependent transcription is increased in malignant mesothelioma (Mizuno, T., Murakami, H., Fujii, M. et al. Oncogene 31, 5117-5122 (2012). https: / / doi.org / 10.1038 / onc.2012.5) and justifies the choice of mesothelioma as the gateway indication for the proof-of-concept clinical trials of novel TEAD inhibitors (https: / / www.bolderscience.com / trial / nct04665206 / ). Cell lines (CCLE) achieve much higher scores, probably because they are clonal populations of progressive tumor cells. The mesothelioma cell lines tested are among the top scorers. We also confirm published observations that TEAD-dependent transcription is increased in cervical cancers with YAP1 amplification (Figure 7; Lorenzetto E, et al. Oncotarget. 2014; 5(9):2608-21; doi:10.18632 / oncotarget.1844).

[0243] Prognostic value of the TEAD-500 score. The mesothelioma cohort (TCGA MESO) was divided into two equal groups according to the TEAD-500 deR score of the tumor samples. Patients with low scores had a median overall survival of 826 days, which is about 2.3 times longer than the median survival of 361 days for patients with high TEAD-500 scores (Figure 8). All differences were significant by log-rank (p=0.011), Breslow (p=0.004) and Tarone-Ware (p=0.006) tests. When all TCGA indications were pooled and divided into three equal groups according to the TEAD-500 score, we again observed highly significant differences in overall survival between the three groups, with p-values ​​less than 1E-15 for all three statistical tests. The median survival time was only 1583 days in the highest scoring group and 2823 days in the lowest scoring group, compared with 2331 days in the intermediate scoring group (Figure 9).

[0244] Independence of TEAD-500 from classical markers. There are some significant but weak (given the large sample size) correlations between the scores of the optimal discriminant function (Table 4) or the minimal discriminant function (Table 5) and the RNA levels of CCN1 and CCN2, two markers commonly used to measure YAP1 or TEAD activity. Significant correlations are only observed in some cancer indications or subtypes. These correlations can be interpreted as further validation of our methodology. However, in the majority of cohorts, the discriminant scores appear to be independent predictors of TEAD pathway activity, especially in tissues where CCN1 and CCN2 biology are unrelated.

[0245] [Table 57]

[0246] [Table 58]

[0247] [Table 59]

[0248] [Table 60]

[0249] The performance of discriminant functions as classifiers. To evaluate the performance of the classifier, several metrics can be calculated from the number of positive (P; TEAD-500>0.055) and negative (N; TEAD-500<0.055) samples, as well as the true positive (TP), true negative (TN), false positive (FP) and false negative (FN) predictions. Positive prediction (PP) is the total TP+FP. Negative prediction is the total TN+FN. The prevalence of positive samples in a population (cohort) is the proportion of positives, P / (P+N). The accuracy of prediction is the proportion of correctly classified samples (TP+TN) / (P+N). Precision, also called positive predictive value (PPV), is the proportion of true positives among all predicted positives, TP / PP. False discovery rate (FDR) is the proportion of false positives among predicted positives, FP / PP or 1-PPV. False dropout rate (FOR) is the proportion of predicted negatives that are true negatives, FN / PN or 1-NPV (negative predictive value). Negative predictive value (NPV) is the proportion of predicted negatives that are true negatives, TN / PN or 1-FOR. Sensitivity, also known as true positive rate (TPR), recall, or predictive power, is the proportion of positives that are truly predicted as such, TP / P. Specificity, selectivity, or true negative rate (TNR) is the proportion of negatives that are truly predicted as such, TN / N. False negative rate (FNR) or miss rate is the proportion of positives that are incorrectly predicted to be negatives, FN / P.

[0250] The F-score or F-measure is a measure of the accuracy of a test commonly used in binary classification statistical analysis. It is calculated from the precision and sensitivity of the test as follows: F score = (2 x PPV x TPR) / (PPV + TPR) or 2 x TP / (2 x TP + FP + FN)

[0251] Perhaps the most frequently used single measure of a classifier's performance is its informativeness, defined in terms of sensitivity (true positive rate) and specificity (true negative rate) as TPR+TNR-1, i.e., Youden's J statistic (also called Youden's index). This is a single statistic that captures the performance of a binary diagnostic test.

[0252] The values ​​of these metrics for all our discriminant classifiers, whether they are optimal or minimal, are shown in Table 6. Forty of the 57 disclosed classifiers achieve perfect discrimination (informativeness=1) of tumors with active versus inactive TEAD pathways, as defined by our general TEAD-500 signature. Another 10 classifiers have superior performance, achieving informativeness values ​​of 0.9 or higher. Another five classifiers raise the probability of correct patient selection from 18% or less than 10% (prevalence or prior probability values) to the range of 85%-90%. Even the most trivial functions applied to the THYM and READ cohorts provide informed correct decisions at rates of 59% and 76%, rather than the 4% and 7% achievable by chance.

[0253] [Table 61]

[0254] [Table 62]

[0255] [Table 63]

[0256] [Table 64]

[0257] Example 2 Mesothelioma-Comparison of optimal and minimal discriminant function scores with TEAD-500 scores. Figures 10 and 11 show the relationship between the optimal and minimal discriminant function scores, respectively, and the gold standard measure of TEAD-pathway activity according to the TEAD-500 score. The TEAD-500 score varies continuously along the horizontal axis, but is divided by an arbitrary threshold into TEAD-active or TEAD-inactive groups (mesothelioma cells that respond or do not respond to siRNA knockout of YAP1) based on a series of experimental observations. The optimal discriminant function score on the vertical axis (Figure 10) comfortably separates the samples into two clearly distinct groups, using fewer predictor genes than TEAD-500 to predict mesothelioma tumor response (assuming that the primary tumor is comparable to our cell line). Based on even fewer predictors, the minimal discriminant function score for the same indication still separates the two groups (Figure 11). However, the distance between the groups is shorter and the risk of misclassification of new samples is greater, especially when the collected data is incomplete. The scores of the optimal and minimal discriminant functions are compared in Figure 12, which shows the coordinates of the midpoints of each group for the two functions.

[0258] Discriminant scores and class calculation tables. Tables 7 and 8 present a simulation of sample discrimination. Two mesothelioma samples are used as an example. Sample 1 has an active TEAD pathway, while in sample 2 the pathway is inactive according to the TEAD-500 score. The optimal set of predictors is listed in column A and its coefficients are listed in column B. A subset of the predictors is used for the minimal discriminant function, its coefficients are listed in column C. Columns D and E show the quantile-normalized expression data of these samples and genes. The fractional rank of the RNA level of the predictors among the genes in the TEAD-500 list is in columns F and G. Columns H and I contain the product of the fractional rank of the genes in the two samples (columns F and G) multiplied by the coefficient of the genes in the optimal function (column B). Similarly, columns J and K contain the product of the fractional rank of the genes in the two samples (columns F and G) multiplied by the coefficient of the genes in the minimal function (column C).

[0259] Row 29 contains the constants for the optimum and minimum functions in cells B29 and C29, respectively. These constants are therefore added to the sum of the products in columns H through K. The total scores for the samples are shown in row 31. The distance of each score from each center point is given by: Distance = Max(score, centerpoint) - Min(score, centerpoint) It is measured as:

[0260] In this example, sample 1 scores closer to the midpoint of the active group (2.76 using the optimal function, or 0.21 using the minimal function) than to the midpoint of the inactive group (optimal distance = 14.96; minimal distance = 5.71). It is therefore safely classified as active. Similarly, comparing the distance of sample 2's score (I31 for optimal, K31 for minimal) to the midpoints of the two functions of the two groups, we see that both scores are closer to the midpoint of the inactive group (compare I32 and I33 for optimal, and K32 and K33 for minimal). Sample 2 is therefore classified as inactive.

[0261] [Table 65]

[0262] [Table 66]

[0263] [Table 67]

[0264] Example 3: Determining the discriminant score (DS) of the optimal discriminant function (DS) Score Determination The dimensionality of the test was reduced from the TEAD500 panel to a subset of the most informative genes applicable to a particular cancer type. A panel (or set) of these small genes makes it possible to predict the status of the TEAD complex (active or inactive) in samples from a given cancer indication or subtype.

[0265] The optimal discriminant function determined for the set of genes a) to mmmm) can be used to calculate a discriminant score (DS) that is compared to a predefined threshold for classifying a cancer or tumor sample as a TEAD-active or TEAD-inactive cancer.

[0266] The (DS) score for a given cancer indication may be determined as follows: 1. Determining the expression levels of genes listed in the best discriminant function of a gene set associated with a particular tumor in tumor samples of individuals suffering from cancer. The expression levels can be determined by RNA sequencing, NGS, RT-qPCR, or any other reliable method. 2. For each gene tested, converting the expression level of the gene to a fractional rank (which, as defined herein, is the rank of the gene divided by the number of genes tested (i.e., expression of the gene relative to all genes tested, the gene with the lowest expression level is given rank 1, the next highest expression level is given rank 2, and so on until the most highly expressed gene is given the highest rank; equal expression levels are given an average rank, e.g., two genes with 0 expression are given 1.5)). 3. Multiply the fractional rank of each gene by the corresponding coefficient provided by the optimal discriminant function of the gene set associated with the indication to determine the fractional rank of each gene (Pgene I ) to obtain the product. 4. The discriminant score (DS) of the tested gene set in the tumor sample is calculated using the formula: (DS) = ΣP Gene i + constant coefficient of the discriminant function of the gene set being tested, According to the above, all discriminant genes Pgene i The formula is calculated as the sum of the products of the gene expression and the coefficient of the discriminant function (where i are the genes listed in the discriminant function of the gene set to be tested, and Pgene i Gene i Fraction rank × Gene i ). For example, if the optimal discriminant function for the indication to which the sample belongs has 10 genes, we multiply the fractional rank of each gene by the gene's corresponding discriminant coefficient (known as the loading) and add the resulting product to a constant coefficient of the optimal discriminant function. 5. For the gene set tested, determine the TEAD activity status of the sample by comparing (DS) to the thresholds for each indication listed in Table 3.

[0267] [Table 68]

[0268] [Table 69]

[0269] [Table 70]

[0270] Depending on the indication, (DS) must be either below or above a given threshold to qualify a cancer as a TEAD-active cancer.

[0271] Performance of the discriminant score (DS) The performance of the discriminant scores (DS) using the thresholds in Table 6 was evaluated from the confusion matrix (true positive, true negative, false positive and false negative counts) according to standard epidemiological metrics (https: / / en.wikipedia.org / wiki / Sensitivity_and_specificity) and is presented in Table 9.

[0272] To evaluate the performance of the classifier, several metrics can be calculated from the number of positive (P) and negative (N) samples (determined by comparing DS to the thresholds in Table 6), as well as the true positive (TP), true negative (TN), false positive (FP) and false negative (FN) predictions.

[0273] TP is a subject with a TEAD active cancer who is correctly identified as having a TEAD active cancer; FP is a subject with a TEAD inactive cancer who is incorrectly identified as having a TEAD active cancer; TN is a subject with a TEAD inactive cancer who is correctly identified as having a TEAD inactive cancer; and FN is a subject with a TEAD active cancer who is incorrectly identified as having a TEAD inactive cancer.

[0274] Positive predictive (PP) is the total TP+FP. Negative predictive (NP) is the total TN+FN. The prevalence of positive samples in a population (cohort) is the proportion of positives, P / (P+N). The accuracy of prediction is the proportion of correctly classified samples, (TP+TN) / (P+N). Precision, also known as positive predictive value (PPV), is the proportion of true positives among all predicted positives, TP / PP. False discovery rate (FDR) is the proportion of false positives among predicted positives, FP / PP or 1-PPV. False dropout rate (FOR) is the proportion of predicted negatives that are true negatives, FN / PN or 1-NPV (negative predictive value). Negative predictive value (NPV) is the proportion of predicted negatives that are true negatives, TN / PN or 1-FOR. Sensitivity, also known as true positive rate (TPR), recall, or predictive power, is the proportion of true predicted positives as such, TP / P. Specificity, selectivity, or true negative rate (TNR) is the proportion of truly predicted negatives, TN / N. False negative rate (FNR) or miss rate is the proportion of positives that are incorrectly predicted to be negatives, FN / P.

[0275] The "F1 score" in Table 9 is a measure of the accuracy of the test. It is calculated from the accuracy and sensitivity of the test as follows: F1 score = (2 x PPV x TPR) / (PPV + TPR) or 2 x TP / (2 x TP + FP + FN)

[0276] "Informativeness" in Table 9 is a measure of the performance of the method for each cancer type, defined as TPR+TNR-1, in terms of sensitivity (true positive rate) and specificity (true negative rate).

[0277] The values ​​of these metrics for all discriminant scores (DS) are shown in Table 8. Twenty of the 43 disclosed DS achieve perfect discrimination (informativeness = 1) of tumors with active TEAD pathway. Another 10 (DS) have excellent performance achieving informativeness values ​​of 0.9 or higher. Another 5 (DS) have correct performance patients achieving informativeness values ​​in the range of 85-90%, 8 (DS) have moderate performance ranging from 0.6 to less than 0.85, and only one (DS) has a performance just below 0.5 (0.46).

[0278] [Table 71]

[0279] [Table 72]

[0280] [Table 73]

[0281] The results show that the discriminant scores (DS) obtained with the optimal discriminant functions defined for the set of genes a)–mmmm) and the associated cancer indications are useful and efficient tools for classifying patients' cancers as TEAD-active or TEAD-inactive and for managing patients' treatment.

Claims

1. 1. Use of a transcriptional signature for characterizing TEAD activity of a cancer in a subject in need thereof, wherein the transcriptional signature is obtained by measuring the expression levels of genes of a gene set in a biological sample of a tumor of the cancer, the gene set comprising: Genes 220 - 249 of gene set (1): AASS, ABAT, ACAT2, ADAMTS1, ADM, ADRB2, AMOT, ANXA3, ARHGAP11A, ARHGDI B, AURKB, AVPI1, AXL, AZIN1, B4GALT4, BCAT1, BIRC5, BTG3, C4BPB, CAP2, CAV1, CAVIN1, CCBE1, CCDC80, CCN1, CCN2, CDC25A, CDC6, CDCA3, CDCA4, CDCA5, CDCA8, CDH4, CDK2, CDK6, CDV3, CENPA, CENPI, CENPM, CENPN, CHRNB1, CHST13, CKS2, CLDN1, CLIC3, CNN3, COBL, COL8A1, COTL1, CPA4, CRIM1, CRY1, CTH, CXCL1, CYTH3, DAPK1, DCLRE1B, DDAH1, DHCR7, DHFR, DIAPH3, DKK1, DLL1, DONSON, DUSP14, DUT, EBP, EIF2AK3, EMG1, EPHA2, EPS8L2, ESM1, ETS1, EXO1, EXOSC2, F3, FAHD2A, FAM83D, FANCA, FAT4, FDPS, FEN1, FMR1, FST, FSTL1, FSTL3, GADD45A, GADD45B, GINS1, GPC6, GPR176, GPRC5A, GPRC5B, GRAMD2B, HASPIN, HEG1, HEXB, HPS5, HSPB11, IDI1, IGFBP7, IKBIP, IL6, ITGB2, JDP2, JPH2, KPNA2, KRT8, KRT80, LCA5, LHFP L6, LMCD1, LMN B2, LRP8, LRR FIP2, LSM5, LYPD6, LYRM1, MAD2L1, MAP6D1, MATN2, MATN3, MCM10, MCM2, MCM5, MDC1, METRNL, MICB, MID1, MRPL33, MSRB3, MVD, MXR A7, NCAPD3, NEDD4, NEDD4L, NEK2, NEXN, NFIB, NNMT, NOC3L, NTN4, NUAK1, NUAK2, NUDCD1, NUP107, NUP37, OGFRL1, OLFM L3, OLR1, OXCT1, PAK2, PCBD1, PCNA, PDLIM2, PDZD2, PEPD, PH LPP1, PKMYT1, PK P2, PK P4, PLECE1, PLEKH A7, PLK2, PLOD2, PP I H,PRPS1, PRPS2, PRSS23, PSG2, PSG6, PSG7, PSG9, PVR, PXMP2, QDPR, QKI, RAB11FI P1, RAB32, RACGAP1, RBM24, RBMS2, RCN2, RFC4, RND3, RNF144B, ROR1, RPS24, SC D5, SCML1, SDC2, SEC14L1, SGK1, SGMS2, ​​SGTB, SH3RF1, SHCBP1, SKP2, SLC25A23 , SLC25A3, SLC38A5, SLC3A2, SLC7A1, SLC7A5, SMPD4, SNAPC1, SNX24, SORT1, SPA G1, SPATA5, STK3, STX11, STXBP6, SUSD2, SUV39H1, SYDE2, TACC3, TAGLN, TEAD1, TEAD4, TENT5B, TGM2, THBS1, TK1, TMEM139, TMEM160, TNFAIP3, TNFRSF12A, TNNC1, TPM1, TPX2, TRIP13, TSPAN2, TTF2, TUBB6, TUFT1, TYMS, UAP1, UBE2C, UGCG, UHRF1, VKORC1L1, WWC1, WWC2, YAP1, ZBED2, ZDHHC18, ZNF488, and ZNF704; and Genes 210 - 233 of gene set (2): AASDH, ABCA1, ABCC5, ABI3BP, ABLIM3, ACADVL, ACOT11, ACox2, ACSL5, ADAM28, AGL, AGPAT4, ALDH3A2, ANKRD12, ANKRD22, ANKRD29, ANKRD42, ANTX2, APBB3, ARAP3, ARHGEF2, ASF1A, ATP7A, ATXN1, BCL11B, BHLHE41, BMF, CA2, CASP1, CBR3, CCNG2, CDC42EP4, CDK1, CEBPB, CELSR3, CLCN3, CLDN4, COL6A1, COL6A2, CPE, CRABP2, CROT, CSRNP2, CSTA, CTNNBIP1, CTsb, CTsk, CXXC5, CYP1B1, CYP27C1, DDR1, DEDD2, DHX32, DIAPH2, DSC2, DSG3, DUSP6, DYNC2LI1, ELN, EPS8L3, ERAP2, FAM102A, FAM117B, FAM83B, FAM89B, FERMT1, FKBP2, FOS, FTH1, FXYD3, GDPD1, GOLGA5, GOLPH3L, GPNMB, GPRC5C, GRB10, GSN, HAS3, HBP1, HDAC1, HDHD2, HEY1, HOXA5, IFI44, IGSF3, IGSF9, INTS3, IRAK2, IRF9, IRX5, ITGA2, KCNMA1, KCNMB3, KCNN4, KIFAP3, KLF10, KLF13, KLHL3, KLK11, KRCC1, KRIT1, KRTDAP, LMTK3, LRP10, LTBP4, LXN, LYPD3, MALL, MANSC1, MAPK13, MARCKSL1, MFSD1, MFSD5, MGST2, MGST3, MLLT11, MLP, MMP13, MSX2, MTMR11, MTMR9, MTSS1, MYO1A, NAGK, NAPEPLD, NCOA3, NFIL3, NPAS2, NRIP1, OAS1, OAS2, OASL, OFD1, OSBPL7, OTUB2, OVOL1, PAG1, PAK1, PCDHB2, PCDHB9, PCGF3, PCMTD2, PERP, PHF21A, PIK3C2B, PIK3R1, PIK3R2, PIK3R3, PIP4P2, PJ A2, PKI A, PLA2G4C, PNR C1, PPP1R11, PRRX2, PTPRE, PYGB,RAC2, RALGPS1, RAPGEFL1, RBM23, RBM45, RBM47, RBP1, REEP6, RGL2, RGS17, R HOC, S100A14, SAMD9, SEC14L2, SECISBP2, SH3PXD2A, SH3TC1, SHROOM2, SLC14 A1, SLC1A2, SLC30A9, SLC35C1, SLC37A2, SLC39A11, SLFN5, SLITRK6, SLK, SM OC1, SNCG, SP1, SPIRE2, SPRY4, SQSTM1, SRD5A3, SSPN, STMN3, STX1A, TBX3, TC Use comprising any of F25, TDO2, TET2, TFF1, TLR3, TMC4, TMC7, TMEM140, TMEM144, TMEM45B, TP53INP1, TP63, TPD52L1, TRAPPC6B, TRIB1, TRIB2, TRIM13, TRIM31, TRIM38, TRIOBP, TRIP11, TSC22D1, TSPAN1, TTC17, TTLL3, TUBB3, UBC, ULK1, VAMP8, VGF, VPS52, VSNL1, WDR13, ZCWPW1, ZNF292, ZNF467, ZNF75D, and ZSWIM7.

2. 2. The use of claim 1, wherein the set of genes comprises 430, 431, 432, 433, 434, 436, 437, or 482 genes.

3. 3. The use according to claim 1 or 2, wherein the cancer is a cancer involving a solid tumor.

4. 4. The use according to claim 3, wherein the solid tumor is in the lung, colon, ovary, cervix, uterus, peritoneum, testis, penis, tongue, lymph node, pancreas bone, breast, prostate, soft tissue, connective tissue, kidney, liver, brain, thyroid, or skin.

5. The cancer is selected from the group consisting of adrenocortical carcinoma, urothelial carcinoma of the bladder, invasive breast carcinoma, squamous cell carcinoma of the cervix and endocervical adenocarcinoma, cholangiocarcinoma, consensus molecular subtype 1 of colorectal cancer, consensus molecular subtype 2 of colorectal cancer, consensus molecular subtype 3 of colorectal cancer, consensus molecular subtype 4 of colorectal cancer, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal cancer, glioblastoma multiforme, 5. The use according to any one of claims 1 to 4, wherein the cancer is selected from head and neck squamous cell carcinoma, kidney clear cell carcinoma, kidney papillary cell carcinoma, brain low grade glioma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid carcinoma, thymoma, uterine endometrial carcinoma, and uterine carcinosarcoma.

6. a) measuring the expression levels of genes in a transcriptome obtained from said biological sample; b) for each gene of said gene set, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes from said transcriptome; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (1) and calculating their average fractional rank (MFR positive); d) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR positive); e) calculating the dgR score as MFR positive minus MFR negative; Or, a) determining in said biological sample the expression level of each gene of said set of genes; b) for each gene of said gene set, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes from said gene set; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (1) and calculating their average fractional rank (MFR positive); d) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR positive); e) calculating the deR score as MFR positive minus MFR negative; Including, The use according to any one of claims 1 to 5, wherein the cancer is TEAD active when the dgR or deR score is above about 0.055, and the cancer is TEAD inactive when the dgR or deR score is below about 0.

055.

7. 1. Use of a transcriptional signature for characterizing TEAD activity of a cancer in a subject in need thereof, wherein the transcriptional signature is obtained by measuring the expression levels of genes of a gene set in a biological sample of a tumor of the cancer, the gene set being selected from the group consisting of sets a) to mmmm) of Table 1.

8. The cancer is - if the tumor is an adrenocortical carcinoma (ACC), the gene set of the transcriptional signature is gene set a) or b); - if the tumor is a bladder urothelial carcinoma (BLCA), said gene set of said transcriptional signature is gene set c); - if the tumor is a breast invasive carcinoma (BRCA) tumor, the gene set of the transcriptional signature is gene set d); - if BRCA basal tumour, the gene set of the transcriptional signature is gene set e) or f); - if a BRCA non-basal tumour, the gene set of the transcriptional signature is gene set g) or h); - if the tumor is a cervical squamous cell carcinoma or cervical adenocarcinoma (CESC), the gene set of the transcriptional signature is gene set i); - if the tumor is a cholangiocarcinoma (CHOL), the gene set of the transcriptional signature is gene set j) or k); - if the tumor is a colon adenocarcinoma (COAD), the gene set of the transcriptional signature is gene set l); - if the tumor is a COAD CMS1 tumor, the gene set of the transcriptional signature is gene set m) or n); - if the tumor is a COAD CMS2 tumor, the gene set of the transcriptional signature is gene set o) or p); - if the tumor is a COAD CMS4 tumor, the gene set of the transcriptional signature is gene set q) or q1); - if the tumor is a lymphoid neoplasm diffuse large B-cell lymphoma (dLBc), the gene set of the transcriptional signature is gene set r) or s); - if the tumor is an esophageal cancer (ESCA), the gene set of the transcriptional signature is gene set t) or u); - if the tumor is a glioblastoma multiforme (GBM), the gene set of the transcriptional signature is gene set v) or w); - if the tumor is a head or neck squamous cell carcinoma (HNSC), the gene set of the transcriptional signature is gene set x); - if a kidney chromophobe (KICH) tumor, the gene set of the transcriptional signature is gene set y) or z); - if the tumor is a kidney renal clear cell carcinoma (KIRC), the gene set of the transcriptional signature is gene set aa); - if a kidney papillary cell carcinoma (KIRP) tumor, the gene set of the transcriptional signature is gene set bb) or cc); - if a brain low grade glioma (LGG) tumor, the gene set of the transcriptional signature is gene set dd) or ee); - if a liver hepatocellular carcinoma (LIHC) tumor, said gene set of said transcriptional signature is gene set ff); - if the tumor is a LIHC S1 tumor, the gene set of the transcriptional signature is gene set gg) or hh); - if the tumor is a LIHC S2 tumor, the set of genes of the transcriptional signature are genes ii) or jj); - if the tumor is a LIHC S3 tumor, the gene set of the transcriptional signature is gene set kk) or ll); - if the tumor is a lung adenocarcinoma (LUAD), the gene set of the transcriptional signature is gene set mm); - if LUAD proximal inflammatory tumor, the gene set of the transcriptional signature is gene set nn) or oo); - if LUAD proximal proliferative tumor, the gene set of the transcriptional signature is gene set pp) or qq); - if the tumor is a lung squamous cell carcinoma (LUSC), the gene set of the transcriptional signature is gene set rr); - if LUSC basal tumor, the gene set of the transcriptional signature is gene set ss) or tt); - if LUSC classical tumor, said gene set of said transcriptional signature is gene set uu); - if it is a LUSC primitive tumor, the gene set of the transcriptional signature is gene set vv) or ww); - if a LUSC secreting tumor, the gene set of the transcriptional signature is gene set xx) or yy); - if the tumor is a malignant mesothelioma (MESO) tumor, the gene set of the transcriptional signature is gene set zz) or aaa); - if the tumor is an ovarian serous cystadenocarcinoma (OV), the gene set of the transcriptional signature is gene set bbb); - if the tumor is OV differentiated, the gene set of the transcriptional signature is a combination of gene set ccc) or ddd); - if the tumor is an OV-immunoreactive tumor, the gene set of the transcriptional signature is gene set eee) or fff); - if OV mesenchymal tumor, the gene set of the transcriptional signature is gene set gggg) or hhh); - if the tumor is an OV-proliferating tumor, the gene set of the transcriptional signature is gene set iii) or jjj); - if the tumor is a pancreatic adenocarcinoma (PAAD), the gene set of the transcriptional signature is gene set kkk) or kkk1); - if the tumor is a rectal adenocarcinoma (READ), the gene set of the transcriptional signature is gene set lll); - if the tumor is a READ CMS1 tumor, then the gene set of the transcriptional signature is gene set mmm); - if a READ CMS2 tumor, then the gene set of the transcriptional signature is gene set nnn) or ooo); - if the tumor is a READ CMS4 tumor, then the gene set of the transcriptional signature is gene set ppp) or ppp1); - READ unclassifiable tumor, the gene set of the transcriptional signature is gene set qqq) or qqq1); - if the tumor is a sarcoma (SARC), the gene set of the transcriptional signature is gene set rrr); - if the tumor is a cutaneous melanoma (SKCM) tumor, the gene set of the transcriptional signature is gene set sss); - SKCM immune tumor, then the gene set of the transcriptional signature is gene set ttt); - if SKCM keratin tumor, the gene set of the transcriptional signature is gene set uuu) or vvv); - if the tumor is a SKCM low MITF tumor, then the gene set of the transcriptional signature is gene set www) or xxx); - if the tumor is a gastric adenocarcinoma (STAD), the gene set of the transcriptional signature is gene set yyy); - if a STAD MSI tumor, the gene set of the transcriptional signature is gene set zzz) or aaaa); - if a STAD MSS_EMT tumor, the gene set of the transcriptional signature is gene set bbbb) or cccc); - if the tumor is a STAD MSS_TP53-tumor, the gene set of the transcriptional signature is gene set dddd) or eeee); - if a STAD MSS_TP53+ tumor, the gene set of the transcriptional signature is gene set ffff) or gggg); - if the tumor is a testicular germ cell tumor (TGCT), the gene set of the transcriptional signature is gene set hhhh) or iiii); - if the tumor is a thyroid cancer (THYM), then the gene set of the transcriptional signature is gene set jjjj); - if the tumor is a uterine endometrial carcinoma (UCEC), the gene set of the transcriptional signature is gene set kkkk); - the use according to claim 7, wherein in the case of a uterine carcinosarcoma (UCS) tumor, the gene set of the transcriptional signature is gene set lllll) or mmmm).

9. a) determining in said biological sample the expression level of each gene of the set of genes according to claim 1 or 2; b) for each gene of the gene set shown in Table 1, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes of said gene set according to claim 1 or 2; c) multiplying each fractional rank obtained in step b) by a coefficient associated with each gene of said gene set in Table 1 (said coefficients for said genes and gene sets are shown in Table 2) to obtain a product for each gene; d) summing the products obtained in step c) to obtain a discriminant score (S); e) comparing the discriminant score (S) obtained in step d) with the midpoints of the TEAD active (A) and TEAD inactive (I) groups shown in Table 2 for said gene set in Table 1; f) determining whether (S) is closer to (A) than to (I) according to the formula max(S,A)-min(S,A)<max(S,I)-min(S,I); and g) characterizing the sample as having TEAD activity if (S) is closer to (A) than to (I).

10. The cancer is selected from the group consisting of adrenocortical carcinoma (ACC) tumors, bladder urothelial carcinoma (BLCA) tumors, breast invasive carcinoma (BRCA) tumors, BRCA basal tumors, BRCA non-basal tumors, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors, cholangiocarcinoma (CHOL) tumors, colon adenocarcinoma (COAD) tumors, COAD CMS1 tumors, COAD CMS4 tumors, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumors, esophageal carcinoma (ESCA) tumors, glioblastoma multiforme (GBM) tumors, head and neck squamous cell carcinoma (HNSC) tumors, kidney chromophobe (KICH) tumors, kidney renal clear cell carcinoma (KIRC) tumors, kidney renal papillary cell carcinoma (KIRP) tumors, brain low grade glioma (LGG) tumors, liver hepatocellular carcinoma (LIHC) tumors, LIHC S1 tumor; LIHC S2 tumor; LIHC S3 tumor; Lung adenocarcinoma (LUAD) tumor; Lung squamous cell carcinoma (LUSC) tumor; LUSC basal tumor; LUSC classical tumor; LUSC primitive tumor; LUSC secretory tumor; Malignant mesothelioma (MESO) tumor; Pancreatic adenocarcinoma (PAAD) tumor; Rectal adenocarcinoma (READ) tumor; READ CMS1 tumor; READ CMS2 tumor; READ CMS4 tumor; Sarcoma (SARC) tumor; Cutaneous melanoma (SKCM) tumor; SKCM immune tumor; SKCM keratin tumor; STAD MSI tumor; STAD MSS_EMT tumor; Testicular germ cell tumor (TGCT) tumor; Uterine endometrial carcinoma (UCEC) tumor; Uterine carcinosarcoma (UCS) tumor, a) measuring in said biological sample the expression level of each gene of said tumor associated gene set, said gene set being selected from the group consisting of Set a), Set c), Set d), Set e), Set g), Set i), Set j), Set l), Set m), Set q), Set r), t), Set v), Set x), Set y), Set aa), Set bb), Set dd), Set ff), Set gg), Set ii), Set kk), Set mm), Set rr), Set ss), Set uu), Set vv), Set xx), Set zz), Set kkk), Set lll), Set mmm), Set nnn), Set ppp), Set rrr), Set sss), Set tt), Set uuu), Set zzz), Set bbbb), Set hhhh), Set kkkk), and Set llll) of Table 1; b) for each gene of said gene set, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes in said gene set; c) multiplying each fractional rank obtained in step b) by a coefficient associated with each gene of the gene set (the coefficients for the genes and gene sets are shown in Table 2) to obtain the product (P ｉ ) to obtain; d) determining a discriminant score (DS) for the gene set, the discriminant score (DS) being the product P obtained in step c) ｉ plus a constant coefficient for said gene set as shown in Table 2; e) comparing, for the gene set, the discriminant score (DS) obtained in step d) with a threshold associated with the gene set (the threshold is shown in Table 3) to determine whether the cancer is TEAD active or TEAD inactive.

11. The use according to any one of claims 1 to 10, wherein the biological sample is a sample from blood, plasma, serum, cerebrospinal fluid (CSF), tissue, cells or a tumor biopsy.

12. The use according to any one of claims 1 to 11 for characterizing a subject as having a TEAD-active or TEAD-inactive cancer.

13. The use of any one of claims 1 to 11 for predicting whether a subject will respond positively to a TEAD pathway inhibitor.

14. The use of claim 12 or 13, wherein the subject is administered a TEAD pathway inhibitor if the subject is characterized as having a TEAD-active cancer or if the subject is predicted to respond positively to a TEAD pathway inhibitor.

15. 1. A method for characterizing TEAD activity of a cancer in a subject in need thereof, using a biological sample of a tumor of said cancer, comprising at least: a) measuring the expression levels of genes in a transcriptome obtained from said biological sample; b) for each gene of the gene set according to claim 1 or 2, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of the gene by the number of genes from the transcriptome; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of said gene set (1) and calculating their average fractional rank (MFR positive); d) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of gene set (2) and calculating their average fractional rank (MFR positive); and e) calculating the dgR score as MFR positive - MFR negative; Or, a) measuring the expression level of each gene of the gene set according to claim 1 or 2 in said biological sample; b) for each gene of the gene set according to claim 1 or 2, converting the expression level of said gene obtained in step a) into a fractional rank by dividing the rank of said gene by the number of genes from said gene set; c) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of said gene set (1) and calculating their average fractional rank (MFR positive); d) isolating from the fractional ranks obtained in step b) the fractional ranks obtained for the genes of said gene set (2) and calculating their average fractional rank (MFR negative); e) calculating the deR score as MFR positive - MFR negative; When the dgR or deR score is greater than about 0.055, the cancer is TEAD active, and when the dgR or deR score is less than or equal to about 0.055, the cancer is TEAD inactive.

16. 1. A method for characterizing TEAD activity of a cancer in a subject in need thereof, comprising using a biological sample of a tumor of said cancer, comprising: a) measuring the expression level of each gene of the gene set according to claim 1 or 2 in said biological sample; b) for each gene of the gene set shown in Table 1, converting the expression level of the gene obtained in step a) into a fractional rank by dividing the rank of the gene by the number of genes of the gene set according to claim 1 or 2; c) multiplying each fractional rank obtained in step b) by a coefficient associated with each gene of said gene set in Table 1 (said coefficients for said genes and gene sets are shown in Table 2) to obtain a product for each gene; d) summing the products obtained in step c) to obtain a discriminant score (S); e) comparing the discriminant score (S) obtained in step d) with the midpoints of the TEAD active (A) and TEAD inactive (I) groups shown in Table 2 for the gene set in Table 1; f) determining whether (S) is closer to (A) than to (I) according to the formula max(S,A)-min(S,A)<max(S,I)-min(S,I); and g) characterizing the cancer as TEAD active if (S) is closer to (A) than to (I).

17. A method for characterizing TEAD activity of a cancer in a subject in need thereof, the cancer being selected from the group consisting of adrenocortical carcinoma (ACC) tumors; bladder urothelial carcinoma (BLCA) tumors; breast invasive carcinoma (BRCA) tumors; BRCA basal tumors; BRCA non-basal tumors; cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors; cholangiocarcinoma (CHOL) tumors; colon adenocarcinoma (COAD) tumors; COAD CMS1 tumors; COAD CMS4 tumor; Lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) tumor; Esophageal carcinoma (ESCA) tumor; Glioblastoma multiforme (GBM) tumor; Head and neck squamous cell carcinoma (HNSC) tumor; Kidney chromophobe (KICH) tumor; Kidney renal clear cell carcinoma (KIRC) tumor; Kidney renal papillary cell carcinoma (KIRP) tumor; Brain low grade glioma (LGG) tumor; Liver hepatocellular carcinoma (LIHC) tumor; LIHC S1 tumor; LIHC S2 tumor; LIHC S3 tumor; lung adenocarcinoma (LUAD) tumor; lung squamous cell carcinoma (LUSC) tumor; LUSC basal tumor; LUSC classical tumor; LUSC primitive tumor; LUSC secretory tumor; malignant mesothelioma (MESO) tumor; pancreatic adenocarcinoma (PAAD) tumor; rectal adenocarcinoma (READ) tumor; READ CMS1 tumor; READ CMS2 tumor; READ CMS4 tumor; sarcoma (SARC) tumor; cutaneous melanoma (SKCM) tumor; SKCM immune tumor; SKCM keratin tumor; STAD MSI tumor; STAD MSS_EMT tumor; testicular germ cell tumor (TGCT) tumor; uterine endometrial carcinoma (UCEC) tumor; uterine carcinosarcoma (UCS) tumor, using a biological sample of a tumor of said cancer, at least: a) measuring in the biological sample the expression level of each gene of the set of genes associated with the tumor, wherein the set of genes is selected from the group consisting of Set a), Set c), Set d), Set e), Set g), Set i), Set j), Set l), Set m), Set q), Set r), t), Set v), Set x), Set y), Set aa), Set bb), Set dd), Set ff), Set gg), Set ii), Set kk), Set mm), Set rr), Set ss), Set uu), Set vv), Set xx), Set zz), Set kkk), Set lll), Set mmm), Set nnn), Set ppp), Set rrr), Set sss), Set tt), Set uuu), Set zzz), Set bbbb), Set hhhh), Set kkkk), and Set llll) of Table 1; b) for each gene of said gene set, converting the expression level of said gene into a fractional rank by dividing the rank of said gene by the number of genes in said gene set; c) multiplying each fractional rank obtained in step b) by a coefficient associated with each gene of the gene set (the coefficients for the genes and gene sets are shown in Table 2) to obtain the product (P ｉ ) to obtain d) determining a discriminant score (DS) for the gene set, the discriminant score (DS) being determined by multiplying the product P ｉ plus a constant coefficient for the gene set shown in Table 2; e) comparing the discriminant score (DS) obtained in step d) for the gene set with a threshold associated with the gene set (the threshold is shown in Table 3) to determine whether the cancer is TEAD active or TEAD inactive.

18. A kit comprising a solid support comprising a panel of nucleic acids for determining the transcription of the set of genes according to claim 1, 2 or 7.

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