Gene transcripts as features of TEAD-active cancers

Abstract

The present invention relates to an isolated set of gene transcripts (A) or an isolated set of genes (B) from a set of genes consisting of a subset (1) of genes consisting of ADM, AXL, BIRC5, CDV3, CRIM1, CTGF, CYR61, FSTL1, GADD45A, KRT8, LMNB2, MATN2, PKP4, RND3, RPS24, SEC14L1, SGK1, SLC25A3, SLC3A2, TNFRSF12A, TPM1, TPX2, TUBB6 and a subset (2) of genes consisting of CTSB, FTH1, SQSTM1, TCF25, UBC, and the use thereof in a method for diagnosing TEAD-active cancers, the isolated gene set (B) consists of at least one gene transcript from a gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9 and EIF4A1.

Description

Technical Field

[0001] This disclosure relates to the field of gene transcripts, which are characteristic of TEAD-active cancers.

[0002] This disclosure relates to the measurement of gene transcript sets and their levels, their use and methods for characterizing cancer, monitoring cancer evolution and cancer response to TEAD inhibitor therapy, and tools for screening TEAD inhibitor candidate compounds. Background Technology

[0003] In normal tissues, transcriptional cofactors YAP1 and WWTR1 bind to transcription factors of the TEAD family (TEAD1, TEAD2, TEAD3, and TEAD4) (collectively, “TEAD”) to form an active protein complex. This complex recognizes and binds to specific sequence motifs in the promoters of target genes, initiating or repressing their transcription. Thus, cell proliferation, survival, plasticity, and migration essential for normal biological processes such as organ growth and wound healing are regulated by TEAD (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 WWTR1 are normally phosphorylated by kinases in the Hippo pathway, thus remaining in the cytoplasm and being degraded. In this case, there is no active transcriptional complex in the nucleus (Totaro A, et al. Nat Cell Biol. 2018 Aug; 20(8):888-899).

[0004] In recent years, YAP1, WWTR1, their TEAD mate, and their upstream regulators (especially the tumor suppressor pathway known as the Hippo pathway) have attracted increasing attention in cancer research (Zhang N et al. Dev Cell. July 20, 2010; 19(1):27-38. doi:10.1016 / j.devcel.2010.06.015; Lu L et al. Proc Natl Acad Sci US A.2010; 107(4):1437-42. doi:10.1073 / pnas.0911427107; Nishio M et al. Proc Natl Acad Sci U SA.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 dysregulations of YAP1, WWTR1, or any of their upstream regulators can lead to insufficient phosphorylation and degradation of the proteins. In the absence of phosphorylation and degradation, transcriptional cofactors enter the nucleus, bind to TEAD, and initiate oncogenic transcription. Increased activity of any component of the complex can increase or decrease the transcription of downstream effectors (TEAD-dependent transcription). Some regulated genes are direct targets of TEAD carrying TEAD-binding motifs (Zanconato, F. et al. Nat Cell Biol 2015:17:1218-1227). Others are indirectly regulated. Therefore, the Hippo-YAP1 / WWTR1-TEAD pathway (hereinafter “TEAD pathway” or “TEAD signaling”) may directly induce tumorigenesis or make existing tumors resistant to targeted therapy (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).

[0005] For example, mutations and deletions of Hippo genes (such as LATS2 and NF2) account for a large proportion of malignant mesothelioma tumors (Sekido Y et al. Cancers (Basel). 2018; 10(4):90. doi:10.3390 / cancers10040090). YAP1 has been found to be locally amplified and overexpressed at both the RNA and protein levels in several types of tumors (especially 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 may give tumor cells a mesenchymal stem cell-like phenotype, as well-known stem cell transcription factors (such as SOX2, OCT3 / 4, NANOG, and MYC) are also controlled by TEAD (Bora-Singhal N et al. Stem Cells. 2015; 33(6):1705-18. doi:10.1002 / stem.1993). Cancers with similar TEAD activity in their tumor cells are referred to as TEAD-active cancers.

[0006] To measure the transcriptional activity of such complexes, estimate the proportion of cases in which TEAD-dependent transcription may be responsible for tumor development or evolution, and evaluate the target binding and pharmacodynamics of novel TEAD pathway inhibitors, sensitive and easily implementable gene transcript signatures (or transcriptional signatures) are required.

[0007] Liquid biopsy holds immense significance for non-invasive monitoring of cancer progression and response to treatment. Extracellular vesicles (EVs) (such as exosomes, microvesicles (exosomes), apoptotic bodies, exosome-like structures, etc.) are nanoparticles present in all biological fluids (body fluids). These cell-derived, small, secretory vesicles deliver biological information through surface-to-surface interactions or by transporting bioactive molecules to the cytoplasm of recipient cells. Because EVs carry or encapsulate the contents of their parent cells, their contents can be used as biomarkers for tracking drug activity. Raposo and Willem Stoorvogel. 2013. Extracellular vesicles: exosomes, microvesicles, and friends. J. Cell Biol.: 200(4):373-83; Nunes et al.; 2020. Tumor-derived Extracellular Vesicles (EVs) expressing TGFβ as potential biomarkers for anti-TGFβ antibodyactivity and drug activity in liquid biopsy.AACR2020; Poster No. 773; Urabe et al.; 2020. Extracellular vesicles as biomarkers and therapeutic targets for cancer. Am.J. Physiol Cell Physiol.:318(1); Calvet, L., Dos-Santos, O., Spanakis, E. et al. 2022. YAP1 is essential for malignant mesothelioma tumor maintenance.BMCCancer 22,639.doi.org / 10.1186 / s12885-022-09686).

[0008] Therefore, there is a need for a new set of gene transcripts that can be used to diagnose or monitor cancers with TEAD activity.

[0009] Cost-effective diagnostic tools and methods are needed to diagnose or monitor TEAD-active cancers.

[0010] There is a need for new gene transcripts that can be used as a characteristic of TEAD-active cancers, and that can be used in a simple, reliable and robust manner.

[0011] An improved set of gene transcripts is needed that can be used in robust and reliable methods for characterizing cancer as having TEAD activity, for monitoring or predicting cancer evolution or cancer response to TEAD inhibitor therapy, or for screening new TEAD inhibitor candidate compounds.

[0012] A new set of gene transcripts is needed, whose levels can be measured to characterize TEAD-active cancers.

[0013] There is a need for improved non-invasive methods to obtain gene transcript signatures for characterizing, diagnosing, or monitoring TEAD-active cancers.

[0014] There is a need for new, simple, and reliable methods for characterizing gene transcripts in TEAD-active cancers.

[0015] There is a need for a new set of gene transcripts that can be obtained from extracellular vesicles as a characteristic of TEAD-active cancers.

[0016] The purpose of this disclosure is to satisfy all or some of the stated requirements.

[0017] summary

[0018] For one of its purposes, this disclosure relates to a separate set of gene transcripts (hereinafter referred to as gene transcript set (A)) from a gene set, said gene set comprising: a subset of genes (1) consisting of ADM, AXL, BIRC5, CDV3, CRIM1, CTGF, CYR61, FSTL1, GADD45A, KRT8, LMNB2, MATN2, PKP4, RND3, RPS24, SEC14L1, SGK1, SLC25A3, SLC3A2, TNFRSF12A, TPM1, TPX2, and TUBB6, and a subset of genes (2) consisting of CTSB, FTH1, SQSTM1, TCF25, and UBC. Depending on the context, (A) may refer to the gene set or its corresponding gene transcript set.

[0019] The isolated gene transcript set (A) disclosed herein is obtained from a gene set consisting of a first gene subset (1) and a second gene subset (2). The first gene subset (1) consists of ADM, AXL, BIRC5, CDV3, CRIM1, CTGF, CYR61, FSTL1, GADD45A, KRT8, LMNB2, MATN2, PKP4, RND3, RPS24, SEC14L1, SGK1, SLC25A3, SLC3A2, TNFRSF12A, TPM1, TPX2, and TUBB6. The second gene subset (2) consists of CTSB, FTH1, SQSTM1, TCF25, and UBC.

[0020] According to one of its purposes, this disclosure relates to a separate set of gene transcripts (hereinafter referred to as gene transcript set (B)) consisting of at least one gene transcript from a gene set, said gene set comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1. Depending on the context, (B) may refer to a gene set or its corresponding set of gene transcripts.

[0021] According to one of its purposes, this disclosure relates to a separate set of gene transcripts (hereinafter referred to as gene transcript set (B)) consisting of at least two gene transcripts, one of which is a gene transcript from gene DLC1, and the at least second gene transcript is a gene set consisting of AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9 and EIF4A1.

[0022] As shown in the Examples section, the newly proposed set of gene transcripts (A) or (B) can be used as a reliable and sensitive feature for characterizing and monitoring TEAD-active cancers. Furthermore, as shown in the Examples section, the gene transcripts can be readily isolated from extracellular vesicles (EVs). Measurements of their levels provide transcriptional signatures that can identify TEAD-active cancers and monitor changes in TEAD activity, thereby monitoring or predicting cancer evolution or cancer response to treatment or screening for new TEAD inhibitor therapies.

[0023] In the embodiments, the utility of EV-encapsulated and carried mRNA has been assessed as a transcriptional signature of TEAD pathway activity, and it has been shown that EVs can be used as a source of gene transcripts to measure the signature of TEAD-active cancers. EVs can be isolated from various bodily fluid samples, such as urine, saliva, or blood samples, to implement non-invasive methods (e.g., by obtaining saliva, blood, or urine samples) to measure the transcriptional signature of TEAD-active cancers.

[0024] Furthermore, examples demonstrate that the newly disclosed set of gene transcripts isolated from EVs can be used to monitor the inhibition of the YAP1 / TEAD signaling pathway by TEAD inhibitors (TEADi).

[0025] The examples further demonstrate that a newly identified set of gene transcripts obtained from EVs exfoliated from tumor cell lines can be used to calculate TEAD activity scores, and the calculated scores are correlated with TEAD activity scores calculated using either the TEAD-500 signature (previously described in PCT / EP2023 / 057332) or the YAP1-TEAD activity signature (Calvet et al. (BMC Cancer, 2022, 22:639, doi.org / 10.1186 / s12885-022-09686-y)) and parental cell mRNA. The results indicate that TEAD activity in parental tumor cells can be reliably measured in secreted EVs using the newly identified set of gene transcripts and their levels. Therefore, the set of gene transcripts obtained from EVs provides relevant pharmacodynamic (PD) biomarkers for monitoring TEAD activity and the TEAD-inhibiting effects of TEAD inhibitor therapy.

[0026] Newly identified sets of gene transcripts and their levels obtained from extracellular vesicles can be advantageously translated into non-invasive, liquid biopsy-based transcriptomic signatures and biomarkers for clinical use.

[0027] The identified set of gene transcripts may be used for a variety of clinical and research-related utility, including but not limited to monitoring the activity of the TEAD complex and evaluating the in vitro or in vivo pharmacodynamics of novel inhibitors of the TEAD pathway, diagnosing the development or evolution of tumors attributable to the activation of the TEAD complex, recruiting patients with active TEAD to clinical trials designed to evaluate TEAD pathway inhibitors, predicting survival, response, and benefit of anti-TEAD pathway therapy alone or in combination with other treatments, estimating the proportion of cases in each cancer indication or subtype in the cohort (where TEAD-dependent transcription may lead to tumor development or evolution into treatment resistance), and measuring TEAD-dependent transcription in any tissue under any condition (whether pathological or not) in the laboratory or clinical setting.

[0028] In some embodiments, the gene transcript set (B) may consist of at least two, three, four, five, six, or seven gene transcripts from the gene set, or eight gene transcripts from the gene set, which consists of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0029] In some embodiments, the gene transcript set (B) may consist of a gene set comprising at least two gene transcripts, one of which is a gene transcript from gene DLC1, and the at least second gene transcript is derived from a gene set consisting of: AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0030] In some embodiments, the gene transcript set disclosed herein may be obtained from isolated extracellular vesicles.

[0031] For another purpose, this disclosure relates to isolated extracellular vesicles comprising a set of gene transcripts as disclosed herein.

[0032] In some embodiments, this disclosure relates to isolated extracellular vesicles comprising a set (A) of gene transcripts obtained from a gene set, said gene set consisting of a first gene subset (1) and a second gene subset (2), the first gene subset (1) consisting of ADM, AXL, BIRC5, CDV3, CRIM1, CTGF, CYR61, FSTL1, GADD45A, KRT8, LMNB2, MATN2, PKP4, RND3, RPS24, SEC14L1, SGK1, SLC25A3, SLC3A2, TNFRSF12A, TPM1, TPX2, and TUBB6, and the second gene subset (2) consisting of CTSB, FTH1, SQSTM1, TCF25, and UBC.

[0033] In some embodiments, this disclosure relates to isolated extracellular vesicles comprising a set of gene transcripts consisting of at least one gene transcript from a gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0034] In some embodiments, the set of gene transcripts according to this disclosure may be used as biomarkers of TEAD activity, for measuring or characterizing TEAD activity in cancer or cell cultures, for use in screening methods for TEAD inhibitor candidate compounds, or for use in cancer diagnostic methods.

[0035] Cancer diagnostic methods may be selected from methods for characterizing the TEAD activity status of cancer, methods for measuring TEAD activity in biological samples, methods for predicting the response of cancer to TEAD inhibitor therapy, methods for monitoring the response of cancer to TEAD inhibitor therapy, methods for predicting cancer progression or regression with TEAD activity, and methods for monitoring cancer progression or regression with TEAD activity.

[0036] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for measuring TEAD activity in biological samples.

[0037] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for characterizing TEAD activity in biological samples.

[0038] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for measuring TEAD activity from cancer in subjects in need.

[0039] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for characterizing the TEAD activity status of cancer in subjects in need.

[0040] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for predicting the response of cancer to TEAD inhibitor therapy in subjects in need. Subjects may be known or believed to have TEAD-active cancer.

[0041] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for monitoring the response of cancer to TEAD inhibitor therapy in subjects of need. Subjects may be known or believed to have TEAD-active cancer.

[0042] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for predicting cancer progression or regression in subjects who may know or be believed to have TEAD-active cancer.

[0043] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for monitoring cancer progression or regression in subjects who may be known or believed to have TEAD-active cancer.

[0044] For another purpose, this disclosure relates to the use of the gene transcript set disclosed herein for screening candidate compounds for TEAD inhibitors.

[0045] In some implementations, the gene transcript set can be obtained from isolated extracellular vesicles.

[0046] For another purpose, this disclosure relates to the use of isolated extracellular vesicles for the purposes described above.

[0047] In some implementations, the level of each gene transcript can be obtained.

[0048] In some implementations, transcriptional signatures can be obtained at the gene transcript level.

[0049] In some implementations, the obtained transcriptional signatures can be compared with reference transcriptional signatures.

[0050] In some implementations, the observed deviation between the obtained transcriptional signature and the reference transcriptional signature may indicate whether the cancer is TEAD-active or TEAD-inactive, or whether the cancer is responsive or unresponsive to treatment with the TEAD inhibitor, or whether treatment with the TEAD inhibitor is effective or ineffective, or whether the TEAD-active cancer is prone to progression or regression, or whether the TEAD-active cancer is progressing or regressing, or whether the TEAD inhibitor candidate compound is effective or ineffective.

[0051] In some implementations, gene transcript levels may be mathematically normalized.

[0052] In some implementations, when using a set of gene transcripts (A), mathematical normalization can be performed by calculating the deR score using a method that includes the following steps:

[0053] a) For each gene transcript in the gene set, the level of each gene transcript is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0054] b) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step a) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0055] c) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step a) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and d) Calculate the deR score as MFR-subset (1) minus MFR-subset (2).

[0056] In some implementations, when using a set of gene transcripts (from gene set (B)) consisting of at least one gene transcript from a gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1, mathematical normalization can be performed to calculate a (S) score according to a method comprising the following steps:

[0057] a) Multiply each level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0058] b) The product P gene obtained in step a) i Summation (ΣP gene) i Add a constant to obtain the (S) score: (ΣP gene) i +constant),

[0059] The coefficients and constants used in steps a) and b) were previously obtained through stepwise multiple linear regression analysis, which correlated (i) the level of the gene transcripts previously obtained in the first biological sample with (ii) the level of gene transcripts of the TEAD-500 feature previously measured in the second biological sample, wherein the TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes of the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes of the gene subset (2). The first and second biological samples represent the same TEAD-active cancer. The first and second biological samples represent cancers considered for use according to this disclosure. The first sample may be a body fluid sample or a portion thereof. The second sample may be a cancer cell sample.

[0060] In some embodiments, in the methods and uses of this disclosure, a stepwise multiple linear regression analysis can be performed between (i) the level of the gene transcript and (ii) the TEAD score obtained by the level of the gene transcript characterized by TEAD-500.

[0061] In some implementations, the calculated deR or (S) score can be compared with a reference value.

[0062] In some implementations, the observed deviation between the calculated deR or (S) score and a reference value may indicate whether the cancer is TEAD-active or TEAD-inactive, or whether the cancer is responsive or unresponsive to treatment with the TEAD inhibitor, or whether treatment with the TEAD inhibitor is effective or ineffective, or whether the TEAD-active cancer is prone to progression or regression, or whether the TEAD-active cancer is progressing or regressing, or whether the TEAD inhibitor candidate compound is effective or ineffective.

[0063] In some implementations, the reference value may be a first deR or (S) score, and the deR or (S) score to be compared with the reference value may be a second deR or (S) score measured after the first deR or (S) score.

[0064] In some implementations, the level of each gene transcript can be obtained by RNA sequencing (RNA-seq) and can be quantified as FPKM (fragments of transcript per kilobase per million mapped reads).

[0065] For another purpose, this disclosure relates to a method for characterizing the TEAD activity status of cancer in a subject in need.

[0066] In some embodiments, the method may include using a set of gene transcripts from a gene set (A) disclosed herein, and the method then includes at least the following steps:

[0067] a) Obtain the level of each gene transcript in the gene set from biological samples obtained from the subject.

[0068] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0069] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0070] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0071] e) Calculate the deR score as MFR-subset(1) minus MFR-subset(2).

[0072] In some embodiments, the method may include using a set of gene transcripts from a gene set (B) disclosed herein, and the method then includes at least the following steps:

[0073] a) Obtain the level of each gene transcript in the gene set from biological samples obtained from the subject.

[0074] b) Multiply each obtained level of each gene transcript in the gene set obtained in step a) by the coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0075] c) The product P gene obtained in step b) i Summation (ΣP gene) i Add a constant to obtain the (S) score: (ΣP gene) i +constant),

[0076] The coefficients and constants used in steps b) and c) were previously obtained by stepwise multiple linear regression analysis, which correlated (i) the level of the gene transcripts previously obtained in a first biosample from a subject with said cancer with (ii) the level of gene transcripts of the TEAD-500 signature measured in a second biosample from said subject’s cancer cells, wherein said TEAD-500 signature comprises gene transcripts containing any of the following gene sets: any of 220 to 249 genes in gene subset (1) and any of 210 to 233 genes in gene subset (2) disclosed in Table 2 below. The subjects who obtained the first and second biosamples may be the same as or different from the subjects who obtained the biosamples used to calculate the (S) score. The first and second biosamples represent cancers considered according to the method of this disclosure.

[0077] In some embodiments, in the method of this disclosure, a stepwise multiple linear regression analysis can be performed to correlate (i) the level of the gene transcript with (ii) the TEAD score obtained by the level of the gene transcript characterized by TEAD-500.

[0078] According to another purpose of this disclosure, therein is a method for monitoring the progression or regression of cancer in a subject suspected or known to have TEAD-active cancer, the method comprising using a set of gene transcripts from a gene set (A), and the method comprising at least the following steps:

[0079] a) Obtain the level of each gene transcript in the gene set for the first time from a first biological sample obtained from the subject.

[0080] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0081] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0082] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0083] e) Calculate the first deR score as MFR-subset(1) minus MFR-subset(2).

[0084] f) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the subject a second time (after the first time).

[0085] g) For each gene transcript in the gene set, convert the level of each gene transcript obtained in step f) into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0086] h) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0087] i) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0088] e) Calculate the second deR score as MFR-subset (1) minus MFR-subset (2), and

[0089] k) Compare the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores indicates the progression or regression of the cancer.

[0090] According to another purpose of this disclosure, therein is a method for monitoring the progression or regression of cancer in a subject suspected of or known to have TEAD-active cancer, the method comprising using a set of gene transcripts from a gene set (B), and the method comprising at least the following steps:

[0091] a) Obtain the level of each gene transcript in the gene set for the first time from a first biological sample obtained from the subject.

[0092] b) Multiply each obtained level of each gene transcript in the gene set obtained in step a) by the coefficient associated with each gene to obtain the product of each gene (Pgene). i Among them, genes i (Refers to the genes listed in the gene set);

[0093] c) The product P gene obtained in step b) i Summation (ΣP gene) i And add a constant to obtain the first (S) score: (ΣP gene) i +constant),

[0094] d) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the subject a second time (after the first time).

[0095] e) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0096] f) The product P gene obtained in step e) i Summation (ΣP gene) i And add a constant to obtain the second (S) score: (ΣP gene) i + constant), and g) compare the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores indicates the progression or regression of the cancer,

[0097] Wherein, the coefficients and constants used in steps b), c), e), and f) were previously obtained by stepwise multiple linear regression analysis, which correlated (i) the level of the gene transcripts previously obtained in a third biosample obtained from a subject with said cancer with (ii) the level of gene transcripts of the TEAD-500 signature obtained in a fourth biosample obtained from said subject’s cancer cells, wherein said TEAD-500 signature comprises gene transcripts containing any of the following gene sets: such as any of the 220 to 249 genes in gene subset (1) disclosed thereafter in Table 2 and any of the 210 to 233 genes in gene subset (2). The subjects who obtained the third and fourth biosamples may be the same as or different from the subjects who obtained the biosamples used to calculate the (S) score. The third and fourth biosamples represent cancers considered according to the method of this disclosure.

[0098] According to another purpose of this disclosure, therein is a method for monitoring the response of a subject with suspected or known TEAD-active cancer to TEAD inhibitor therapy, the method comprising using a set of gene transcripts from a gene set (A), and the method comprising at least the following steps:

[0099] a) Obtain the level of each gene transcript in the gene set from a first biological sample obtained from the subject prior to administration of TEAD inhibitor treatment.

[0100] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0101] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0102] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0103] e) Calculate the first deR score as MFR-subset(1) minus MFR-subset(2).

[0104] f) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the subject after treatment with the TEAD inhibitor.

[0105] g) For each gene transcript in the gene set, convert the level of each gene transcript obtained in step f) into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0106] h) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0107] i) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0108] e) Calculate the second deR score as MFR-subset (1) minus MFR-subset (2), and

[0109] k) Compare the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores indicates whether TEAD inhibitor treatment is effective or ineffective.

[0110] According to another purpose of this disclosure, therein is a method for monitoring the response of a subject with suspected or known TEAD-active cancer to TEAD inhibitor treatment, the method comprising using a set of gene transcripts from a gene set (B), and the method comprising at least the following steps:

[0111] a) Obtain the level of each gene transcript in the gene set from a first biological sample obtained from the subject prior to administration of TEAD inhibitor treatment.

[0112] b) Multiply each obtained level of each gene transcript in the gene set obtained in step a) by the coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0113] c) The product P gene obtained in step b) i Summation (ΣP gene) i And add a constant to obtain the first (S) score: (ΣP gene) i +constant),

[0114] d) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the subject after treatment with the TEAD inhibitor.

[0115] e) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0116] f) The product P gene obtained in step e) i Summation (ΣP gene) i And add a constant to obtain the second (S) score: (ΣP gene) i + constant), and

[0117] g) Compare the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores indicates whether TEAD inhibitor treatment is effective or ineffective.

[0118] Wherein, the coefficients and constants used in steps b), c), e), and f) were previously obtained by stepwise multiple linear regression analysis, which correlated (i) the level of the gene transcripts previously obtained in a third biosample obtained from a subject with said cancer with (ii) the level of gene transcripts of the TEAD-500 signature obtained in a fourth biosample obtained from said subject’s cancer cells, wherein said TEAD-500 signature comprises gene transcripts containing any of the following gene sets: such as any of the 220 to 249 genes in gene subset (1) disclosed thereafter in Table 2 and any of the 210 to 233 genes in gene subset (2). The subjects who obtained the third and fourth biosamples may be the same as or different from the subjects who obtained the biosamples used to calculate the (S) score. The third and fourth biosamples represent cancers considered according to the method of this disclosure.

[0119] According to another purpose of this disclosure, therein lies a method for screening TEAD inhibitor candidates for inhibiting TEAD activity, the method comprising using a set of gene transcripts from a gene set (A), and the method comprising at least the following steps:

[0120] a) Obtaining the level of each gene transcript in the gene set from a first biological sample obtained from the supernatant of a cell culture, the first biological sample being obtained prior to contacting the cell culture with the TEAD inhibitor candidate compound.

[0121] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0122] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0123] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0124] e) Calculate the first deR score as MFR-subset(1) minus MFR-subset(2).

[0125] f) Obtaining the level of each gene transcript in the gene set from a second biological sample obtained from the supernatant of the cell culture, the second biological sample being obtained after contacting the cell culture with the TEAD inhibitor candidate compound.

[0126] g) For each gene transcript in the gene set, convert the level of each gene transcript obtained in step f) into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0127] h) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0128] i) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0129] e) Calculate the second deR score as MFR-subset (1) minus MFR-subset (2), and

[0130] k) Compare the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores indicates whether the TEAD inhibitor candidate compound is effective or ineffective for inhibiting TEAD activity.

[0131] Suitable cells for use in cell cultures according to this disclosure may be TEAD active cells.

[0132] According to another purpose of this disclosure, therein lies a method for screening TEAD inhibitor candidates for inhibiting TEAD activity, the method comprising using a set of gene transcripts from a gene set (B), and the method comprising at least the following steps:

[0133] a) Obtaining the level of each gene transcript in the gene set from a first biological sample obtained from the supernatant of a cell culture, the supernatant being obtained prior to contacting the cell culture with the TEAD inhibitor candidate compound.

[0134] b) Multiply each obtained level of each gene transcript in the gene set obtained in step a) by the coefficient associated with each gene to obtain the product of each gene (Pgene). i Among them, genes i (Refers to the genes listed in the gene set);

[0135] c) The product P gene obtained in step b) i Summation (ΣP gene) i And add a constant to obtain the first (S) score: (ΣP gene) i +constant),

[0136] d) Obtaining the level of each gene transcript of the gene set from a second biological sample obtained from the supernatant of the cell culture, the supernatant being obtained after contacting the cell culture with the TEAD inhibitor candidate compound.

[0137] e) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0138] f) The product P gene obtained in step e) i Summation (ΣP gene) i And add a constant to obtain the second (S) score: (ΣP gene) i+ constant), and

[0139] g) Compare the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores indicates whether TEAD inhibitor treatment is effective or ineffective.

[0140] Wherein, the coefficients and constants used in steps b), c), e), and f) were previously obtained by stepwise multiple linear regression analysis, which correlated (i) the level of the gene transcripts previously obtained in a third biological sample from the supernatant of the cell culture with (ii) the level of gene transcripts of the TEAD-500 feature obtained in a fourth biological sample from the cell culture derived from the subject's cancer cells, wherein the TEAD-500 feature comprises gene transcripts containing any of the following gene sets: such as any of the 220 to 249 genes of the gene subset (1) and any of the 210 to 233 genes of the gene subset (2) disclosed thereafter in Table 2. The cell cultures from which the third and fourth biological samples were obtained may be the same as or different from the cell cultures from which the biological samples used to calculate the (S) score were obtained.

[0141] Suitable cells for use in cell cultures according to this disclosure may be TEAD active cells.

[0142] In some implementations, the gene transcript set can be obtained from isolated extracellular vesicles.

[0143] In some implementation schemes, the cancer may be selected from mesothelioma, adrenocortical carcinoma, urothelial carcinoma of the bladder, invasive breast cancer, cervical squamous cell carcinoma and cervical 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, colonic adenocarcinoma, lymphoma diffuse large B-cell lymphoma, esophageal cancer, glioblastoma multiforme, head and neck squamous cell carcinoma, clear cell carcinoma of the kidney, papillary cell carcinoma of the kidney, low-grade glioma of the brain, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, melanoma of the skin, gastric adenocarcinoma, testicular germ cell tumor, thyroid cancer, thymoma, endometrial cancer of the uterine body and uterine carcinosarcoma.

[0144] In some implementations, the cancer may be mesothelioma.

[0145] In some implementations, the cancer may be colorectal cancer.

[0146] For another purpose, this disclosure relates to a kit comprising a solid support containing a set of nucleic acids for obtaining gene transcript levels of a gene transcript set as disclosed herein. Attached Figure Description

[0147] Figure 1 This indicates the process from cell processing to the separation and analysis of extracellular vesicles (EVs).

[0148] Figure 2 : Indicates the TEAD activity in parental cells after treatment with the TEAD inhibitor (TEADi).

[0149] Figure 3 : Indicates the percentage of genes with TEAD-500 signatures detected in EV mRNA.

[0150] Figure 4 : Indicates the comparison of TEAD characteristic spectra between extracellular vesicles (EVs) and parental cells in TEADi dose response analysis.

[0151] Figure 5 The effect of TEAD inhibitor (TEADi) dosage on TEAD activity in parental cells was measured by extracellular vesicles (EVs) and TEAD characteristics defined by 28 genes in parental cells.

[0152] Figure 6 : Indicates TEAD activity measured in extracellular vesicles (EVs) using selected markers (n=4) and in parental cells using TEAD-500 features (n=500). Detailed Implementation

[0153] definition

[0154] Unless otherwise defined herein, scientific and technical terms used in connection with this disclosure shall have the meanings commonly understood by those skilled in the art. For example, *Concise Dictionary of Biomedicine and Molecular Biology*, Juo, Pei-Show, 2nd Edition, 2002, CRC Press; *Dictionary of Cell and Molecular Biology*, 3rd Edition, 1999, Academic Press; and *Oxford Dictionary of Biochemistry and Molecular Biology*, revised edition, 2000, Oxford University Press can provide a general dictionary for those skilled in the art of the art regarding the many terms used in this disclosure. While similar or equivalent methods and materials described herein may also be used in the practice or testing of this disclosure, exemplary methods and materials are described below. In the event of any conflict, this specification (including definitions) shall prevail. Generally, the nomenclature and techniques used in conjunction with those described herein in cell and tissue culture, molecular biology, virology, immunology, microbiology, genetics, analytical chemistry, synthetic organic chemistry, medical and medicinal chemistry, and protein and nucleic acid chemistry and hybridization are those well-known and commonly used in the art. Enzymatic reactions and purification techniques are performed according to the manufacturer's instructions, as commonly practiced in the art, or as described herein. Furthermore, unless the context requires otherwise, singular terms will include plurals and plural terms will include singulars.

[0155] Units, prefixes, and symbols are represented in their form accepted by the International System of Units (SI). Numerical ranges include the numbers defining the ranges. Unless otherwise specified, amino acid sequences are written from left to right in the amino-to-carboxyl direction. The headings provided herein are not intended to limit any aspect of this disclosure. Therefore, the terms immediately following the definitions are defined more fully by referring to the specification (in its entirety).

[0156] All publications and other references mentioned herein are incorporated in their full text by way of citation. While this document cites numerous documents, such citations do not constitute an acknowledgment that any of those documents constitutes common general knowledge in the art.

[0157] Throughout this specification and in the embodiments, the words “have” and “comprise”, or variations such as “has / having” or “comprises / comprising”, should be understood to imply that a whole or group of wholes is included, but not to exclude any other whole or group of wholes. It should be understood that wherever the term “comprises” is used to describe an aspect herein, similar aspects described in the form of “consisting of” and / or “substantially consisting of” are also provided.

[0158] It should be noted that an entity “a / an” refers to one or more species of the entity; for example, “a / an nucleotide sequence” is understood to mean one or more nucleotide sequences. Therefore, the terms “a / an”, “one or more”, and “at least one” are used interchangeably herein.

[0159] Furthermore, the use of "and / or" herein should be interpreted as a specific disclosure of each of the two specified features or components, having or not having the other specified feature or component. Therefore, the term "and / or" as used herein in phrases such as "A and / or B" is intended to include "A and B", "A or B", "A" (alone), and "B" (alone). Similarly, the term "and / or" as used in phrases such as "A, B, and / or C" is intended to cover each of the following: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

[0160] The term “about” or “approximately” is used herein to mean approximately, roughly, approximately, or around. When the term “about” is used in conjunction with a numerical range, it defines the range by extending the boundaries to be above and below the stated value. Generally, the term “about” can define a value to be above and below a specified value by, for example, a variation of around 10% (higher or lower). In some embodiments, the term indicates a deviation from the indicated value of ±10%, ±5%, ±4%, ±3%, ±2%, ±1%, ±0.9%, ±0.8%, ±0.7%, ±0.6%, ±0.5%, ±0.4%, ±0.3%, ±0.2%, ±0.1%, ±0.05%, or ±0.01%. In some embodiments, “about” indicates a deviation from the indicated value of ±10%. In some embodiments, “about” indicates a deviation from the indicated value of ±5%. In some embodiments, “about” indicates a deviation from the indicated value of ±4%. In some embodiments, “about” indicates a deviation from the indicated value of ±3%. In some embodiments, "about" indicates a deviation from the indicated value of ±2%. In some embodiments, "about" indicates a deviation from the indicated value of ±1%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.9%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.8%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.7%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.6%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.5%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.4%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.3%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.1%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.05%. In some embodiments, "about" indicates a deviation from the indicated value of ±0.01%.

[0161] When referring to biological samples, gene transcripts, populations or subpopulations of gene transcripts, sets of gene transcripts, extracellular vesicles, or populations or subpopulations of extracellular vesicles, “obtained,” “purified,” and “isolated” mean that the element referred to has been separated from other substances or components originally present in the mixture or its natural environment. As used herein, the term “purified” specifically means the presence of at least 75%, 85%, 95%, or 98% by weight of the same type of element.

[0162] As used herein, the terms "subject" or "patient" refer to mammals such as rodents, felines, canines, and primates. Specifically, according to this disclosure, the subject is a human being. The subject in need may be a subject who has known or presumed to have cancer. In some embodiments, the subject in need may be a subject who has known or presumed to have TEAD-active cancer. In some embodiments, the subject may be an animal model of TEAD-active cancer.

[0163] "Biomarker" is defined as a biomolecule or set of biomolecules, such as RNA molecules or a set of RNA molecules, that is differentially present, increased, or decreased in a biological sample obtained from a subject or set of subjects with a first phenotype (e.g., having a disease, such as cancer or TEAD-active cancer) compared to a biological sample obtained from a subject or set of subjects with a second phenotype (e.g., not having the disease or not having TEAD-active cancer). In use, biomarkers are isolated from the subject.

[0164] Features, gene features, and transcriptional features. As used herein, those terms and expressions are intended to refer to patterns of transcriptional levels in a gene set or patterns of gene transcript levels that may be associated with TEAD-active cancer or tumors. Transcriptional features can be obtained by measuring the levels of gene transcripts disclosed herein in a biological sample. In a biological sample, the levels of all or part of the gene transcripts that may be present in the biological sample can be measured (or obtained).

[0165] In this article, "gene transcripts," "transcription," and "gene expression" are used interchangeably, referring to the production of RNA from genes. Increased or decreased transcription or expression of genes can lead to increased or decreased RNA production.

[0166] Gene expression level. As used herein, the term “gene expression level” refers to the level of RNA (or gene transcript) transcribed from any given gene. The level of gene transcript obtained by RNA-seq can be expressed as the number of fragments per kilobase of transcript per million mapped reads (FPKM).

[0167] Levels of gene transcripts. As used herein, the term "level of gene transcripts" is intended to refer to a measure of gene expression or transcription in a biological sample. It reflects the amount of RNA produced by a gene through transcription. Levels of gene transcripts can be obtained or measured by various methods known in the art, such as reverse transcription polymerase chain reaction (RT-PCR), microarrays, and RNA sequencing (RNA-seq). In this disclosure, the terms "a level of gene transcripts" and "a gene transcript level" are used interchangeably. In this disclosure, the terms "levels of gene transcripts" and "gene transcript levels" are used interchangeably.

[0168] FPKM (fragments per kilobase of transcript per million mapped reads) is a measure of gene expression level (or gene transcript level), which takes into account the number of RNA sequencing reads normalized to the total number of reads in a sample, mapped to a specific gene and gene length.

[0169] Positive effectors. Positive effectors are genes whose transcript levels are positively correlated with TEAD activity. These positive effectors increase when the TEAD pathway is activated in some way, or decrease when the TEAD pathway is inhibited. Positive effectors do not necessarily respond in both directions of TEAD regulation. For example, a gene that is not normally expressed in a tissue may increase with TEAD activation but not be expected to decrease with TEAD inhibition. Even at high levels of normal expression, positive effector genes are not necessarily sensitive to both TEAD activation and inhibition.

[0170] Negative effectors. Negative effectors are genes whose transcript levels are negatively correlated with TEAD activity. These negative effectors decrease upon TEAD activation or increase upon TEAD inhibition. Like positive effectors, negative effectors do not necessarily respond to TEAD regulation in both directions. For example, genes not normally expressed in a tissue may increase upon TEAD pathway inhibition but not be expected to decrease upon TEAD activation. Even at high levels of normal expression, negative effector genes may be sensitive to TEAD activation but immune to TEAD inhibition, and vice versa.

[0171] The TEAD-500 signature gene list. A list of 430 to 482 of the most significant positive or negative gene effectors regulated by TEAD. These genes were selected through differential expression analysis of 18 published expression array datasets. Some genes are direct targets of TEAD transcription factors, meaning they present TEAD recognition motifs in their promoters, but most are subject to secondary or indirect regulation by the TEAD pathway. The “TEAD-500” signature gene list comprises approximately 90% of the 482 genes. Dropout rates of up to at least 10% have a negligible impact on the score. This is one reason for the high robustness of the TEAD-500. The TEAD-500 signature is published by Calvet et al. (BMC Cancer, 2022, 22:639, doi.org / 10.1186 / s12885-022-09686-y).

[0172] Extracellular vesicles (EVs) are defined as vesicles shed from cells, such as exosomes, microvesicles (or extranuclear mitochondria), apoptotic bodies, cancer bodies, and exosome-like vesicles. EVs are small, cell-derived membrane vesicles secreted by cells that deliver biological information through surface-to-surface interactions or by transporting bioactive molecules to the cytoplasm of recipient cells. EVs released by cells carry the cargo of their original cells and can be a good tool for tracking drug activity.

[0173] The term "sample" or "biosample" is intended to refer to biological material obtained (or purified or isolated) from a subject or from a cell culture. A biosample may contain any biological material suitable for detecting biomarkers (i.e., gene transcripts) and may comprise cellular material and / or non-cellular material (such as extracellular vesicles). A biosample may be a bodily fluid sample. Bodily fluid samples can be isolated from any suitable biological fluid (such as, for example, blood, plasma, serum, saliva, urine, or cerebrospinal fluid (CSF)). In some embodiments, the biosample is a plasma, serum, saliva, or urine sample.

[0174] Depending on the context, a “reference value” or “threshold” is intended to refer to the level of a gene transcript, or the level of multiple gene transcripts, or transcriptional signature, or deR score, or (S) score, which indicates a specific disease state, phenotype (such as cancer) or its absence in the relevant subject, or a combination of disease state, phenotype or its absence.

[0175] TEAD-active cancer. As used herein, “TEAD-active cancer” is intended to refer to cancer cells in which the TEAD pathway is dysregulated, leading to oncogenic transcription, cancer cell / tumor development or evolution, and which can respond to treatment with TEAD inhibitors (TEADi).

[0176] TEAD Feature. As used herein, “TEAD feature” or “TEAD-500 feature” refers to a transcriptional feature obtained by measuring the expression levels of genes in a gene set, which includes any of the 220 to 249 positive-effect genes and any of the 210 to 233 negative-effect genes listed in Table 2 of this document. By extension, depending on the context, “TEAD feature” can refer to the relevant gene set.

[0177] Scoring. The TEAD-500 score (referred to herein as the "TEAD score") is a procedure for calculating TEAD pathway activity in a sample over a continuous numerical scale. In some implementations, the score is based on the average fractional rank of transcriptional levels of positive and negative effectors.

[0178] Fractional rank. In an array of values ​​(such as gene transcript levels) in the transcriptome of a gene set, each value is replaced by its rank, with the smallest value being rank 1. The fractional rank of a gene is equal to its rank (determined by its gene transcript level) divided by the number of genes in the set (the largest rank).

[0179] Difference in effector ranking (deR). Converting TEAD-500 characteristic transcript levels (430 to 482 or 500 gene transcripts) to fractional rank.

[0180] The TEAD activity status is invoked (binned). This is a binary transformation of the continuous deR score of TEAD activity to one of two discrete state values: "active" or "inactive". In some implementations, we empirically set the deR threshold to 0.055. This value was chosen based on experiments with human dermatoma cell lines. We observed that cell lines with deR scores below 0.055 were unresponsive to YAP1-siRNA treatment, while cell lines with higher scores ceased growth after YAP1 was knocked out. The response threshold may vary for different tissues or treatments. A score of 0.055 roughly corresponds to the 88th percentile of scores observed in cancer cells in the Cancer Genome Atlas (TCGA) cohort (which aggregates all indications). TEAD is considered active if the deR score of TEAD-500 is greater than 0.055; otherwise, it is inactive.

[0181] Mathematical normalization. Mathematical normalization is the process of converting data or values ​​into a standard or universal scale.

[0182] TEAD pathway inhibitors or TEAD inhibitors (TEADi). As used herein, TEAD pathway inhibitors are any small or large molecule compounds that inhibit the HIPPO-YAP / WWTR1 / TEAD pathway.

[0183] As used herein, “administer” means to deliver the composition described herein to a subject. The composition may be administered to a subject using methods known in the art. In particular, the composition may be administered intravenously, subcutaneously, intramuscularly, intradermally, or via any mucosal surface (e.g., orally, sublingually, buccally, nasally, rectally, vaginally, or via the lungs). In some embodiments, intravenous administration is performed. In some embodiments, subcutaneous administration is performed.

[0184] The terms “treat” or “treatment” refer to the administration of a compound or composition according to this disclosure for the purpose of curing, eradicating, alleviating, reducing, altering, remedying, relieving, improving, or influencing the symptoms of a disorder or condition, or preventing or delaying the onset of symptoms or complications, or statistically significantly halting or inhibiting the further development of a disorder. More specifically, “treating” or “treatment” includes any method that achieves a beneficial or desired outcome in a subject’s cancer condition. Beneficial or desired clinical outcomes may include, but are not limited to, alleviating or relieving one or more cancer symptoms or conditions, reducing or lessening the severity of a cancer disease or cancer symptoms, stabilizing (i.e., not worsening) the state of a cancer disease or cancer symptoms, preventing the spread of a cancer disease or cancer symptoms, delaying or slowing the progression of a cancer disease or cancer symptoms, alleviating or slowing the state of a cancer disease, reducing the recurrence of a cancer disease, and alleviating (whether partial or complete, whether detectable or undetectable). In other words, as used herein, “treatment” includes any cure, relief, or reduction of a cancer disease or symptom. “Relief” of symptoms or disease means a reduction in the severity or frequency of the disease or symptom, or the elimination of the disease or symptom.

[0185] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Although any methods and materials similar to or equivalent to those described and materials herein may also be used in the practice or testing of this disclosure. All publications mentioned herein are incorporated herein by reference to disclose and describe methods and / or materials relating to the cited publications.

[0186] A list of sources, ingredients and components as described below is provided, so that combinations and mixtures thereof are also considered and are within the scope of this document.

[0187] It should be understood that each maximum numerical limit given throughout this specification includes each lower numerical limit, as if such lower numerical limit were explicitly stated herein. Each minimum numerical limit given throughout this specification includes each higher numerical limit, as if such higher numerical limit were explicitly stated herein. Each numerical range given throughout this specification includes each narrower numerical range falling within such a wider numerical range, as if such narrower numerical range were explicitly stated herein.

[0188] All lists of items (such as ingredient lists) are intended and should be interpreted as Markush groups. Therefore, all lists can be interpreted and understood as "items selected from the groups composed of the lists of items" and their combinations and mixtures.

[0189] The references herein may be to the trade names of components, including the various ingredients used in this disclosure. The inventors herein do not intend to be limited to materials under any particular trade name. In the description herein, materials equivalent to those mentioned under trade names (e.g., materials obtained from different sources under different names or reference numbers) may be substituted and used. Detailed Implementation

[0190] Gene Transcript Collection

[0191] In some implementations, the gene transcript set (A) from the gene set may consist of the following: a subset of genes (1) consisting of ADM, AXL, BIRC5, CDV3, CRIM1, CTGF, CYR61, FSTL1, GADD45A, KRT8, LMNB2, MATN2, PKP4, RND3, RPS24, SEC14L1, SGK1, SLC25A3, SLC3A2, TNFRSF12A, TPM1, TPX2 and TUBB6, and a subset of genes (2) consisting of CTSB, FTH1, SQSTM1, TCF25 and UBC.

[0192] The gene transcript set (A) was obtained from a gene set consisting of a first gene subset (1) and a second gene subset (2). The first gene subset (1) consists of the following: ADM, AXL, BIRC5, CDV3, CRIM1, CTGF, CYR61, FSTL1, GADD45A, KRT8, LMNB2, MATN2, PKP4, RND3, RPS24, SEC14L1, SGK1, SLC25A3, SLC3A2, TNFRSF12A, TPM1, TPX2, and TUBB6. The second gene subset (2) consists of CTSB, FTH1, SQSTM1, TCF25, and UBC.

[0193] In some embodiments, the gene transcript set (B) may consist of at least one gene transcript from the gene set, which consists of: DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0194] In some implementations, the gene transcript set (B) may consist of at least 2, 3, 4, 5, 6, or 7 gene transcripts from said gene set, or 8 gene transcripts from said gene set.

[0195] In some implementations, the gene transcript set (B) may consist of at least two gene transcripts from the gene set, which consists of: DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0196] In some embodiments, the gene transcript set (B) may consist of a gene set consisting of at least two gene transcripts, one of which is a gene transcript from gene DLC1 and at least the second gene transcript comes from a gene set consisting of: AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0197] In some embodiments, the gene transcript set (B) may consist of a gene set derived from DLC1 and AKAP2, and a gene transcript set optionally selected from at least one gene of CANX, SAFB2, EIF4H, NDUFS5, SEPT9 and EIF4A1.

[0198] In some embodiments, the gene transcript set (B) may consist of a gene set consisting of DLC1, AKAP2 and CANX, and a gene transcript set optionally selected from at least one gene of SAFB2, EIF4H, NDUFS5, SEPT9 and EIF4A1.

[0199] In some embodiments, the gene transcript set (B) may consist of a gene set consisting of DLC1, AKAP2, CANX and SAFB2, and a gene transcript set optionally selected from at least one gene of EIF4H, NDUFS5, SEPT9 and EIF4A1.

[0200] In some embodiments, the gene transcript set (B) may consist of a gene set derived from DLC1, AKAP2, CANX, SAFB2 and EIF4H, and a gene transcript set optionally selected from at least one gene from NDUFS5, SEPT9 and EIF4A1.

[0201] In some embodiments, the gene transcript set (B) may consist of a gene set derived from DLC1, AKAP2, CANX, SAFB2, EIF4H and NDUFS5, and a gene transcript set optionally selected from at least one gene selected from SEPT9 and EIF4A1.

[0202] In some implementations, the gene transcript set (B) may consist of a gene set derived from DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, and SEPT9, and optionally a gene transcript set derived from EIF4A1.

[0203] In some implementations, the gene transcript set (B) may consist of gene transcripts from gene DLC1.

[0204] In some implementations, the gene transcript set (B) may consist of a gene transcript set derived from the gene set consisting of DLC1 and AKAP2.

[0205] In some implementations, the gene transcript set (B) may consist of a gene transcript set derived from the gene set consisting of DLC1, AKAP2, and CANX.

[0206] In some implementations, the gene transcript set (B) may consist of a gene transcript set derived from the gene set consisting of DLC1, AKAP2, CANX and SAFB2.

[0207] In some implementations, the gene transcript set (B) may consist of a gene transcript set derived from the gene set consisting of DLC1, AKAP2, CANX, SAFB2, and EIF4H.

[0208] In some implementations, the gene transcript set (B) may consist of a gene transcript set derived from the gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, and NDUFS5.

[0209] In some implementations, the gene transcript set (B) may consist of a gene transcript set derived from the gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, and SEPT9.

[0210] In some implementations, the gene transcript set (B) may consist of a gene transcript set derived from a gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0211] Gene transcripts can be isolated or purified gene transcripts.

[0212] In some implementations, the gene transcript set may be an isolated or purified gene transcript set.

[0213] In some implementations, the gene transcript may be an RNA molecule.

[0214] Measurement / RNA sequencing

[0215] In some implementations, levels of one or more gene transcripts can be obtained.

[0216] In some implementations, the level of gene transcripts can be the level of nucleic acid expression, such as RNA levels (e.g., mRNA levels) or DNA expression levels. Any suitable method for determining nucleic acid expression levels (or gene transcript levels) can be used.

[0217] In some implementations, the level of gene transcripts can be measured by methods selected from: RNA sequencing (RNA-seq), next-generation sequencing (NGS), digital PCR, droplet digital PCR (ddPCR), RT-qPCR, qPCR, multiplex qPCR microarray analysis, gene expression serial analysis (SAGE), whole genome sequencing (WGS), and Mass-ARRAY technology.

[0218] For example, gene transcript levels can be determined using the following methods: RNA ACCESS protocol or TRUSEQ RIBO-ZERO00 protocol (ILLUMINA), RT-qPCR, qPCR, multiplex qPCR or RT-qPCR, microarray analysis, SAGE, Mass-ARRAY technology or a combination thereof.

[0219] Furthermore, such methods may include one or more steps that allow determination of the level of target RNA in a biological sample, for example, by simultaneously examining the level of a comparative control RNA sequence of a "housekeeping" gene (such as a member of the actin family). In some embodiments, the sequence of the amplified target cDNA can be determined.

[0220] In some implementations, the gene transcript is an RNA molecule, and RNA-seq is used to determine the level.

[0221] In the implementation plan, the gene transcript is an mRNA molecule.

[0222] RNA-seq enables qualitative and quantitative measurement of gene expression at the transcriptome level and involves sequencing RNA molecules from a sample and mapping them to a reference genome or transcriptome. Different RNA-seq methods are known in the field (Zhang et al. (2020). Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis. Scientific Reports 10:20765. www.nature.com / articles / s41598-020-76881-x).

[0223] RNA-seq typically involves the preparation of RNA sequencing libraries. Standard protocols can be used to prepare RNA sequencing libraries. For example, RNA can be fragmented and reverse transcribed into cDNA. An adaptor can be added to the cDNA, and the library can be amplified using PCR. An adaptor is a short DNA sequence that allows the fragment to be amplified by PCR and provides a barcode or identifier for the sequencing platform. The RNA library can then be sequenced using any method known in the art, such as next-generation sequencing technology, to obtain RNA-seq data.

[0224] Typically, RNA-seq data analysis involves the following steps: pruning to remove adaptor sequences and inferior nucleotides, alignment with a reference genome or transcriptome, counting or quantifying by assigning readings to genes or transcripts, normalizing sequencing readings, and usually cross-conditional differential expression (DE) analysis.

[0225] The levels of gene transcripts can be quantified using software such as Cufflinks or RSEM. Differential expression analysis can be performed using software such as DESeq2 or edgeR.

[0226] Optional methods include protocols for examining or detecting RNA (such as target RNA) in samples using microarray technology.

[0227] Using a nucleic acid microarray, test and control RNA samples are reverse transcribed and labeled to generate cDNA probes. The probes are then hybridized to a nucleic acid microarray immobilized on a solid support. The array is configured such that the sequence and location of each member in the array are known. For example, any gene transcript described herein can be arranged on a solid support. Hybridization of the labeled probe with a specific array member indicates that the sample from which the probe originates expresses the gene.

[0228] The level of gene transcripts can be measured and quantified using various methods, such as microarray analysis or RNA sequencing (RNA-seq), as previously detailed. The level of gene transcripts can be expressed using FPKM (fragments of transcript per kilobase per million mapped reads), a normalized measure that takes into account transcript length and the total number of reads generated by RNA sequencing.

[0229] FPKM works by dividing the number of fragments (or readings) mapped to a specific gene transcript by the transcript length (in kilobases), and then normalizing this value to the total number of mapped fragments in the sample (in millions). This allows for comparisons of expression levels between different genes and different samples.

[0230] Other suitable measures of gene transcript levels include TPM (number of transcripts per million) and RPKM (number of transcripts per million mapped readings per kilobase).

[0231] TPM (Transcription Per Million) is a method for normalizing gene transcript levels in RNA sequencing data. TPM considers both gene transcript length and the total number of reads generated by RNA sequencing, but normalizes gene transcript levels by dividing the reads mapped to a specific gene transcript by the total reads in the sample, and then multiplying this value by one million. This allows for comparisons of levels between different gene transcripts and different samples, taking into account differences in gene transcript length and sequencing depth.

[0232] RPKM (Reads per kilobase of transcript per million mapped reads) is a method for quantifying the level of gene transcripts in RNA sequencing data. Like FPKM and TPM, RPKM takes into account both the length of gene transcripts and the total reads generated by RNA sequencing.

[0233] RPKM works by dividing the readings mapped to a specific gene transcript by the length of the gene transcript (in kilobases), and then dividing this value by the total number of mapped readings in the sample (in millions). The resulting value represents the level of the gene transcript, expressed as readings per kilobase per million mapped readings. This allows for comparisons of levels between different gene transcripts and different samples, taking into account differences in transcript length and sequencing depth.

[0234] In some embodiments, the levels of gene transcripts disclosed herein are measured by RNA sequencing (RNA-seq) and expressed / quantified as FPKM (fragments of transcript per kilobase per million mapped readings).

[0235] biological samples

[0236] In some embodiments, gene transcripts can be obtained from a biological sample or a portion thereof. A portion of the biological sample applicable to this disclosure may be an extract of such a sample.

[0237] Biological samples can be obtained from subjects in need, cell cultures, or animal models (such as animal models of cancer).

[0238] Cells suitable for use in this disclosure may be TEAD-active cells. Examples of TEAD-active cells suitable for cell cultures according to this disclosure may be mesothelioma cell lines, NCI-H226, Mero-14, and SPC212. Examples of TEAD-inactive cells suitable for use as controls according to this disclosure may be the unresponsive colon cell line HCT116.

[0239] Examples of animal models of cancer with TEAD activity include mouse models of colorectal cancer, lung cancer, liver cancer, and breast cancer, wherein TEAD or its cofactor genes are deleted or mutated, or TEAD or its cofactors are overexpressed or knocked down.

[0240] Biological samples can be body fluid samples or cell culture supernatants.

[0241] Bodily fluid samples can be isolated from subjects who require them.

[0242] Body fluid samples can be selected from blood samples, plasma samples, urine samples, cerebrospinal fluid (CSF) samples, bronchoalveolar fluid samples, nasal secretion samples, breast milk samples, semen samples, and saliva samples.

[0243] In some implementations, the body fluid sample may be a blood sample.

[0244] In some implementations, the body fluid sample may be a plasma sample.

[0245] In some implementations, the body fluid sample may be a urine sample.

[0246] In some implementations, the body fluid sample may be a saliva sample.

[0247] In some implementations, the biological sample or a portion thereof may contain extracellular vesicles.

[0248] Body fluid samples can be obtained by any method known in the art. For example, acupuncture or passive drainage can be used to obtain blood samples, aspiration can be used to obtain saliva samples, lumbar puncture can be used to obtain CSF samples, midstream clean-clean urine collection or intubation can be used to obtain urine samples, and bronchoalveolar lavage can be used to obtain bronchoalveolar samples.

[0249] Obtain a sufficient quantity of biological sample to contain the biological material required to obtain the gene transcripts of this disclosure. Those skilled in the art know how to adjust the quantity of biological sample according to its nature and source.

[0250] For example, depending on the liquid sample, the sample volume may range from about 1 ml (saliva sample) to about 250 ml (bronchopneumonic sample).

[0251] Biological samples or portions thereof applicable to this disclosure may contain extracellular vesicles (EVs).

[0252] extracellular vesicles

[0253] In some embodiments, the gene transcripts of this disclosure can be obtained from isolated extracellular vesicles. EVs can be obtained from biological samples as disclosed herein.

[0254] In some implementations, gene transcripts can be extracted from extracellular vesicles.

[0255] Extracellular vesicles (EVs) are small, membrane-bound vesicles released from cells into the extracellular environment. Based on their biogenesis, size, and contents, EVs can be classified into different subtypes, including exosomes, microvesicles (or extranuclear mitochondria), apoptotic bodies, cancer bodies, or exosome-like vesicles. EVs have been found to play an important role in intercellular communication because they can transport various biomolecules, such as proteins, lipids, and nucleic acids, between cells.

[0256] In some embodiments, this disclosure relates to isolated extracellular vesicles comprising a set of gene transcripts as disclosed herein.

[0257] In some embodiments, this disclosure relates to isolated extracellular vesicles comprising a set (A) of gene transcripts obtained from a gene set, said gene set consisting of a first gene subset (1) and a second gene subset (2), the first gene subset (1) consisting of ADM, AXL, BIRC5, CDV3, CRIM1, CTGF, CYR61, FSTL1, GADD45A, KRT8, LMNB2, MATN2, PKP4, RND3, RPS24, SEC14L1, SGK1, SLC25A3, SLC3A2, TNFRSF12A, TPM1, TPX2, and TUBB6, and the second gene subset (2) consisting of CTSB, FTH1, SQSTM1, TCF25, and UBC.

[0258] In some embodiments, this disclosure relates to isolated extracellular vesicles comprising a set of gene transcripts consisting of at least one gene transcript from a gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0259] In some implementations, each extracellular vesicle in the extracellular vesicle set may contain all the gene transcripts of the gene transcript set described herein.

[0260] In some implementations, each extracellular vesicle in the extracellular vesicle set may contain at least a portion of the gene transcripts of the gene transcript set described herein, and all extracellular vesicles in the EV set together may contain all the gene transcripts of the gene transcript set.

[0261] In some implementations, extracellular vesicles may be selected from exosomes, microvesicles (or extranuclear mitochondria), apoptotic bodies, cancer bodies, exosome-like vesicles, and combinations thereof.

[0262] In some implementations, extracellular vesicles can be isolated from biological samples (e.g., body fluid samples).

[0263] In some implementations, extracellular vesicles can be purified from biological samples (e.g., body fluid samples).

[0264] Biological samples can be described in detail previously.

[0265] Various methods for isolating and purifying extracellular vesicles (EVs) from suitable biological samples are known in the art. As an example of a method, one could be:

[0266] Ultracentrifugation: This type of method involves high-speed centrifugation of the sample to separate EVs from other cellular components.

[0267] Size exclusion chromatography (SEC): This type of method involves using a bead-filled column to separate EVs according to their size, the column allowing separation based on the hydrodynamic diameter of the particles. Another type of SEC can be ultracentrifugation using a sucrose gradient.

[0268] Immunoaffinity-based methods: These methods involve isolating and purifying EVs from a sample using antibodies or other ligands that specifically bind to EV surface markers (such as CD63 or CD9). An example of an immunoaffinity-based method could be a membrane-based affinity column method.

[0269] Membrane-based affinity columns: These methods involve chromatographic columns that use membranes with affinity for EVs to selectively capture and purify EVs from a sample. The membranes contain ligands that bind to specific EV surface markers (e.g., the four-permeable-membrane proteins CD63 or CD9) to allow for the high-purity and specific separation of EVs, such as cytosomes and microvesicles.

[0270] The workflow using membrane-based affinity columns typically involves binding the EVs of a sample to a membrane, washing away unwanted substances, and then eluting the purified EVs from the membrane using a buffer solution.

[0271] Microfluidic-based methods: These methods involve using microfluidic devices to separate and purify EVs based on their size, shape, and other physical properties.

[0272] Various methods known in the art can be used to separate EVs and extract gene transcript contents. For example, differential ultracentrifugation, methods using size-based filters (e.g., EXOMIR, Biosystocientific), antibody-based capture methods (e.g., IMMUNOBEADS, HANSABIOMED), polymer-based precipitation reagent methods (e.g., LIFE TECHNOLOGIES, SYSTEM BIOSCIENCES INC.), or column-based methods (membrane-based affinity columns) can be used.

[0273] In some implementations, membrane-based affinity columns (EXOEASY, MAXI kits, QIAGEN) can be used to separate EVs from biological samples (e.g., blood or urine samples or supernatants of cultured cells). Membrane-based affinity column methods may include the following steps:

[0274] Membrane-based affinity columns are prepared by immobilizing specific ligands on a column matrix. Suitable ligands can be antibodies or aptamers that specifically bind to EVs.

[0275] Biological samples are prepared by removing cells and cell debris through centrifugation or filtration to obtain a supernatant or filtrate containing the target EV.

[0276] Under conditions suitable for EV binding to immobilized ligands, the EV-containing sample was loaded onto a prepared column, and the column was washed with a suitable buffer to remove any unbound material.

[0277] Elute EVs by appropriately changing the buffer conditions (such as changing the pH or salt concentration).

[0278] In some implementations, extracellular vesicles can be centrifuged into a membrane-based affinity column.

[0279] The separated EVs can be quantified and characterized using a variety of techniques known in the art, such as electron microscopy, nanoparticle tracking analysis, or immunoblotting.

[0280] Gene transcripts can be extracted, isolated, and sequenced from isolated EVs using any method known in the art.

[0281] Suitable methods for extracting and isolating gene transcripts of this disclosure may include at least the following steps:

[0282] EVs were lysed and the released gene transcripts were obtained from the lysates.

[0283] Load the lysate onto an affinity column, such as a centrifuge column.

[0284] Wash the affinity column with a suitable buffer solution to remove any unbound material.

[0285] Elution of gene transcripts.

[0286] The quality and quantity of gene transcripts can be assessed by any method known in the art, such as using a bioanalyzer or spectrophotometer.

[0287] Uses and methods

[0288] use

[0289] In some implementations, such as the set of gene transcripts disclosed herein, the gene transcripts may be used as biomarkers for TEAD activity.

[0290] In some implementations, such as the gene transcript set disclosed herein, the TEAD activity of cancer can be measured or characterized.

[0291] In some implementations, such as the gene transcript set disclosed herein, the TEAD activity of a biological sample can be measured or characterized.

[0292] In some implementations, such as the set of gene transcripts disclosed herein, the gene transcripts may be used in a screening method for TEAD inhibitor candidate compounds.

[0293] In some implementations, such as the set of gene transcripts disclosed herein, the gene transcripts may be used in cancer diagnostic methods.

[0294] Cancer diagnostic methods may be selected from methods for characterizing the TEAD activity status of cancer, methods for measuring TEAD activity in biological samples, methods for predicting the response of cancer to TEAD inhibitor therapy, methods for monitoring the response of cancer to TEAD inhibitor therapy, methods for predicting cancer progression or regression with TEAD activity, and methods for monitoring cancer progression or regression with TEAD activity.

[0295] In some embodiments, this disclosure relates to the use of gene transcript sets as disclosed herein for characterizing the TEAD activity status of biological samples.

[0296] In some embodiments, this disclosure relates to the use of a set of gene transcripts as disclosed herein for characterizing the TEAD activity status of cancer in a subject of need.

[0297] In some embodiments, this disclosure relates to the use of the gene transcript set disclosed herein for measuring TEAD activity in biological samples.

[0298] In some embodiments, this disclosure relates to the use of the gene transcript set disclosed herein for measuring TEAD activity in bodily fluid samples of subjects in need.

[0299] In some embodiments, this disclosure relates to the use of the gene transcript set disclosed herein for measuring TEAD activity in cancer cell samples from a subject in need.

[0300] In some embodiments, this disclosure relates to the use of the gene transcript set disclosed herein for predicting the response of cancers characterized as TEAD activity to TEAD inhibitor therapy in subjects of need.

[0301] In some embodiments, this disclosure relates to the use of the gene transcript set disclosed herein for monitoring the response of cancers characterized as TEAD activity to TEAD inhibitor therapy in subjects of need.

[0302] In some implementations, this disclosure relates to the use of the gene transcript set disclosed herein for predicting cancer progression or regression in subjects of need.

[0303] In some implementations, this disclosure relates to the use of the gene transcript set disclosed herein for monitoring cancer progression or regression in subjects of need.

[0304] Subjects who are known or presumed to have TEAD-active cancer may be eligible.

[0305] The cancers considered in this disclosure may be TEAD-active cancers.

[0306] In some embodiments, this disclosure relates to the use of the gene transcript set disclosed herein for screening TEAD inhibitor candidate compounds.

[0307] In some implementations, the gene transcript set may be obtained from biological samples, such as those detailed above.

[0308] In some implementations, the set of gene transcripts can be obtained from extracellular vesicles, such as those detailed above.

[0309] In some embodiments, extracellular vesicles containing the gene transcript set of this disclosure may be used for the gene transcript indicated above, wherein the gene transcripts are extracted from the extracellular vesicles.

[0310] In some implementations, the level of each gene transcript in the gene set is obtained, for example as detailed above.

[0311] The obtained gene transcript levels can characterize the TEAD activity status of cancer or biological samples.

[0312] The obtained gene transcript levels can serve as a measure of TEAD activity in cancer or biological samples.

[0313] In some implementations, the obtained gene transcript levels can be compared with reference values.

[0314] In some implementations, observed deviations relative to reference values ​​may indicate whether the cancer is TEAD-active or TEAD-inactive, or whether the cancer is responsive or unresponsive to TEAD inhibitor treatment, or whether TEAD inhibitor treatment is effective or ineffective, or whether the cancer is progressing or regressing, or whether the TEAD inhibitor candidate compound is effective or ineffective.

[0315] In some implementations, transcriptional signatures can be obtained at the gene transcript level.

[0316] The obtained transcriptional features can be compared with reference transcriptional features.

[0317] In some implementations, the observed deviation between the obtained transcriptional signature and the reference transcriptional signature may indicate that the cancer is TEAD-active or TEAD-inactive, or that the cancer is responsive or unresponsive to TEAD inhibitor treatment, or that TEAD inhibitor treatment is effective or ineffective, or that the cancer is progressing or regressing, or that the TEAD inhibitor candidate compound is effective or ineffective.

[0318] In some implementations, the obtained gene transcript levels can be mathematically normalized.

[0319] When using a set of gene transcripts (A), mathematical normalization can be used to calculate the deR score. The deR score can be calculated using a method that includes the following steps:

[0320] a) For each gene transcript in the gene set, the level of each gene transcript is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0321] b) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step a) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0322] c) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step a) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and d) Calculate the deR score as MFR-subset (1) minus MFR-subset (2).

[0323] The rank of genes within a gene set is obtained by assigning rank 1 to the gene with the lowest level of gene transcript, rank 2 to the gene with the second highest level of gene transcript, and so on, until the gene with the highest level of gene transcript is assigned the highest rank. Genes with the same level of gene transcript are assigned an average rank; for example, two genes with the same level of gene transcript and an expression level of 0 are assigned rank 1.5.

[0324] When using a set of gene transcripts (B), the (S) score is calculated using mathematical normalization. The (S) score can be calculated using a method that includes the following steps:

[0325] a) Multiply each level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0326] b) The product P gene obtained in step a) i Summation (ΣP gene) i Add a constant to obtain the (S) score:

[0327] (ΣP ​​gene) i +constant),

[0328] The coefficients and constants used in steps a) and b) were previously obtained by stepwise multiple linear regression analysis, which correlated (i) the level of the gene transcripts previously obtained in the first biological sample with (ii) the level of gene transcripts of the TEAD-500 feature obtained in the second biological sample, wherein the TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes of the gene subset (1) disclosed in Table 2 provided thereafter and any of the 210 to 233 genes of the gene subset (2).

[0329] In some embodiments, in the methods and uses of this disclosure, a stepwise multiple linear regression analysis can be performed between (i) the level of the gene transcript and (ii) the TEAD score obtained by the level of the gene transcript characterized by TEAD-500.

[0330] The coefficients and constants are specific to a given set of gene transcripts (B), a given cancer, a given method for determining the levels of gene transcripts, and therefore a given pattern for determining / quantifying the levels. Therefore, for a given cancer, coefficients and constants must be calculated for each implementation of the set of gene transcripts (B) (e.g., any combination containing one to eight gene transcripts from the set of gene transcripts (B)). For the set of gene transcripts (B), coefficients and constants must be calculated for each cancer type.

[0331] The calculated coefficients and constants depend on the number of gene transcripts used for the gene transcript set (B) and the method used to obtain the levels of the gene transcripts, and therefore the pattern used to determine / quantify the levels of the transcripts. The method used to obtain the levels of the gene transcripts for the gene set (B) does not necessarily have to be the same as the method used to measure the levels of the gene transcripts used for the association of TEAD-500 features.

[0332] Examples of coefficients and constants applicable to mesothelioma and gene transcript set (B) (containing 8 gene transcripts, with levels determined by FKPM) are presented in Table 1 below:

[0333] Table 1

[0334] A set of gene transcripts containing 8 genes (B) coefficient DLC1 0.031 AKAP2 0.036 CANX -0.023 SAFB2 0.040 EIF4H 0.008 NDUFS5 -0.011 SEPT9 -0.009 EIF4A1 0.005 constant -0.155

[0335] The first and second biological samples can be obtained from subjects, cultured cells, or TEAD cancer animal models.

[0336] The first and second biological samples represent the same TEAD-active cancer, the same cell culture, or the same TEAD-active cancer animal model. The first and second biological samples represent cancer or TEAD-active cancer cell cultures or animal models (considered for use according to this disclosure). Suitable cells for the cell cultures according to this disclosure may be TEAD-active cells.

[0337] The first sample can be a body fluid sample or a portion thereof. The second sample can be a cancer cell sample.

[0338] The first and second biological samples can be taken from the same subject.

[0339] In some implementations, the subjects from which the first and second biological samples are obtained for calculating coefficients and constants by stepwise multiple linear regression analysis may be the same or different from the subjects from whom the (S) score is calculated.

[0340] When different, the subjects calculating the coefficients and constants may be reference subjects or a reference subject group known to have TEAD-active cancer. Since the coefficients and constants are specific to a given cancer, the subjects calculating the (S) score must have, or must be presumed to have, the same type of cancer as the reference subjects or the reference subject group (i.e., subjects from whom biological and cancer cell samples were previously obtained).

[0341] Two types of biological samples were obtained from reference subjects: (a) a first biological sample (e.g., a body fluid sample or a portion thereof) that is known or presumed to contain EVs, and (a) a second biological sample from cancer cells.

[0342] The levels of gene transcripts characteristic of TEAD-500 (as detailed later) were obtained from cancer cell biological samples, and the levels of gene transcripts of gene set (B) were obtained from biological samples known to contain EVs.

[0343] The TEAD-500 feature may include gene transcripts containing the following gene sets: any of the 220 to 249 genes in the gene subset (1) disclosed in Table 2 below and any of the 210 to 233 genes in the gene subset (2).

[0344] A stepwise multiple linear regression analysis is performed (which correlates (i) the levels of gene transcripts in gene transcript set (B) with (ii) the levels of gene transcripts characterized by TEAD-500) to obtain coefficients and constants for the cancer and the gene transcript set (B). In some embodiments, the correlation using stepwise multiple linear regression analysis can be performed between (i) the levels of the gene transcripts and (ii) the TEAD scores obtained using the levels of gene transcripts characterized by TEAD-500.

[0345] In some implementations, the subjects from whom the first and second biological samples used to calculate coefficients and constants via stepwise multiple linear regression analysis are the same subjects from whom the (S) score is calculated. In such cases, the coefficients and constants can be calculated as described above. The (S) score calculated using the coefficients and constants can be calculated on different biological samples obtained from said subjects, for example, at different subsequent time points. Such (S) scores can be used to track the progression or regression of TEAD-active cancer, or the response of TEAD-active cancer to TEAD inhibitor therapy.

[0346] In some implementations, the first biological sample for obtaining the level of gene transcripts may be a body fluid sample or a portion thereof, as previously detailed. The second biological sample may be a cancer cell sample.

[0347] In some implementations, the first and second biological samples can be obtained from cultured cells.

[0348] The first biological sample from cultured cells can be the cell supernatant.

[0349] A second biological sample derived from cultured cells can be cultured cells.

[0350] In some implementations, the cell cultures from which the first and second biological samples (i.e., previously acquired biological samples) for calculating coefficients and constants by stepwise multiple linear regression analysis are obtained may be the same as or different from the cell cultures from which the (S) score is calculated.

[0351] Suitable cells for use in cell cultures according to this disclosure may be TEAD active cells.

[0352] In some implementations, the first and second biological samples can be obtained from animal models of TEAD-active cancer.

[0353] The first biological sample may be a body fluid sample or a portion thereof, as described above.

[0354] The second biological sample can be a cancer cell sample.

[0355] In some implementations, the animal models from which the first and second biological samples (i.e., previously acquired biological samples) from which coefficients and constants are obtained by stepwise multiple linear regression analysis may be the same as or different from the animals from which the (S) score is calculated.

[0356] In some implementations, the deR or (S) score is calculated in this way.

[0357] The calculated deR or (S) score can be used to characterize the TEAD activity status of cancer or biological samples in subjects in need, or to measure TEAD activity in biological samples (e.g., subjects in need).

[0358] In some implementations, the calculated deR or (S) score is compared with a reference value (e.g., a reference deR or (S) score).

[0359] Reference deR or (S) scores can be obtained from reference subjects or reference subject groups, or reference cell cultures, or animal models of cancer with reference TEAD activity.

[0360] The comparison of the calculated deR or (S) score with the reference deR or (S) score can be used to predict the progression or regression of a subject's TEAD-active cancer, or to predict the response of a subject's TEAD-active cancer to TEAD inhibitor therapy, or to characterize the TEAD activity status of a subject's cancer or biological sample, or to measure TEAD activity in a biological sample, or to screen TEAD inhibitor candidate compounds.

[0361] In some implementations, a calculated deR or (S) score that is lower or higher than a reference deR or (S) score may indicate whether the cancer is TEAD active or TEAD inactive, or whether the cancer is responsive or unresponsive to TEAD inhibitor treatment, or whether TEAD inhibitor treatment is effective or ineffective, or whether TEAD-active cancer progresses or regresses, or whether a TEAD inhibitor candidate compound is effective or ineffective.

[0362] In some implementations, a lower calculated deR or (S) score compared to a reference deR or (S) score may indicate that the cancer is TEAD inactivated, or that the cancer is responsive to TEAD inhibitor treatment, or that TEAD inhibitor treatment is effective, or that TEAD-active cancer has regressed, or that a TEAD inhibitor candidate compound is effective.

[0363] In some implementations, a higher calculated deR or (S) score compared to a reference deR or (S) score may indicate that the cancer is TEAD-active, or that the cancer is unresponsive to TEAD inhibitor treatment, or that TEAD inhibitor treatment is ineffective, or that TEAD-active cancer is progressing, or that the TEAD inhibitor candidate compound is ineffective.

[0364] In some implementations, cancer may be characterized as TEAD active when the (S) score is likely greater than a reference value of about 0.055, and cancer may be characterized as TEAD inactive when the (S) score is likely less than or equal to a reference value of about 0.055.

[0365] In some implementations, cancer may be predicted to be responsive to TEAD inhibitor treatment when the (S) score is likely to be greater than a reference value of about 0.055, and cancer may be characterized as unresponsive to TEAD inhibitor treatment when the (S) score is likely to be less than or equal to a reference value of about 0.055.

[0366] In some implementations, TEAD inhibitor treatment may be observed to be ineffective against TEAD-active cancers when the (S) score is likely to be greater than a reference value of about 0.055, and TEAD inhibitor treatment may be observed to be effective against TEAD-active cancers when the (S) score is likely to be less than or equal to a reference value of about 0.055.

[0367] In some implementations, TEAD-active cancer can be predicted or detected as progressing when the (S) score is likely to be greater than a reference value of about 0.055, and TEAD-active cancer can be predicted or detected as regressing when the (S) score is likely to be less than or equal to a reference value of about 0.055.

[0368] In some implementations, a TEAD inhibitor candidate compound may be ineffective in inhibiting TEAD activity when the (S) score is likely to be greater than a reference value of about 0.055, and a TEAD inhibitor candidate compound may be effective in inhibiting TEAD activity when the (S) score is likely to be less than or equal to a reference value of about 0.055.

[0369] In some implementations, the reference value may be a first deR or (S) score, and the deR or (S) score to be compared with the reference value may be a second deR or (S) score measured after the first deR or (S) score.

[0370] The first and second deR or (S) scores can be calculated from biological samples obtained from the same subject or the same cell culture.

[0371] The comparison of the first deR or (S) score with the second deR or (S) score (or even further subsequent deR or (S) scores) can be used to monitor the progression or regression of TEAD-active cancer in a subject, or to monitor the response of TEAD-active cancer in a subject to TEAD inhibitor therapy, or to screen TEAD inhibitor candidate compounds.

[0372] In some implementations, a second deR or (S) score lower than the first deR or (S) score may indicate that TEAD activity is inhibited.

[0373] In some implementations, the first deR or (S) score may be measured in a biological sample obtained from a subject with known or presumed TEAD-active cancer and the second deR or (S) score may be measured in a second biological sample obtained from the subject after the first biological sample, and wherein the second deR or (S) score being lower than the first deR or (S) score may indicate that the subject's cancer is regressing.

[0374] In some implementations, a first deR or (S) score may be measured in a biological sample obtained from a subject with known or presumed TEAD-active cancer and a second deR or (S) score may be measured in a second biological sample obtained from the subject after the first biological sample, wherein a second deR or (S) score higher than the first deR or (S) score may indicate that the subject's cancer is progressing.

[0375] In some implementations, the first deR or (S) score can be measured in a first biological sample obtained from a subject with known or presumed TEAD-active cancer prior to treatment with the TEAD inhibitor, and the second deR or (S) score can be measured in a second biological sample obtained from the subject after treatment with the TEAD inhibitor, wherein a second deR or (S) score lower than the first deR or (S) score may indicate that the TEAD inhibitor treatment is effective in the subject.

[0376] In some implementations, the first deR or (S) score may be measured in a first biological sample obtained from a subject with known or presumed TEAD-active cancer prior to treatment with the TEAD inhibitor, and the second deR or (S) score may be measured in a second biological sample obtained from the subject after treatment with the TEAD inhibitor, wherein a second deR or (S) score higher than the first deR or (S) score may indicate that the TEAD inhibitor treatment is ineffective in the subject.

[0377] In some embodiments, a first deR or (S) score may be measured in a first biological sample obtained from the supernatant of an animal model of TEAD-active cancer or a cell culture, the first biological sample being obtained before contacting the cell culture with the TEAD inhibitor candidate compound or before administering the TEAD inhibitor candidate compound to the animal model of TEAD-active cancer, and a second deR or (S) score may be measured in a second biological sample obtained from the animal model of TEAD-active cancer or the supernatant of the cell culture, the second biological sample being obtained after contacting the cell culture with the TEAD inhibitor candidate compound or after administering the TEAD inhibitor candidate compound to the animal model of TEAD-active cancer, and wherein a second deR or (S) score lower than the first deR or (S) score may indicate that the TEAD inhibitor candidate compound is effective.

[0378] In some implementations, a first deR or (S) score may be measured in a first biological sample obtained from the supernatant of an animal model of TEAD-active cancer or a cell culture, the first biological sample being obtained before contacting the cell culture with the TEAD inhibitor candidate compound or before administering the TEAD inhibitor candidate compound to the animal model of TEAD-active cancer, and a second deR or (S) score may be measured in a second biological sample obtained from the animal model of TEAD-active cancer or the supernatant of the cell culture, the second biological sample being obtained after contacting the cell culture with the TEAD inhibitor candidate compound or after administering the TEAD inhibitor candidate compound to the animal model of TEAD-active cancer, and wherein a second deR or (S) score higher than the first deR or (S) score may indicate that the TEAD inhibitor candidate compound is ineffective.

[0379] Suitable cells for use in cell cultures according to this disclosure may be TEAD active cells.

[0380] method

[0381] In some embodiments, this disclosure relates to a method for characterizing or measuring the TEAD activity status of cancer in a subject of need, the method comprising using a set of gene transcripts from a gene set (A), and the method comprising at least the following steps:

[0382] a) Obtain the level of each gene transcript in the gene set from biological samples obtained from the subject.

[0383] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0384] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0385] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0386] e) Calculate the deR score as MFR-subset(1) minus MFR-subset(2).

[0387] In some embodiments, this disclosure relates to a method for characterizing or measuring the TEAD activity status of cancer in a subject of need, the method comprising using a set of gene transcripts from a gene set (B), and the method comprising at least the following steps:

[0388] a) Obtain the level of each gene transcript in the gene set from biological samples obtained from the subject.

[0389] b) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0390] c) The product P gene obtained in step b) i Summation (ΣP gene) i Add a constant to obtain the (S) score: (ΣP gene) i +constant),

[0391] Wherein, the coefficients and constants used in steps b) and c) can previously be obtained by stepwise multiple linear regression analysis, which correlates (i) the level of the gene transcripts previously obtained in a first biological sample obtained from a subject with said cancer with (ii) the level of gene transcripts of the TEAD-500 feature obtained in a second biological sample obtained from said subject’s cancer cells, wherein said TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes of the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes of the gene subset (2).

[0392] In some implementations, a stepwise multiple linear regression analysis can be performed to correlate (i) the level of the gene transcript with (ii) the TEAD score obtained by analyzing the level of the gene transcript characterized by TEAD-500.

[0393] As described above, the subjects who obtain the first and second biological samples may be the same as or different from the subjects who obtain the biological samples used to calculate the (S) score. The first and second biological samples represent cancers considered according to the method of this disclosure.

[0394] In some implementations, if the deR or (S) score is greater than a reference value (e.g., about 0.055 for the (S) score), the cancer can be characterized as TEAD active, and if the deR or (S) score is less than or equal to a reference value (e.g., about 0.055 for the (S) score), the cancer can be characterized as TEAD inactive.

[0395] In some embodiments, this disclosure relates to a method for monitoring the progression or regression of cancer in a subject suspected of or known to have TEAD-active cancer, the method comprising using a set of gene transcripts from a gene set (A), and the method comprising at least the following steps:

[0396] a) Obtain the level of each gene transcript in the gene set from the first biological sample obtained from the subject.

[0397] b) For each gene transcript in the gene set, convert the level of each gene transcript obtained in step a) into a fractional rank by dividing the rank of the gene by the number of genes from the gene set.

[0398] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0399] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0400] e) Calculate the first deR score as MFR-subset(1) minus MFR-subset(2).

[0401] f) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the subject (after the first time).

[0402] g) For each gene transcript in the gene set, convert the level of each gene transcript obtained in step f) into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0403] h) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0404] i) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0405] e) Calculate the second deR score as MFR-subset (1) minus MFR-subset (2), and

[0406] k) Compare the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores may indicate the progression or regression of the cancer.

[0407] In some implementations, a second deR score may be greater than a first deR score, in which case cancer progression may be observed, and a second deR score may be lower than a first deR score, in which case cancer regression may be observed.

[0408] In some embodiments, this disclosure relates to a method for monitoring the progression or regression of cancer in a subject suspected of or known to have TEAD-active cancer, the method comprising using a set of gene transcripts from a gene set (B), and the method comprising at least the following steps:

[0409] a) Obtain the level of each gene transcript in the gene set from the first biological sample obtained from the subject.

[0410] b) Multiply each level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0411] c) The product P gene obtained in step b) i Summation (ΣP gene) i And add a constant to obtain the first (S) score: (ΣP gene) i +constant),

[0412] d) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the subject (after the first).

[0413] e) Multiply each level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i Among them, genes i (Refers to the genes listed in the gene set);

[0414] f) The product P gene obtained in step e) i Summation (ΣP gene)i And add a constant to obtain the second (S) score: (ΣP gene) i + constant), and

[0415] g) Compare the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores may indicate the progression or regression of the cancer.

[0416] Wherein, the coefficients and constants used in steps b), c), e) and f) can previously be obtained by stepwise multiple linear regression analysis, which correlates (i) the level of the gene transcript previously obtained in a third biological sample obtained from a subject with said cancer with (ii) the level of the gene transcript of the TEAD-500 feature obtained in a fourth biological sample obtained from the subject's cancer cells, wherein the TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes of the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes of the gene subset (2).

[0417] In some implementations, a stepwise multiple linear regression analysis can be performed to correlate (i) the level of the gene transcript with (ii) the TEAD score obtained by analyzing the level of the gene transcript characterized by TEAD-500.

[0418] The subjects who obtain the third and fourth biological samples may be the same or different from the subjects who obtain the first and second biological samples used to calculate the (S) score. The third and fourth biological samples represent cancers considered according to the method of this disclosure.

[0419] In some implementations, cancer progression may be observed if the second (S) score is greater than the first (S) score, and cancer regression may be observed if the second (S) score is lower than the first (S) score.

[0420] In some embodiments, this disclosure relates to a method for diagnosing and treating a subject in need, the method comprising the steps of characterizing the TEAD activity status of the subject's cancer, and, if the cancer is characterized as TEAD active, administering a TEAD inhibitor to the subject for treatment.

[0421] in

[0422] For a method that includes using a set of gene transcripts from a gene set (A) to characterize the TEAD activity status of cancer, the method includes at least the following steps:

[0423] a) Obtain the level of each gene transcript in the gene set from biological samples obtained from the subject.

[0424] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0425] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0426] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0427] e) Calculate the deR score as MFR-subset(1) minus MFR-subset(2).

[0428] f) Compare the calculated deR score obtained in step e) with a reference deR score, wherein the observed deviation between the calculated deR score and the reference deR score may indicate TEAD-active cancer, and g) If the cancer is characterized as TEAD-active, administer a TEAD inhibitor to the subject.

[0429] In some embodiments, this disclosure relates to a method for diagnosing and treating a subject in need, the method comprising the steps of characterizing the TEAD activity status of the subject's cancer, and, if the cancer is characterized as TEAD active, administering a TEAD inhibitor to the subject for treatment.

[0430] in

[0431] For a method that includes using a set of gene transcripts from gene set (B) to characterize the TEAD activity status of cancer, the method includes at least the following steps:

[0432] a) Obtain the level of each gene transcript in the gene set from biological samples obtained from the subject.

[0433] b) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0434] c) The product P gene obtained in step b)i Summation (ΣP gene) i Add a constant to obtain the (S) score: (ΣP gene) i +constant),

[0435] d) Compare the calculated (S) score obtained in step e) with a reference (S) score, wherein an observed deviation between the calculated (S) score and the reference (S) score may indicate TEAD-active cancer, and e) if the cancer is characterized as TEAD-active, administer a TEAD inhibitor to the subject.

[0436] Wherein, the coefficients and constants used in steps b) and c) can previously be obtained by stepwise multiple linear regression analysis, which correlates (i) the level of the gene transcripts previously obtained in a first biological sample obtained from a subject with said cancer with (ii) the level of gene transcripts of the TEAD-500 feature obtained in a second biological sample obtained from said subject’s cancer cells, wherein said TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes of the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes of the gene subset (2).

[0437] In some implementations, a stepwise multiple linear regression analysis can be performed to correlate (i) the level of the gene transcript with (ii) the TEAD score obtained by analyzing the level of the gene transcript characterized by TEAD-500.

[0438] The subjects who obtain the first and second biological samples may be the same as or different from the subjects who obtain the biological samples used to calculate the (S) score. The first and second biological samples represent cancers considered according to the method of this disclosure.

[0439] In some implementations, the deR or (S) score may be greater than a reference value (e.g., for the (S) score, approximately 0.055), in which case the cancer may be characterized as TEAD-active and TEAD inhibitor treatment may be administered to the subject.

[0440] In some implementations, the deR or (S) score may be less than or equal to a reference value (e.g., for the (S) score, approximately 0.055), in which case the cancer may be characterized as TEAD inactivation and TEAD inhibitor treatment will not be administered to the subject.

[0441] In some embodiments, this disclosure relates to a method for predicting the response of a subject with suspected or known TEAD-active cancer to TEAD inhibitor treatment.

[0442] The method includes using a set of gene transcripts from a gene set (A), and the method includes at least the following steps:

[0443] a) Obtain the level of each gene transcript in the gene set from biological samples obtained from the subject.

[0444] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0445] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0446] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0447] e) Calculate the deR score as MFR-subset(1) minus MFR-subset(2).

[0448] d) Compare the calculated deR score obtained in step e) with the reference deR score, wherein the observed deviation between the calculated deR score and the reference deR score can predict whether the subject responds to or does not respond to TEAD inhibitor treatment.

[0449] In some embodiments, this disclosure relates to a method for predicting the response of a subject with suspected or known TEAD-active cancer to TEAD inhibitor treatment.

[0450] The method includes using a set of gene transcripts from gene set (B), and the method includes at least the following steps:

[0451] a) Obtain the level of each gene transcript in the gene set from biological samples obtained from the subject.

[0452] b) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i Among them, genes i (Refers to the genes listed in the gene set);

[0453] c) The product P gene obtained in step b) i Summation (ΣP gene) iAdd a constant to obtain the (S) score: (ΣP gene) i +constant),

[0454] d) Compare the calculated (S) score obtained in step e) with a reference (S) score, wherein the observed deviation between the calculated (S) score and the reference (S) score can predict whether the subject responds to or does not respond to TEAD inhibitor treatment.

[0455] Wherein, the coefficients and constants used in steps b) and c) can previously be obtained by stepwise multiple linear regression analysis, which correlates (i) the level of the gene transcripts previously obtained in a first biological sample obtained from a subject with said cancer with (ii) the level of gene transcripts of the TEAD-500 feature obtained in a second biological sample obtained from said subject’s cancer cells, wherein said TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes of the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes of the gene subset (2).

[0456] The subjects who obtain the first and second biological samples may be the same as or different from the subjects who obtain the biological samples used to calculate the (S) score. The first and second biological samples represent cancers considered according to the method of this disclosure.

[0457] In some implementations, a deR or (S) score greater than a reference value (e.g., about 0.055 for a (S) score) can predict that the cancer is responsive to TEAD inhibitor treatment, and a deR or (S) score less than or equal to a reference value (e.g., about 0.055 for a (S) score) can characterize the cancer as unresponsive to TEAD inhibitor treatment.

[0458] In some embodiments, this disclosure relates to a method for monitoring the response of a subject with suspected or known TEAD-active cancer to TEAD inhibitor treatment, the method comprising using a set of gene transcripts from a gene set (A), and the method comprising at least the following steps:

[0459] a) Obtain the level of each gene transcript in the gene set from a first biological sample obtained from the subject prior to administration of the TEAD inhibitor treatment.

[0460] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0461] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0462] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0463] e) Calculate the first deR score as MFR-subset(1) minus MFR-subset(2).

[0464] f) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the subject after treatment with the TEAD inhibitor.

[0465] g) For each gene transcript in the gene set, convert the level of each gene transcript obtained in step f) into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0466] h) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0467] i) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0468] e) Calculate the second deR score as MFR-subset (1) minus MFR-subset (2), and

[0469] k) Compare the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores may indicate whether TEAD inhibitor treatment is effective or ineffective.

[0470] In some implementations, when the second deR score is greater than the first deR score, TEAD inhibitor treatment may be observed to be ineffective against cancer, and when the second deR score is lower than the first deR score, TEAD inhibitor treatment may be observed to be effective against cancer.

[0471] In some embodiments, this disclosure relates to a method for monitoring the response of a subject with suspected or known TEAD-active cancer to TEAD inhibitor treatment, the method comprising using a set of gene transcripts from a gene set (B), and the method comprising at least the following steps:

[0472] a) Obtain the level of each gene transcript in the gene set from a first biological sample obtained from the subject prior to administration of the TEAD inhibitor treatment.

[0473] b) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0474] c) The product P gene obtained in step b) i Summation (ΣP gene) i And add a constant to obtain the first (S) score: (ΣP gene) i +constant),

[0475] d) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the subject after treatment with the TEAD inhibitor.

[0476] e) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i Among them, genes i (Refers to the genes listed in the gene set);

[0477] f) The product P gene obtained in step e) i Summation (ΣP gene) i And add a constant to obtain the second (S) score: (ΣP gene) i + constant), and

[0478] g) Compare the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores may indicate whether TEAD inhibitor treatment is effective or ineffective.

[0479] Wherein, the coefficients and constants used in steps b), c), e) and f) can previously be obtained by stepwise multiple linear regression analysis, which correlates (i) the level of the gene transcript previously obtained in a third biological sample obtained from a subject with said cancer with (ii) the level of the gene transcript of the TEAD-500 feature obtained in a fourth biological sample obtained from said subject’s cancer cells, wherein said TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes in the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes in the gene subset (2).

[0480] In some implementations, a stepwise multiple linear regression analysis can be performed to correlate (i) the level of the gene transcript with (ii) the TEAD score obtained by analyzing the level of the gene transcript characterized by TEAD-500.

[0481] The subjects who obtain the third and fourth biological samples may be the same as or different from the subjects who obtain the biological samples used to calculate the (S) score. The third and fourth biological samples represent cancers considered according to the method of this disclosure.

[0482] In some implementations, when the second (S) score is greater than the first (S) score, it may be observed that TEAD inhibitor treatment is ineffective against cancer, and when the second (S) score is lower than the first (S) score, it may be observed that TEAD inhibitor treatment is effective against cancer.

[0483] In the methods disclosed above, the gene transcripts of gene transcript set (A) or (B) can be obtained from the biological samples described above.

[0484] In the methods disclosed above, the gene transcripts of gene transcript set (A) or (B) can be obtained from the body fluid sample as described above.

[0485] In the methods disclosed above, the gene transcripts of gene transcript set (A) or (B) can be obtained from the EV as described above.

[0486] In some embodiments, this disclosure relates to a method for screening TEAD inhibitor candidates for inhibiting TEAD activity, the method comprising using a set of gene transcripts from a gene set (A), and the method comprising at least the following steps:

[0487] a) Obtain the level of each gene transcript in the gene set from a first biological sample obtained from the supernatant of a cell culture, the first biological sample being obtained prior to contacting the cell culture with the TEAD inhibitor candidate compound.

[0488] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0489] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0490] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0491] e) Calculate the first deR score as MFR-subset(1) minus MFR-subset(2).

[0492] f) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from the supernatant of the cell culture, the second biological sample being obtained after contacting the cell culture with the TEAD inhibitor candidate compound.

[0493] g) For each gene transcript in the gene set, convert the level of each gene transcript obtained in step f) into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0494] h) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0495] i) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0496] e) Calculate the second deR score as MFR-subset (1) minus MFR-subset (2), and

[0497] k) Compare the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores may indicate whether the TEAD inhibitor candidate compound is effective or ineffective for inhibiting TEAD activity.

[0498] In some implementations, the second deR score may be greater than the first deR score, in which case the TEAD inhibitor candidate compound may be observed to be ineffective in inhibiting TEAD activity, and when the second deR score may be lower than the first deR score, the TEAD inhibitor candidate compound may be observed to be effective in inhibiting TEAD activity.

[0499] In some embodiments, this disclosure relates to a method for screening TEAD inhibitor candidates for inhibiting TEAD activity, the method comprising using a set of gene transcripts from a gene set (B), and the method comprising at least the following steps:

[0500] a) Obtain the level of each gene transcript in the gene set from a first biological sample obtained from the supernatant of a cell culture, the first biological sample being obtained prior to contacting the cell culture with the TEAD inhibitor candidate compound.

[0501] b) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0502] c) The product P gene obtained in step b) i Summation (ΣP gene) i And add a constant to obtain the first (S) score: (ΣP gene) i +constant),

[0503] d) Obtain the level of each gene transcript of the gene set from a second biological sample obtained from the supernatant of the cell culture, the second biological sample being obtained after contacting the cell culture with the TEAD inhibitor candidate compound.

[0504] e) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0505] f) The product P gene obtained in step e) i Summation (ΣP gene) i And add a constant to obtain the second (S) score: (ΣP gene) i + constant), and

[0506] g) Compare the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores may indicate whether TEAD inhibitor treatment is effective or ineffective.

[0507] Wherein, the coefficients and constants used in steps b), c), e) and f) can previously be obtained by stepwise multiple linear regression analysis, which correlates (i) the level of the gene transcripts previously obtained in a third biological sample obtained from the supernatant of the cell culture with (ii) the level of gene transcripts of the TEAD-500 feature obtained in a fourth biological sample obtained from the cell culture, the fourth biological sample being derived from the subject's cancer cells, wherein the TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes of the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes of the gene subset (2).

[0508] In some implementations, a stepwise multiple linear regression analysis can be performed to correlate (i) the level of the gene transcript with (ii) the TEAD score obtained by analyzing the level of the gene transcript characterized by TEAD-500.

[0509] The cell cultures used to obtain the third and fourth biological samples may be the same as or different from the cell cultures used to obtain the biological samples for calculating the (S) score.

[0510] Suitable cells for use in cell cultures according to this disclosure may be TEAD active cells.

[0511] In some implementations, the second (S) score may be greater than the first (S) score, in which case the TEAD inhibitor candidate compound may be observed to be ineffective in inhibiting TEAD activity, and the second (S) score may be lower than the first (S) score, in which case the TEAD inhibitor candidate compound may be observed to be effective in inhibiting TEAD activity.

[0512] In some implementations, biological samples can be as described above.

[0513] In some implementations, the gene transcript set can be obtained from extracellular vesicles.

[0514] The contact between cell cultures and TEAD inhibitor candidate compounds is carried out under conditions suitable for the compounds to inhibit the TEAD pathway.

[0515] In some embodiments, this disclosure relates to a method for screening TEAD inhibitor candidates for inhibiting TEAD activity, the method comprising using a set of gene transcripts from a gene set (A), and the method comprising at least the following steps:

[0516] a) Obtain the level of each gene transcript in the gene set from a first biological sample obtained from an animal model of TEAD-active cancer, the first biological sample being obtained prior to administration of the TEAD inhibitor candidate compound to the animal model of TEAD-active cancer.

[0517] b) For each gene transcript in the gene set, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0518] c) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0519] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step c) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0520] e) Calculate the first deR score as MFR-subset(1) minus MFR-subset(2).

[0521] f) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from an animal model of TEAD-active cancer, the second biological sample being obtained after administration of the TEAD inhibitor candidate compound to the animal model of TEAD-active cancer.

[0522] g) For each gene transcript in the gene set, convert the level of each gene transcript obtained in step f) into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set.

[0523] h) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (1) or MFR-positive).

[0524] i) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step g) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and

[0525] e) Calculate the second deR score as MFR-subset (1) minus MFR-subset (2), and

[0526] k) Compare the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores may indicate whether the TEAD inhibitor candidate compound is effective or ineffective for inhibiting TEAD activity.

[0527] In some implementations, the second deR score may be greater than the first deR score, in which case the TEAD inhibitor candidate compound may be observed to be ineffective in inhibiting TEAD activity, and when the second deR score may be lower than the first deR score, the TEAD inhibitor candidate compound may be observed to be effective in inhibiting TEAD activity.

[0528] In some embodiments, this disclosure relates to a method for screening TEAD inhibitor candidates for inhibiting TEAD activity, the method comprising using a set of gene transcripts from a gene set (B), and the method comprising at least the following steps:

[0529] a) Obtain the level of each gene transcript in the gene set from a first biological sample obtained from an animal model of TEAD-active cancer, the first biological sample being obtained prior to administration of the TEAD inhibitor candidate compound to the animal model of TEAD-active cancer.

[0530] b) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0531] c) The product P gene obtained in step b) i Summation (ΣP gene) i And add a constant to obtain the first (S) score: (ΣP gene) i +constant),

[0532] d) Obtain the level of each gene transcript in the gene set from a second biological sample obtained from an animal model of TEAD-active cancer, the second biological sample being obtained after administration of the TEAD inhibitor candidate compound to the animal model of TEAD-active cancer.

[0533] e) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0534] f) The product P gene obtained in step e) i Summation (ΣP gene)i And add a constant to obtain the second (S) score: (ΣP gene) i + constant), and

[0535] g) Compare the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores may indicate whether TEAD inhibitor treatment is effective or ineffective.

[0536] Wherein, the coefficients and constants used in steps b), c), e) and f) can previously be obtained by stepwise multiple linear regression analysis, which correlates (i) the level of the gene transcripts previously obtained in a third biological sample obtained from an animal model of TEAD-active cancer with (ii) the level of gene transcripts of the TEAD-500 feature obtained in a fourth biological sample obtained from cancer cells of the animal model, wherein the TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes of the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes of the gene subset (2).

[0537] In some implementations, a stepwise multiple linear regression analysis can be performed to correlate (i) the level of the gene transcript with (ii) the TEAD score obtained by analyzing the level of the gene transcript characterized by TEAD-500.

[0538] The animal models used to obtain the third and fourth biological samples may be the same as or different from the animal models used to obtain the biological samples used to calculate the (S) score.

[0539] The third and fourth biological samples represent cancers considered according to the methods of this disclosure.

[0540] In some implementations, the second (S) score may be greater than the first (S) score, in which case the TEAD inhibitor candidate compound may be observed to be ineffective in inhibiting TEAD activity, and the second (S) score may be lower than the first (S) score, in which case the TEAD inhibitor candidate compound may be observed to be effective in inhibiting TEAD activity.

[0541] In some implementations, the first, second, and third biological samples can be as described above.

[0542] In some implementations, the gene transcript set can be obtained from extracellular vesicles.

[0543] In some implementations, a stepwise multiple linear regression analysis can be performed to correlate (i) the level of the gene transcript with (ii) the TEAD score obtained by analyzing the level of the gene transcript characterized by TEAD-500.

[0544] cancer

[0545] In some implementations, the cancer may be selected from mesothelioma, adrenocortical carcinoma, urothelial carcinoma of the bladder, invasive breast cancer, cervical squamous cell carcinoma and cervical adenocarcinoma, cholangiocarcinoma, colorectal cancer, 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, colonic adenocarcinoma, lymphoma diffuse large B-cell lymphoma, esophageal cancer, glioblastoma multiforme, head and neck squamous cell carcinoma, clear cell carcinoma of the kidney, papillary cell carcinoma of the kidney, low-grade glioma of the brain, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, 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 cancer, thymoma, endometrial cancer of the uterine body and uterine carcinosarcoma.

[0546] In some implementations, the cancer may be mesothelioma.

[0547] In some implementations, the cancer may be colorectal cancer.

[0548] reagent kits and products

[0549] In some implementations, this document provides a kit or article containing probes (such as nucleic acids) for detecting and quantifying any gene transcripts from the gene set disclosed herein.

[0550] In some embodiments, this disclosure relates to a kit comprising a solid support containing a set of nucleic acids for obtaining gene transcripts of gene sets as disclosed herein.

[0551] The kit disclosed herein is suitable for use in the purposes and methods disclosed herein.

[0552] In some implementations, the kit or product may include one or more reagents for preparing biological samples or portions thereof for RNA sequencing analysis.

[0553] In some embodiments, the kit or product further includes one or more reagents for determining the level of the gene transcripts disclosed herein in a biological sample.

[0554] In some implementations, the kit or article may include instructions for using the kit for any of the methods / uses described herein.

[0555] In some embodiments, the kit or article may include a container, a label on the container, and a composition contained within the container, wherein the composition comprises one or more polynucleotides that hybridize under stringent conditions to the gene transcripts listed herein, and the label on the container indicates that the composition can be used to obtain levels of the gene transcripts listed herein, and wherein the kit includes instructions for using polynucleotides to assess the presence of the gene transcripts disclosed herein in a biological sample or a portion thereof and to quantify the levels of said gene transcripts.

[0556] In some embodiments, the kit or product is oligonucleotide-based and may include, for example, (1) an oligonucleotide that hybridizes with a gene transcript, such as a detectable marker oligonucleotide, or (2) a pair of primers for amplifying the gene transcript. In some embodiments, the kit or product may also include buffers, preservatives, or stabilizers. In some embodiments, the kit or product may further include components necessary for detecting the detectable marker, such as enzymes or substrates. In some embodiments, the kit or product may also contain a control sample or a series of control samples that can be measured and compared with the test sample. In some embodiments, each component of the kit or product may be packaged in a separate container, and all various containers may be packaged together with instructions for interpreting the results of assays performed using the kit or product in a single package.

[0557] TEAD pathway inhibitors

[0558] In some implementations, a TEAD pathway inhibitor (or TEAD suppressor) is administered to a subject in need. Subjects in need may be those identified as having TEAD-active cancer (or known to have TEAD-active cancer) or who may respond to a TEAD pathway inhibitor (or are presumed to have TEAD-active cancer) using the methods described herein and the gene transcript set.

[0559] Any TEAD pathway inhibitor known in the art can be administered.

[0560] As used in this article, “TEAD pathway inhibitor” can be any small or large molecule that inhibits the HIPPO-YAP / WWTR1 / TEAD pathway.

[0561] As a suitable example of a TEAD inhibitor, the following can be cited:

[0562] Statins: Statins have been reported to inhibit TEAD activity by reducing the expression of YAP.

[0563] Epigallocatechin gallate (EGCG): EGCG has been shown to inhibit TEAD activity by disrupting the interaction between TEAD and its coactivator YAP.

[0564] Cucurbitacin B: Cucurbitacin B has been shown to inhibit TEAD activity by disrupting the interaction between TEAD and its coactivator YAP.

[0565] CA3: CA3 is a synthetic compound that has been shown to inhibit TEAD activity by disrupting the interaction between TEAD and its coactivator YAP.

[0566] CA-170: CA-170 is a small molecule inhibitor of both PD-L1 and VISTA (a T-cell activation V-domain Ig inhibitor), and has been shown to have TEAD inhibitory activity. A Phase I clinical trial of CA-170 in patients with advanced solid tumors is underway.

[0567] YAPi: YAPi is a monoclonal antibody that targets the YAP protein (a coactivator of TEAD). A Phase I clinical trial of YAPi in patients with advanced solid tumors is underway.

[0568] CYT-0851: CYT-0851 is a small molecule inhibitor of bromolar domain and superterminal domain (BET) proteins, and has been shown to have TEAD inhibitory activity. A Phase I clinical trial of CYT-0851 in patients with advanced solid tumors is underway.

[0569] CB-839: CB-839 is a small molecule inhibitor of glutaminase, and preclinical studies have demonstrated its inhibitory effect on TEAD activity. A phase I / II clinical trial of a combination of CB-839 and a PD-L1 inhibitor in patients with advanced solid tumors is underway.

[0570] Suitable compounds for use in this disclosure may be 1H-indole-acrylamide derivatives having formula (I) disclosed in WO 2021 / 204823:

[0571]

[0572] in

[0573] n is an integer selected from 0 and 1.

[0574] R1 is selected from single bonds and (C1-C4) alkenyl groups.

[0575] R2 is selected from: (C1-C4) alkyl or substituted with one or more fluorine atoms, (C1-C3) alkoxy substituted with one or more fluorine atoms, unsubstituted or substituted with one or more R3 groups, unsubstituted or substituted with one or more R5 groups, (C4-C8) cycloalkyl, unsubstituted or substituted with one or more R6 groups, and NR9R10 groups.

[0576] R3 is selected from unsubstituted or substituted with one or more fluorine atoms (C1-C4) alkyl, cyclopropyl, halogen atom, unsubstituted or substituted with one or more fluorine atoms (C1-C3) alkoxy, pentafluorothioalkyl, nitrile, (C1-C3) trialkylsilyl, (C1-C3) alkylsulfonyl, and unsubstituted or substituted with trifluoromethyl phenyl groups.

[0577] R4 is selected from hydrogen atoms and (C1-C4) alkyl groups.

[0578] R5 is selected from fluorine atoms and trifluoromethyl groups.

[0579] R6 is selected from unsubstituted or substituted phenyl groups, (C1-C4) alkyl groups substituted with one or more fluorine atoms, and fluorine atoms.

[0580] R7 is selected from hydrogen atoms, nitrile groups, and (C1-C4) alkyl groups.

[0581] R8 is selected from hydrogen atoms and unsubstituted or substituted (C1-C4) alkyl groups with bis(C1-C4)alkylamino groups.

[0582] R9 and R10 may be the same or different, and are selected from unsubstituted or substituted (C1-C3) alkyl groups, or pharmaceutically acceptable salts thereof.

[0583] As a suitable 1H-indole-acrylamide derivative having formula (I), N-(1-(3-(trifluoromethyl)benzyl-1H-indole-5-yl)acrylamide can be referenced.

[0584] TEAD-500 Features

[0585] The transcriptional signatures applicable to this disclosure (“TEAD signatures” or “TEAD-500 signatures”) can be obtained by measuring the levels of gene transcripts in a gene set comprising any of 220 to 249 genes in a subset (1) (positive effect genes) and any of 210 to 233 genes in a subset (2) (negative effect genes) as shown in Table 2.

[0586] [Table 2]

[0587]

[0588]

[0589] The levels of gene transcripts characteristic of TEAD can be measured as described above for the levels of gene transcripts in the gene transcript set disclosed herein.

[0590] The levels of gene transcripts characteristic of TEAD were used in this paper for stepwise multiple linear regression analysis, which correlated the levels of gene transcripts of (i) gene set (B) with the levels of gene transcripts characteristic of TEAD-500.

[0591] Transcriptional signatures (“TEAD signatures”) can be obtained by measuring the levels of gene transcripts in a gene set containing any of the 430 to 482 genes, or any of the 435 to 482 genes, or any of the 440 to 482 genes, or any of the 445 to 482 genes, or any of the 450 to 482 genes, or any of the 455 to 482 genes, or any of the 460 to 482 genes, or any of the 465 to 482 genes, or any of the 470 to 482 genes, or any of the 475 to 482 genes, or any of the 480 to 482 genes.

[0592] Transcriptional signatures (“TEAD signatures”) can be obtained by measuring the levels of gene transcripts in a gene set containing any one of the following 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.

[0593] The transcriptional features (“TEAD features”) disclosed herein can be used to determine TEAD scores.

[0594] In some implementations, the TEAD score can be used for stepwise multiple linear regression analysis, which correlates (i) the levels of gene transcripts in the gene transcript set (B) with (ii) the TEAD score calculated using the levels of gene transcripts characterized by TEAD-500.

[0595] In some implementations, the TEAD score is calculated according to the following method:

[0596] a) Measure the level of each gene in the gene set in the biological sample.

[0597] b) For each gene in the gene set, convert the gene expression level obtained in step a) into a fractional rank by dividing the gene's rank by the number of genes from the gene set.

[0598] c) Separate the fractional ranks obtained for the genes in the gene subset (1) from the fractional ranks obtained in step b) and calculate their mean fractional rank (MFR - positive).

[0599] d) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step b) and calculate their average fractional rank (MFR-negative), and e) Calculate the deR score as MFR-positive-MFR-negative.

[0600] The ranking of genes within a gene set is achieved by assigning the gene with the lowest level of gene transcripts to rank 1, the gene with the second highest level of gene transcripts to rank 2, and so on, until the gene with the highest level of gene transcripts is assigned the highest rank. Genes with the same level of gene transcripts are assigned an average rank; for example, two genes with the same level of gene transcripts and an expression level of 0 are assigned a rank of 1.5.

[0601] When the deR score is greater than approximately 0.055, the cancer is TEAD active, and when the deR score is less than or equal to approximately 0.055, the cancer is TEAD inactive.

[0602] [Example]

[0603] The following examples illustrate embodiments of the present invention that are currently known. However, it should be understood that the following are merely examples or descriptions of the application of the principles of the invention. Many modifications and alternative compositions, methods, and systems can be devised by those skilled in the art without departing from the spirit and scope of the invention. Therefore, although the invention has been specifically described above, the following examples provide further details relating to what is now considered to be the most practical and preferred embodiments of the invention.

[0604] Example 1: Materials and Methods

[0605] Cells and culture conditions:

[0606] All cell lines were grown at 37°C in 5% CO2. NCI-H226 (H226) (CRL No.-5826) and HCT116 (CCL No.-247) were purchased from the American Type Culture Collection (ATCC, Manassas, Virginia, USA) and cultured according to the supplier's recommendations. SPC212 (No. 11120717) and Mero-14 (No. 09100101) were purchased from the European Certified Cell Culture Collection (ECACC, Public Health England, Salisbury, UK) and cultured according to the supplier's recommendations. All cell lines were validated at MICROSYNTH AG (Bargach, Switzerland) using short tandem repeat assays. PCR using a GEM kit (BIOVALLEY, Nantel, France) to exclude mycoplasma infection.

[0607] Cell treatment

[0608] Cells were treated for 24 hours with or without (control cells) the TEAD inhibitor (TEADi, four doses (0.0 μM, 0.3 μM, 1.0 μM and 3.0 μM)).

[0609] EVs and cellular RNA were separated and analyzed by RNA-seq, as detailed below.

[0610] Extracellular vesicle (EV) preparation and RNA extraction

[0611] Extracellular vesicles (EVs) were isolated from the supernatants of treated and control cells using membrane-based affinity columns (EXOEASY, MAXI kit, QIAGEN). EV size distribution was quantified and determined using nanoparticle tracking analysis (NS300, MALVERN).

[0612] EV RNA was extracted using the EXORNEASY serum / plasma MAXI plasma kit (QIAGEN).

[0613] Total cellular RNA was extracted and purified using the MIRNEASY mini kit (Qiagen, 217,004). RNA concentration and purity were assessed using an ND-1000 spectrophotometer (NANODROP, THERMO FISHER SCIENTIFIC, Wilmington, Delaware, USA). Quality indicators were RNA integrity value (RIN) and DV200 value (% of RNA fragments ≥200 nt in length). 100 ng of total RNA from each sample was used as input material for library preparation.

[0614] RNA sequencing

[0615] use STRANDED TOTAL RNA-SEQ KIT V2-PICO INPUT MAMMALIAN was used to prepare an RNA-seq library targeting the mRNA of TAKARA BIO USA, INC. Cell line mRNA sequencing was performed using the KAPPA LIBRARY AMPLIFICATION KIT (ILLUMINA). The library was then sequenced on an ILLUMINA NOVASEQ 6000.

[0616] The following steps are performed to prepare an RNA-seq library.

[0617] Step 1: DNA enzyme digestion

[0618] • DNA depletion step: Use DNase I (amplification grade, INVITROGEN, Cat. No. 18068-015) under the following conditions:

[0619] • 8 μl RNA / EV sample, 1 μl 10X DNase I buffer, 1 μl DNase I (amplification grade, 1 U / μl), then incubate at room temperature for 15 minutes.

[0620] • Inactivate the DNase by adding 1 μl of 25 mM EDTA solution and then incubating at 65 °C for 10 minutes.

[0621] • Use RNAESY MINELUTE (QIAGEN COLUMN) to remove EDTA

[0622] Step 2: Use TAKARA BIO USA,INC. RNA seq preparation of STRANDED TOTAL RNA-SEQ KITV2-PICO INPUT MAMMALIAN#634413. Following the supplier's (Takara) recommendations, the same specifications were used.

[0623] A) Scheme: cDNA synthesis

[0624] a. Using Option 2 (Unfragmented): Start with highly degraded RNA (EV) and then follow the steps recommended by TAKARA:

[0625] Option B: Add ILLUMINA ADAPTERS / INDEX

[0626] C) Method: Purify RNA-seq libraries using AMPURE beads.

[0627] D) Method: Deplete ribosomal cDNA using ZAPR V2 and R-PROBES V2.

[0628] E) Protocol: Final amplification of PCR 2-RNA-seq library

[0629] F) Procedure: Purify the final RNA-seq library using AMPURE beads.

[0630] TEAD activity characteristics

[0631] To estimate TEAD activity in cells, transcriptomic features from total cellular RNA (TEAD-500 features) were used (Calvet, L., Dos-Santos, O., Spanakis, E. et al. 2022. YAP1 is essential for malignant mesothelioma tumor maintenance. BMC Cancer 22,639. Doi.org / 10.1186 / s12885-022-09686 or PCT / EP2023 / 057332). YAP1 / TEAD-dependent transcription was scored based on the following differences using RNA-seq data from individual samples.

[0632] deR=Rp-Rn

[0633] Rp is the average fractional rank of positive YAP1 effectors, and Rn is the average fractional rank of negative YAP1 effectors. The deR score is calculated using only the levels of the genes constituting the trait.

[0634] Novel features comprising 28 gene transcripts were identified from Ev-isolated gene transcripts, and these novel features were used to calculate TEAD activity scores according to the method used for “TEAD-500” features, as detailed above or below: Calvet, L., Dos-Santos, O., Spanakis, E. et al. 2022. YAP1 is essential for malignant mesothelioma tumor maintenance. BMC Cancer 22,639. Doi.org / 10.1186 / s12885-022-09686 or PCT / EP2023 / 057332.

[0635] Stepwise multiple linear regression analysis was used to correlate the levels of all readily detectable gene transcripts in EVs (Log(FPKM+0.1)>4 at baseline) with TEAD-500 feature scores measured in cells from which EVs were obtained. This analysis yielded a ensemble of eight genes (each with a specified coefficient) and constant coefficients.

[0636] This second feature set, comprising 1 to 8 gene transcripts, was identified from gene transcripts isolated from EVs. The second feature set was used to predict the TEAD activity score of parental tumor cells. The TEAD activity score was calculated based on the expression levels of downstream TEAD effector genes highly correlated with TEAD regulation, as previously described (Calvet, L., Dos-Santos, O., Spanakis, E. et al. 2022. YAP1 is essential for malignant mesothelioma tumor maintenance. BMCCancer 22,639. Doi.org / 10.1186 / s12885-022-09686 or PCT / EP2023 / 057332).

[0637] For this second feature set, which contains a set of gene transcripts from the gene set, the predicted TEAD activity score is calculated as follows:

[0638] a) Multiply each obtained level of each gene transcript in the gene set by a coefficient associated with each gene to obtain the product of each gene (Pgene). i ), including genes i This refers to the genes listed in the gene set.

[0639] b) The product P gene obtained in step a) i Summation (ΣP gene) i Add a constant to obtain the (S) score: (ΣP gene) i +constant),

[0640] The coefficients and constants used in steps a) and b) were previously obtained by stepwise multiple linear regression analysis, which correlated (i) the level of EV gene transcripts with (ii) the score of the TEAD-500 feature measured in cells from which EVs were obtained.

[0641] Stepwise multiple linear regression analysis was performed using SPSS statistical procedures (IBM; Armonk, NY) to select the gene set expressed in EVs and predict TEAD-500 values ​​in parental cells. This was also used in all other statistical work.

[0642] Example 2: Results

[0643] The effects of TEAD inhibition on intracellular and EV RNA isolated from the supernatant of three responsive mesothelioma cell lines (NCI-H226, Mero-14, and SPC212) and one non-responsive colon cell line (HCT116) were compared. Based on the TEAD inhibitor (TEADi) IC50... 50Measurement (CELL TITER) The response was defined using the PROMEGA protocol. Cell lines (Mero14; Spc212, HCT116, and H226) were treated with four doses of TEADi (0.0 μM, 0.3 μM, 1.0 μM, and 3.0 μM) for 24 hours. EV and tumor RNA were isolated and analyzed by RNA-seq.

[0644] The process from cell processing to Ev isolation and analysis is shown in Figure 1 middle.

[0645] like Figure 2 As shown, the non-responsive colon cell line HCT116 exhibited lower intrinsic TEAD activity than the responsive mesothelioma cell lines H226, Mero14, and SPC112. A significant decrease was observed in all cell lines after treatment, but the effect was more pronounced in responders.

[0646] like Figure 3 As shown, approximately 50% of the genes characteristic of TEAD-500 were detected in the EV RNA of each cell line. Positive effectors specify genes whose expression levels are positively correlated with TEAD activity, while negative effectors specify genes whose expression levels are negatively correlated with TEAD activity.

[0647] like Figure 4 As shown, using features of approximately 500 publicly available genes, the TEAD activity score of EV RNA is generally lower compared to the parental cell score. The correlation between EV and parental cell scores is poor (Mero14 r 2 =0.00; Spc212 r 2 =0.50, HCT116 r 2 =0.03), except for H226 (r 2 =0.66).

[0648] TEAD inhibitors caused a sharp decrease in TEAD activity scores measured in EVs of the H266 cell line, but had no effect on EV values ​​in the other two responders or non-responders.

[0649] like Figure 5 As shown, focusing on a subset of 28 easily detectable TEAD effectors (out of approximately 500) in EVs across three mesothelioma cell lines, the association between TEAD activity observed in parental cells and scores predicted from EV RNA content was improved. A TEADi dose-response effect was observed in EV RNA in two of the three responsive cell lines (H226 and Mero14). In the negative control (HCT116), no association was found between EV and parental cell measurements.

[0650] like Figure 6 As shown, a genome-wide, stepwise regression analysis was performed to identify the set of EV transcripts that could successfully predict the TEAD activity score of parental cells. This analysis only included transcripts that were easily detectable at baseline in the three responsive cell lines with log2FPKM>4. Four transcriptional predictors were selected from this analysis. Using these two features, high correlations were observed in all responsive cell lines (r2: H226 = 0.96, Mero14 = 0.95, Spc212 = 0.89), but the correlation was low in the non-responsive cell line HCT116 (r2 = 0.63).

[0651] Example 3: Conclusion

[0652] Although the yield is relatively low compared to parental cells, EV RNA is suitable for transcriptome analysis.

[0653] A well-defined list of effector genes from a rich collection of EV transcripts can be used to more accurately estimate TEAD activity.

[0654] Novel biomarkers can be identified through whole-genome analysis of EV gene transcripts.

[0655] Characterization of EV gene transcripts identified as TEAD-repressed has the potential to be used as peripheral markers for targeted binding in plasma or other liquid biopsies.

Claims

1. A set of isolated gene transcripts derived from a gene set, said gene set comprising: A subset of genes consisting of ADM, AXL, BIRC5, CDV3, CRIM1, CTGF, CYR61, FSTL1, GADD45A, KRT8, LMNB2, MATN2, PKP4, RND3, RPS24, SEC14L1, SGK1, SLC25A3, SLC3A2, TNFRSF12A, TPM1, TPX2 and TUBB6 (1) and a subset of genes consisting of CTSB, FTH1, SQSTM1, TCF25 and UBC (2).

2. A separate set of gene transcripts, comprising at least two gene transcripts derived from a gene set consisting of: DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

3. The gene transcript set according to claim 2, wherein the gene transcript set comprises a gene set consisting of DLC1, AKAP2, CANX and SAFB2, and a gene transcript set optionally selected from at least one gene of EIF4H, NDUFS5, SEPT9 and EIF4A1.

4. The set of gene transcripts according to any one of claims 1 to 3, wherein the set of gene transcripts is obtained from isolated extracellular vesicles.

5. Isolated extracellular vesicles comprising a set of gene transcripts as defined in claim 1 or a set of gene transcripts comprising at least one gene transcript from a set of genes consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9 and EIF4A1.

6. The gene transcript set according to claim 1, or a gene transcript set consisting of at least one gene transcript from a gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9 and EIF4A1, for use as a biomarker of TEAD activity, for measuring or characterizing TEAD activity in cancer or cell cultures, for use in a TEAD inhibitor candidate compound screening method, or for use in a cancer diagnostic method.

7. The gene transcript set according to claim 1, or a gene transcript set consisting of at least one gene transcript from a gene set consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1, is used for the following purposes: characterizing the TEAD activity status of a biological sample, or measuring TEAD activity in a biological sample, or predicting the response of cancer to TEAD inhibitor treatment in subjects with known or presumed TEAD-active cancer, or monitoring the response of cancer to TEAD inhibitor treatment in subjects with known or presumed TEAD-active cancer, or predicting cancer progression or regression in subjects with known or presumed TEAD-active cancer, or monitoring cancer progression or regression in subjects with known or presumed TEAD-active cancer, or screening TEAD inhibitor candidate compounds.

8. The use according to claim 7, wherein the set of gene transcripts is obtained from isolated extracellular vesicles.

9. The use according to claim 7 or 8, wherein the level of each gene transcript is obtained.

10. The use according to claim 9, wherein the transcriptional signature is obtained from the gene transcript level.

11. The use according to claim 10, wherein the obtained transcriptional signature is compared with a reference transcriptional signature, and wherein the observed deviation between the obtained transcriptional signature and the reference transcriptional signature indicates that the cancer is TEAD-active or TEAD-inactive, or that the cancer is responsive or unresponsive to treatment with the TEAD inhibitor, or that treatment with the TEAD inhibitor is effective or ineffective, or that TEAD-active cancer is prone to progression or regression, or that TEAD-active cancer is progressing or regressing, or that the TEAD inhibitor candidate compound is effective or ineffective.

12. The use according to claim 9 or 10, wherein the gene transcript level is mathematically normalized.

13. The use according to claim 12, When using the gene transcript set according to claim 1, the mathematical normalization calculates the deR score according to a method including the following steps: a) For each gene transcript in the gene set, the level of each gene transcript is converted into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes from the gene set. b) Separate the fractional ranks obtained for the genes in the subset (1) from the fractional ranks obtained in step a) and calculate their average fractional rank (MFR-subset (1) or MFR-positive). c) Separate the fractional ranks obtained for the genes in the subset (2) from the fractional ranks obtained in step a) and calculate their average fractional rank (MFR-subset (2) or MFR-negative), and d) Calculate the deR score as MFR-subset (1) minus MFR-subset (2). or When using a set of gene transcripts consisting of at least one gene transcript from a gene set comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1, the mathematical normalization is calculated according to a method comprising the following steps: a) Multiply each level of each gene transcript in the gene set by the coefficient associated with each gene to obtain a product for each gene (Pgenei), where genei refers to the gene listed in the gene set; b) Sum the products Pgenei obtained in step a) (ΣPgenei) and add a constant to obtain the (S) score: (ΣPgenei + constant). in, The coefficients and constants used in steps a) and b) were previously obtained by stepwise multiple linear regression analysis, which correlated (i) the levels of the gene transcripts previously obtained in the first biological sample with (ii) the levels of gene transcripts of the TEAD-500 feature previously measured in the second biological sample, wherein the TEAD-500 feature comprises gene transcripts containing any of the following gene sets: any of the 220 to 249 genes in the gene subset (1) disclosed in Table 2 and any of the 210 to 233 genes in the gene subset (2), wherein the first and second biological samples represent the same TEAD-active cancer.

14. The use according to claim 13, wherein the reference value is a first deR or (S) score, and the deR or (S) score to be compared with the reference value is a second deR or (S) score measured after the first deR or (S) score.

15. The use according to any one of claims 9 to 14, wherein the level of each gene transcript is obtained by RNA sequencing (RNA-seq) and quantified as FPKM (fragments per kilobase transcript per million mapping reads).

16. The use according to any one of claims 7 to 15, wherein the cancer is selected from mesothelioma, adrenocortical carcinoma, urothelial carcinoma of the bladder, invasive breast carcinoma, cervical squamous cell carcinoma and cervical adenocarcinoma, cholangiocarcinoma, colorectal cancer, 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, colonic adenocarcinoma, diffuse large B-cell lymphoma, esophageal cancer, glioblastoma multiforme, head and neck squamous cell carcinoma, clear cell carcinoma of the kidney, papillary cell carcinoma of the kidney, low-grade glioma of the brain, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, melanoma of the skin, gastric adenocarcinoma, testicular germ cell tumor, thyroid cancer, thymoma, endometrial cancer of the uterine body and uterine carcinosarcoma.

17. A kit comprising a solid support containing a set of nucleic acids for obtaining a set of gene transcripts according to any one of claims 1 to 3.

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