Genetic transcripts as signatures of TEAD-activated cancers

Abstract

The present invention relates to a set of gene transcripts (A) isolated from a set of genes consisting of 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 an isolated set of genes (B) consisting of at least one gene transcript from gene subset (2) consisting of CTSB, FTH1, SQSTM1, TCF25, and UBC, or from a set of genes consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1, as well as their use in a diagnostic method for TEAD-activated cancer.

Description

[Technical Field]

[0001] This disclosure relates to the field of gene transcripts as signatures of TEAD-activated cancers.

[0002] This disclosure relates not only to tools for screening candidate TEAD inhibitor compounds, but also to sets of gene transcripts, and methods for their use in characterizing cancer, cancer evolution, and measurement of their levels for monitoring cancer's response to TEAD inhibitor therapy. [Background technology]

[0003] In normal tissues, the transcription cofactors YAP1 and WWTR1 bind to transcription factors of the TEAD family (TEAD1, TEAD2, TEAD3, and TEAD4), collectively known as "TEAD," to form an active protein complex. This complex recognizes and binds to specific sequence motifs in the promoters of target genes, initiating or inhibiting their transcription. Therefore, cell proliferation, survival, plasticity, and migration, which are necessary for normal biological processes such as organ growth and wound healing, are regulated by TEAD (see Totaro A, et al. Nat Cell Biol. 2018;20(8):888-899. doi:10.1038 / s41556-018-0142-z). In intact adult mammalian tissues, YAP1 and WWTR1 are generally phosphorylated by kinases of the Hippo pathway, thereby retained in the cytoplasm and degraded. In this case, the active transcription complex is not present in the nucleus (Totaro A, et al. Nat Cell Biol 2018. Aug;20(8):888-899).

[0004] In recent years, YAP1, WWTR1, their TEAD partners, and their upstream regulators, particularly the tumor suppressor pathway known as the Hippo pathway, have attracted increasing attention in cancer research (Zhang N et al. Dev Cell. 2010 Jul 20;19(1):27-38.doi:10.1016 / j.devcel.2010.06.015; Lu L, et al. Proc Natl Acad Sci USA. 2010;107(4):1437-42.doi:10.1073 / pnas.0911427107; Nishio M, et al. Proc Natl Acad Sci USA. 2016;113(1):E71-80.doi:10.1073 / pnas.1517188113; Liu-Chittenden Y, et al., Genes Dev.2012;26(12):1300-5.doi:10.1101 / gad.192856.112). Mutations or physiological dysregulation of YAP1, WWTR1, or any of their upstream regulators can result in insufficient phosphorylation and degradation of these proteins. Without phosphorylation and degradation, the transcription 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 of the regulated genes are direct targets of TEAD, possessing 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 referred to as the "TEAD pathway" or "TEAD signaling") can directly induce tumorigenesis or make existing tumors resistant to targeted therapy (Reggiani Fet al. Biochim Biophys Acta Rev Cancer. 2020:1873(1):188341.doi:10.1016 / j.bbcan.2020.188341; Kurppa KJ et al. Cancer Cell. 2020;37(1):104-122.e12.doi:10.1016 / j.ccell.2019.12.006).

[0005] For example, mutations and deletions of Hippo genes such as LATS2 and NF2 account for the majority of malignant mesothelioma tumors (Sekido Y et al. Cancers (Basel). 2018; 10(4): 90. doi: 10.3390 / cancers10040090). YAP1 has been found to be locally amplified and overexpressed at both RNA and protein levels in several types of tumors, particularly cervical cancer (Zanconato F et al. Cancer Cell. 2016; 29(6): 783-803. doi: 10.1016 / j.ccell. 2016.05.005). TEAD-dependent transcriptional activation can give tumor cells a mesenchymal stem cell-like phenotype because TEAD also regulates notorious stem cell transcription factors such as SOX2, OCT3 / 4, NANOG, and MYC (Bora-Singhal N et al. Stem Cells. 2015;33(6):1705-18.doi:10.1002 / stem.1993). Cancers in which tumor cells possess the TEAD activity pathway are called TEAD-active cancers.

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

[0007] The field of fluid biopsy is attracting considerable interest in non-invasive monitoring of cancer progression and evaluation of treatment response. Extracellular vesicles (EVs), such as exosomes, microvesicles (ectosomes), apoptotic bodies, and exosome-like particles, are nanoparticles present in all biological fluids. These small, cell-derived secretory vesicles transmit biological information through surface interactions or by circulating bioactive molecules to and from the cytoplasm of recipient cells. Because extracellular vesicles (EVs) hold or encapsulate the cargo of parent cells, their contents may be useful as biomarkers for tracking drug activity (Graca 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 antibody activity 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., Spankis, E. et al. 2022. YAP1 is essential for malignant mesothelioma tumor maintenance.BMC Cancer 22,639.doi.org / 10.1186 / s12885-022-09686).

[0008] Therefore, a new set of gene transcripts is needed that can be used as a signature for diagnosing or monitoring TEAD-activated cancers.

[0009] There is a need for cost-effective diagnostic tools and methods for diagnosing or monitoring TEAD-activated cancers.

[0010] There is a need for novel gene transcripts that can be used as signatures for TEAD-activated cancers, and these transcripts must be simple, reliable, and robust in their use.

[0011] An improved set of gene transcripts is needed for robust and reliable methods to characterize cancer as a TEAD-activated state, monitor or predict cancer progression and response to TEAD inhibitor therapy, or screen for novel TEAD inhibitor candidate compounds.

[0012] Measuring that level would create a new set of gene transcripts that could be used as a signature for TEAD-activated cancer.

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

[0014] A new, simple, and reliable method is needed to obtain gene transcript signatures of TEAD-activated cancers.

[0015] A new set of gene transcripts is needed as a signature of TEAD-activated cancer, obtainable from extracellular vesicles. [Overview of the Initiative] [Problems that the invention aims to solve]

[0016] The present invention aims to satisfy all or some of these needs. [Means for solving the problem]

[0017] According to one of its purposes, this disclosure relates to a set of gene transcripts isolated from a set of genes consisting of a gene subset (1) comprising 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 gene subset (2) comprising CTSB, FTH1, SQSTM1, TCF25, and UBC, which will hereafter be referred to as the set of gene transcripts (A). Depending on the context, (A) may also refer to the set of genes or the set of their corresponding gene transcripts.

[0018] An isolated set of gene transcripts (A) of the present disclosure is obtained from a set of genes, which consists 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.

[0019] According to one of its purposes, this disclosure relates to an isolated set of gene transcripts (hereinafter referred to as the gene transcript set (B)) consisting of at least one gene transcript from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1. Depending on the context, (B) may also refer to a set of genes or a set of their corresponding gene transcripts.

[0020] According to one of its purposes, this disclosure relates to an isolated set of gene transcripts consisting of at least two gene transcripts (hereinafter referred to as gene transcript set (B)), one of which is a gene transcript derived from the DLC1 gene, and at least the second gene transcript is derived from a set of genes consisting of AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0021] As shown in the Examples section, the newly proposed set of gene transcripts (A) or (B) can be used as a reliable and sensitive signature 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). By measuring their levels, a transcriptional signature that can identify TEAD-active cancers can be obtained, and by monitoring changes in TEAD activity, it is possible to monitor or predict cancer evolution or the cancer's response to treatment, or to screen for new TEAD inhibitor therapies.

[0022] In the examples, the usefulness of mRNA encapsulated and retained in extracellular viable (EVs) was evaluated as a transcriptional signature of TEAD pathway activity, demonstrating that EVs can be used as a source of gene transcripts for measuring the signature of TEAD-active cancer. EVs can be isolated from various bodily fluid samples, such as urine, saliva, or blood samples, and a non-invasive method for measuring the transcriptional signature of TEAD-active cancer can be performed, for example, by obtaining saliva, blood, or urine samples.

[0023] Furthermore, the examples demonstrate that inhibition of the YAP1 / TEAD signaling pathway by TEAD inhibitors (TEADi) can be monitored using a newly disclosed set of gene transcripts isolated from EVs.

[0024] The examples further demonstrate that a newly identified set of gene transcripts obtained from extracellular viable cells (EVs) expelled by tumor cell lines could be used to calculate a YAP1-TEAD activity score, and that the calculated score correlated with the TEAD-activity score calculated from parental cell mRNA when used as the TEAD-500 signature previously described in PCT / EP2023 / 057332, or as the YAP1-TEAD activity signature of Calvet et al. (BMC Cancer, 2022, 22:639, doi.org / 10.1186 / s12885-022-09686-y). The results show that the newly identified set of gene transcripts and their levels can be used to reliably measure the TEAD activity of parental tumor cells in secreted EVs. Therefore, the set of gene transcripts obtained from EVs serves as a suitable pharmacodynamic (PD) marker for monitoring TEAD activity and TEAD inhibition induced by TEAD inhibitor therapy.

[0025] The newly identified sets and levels of gene transcripts from extracellular vesicles offer advantages for clinical applications as non-invasive, fluid-based biopsy-based transcriptional signatures and biomarkers.

[0026] The identified set of gene transcripts can be used in a variety of clinical and research-related utilities, including, but not limited to, monitoring the activity of the TEAD complex, evaluating the pharmacodynamics of novel inhibitors of the TEAD pathway in vitro or in vivo, diagnosing tumors whose development or evolution is attributable to 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 therapies, estimating the proportion of cases in a cohort for each cancer indication or subtype in which TEAD-dependent transcription may be involved in tumor development or evolution to treatment resistance, and measuring TEAD-dependent transcription in the laboratory or clinic, in any tissue, under any conditions, and whether pathological or not.

[0027] In some embodiments, the set of gene transcripts (B) may consist of at least 2, 3, 4, 5, 6, 7, or 8 gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0028] In some embodiments, the set of gene transcripts (B) may consist of a set of genes comprising at least two gene transcripts, one of which is a gene transcript from the gene DLC1, and at least the second gene transcript is derived from a set of genes comprising AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0029] In some embodiments, the set of gene transcripts of this disclosure can be obtained from isolated extracellular vesicles.

[0030] For another purpose, this disclosure relates to an isolated extracellular vesicle containing a set of gene transcripts disclosed herein.

[0031] In some embodiments, the disclosure relates to an isolated extracellular vesicle comprising a set of gene transcripts (A) obtained from a set of genes, the set of genes comprising a first gene subset (1) and a second gene subset (2), the first gene subset (1) comprising 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) comprising CTSB, FTH1, SQSTM1, TCF25 and UBC.

[0032] In some embodiments, the disclosure relates to an isolated extracellular vesicle comprising 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.

[0033] In some embodiments, the set of gene transcripts provided herein may be used as a biomarker for 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.

[0034] Cancer diagnostic methods can be selected from among 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 treatment, methods for monitoring the response of cancer to TEAD inhibitor treatment, methods for predicting the progression or regression of TEAD-active cancer, and methods for monitoring the progression or regression of TEAD-active cancer.

[0035] For another purpose, this disclosure relates to the use of the set of gene transcripts disclosed herein for measuring TEAD activity in biological samples.

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

[0037] For another purpose, this disclosure relates to the use of the set of gene transcripts disclosed herein for measuring the TEAD activity of cancers from subjects requiring it.

[0038] For another purpose, this disclosure relates to the use of the set of gene transcripts disclosed herein to characterize the TEAD activity status of cancer in subjects requiring it.

[0039] For another purpose, this disclosure relates to the use of a set of gene transcripts disclosed herein for predicting the cancer response to TEAD inhibitor therapy in subjects requiring TEAD inhibitor therapy. Subjects are known to have or may be presumed to have TEAD-active cancer.

[0040] For another purpose, this disclosure relates to the use of a set of gene transcripts disclosed herein for monitoring the cancer response to TEAD inhibitor therapy in subjects requiring TEAD inhibitor therapy. Subjects are known to have or may be presumed to have TEAD-active cancer.

[0041] For another purpose, this disclosure relates to the use of a set of gene transcripts disclosed herein for predicting the progression or regression of cancer in subjects requiring such use. Subjects are known to have or may be presumed to have TEAD-active cancer.

[0042] For another purpose, this disclosure relates to the use of a set of gene transcripts disclosed herein for monitoring the progression or regression of cancer in subjects requiring such use. Subjects are known to have or may be presumed to have TEAD-activated cancer.

[0043] For another purpose, this disclosure relates to the use of the set of gene transcripts disclosed herein for screening candidate compounds for TEAD inhibitors.

[0044] In some embodiments, a set of gene transcripts can be obtained from isolated extracellular vesicles.

[0045] For another purpose, this disclosure relates to the use of isolated extracellular vesicles for the above-mentioned uses.

[0046] In some embodiments, the level of each gene transcript can be obtained.

[0047] In some embodiments, the transcription signature can be obtained at the gene transcript level.

[0048] In some embodiments, the obtained transcription signature can be compared with a reference transcription signature.

[0049] In some embodiments, the observed deviation between the obtained transcription signature and the reference transcription 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 TEAD-active cancer is prone to progression or regression, or that TEAD-active cancer is progressing or regressing, or that a candidate TEAD inhibitor compound is effective or ineffective.

[0050] In some embodiments, gene transcript levels may be subject to mathematical normalization.

[0051] In some embodiments, when a set of gene transcripts (A) is used, mathematical normalization is performed to obtain the deR score in the following steps: a) For each gene transcript in the set of genes, the rank of the gene having the level of the gene transcript is divided by the number of genes in the set of genes to convert the level of each gene transcript into a fractional rank, b) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step a), and calculate their average fractional ranks (MFR subset (1) or MFR positive), c) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step a), and calculate their average fractional ranks (MFR subset (2) or MFR negative), d) The deR score is calculated by subtracting MFR subset (2) from MFR subset (1), It can be calculated according to a method that includes [a specific method].

[0052] In some embodiments, when a set of gene transcripts (a set of gene transcripts from set (B)) is used, consisting of at least one gene transcript from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1, mathematical normalization is performed to obtain the (S) score in the following steps: a) Multiply each level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, b) The product Pgene obtained in step a) i (ΣPgene i (S) score: (ΣPgene) i To obtain (a constant), It can be calculated according to a method that includes, Here, the coefficients and constants used in steps a) and b) may be obtained in advance by stepwise multiple linear regression analysis correlating (i) the level of the gene transcript previously obtained in a first biological sample with (ii) the level of the TEAD-500 signature gene transcript previously measured in a second biological sample, wherein the TEAD-500 signature includes gene transcripts of a set of genes consisting of any 220 to 249 of the gene subset (1) disclosed in Table 2 and any 210 to 233 of the genes of gene subset (2). The first and second biological samples represent the same TEAD-activated cancer. The first and second biological samples represent the cancer for which the use of this disclosure is intended. The first sample may be a bodily fluid sample or a fraction thereof. The second sample may be a cancer cell sample.

[0053] In some embodiments, the correlation performed using stepwise multiple linear regression analysis in the methods and uses of the present disclosure may be between (i) the expression level of a gene transcript and (ii) the TEAD score obtained from the expression level of a gene transcript of the TEAD-500 signature.

[0054] In some embodiments, the calculated deR or (S) score can be compared to a baseline value.

[0055] In some embodiments, the observed deviation between the calculated deR or (S) score and the reference value 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 TEAD-active cancer is sensitive to progression or regression, or that the TEAD-active cancer progresses or regresses, or that an effective or ineffective TEAD inhibitor candidate compound is effective or ineffective.

[0056] In some embodiments, the reference value may be a first deR or (S) score, and the deR or (S) score compared to the reference value may be a second deR or (S) score measured subsequently to the first deR or (S) score.

[0057] In some embodiments, the level of each gene transcript can be obtained by RNA sequencing (RNA-seq) and quantified as FPKM (Fragments Per Kilobase of transcript per Million mapped reads).

[0058] For another purpose, this disclosure relates to a method for characterizing the TEAD activity status of cancer in subjects where this is required.

[0059] In some embodiments, the method may include the use of a set of gene transcripts from the set of genes (A) disclosed herein, and then the method may include at least the following steps: a) Obtaining the level of each gene transcript of the set of genes from a biological sample obtained from the subject; b) For each gene transcript of the set of genes, converting the level of each gene transcript obtained in step a) into a fractional rank by dividing the rank of the gene having the level of the gene transcript by the number of genes in the set of genes; c) Separating the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b) and calculating their average fractional rank (MFR subset (1) or MFR positive); d) Separating the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c) and calculating their average fractional rank (MFR subset (2) or MFR negative); e) Calculating the deR score by subtracting MFR subset (2) from MFR subset (1).

[0060] In some embodiments, the method may include the use of a set of gene transcripts from the set of genes (B) disclosed herein, and then the method may include at least the following steps: a) Obtaining the level of each gene transcript of the set of genes from a biological sample obtained from the subject; b) Multiplying each obtained level of each gene transcript of the set of genes obtained in step a) by a coefficient associated with each gene to obtain, for each gene, a product (Pgene i ), where gene i refers to the gene listed in the set of genes; c) Summing the products Pgene i (ΣPgene s i ) and adding a constant to obtain an (S) score: (ΣPgene i ) + constant). Includes, The coefficients and constants used in steps b) and c) are obtained in advance by stepwise multiple linear regression analysis, which correlates (i) the level of a gene transcript obtained in advance from a first biological sample obtained from a subject with cancer with (ii) the level of a gene transcript of the TEAD-500 signature measured in a second biological sample obtained from the subject, the second biological sample being derived from cancer cells from the subject, and the TEAD-500 signature subsequently includes gene transcripts of a set of genes that include any of 220 to 249 of the gene subset (1) and any of 210 to 233 of the genes of gene subset (2) disclosed in Table 2. The subjects from which the first and second biological samples are taken may be the same as or different from the subjects from which the biological samples are taken to calculate the (S) score. The first and second biological samples are representative of the cancers targeted by the method of this disclosure.

[0061] In some embodiments, the correlation performed using stepwise multiple linear regression analysis in the method of the present disclosure may be between (i) the expression level of a gene transcript and (ii) the TEAD score obtained from the expression level of the gene transcript of the TEAD-500 signature.

[0062] For another purpose, the present disclosure relates to a method for monitoring the progression or regression of cancer in subjects suspected or known to have TEAD-activated cancer, the method comprising the use of a set of gene transcripts from a set of genes (A), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the gene set from a first biological sample obtained from the subject at a first time point, b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The first deR score is calculated by subtracting MFR subset (2) from MFR subset (1), f) Obtaining the level of each gene transcript of the gene set from a second biological sample obtained from the subject at a second time point following the first time point, g) For each gene transcript in the set of genes, the level of each gene transcript obtained in step f) 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 in the set of genes, h) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (1) or MFR positive), i) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The second deR score is calculated by subtracting MFR subset (2) from MFR subset (1), k) Comparing the first deR score with the second deR score, wherein the observed deviation between the first deR score and the second deR score indicates progression or regression of the cancer. Includes.

[0063] For another purpose, the present disclosure relates to a method for monitoring the progression or regression of cancer in subjects presumed or known to have TEAD-activated cancer, the method comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the gene set from a first biological sample obtained from the subject at a first time point, b) Multiply each level obtained from each gene transcript of the set of genes obtained in step a) by a coefficient associated with each gene, and for each gene, the product (Pgene) i Here gene i (referring to the genes listed in the aforementioned set of genes) and c) The product Pgene obtained in step b) i (ΣPgene i The first (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), d) Obtaining the level of each gene transcript of the gene set from a second biological sample obtained from the subject at a second time point following the first time point, e) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, f) The product Pgene obtained in step e) i (ΣPgene i The second (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), g) Comparing 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. Includes, The coefficients and constants used in steps b), c), e), and f) are obtained in advance by stepwise multiple linear regression analysis, which correlates (i) the level of a gene transcript obtained in a third biological sample taken from a subject with cancer with (ii) the level of a gene transcript of the TEAD-500 signature taken from a fourth biological sample taken from the subject, the fourth biological sample being derived from cancer cells from the subject, and the TEAD-500 signature subsequently includes gene transcripts of a set of genes that include any of 220 to 249 of the gene subset (1) and any of 210 to 233 of the genes of gene subset (2) disclosed in Table 2. The subjects from which the third and fourth biological samples were taken may be the same as or different from the subjects from which the biological samples were taken to calculate the (S) score. The third and fourth biological samples are representative of the cancers targeted by the method of this disclosure.

[0064] For another purpose, the present disclosure relates to a method for monitoring the response of a subject who is presumed to have or is known to have TEAD-active cancer to TEAD inhibitor therapy, wherein the method comprises the use of a set of gene transcripts from a set of genes (A), and the method comprises at least the following steps: a) Obtaining the level of each gene transcript of the set of genes from a first biological sample collected from the subject before administration of TEAD inhibitor therapy, b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The first deR score is calculated by subtracting MFR subset (2) from MFR subset (1), f) Obtaining the level of each gene transcript of the gene set from a second biological sample collected from the subject before administration of TEAD inhibitor therapy, g) For each gene transcript in the set of genes, the level of each gene transcript obtained in step f) 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 in the set of genes, h) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (1) or MFR positive), i) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The second deR score is calculated by subtracting MFR subset (2) from MFR subset (1), k) A step of comparing the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores indicates effective or ineffective TEAD inhibitor treatment. Includes.

[0065] For another purpose, the present disclosure relates to a method for monitoring the response to TEAD inhibitor therapy in a subject who is presumed to have TEAD-active cancer or who is known to have TEAD-active cancer, the method comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the set of genes from a first biological sample collected from the subject before administration of TEAD inhibitor therapy, b) Multiply each level obtained from each gene transcript of the set of genes obtained in step a) by a coefficient associated with each gene, and for each gene, the product (Pgene)i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, c) The product Pgene obtained in step b) i (ΣPgene i The first (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), d) Obtaining the level of each gene transcript of the set of genes from a second biological sample collected from the subject before administration of TEAD inhibitor therapy, e) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, f) The product Pgene obtained in step e) i (ΣPgene i The second (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), g) Comparing the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores indicates effective or ineffective TEAD inhibitor treatment. Includes, The coefficients and constants used in steps b), c), e), and f) are obtained in advance by stepwise multiple linear regression analysis, which correlates (i) the level of a gene transcript obtained in a third biological sample taken from a subject with cancer with (ii) the level of a gene transcript of the TEAD-500 signature taken from a fourth biological sample taken from the subject, the fourth biological sample being derived from cancer cells from the subject, and the TEAD-500 signature subsequently includes gene transcripts of a set of genes that include any of 220 to 249 of the gene subset (1) and any of 210 to 233 of the genes of gene subset (2) disclosed in Table 2. The subjects from which the third and fourth biological samples were taken may be the same as or different from the subjects from which the biological samples were taken to calculate the (S) score. The third and fourth biological samples are representative of the cancers targeted by the method of this disclosure.

[0066] For another purpose, this disclosure relates to a method for screening candidate TEAD inhibitors that inhibit TEAD activity, the method comprising the use of a set of gene transcripts from a set of genes (A), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the gene set from a first biological sample obtained from the supernatant of the cell culture, which was obtained before contacting the cell culture with the candidate TEAD inhibitor compound, b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The first deR score is calculated by subtracting MFR subset (2) from MFR subset (1), f) Obtaining the level of each gene transcript in the set of genes in the second biological sample obtained from the supernatant of the cell culture after contacting the cell culture with the candidate TEAD inhibitor compound, g) For each gene transcript in the set of genes, the level of each gene transcript obtained in step f) 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 in the set of genes, h) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (1) or MFR positive), i) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The second deR score is calculated by subtracting MFR subset (2) from MFR subset (1), k) Comparing a first deR score with a second deR score, wherein the observed deviation between the first and second deR scores indicates a candidate compound that is effective or ineffective in inhibiting TEAD activity.

[0067] Cells suitable for cell culture according to this disclosure may be TEAD-active cells.

[0068] For another purpose, this disclosure relates to a method for screening candidate TEAD inhibitors that inhibit TEAD activity, the method comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) From a first biological sample obtained from the supernatant of a cell culture, the supernatant was obtained before contacting the cell culture with the candidate TEAD inhibitor compound, and the level of each gene transcript in the set of genes was obtained, b) Multiply each level obtained from each gene transcript of the set of genes obtained in step a) by a coefficient associated with each gene, and for each gene, the product (Pgene) i Here gene i (referring to the genes listed in the aforementioned set of genes) and c) The product Pgene obtained in step b) i (ΣPgene i The first (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), d) From a second biological sample obtained from the supernatant of the cell culture, the supernatant was obtained after contacting the cell culture with the TEAD inhibitor candidate compound, and the levels of each gene transcript in the set of genes were obtained, e) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, f) The product Pgene obtained in step e) i (ΣPgene i The second (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), g) Comparing the first (S) score with the second (S) score, wherein the observed deviation between the first and second (S) scores indicates effective or ineffective TEAD inhibitor treatment. Includes, The coefficients and constants used in steps b), c), e), and f) are obtained in advance by stepwise multiple linear regression analysis, which correlates (i) the level of gene transcripts obtained in a third biological sample obtained from the supernatant of a cell culture with (ii) the level of gene transcripts of the TEAD-500 signature obtained in a fourth biological sample obtained from a cell culture, the fourth biological sample being derived from cancer cells from the subject, and the TEAD-500 signature subsequently includes gene transcripts of a set of genes containing any of 220 to 249 of the gene subset (1) and any of 210 to 233 of the genes of gene subset (2) disclosed in Table 2. The cell cultures from which the third and fourth biological samples are taken may be the same as or different from the cell cultures from which the biological samples are taken to calculate the (S) score.

[0069] Cells suitable for cell culture according to this disclosure may be TEAD-active cells.

[0070] In some embodiments, a set of gene transcripts can be obtained from isolated extracellular vesicles.

[0071] In some embodiments, cancer is defined as adrenocortical carcinoma, urothelial carcinoma of the bladder, invasive breast carcinoma, squamous cell carcinoma of the cervix and intracervical adenocarcinoma, cholangiocarcinoma, consensus molecule subtype 1 of colorectal cancer, consensus molecule subtype 2 of colorectal cancer, consensus molecule subtype 3 of colorectal cancer, consensus molecule subtype 4 of colorectal cancer, colonic adenocarcinoma, and lymphoid neoplasm diffuse large B-cell lymphoid The following can be selected: tumors, esophageal cancer, glioblastoma multiforme, squamous cell carcinoma of the head and neck, renal pellucid cell carcinoma, renal papillary cell carcinoma, low-grade brain glioma, hepatocellular carcinoma of the liver, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid cancer, thymoma, endometrial cancer, and uterine carcinosarcoma.

[0072] In some embodiments, the cancer may be mesothelioma.

[0073] In some embodiments, the cancer may be colorectal cancer.

[0074] For another purpose, this disclosure relates to a kit comprising a solid support comprising a panel of nucleic acids for obtaining gene transcript levels of a set of gene transcripts disclosed herein. [Brief explanation of the drawing]

[0075] [Figure 1] This document outlines the process from cell processing to the isolation and analysis of extracellular vesicles (EVs). [Figure 2] This shows the TEAD activity in parental cells after treatment with a TEAD inhibitor (TEADi). [Figure 3] This shows the percentage of EV mRNA samples that contained genes derived from the TEAD-500 signature. [Figure 4] This shows a comparison of TEAD signature profiles between extracellular vesicles (EVs) and parental cells in TEADi dose-response analysis. [Figure 5] The study demonstrates the dose-response effect of TEAD inhibitors (TEADi) on TEAD activity in parental cells, which is assessed by limited TEAD signatures of 28 genes measured in extracellular vesicles (EVs) and within parental cells. [Figure 6] The TEAD activity measured in extracellular vesicles (EVs) using a selection marker (n=4) and the TEAD activity measured in parental cells using the TEAD-500 signature (n=500) are shown. [Modes for carrying out the invention]

[0076] definition Unless otherwise defined herein, scientific and technical terms used in connection with this disclosure shall have meanings generally understood by those skilled in the art. For example, the Concise Dictionary of Biomedicine and Molecular Biology, Juo, Pei-Show, 2nd ed., 2002, CRC Press; The Dictionary of Cell and Molecular Biology, 3rd ed., 1999, Academic Press; and the Oxford Dictionary of Biochemistry and Molecular Biology, Revised, 2000, Oxford University Press may provide those skilled in the art with a general dictionary of many of the terms used in this disclosure. Exemplary methods and materials are described below, but similar or equivalent methods and materials may also be used in the practice or testing of this disclosure. In the event of any conflict, this specification, including definitions, shall prevail. In general, the nomenclature and techniques used herein in connection with cell and tissue culture, molecular biology, virology, immunology, microbiology, genetics, analytical chemistry, synthetic organic chemistry, pharmaceutical and pharmaceutical chemistry, and protein and nucleic acid chemistry and hybridization are well known and commonly used in the art. Enzyme reactions and purification techniques are performed as commonly practiced in the art or as described herein, in accordance with the manufacturer's specifications. Furthermore, unless otherwise required by context, singular terms shall include plural terms and plural terms shall include singular terms.

[0077] Units, prefixes, and symbols are given in their International System of Units (SI) approved forms. Numerical ranges include the number defining the range. Unless otherwise indicated, amino acid sequences are written from left to right in the amino-carboxyl direction. The headings provided herein are not limitations on the various aspects of this disclosure. Thus, terms defined immediately thereafter are defined in more detail by referring to this specification as a whole.

[0078] All publications and other references mentioned herein are incorporated by reference in their entirety. While several documents are cited herein, the citations do not constitute an endorsement that any of those documents form part of the common technical knowledge in the art.

[0079] Throughout this specification and its embodiments, variations of the words “have” and “comprise,” or “has,” “having,” “comprises,” or “comprising,” will be understood to mean that they encompass the specified integer or group of integers, but do not exclude any other integer or group of integers. Whenever an aspect is described herein in the language of “comprise,” it will be understood that other similar aspects are also provided, described in the terms “consist of” and / or “essentially consisting of.”

[0080] It should be noted that the terms “one (a)” or “one (an)” entity refer to one or more of those entities; for example, “nucleotide sequence” is understood to represent one or more nucleotide sequences. Thus, the terms “one (a)” (or “one (an)”), “one or more” and “at least one” can be used interchangeably herein.

[0081] Furthermore, as used herein, “and / or” should be interpreted as the specific disclosure of each of two particular features or components that have or do not have the other. Accordingly, the term “and / or” as used in phrases such as “A and / or B” is intended herein 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 include each of the following situations: 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).

[0082] The terms “approximately” or “about” are used herein to mean roughly, roughly, around, within or within a range. When the term “about” is used in conjunction with a numerical range, it modifies the range by extending the upper and lower boundaries of the stated numerical value. Generally, the term “about” can modify numerical values ​​above and below a stated value, for example, by 10 percent, or by a higher or lower variance. In some embodiments, the term indicates a deviation 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% from the stated numerical value. In some embodiments, “about” indicates a deviation of ±10% from the stated numerical value. In some embodiments, “about” indicates a deviation of ±5% from the stated numerical value. In some embodiments, “about” indicates a deviation of ±4% from the stated numerical value. In some embodiments, "approximately" indicates a deviation of ±3% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±2% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±1% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.9% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.8% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.7% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.6% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.5% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.4% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.3% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.1% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.05% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.01% from the indicated value.

[0083] "Obtained," "purified," and "isolated" mean, when referring to a biological sample, gene transcript, population or subpopulation of gene transcripts, set of gene transcripts, extracellular vesicles, population or subpopulation of extracellular vesicles, that the indicated element has been separated from other substances or components that were originally present in the mixture or in its natural environment. As used herein, the term "purified" means, in particular, that the same type of component is present in an amount of at least 75% by weight, 85% by weight, 95% by weight, or 98% by weight.

[0084] As used herein, the terms “subject” or “patient” mean mammals such as rodents, cats, dogs, and primates. In particular, the subject according to this disclosure is human. The subject requiring this is a subject known to or presumed to have cancer. In some embodiments, the subject requiring this may be a subject known to or presumed to have TEAD-activated cancer. In some embodiments, the subject may be an animal model of TEAD-activated cancer.

[0085] "Biomarker" is intended to refer to a biological molecule or set of biological molecules, such as an RNA molecule or set of RNA molecules, that is differentially present, increased, or decreased in a biological sample obtained from a subject or group of subjects having a first phenotype, such as cancer or TEAD-active cancer, compared to a biological sample from a subject or group of subjects having a second phenotype, such as no disease or no TEAD-active cancer. During use, the biomarker is isolated from the subject.

[0086] Signature, gene signature, and transcription signature. As used herein, these terms and expressions are intended to refer to a pattern of transcriptional levels of a set of genes, or a pattern of levels of gene transcripts, which may be associated with TEAD-activated cancer or tumor. A transcription signature can be obtained by measuring the levels of the gene transcripts of this disclosure in a biological sample. In a biological sample, the levels of all or some of the gene transcripts that are thought to be present can be measured (or obtained).

[0087] In this specification, “gene transcript,” “transcription,” and “gene expression” are used interchangeably to refer to the production of RNA from a gene. Gene transcription or expression can increase or decrease, resulting in an increase or decrease in RNA production.

[0088] 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 may be expressed as Fragments Per Kilobase of transcript per Million mapped reads (FPKM).

[0089] Level of gene transcript. As used herein, the term “level of gene transcript” is intended to refer to a measure of the extent to which a gene is expressed or transcribed in a biological sample. This reflects the amount of RNA produced from the gene by transcription. Levels of gene transcript 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 “level of gene transcript” and “gene transcript level” are used interchangeably. In this disclosure, the terms “level of gene transcript” or “gene transcript level” are used interchangeably.

[0090] FPKM (Fragments Per Kilobase of Transcript Per Million Mapped Reads) is a measure of gene expression level (or gene transcript level) that takes into account the number of RNA sequencing reads mapped to a particular gene and the gene length, and is normalized by the total number of reads in the sample.

[0091] Positive effectors. Positive effectors are genes whose gene transcript levels are positively correlated with TEAD activity. This increases when the TEAD pathway is activated in any way, or decreases when the TEAD pathway is inhibited. Positive effectors are not necessarily expected to respond in both directions of TEAD regulation. For example, a gene that is deficient in normal expression in tissues may be seen to increase with TEAD activation, but not necessarily decrease with TEAD inhibition. Even at higher normal levels of expression, positive effector genes may not necessarily be sensitive to both TEAD activation and inhibition.

[0092] Negative effectors. Negative effectors are genes whose gene transcript levels are negatively correlated with TEAD activity. They decrease with TEAD activation or increase with TEAD inhibition. Similar to positive effectors, negative effectors are not necessarily expected to respond in both directions of TEAD regulation. For example, a gene that is deficient in normal expression in tissues may be seen to increase with TEAD pathway inhibition, but not necessarily decrease with TEAD activation. Even at higher normal expression levels, negative effector genes may be sensitive to TEAD activation but unaffected by TEAD inhibition, or vice versa.

[0093] TEAD-500 Signature Gene List. A list of the 430–482 most significant positive or negative gene effectors of TEAD regulation. These genes were selected by differential expression analysis of 18 publicly available expression sequence datasets. Some of the genes are direct targets of TEAD transcription factors, i.e., they present TEAD recognition motifs to their promoters, but most of these genes are subject to secondary or indirect regulation by the TEAD pathway. The "TEAD-500" signature gene list consists of 482 genes, representing approximately 90% of the 482 genes. Dropouts of at least 10% have negligible impact on the score. This is one reason why TEAD-500 is so robust. The TEAD-500 signatures are disclosed in Calvet et al. (BMC Cancer, 2022, 22:639, doi.org / 10.1186 / s12885-022-09686-y).

[0094] Extracellular vesicles (EVs) refer to vesicles released from cells, such as exosomes, microvesicles (or ectosomes), apoptotic bodies, oncosomes, and exosome-like vesicles. EVs are small, cell-derived membrane vesicles secreted by cells that transmit biological messages through surface-to-surface interactions or by transporting bioactive molecules into the cytoplasm of recipient cells. Cell-released EVs can hold the original cell's cargo and serve as a valuable tool for tracking drug activity.

[0095] "Sample" or "biological sample" is intended to refer to biological material obtained from (or purified or isolated from) a subject or cell culture. A biological sample may contain any biological material suitable for detecting biomarkers, i.e., gene transcripts, and may include cellular and / or non-cellular material such as extracellular vesicles. A biological sample may also be a bodily fluid sample. A bodily fluid sample may be isolated from any suitable bodily fluid, such as blood, blood plasma, blood serum, saliva, urine, or cerebrospinal fluid (CSF). In some embodiments, a biological sample is a blood plasma sample, a blood serum sample, a saliva sample, or a urine sample.

[0096] "Reference value" or "threshold" is intended to refer, depending on the context, to a level of gene transcript, or a level of gene transcript, or transcription signature, or a deR score, or (S) score, which indicates a specific disease state, phenotype, e.g., cancer or its absence, or combination of disease states, phenotypes, or their absence in the subject concerned.

[0097] TEAD-activated cancer. As used herein, “TEAD-activated cancer” is intended to refer to cancer in which cancer cells have an abnormally regulated TEAD pathway that leads to oncogenic transcription, cancer cell / tumor development or evolution, and which is capable of responding to treatment with TEAD inhibitors (TEADi).

[0098] TEAD Signature. As used herein, “TEAD Signature” or “TEAD-500 Signature” is intended to refer to the transcriptional signature obtained by measuring the gene expression levels of a set of genes, including any of the 220–249 positive effector genes and any of the 210–233 negative effector genes listed in Table 2 herein. Extended, depending on the context, “TEAD Signature” may refer to the set of genes in question.

[0099] Scoring. TEAD-500 scoring (hereinafter referred to as "TEAD score") is a procedure for calculating the activity of the TEAD pathway in a sample on a continuous scale of values. In some embodiments, the score is based on the mean fractional rank of the transcript levels of positive and negative effectors.

[0100] Fractional rank. In a set of genes' transcriptome, each value is assigned a rank, with the lowest rank being 1. The fractional rank of a gene is equal to its rank (determined by the level of its transcript) divided by the number of genes in the set (highest rank).

[0101] Effector rank difference (deR). Converts the levels of TEAD-500 signature transcripts (430-482 or 500 gene transcripts) into fractional ranks.

[0102] TEAD activity state invocation (binning). This involves binary conversion of the continuous deR score of TEAD activity into two discrete state values: "active" or "inactive". In some embodiments, the inventors empirically set the deR threshold to 0.055. This value was selected from experiments using human mesothelioma cell lines. The inventors observed that cell lines with a deR score of less than 0.055 did not respond to YAP1-siRNA treatment, while cell lines with higher scores stopped growing when YAP1 was knocked out in this manner. The response threshold may vary depending on the tissue or treatment. A score value of 0.055 roughly corresponds to the 88th percentile of scores observed in cancer cells in the Cancer Genome Atlas (TCGA) cohort (pooling all indications). If the deR score of TEAD-500 is greater than 0.055, TEAD is called active; otherwise, it is called inactive.

[0103] Mathematical normalization. Mathematical normalization is the process of transforming data or values ​​into a standardized or common scale.

[0104] TEAD pathway inhibitor or TEAD inhibitor (TEADi). As used herein, a TEAD pathway inhibitor is any small or large molecule compound that inhibits the HIPPO-YAP / WWTR1 / TEAD pathway.

[0105] As used herein, “administer” or “to administer” means to deliver the compositions described herein, for example, lipid nanoparticles, to a target. The compositions may be administered to a target using methods known in the art. In particular, the compositions may be administered intravenously, subcutaneously, intramuscularly, intradermally, or via any mucosal surface, for example, orally, sublingually, phalanxally, transnasally, rectally, transvaginally, or via the pulmonary route. In some embodiments, the administration is intravenous. In some embodiments, the administration is subcutaneous.

[0106] The terms “to treat,” “treatment,” or “therapy” refer to the administration or consumption of any composition disclosed herein for the purpose of curing, healing, alleviating, reducing, altering, correcting, improving, enhancing, or influencing the symptoms of a disorder or condition, or for the purpose of preventing or delaying the onset of symptoms or complications, or otherwise stopping or inhibiting the further onset of the disorder in a statistically significant manner. More specifically, “to treat” or “therapy” includes any approach to obtain a beneficial or desired outcome in the cancerous condition of interest. Beneficial or desired clinical outcomes may include, but are not limited to, the reduction or improvement of one or more cancerous symptoms or conditions, the reduction or decrease in the severity of the cancerous disease or cancerous symptoms, the stabilization of the cancerous disease or cancerous symptoms, i.e., the prevention of worsening, the prevention of the spread of the cancerous disease or cancerous symptoms, or the delay or slowing of the progression of the cancerous disease or cancerous symptoms. This includes improvement or alleviation of the cancerous disease condition, reduction of cancerous disease recurrence, and remission, whether partial or whole, and whether detectable or not. In other words, as used herein, “treatment” includes any cure, improvement, or alleviation of the cancerous disease or symptoms. "Reduction" of symptoms or disease means a decrease in the severity or frequency of the disease or symptoms, or the elimination of the disease or symptoms.

[0107] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which this disclosure belongs. However, any methods and materials similar to or equivalent to those described herein may also be used in the implementation or testing of this disclosure. All publications referenced herein are incorporated herein by reference to disclose and explain such methods and / or materials in the context in which those publications are cited.

[0108] The following list of suppliers, raw materials, and ingredients, including their combinations and mixtures, is enumerated as is within the scope of this specification.

[0109] It should be understood that any upper limit of any numerical limitation provided throughout this specification encompasses all lower numerical limitations, as those expressly stated herein. Any lower limit of any numerical limitation provided throughout this specification encompasses all higher numerical limitations, as those expressly stated herein. Any numerical range provided throughout this specification encompasses all narrower numerical ranges within such wider ranges, as those expressly stated herein.

[0110] For example, all lists of items, such as a list of raw materials, are intended to be and must be interpreted as Markush groups. Therefore, all lists can be read and interpreted as items "selected from a group consisting of lists of items and combinations and mixtures thereof."

[0111] In this specification, trademark names may be used to refer to components, including various raw materials, used in this disclosure. The inventors do not intend to limit themselves in this specification to any particular trademarked material. In the description herein, materials equivalent to those referenced by trademark names (e.g., those available from different suppliers under different names or reference numbers) may be substituted and used.

[0112] Modes for carrying out the invention A set of gene transcripts In some embodiments, the set of gene transcripts from a set of genes (A) may consist of a gene subset (1) comprising 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 gene subset (2) comprising CTSB, FTH1, SQSTM1, TCF25, and UBC.

[0113] A set of gene transcripts (A) is obtained from a set of genes 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.

[0114] In some embodiments, the set of gene transcripts (B) may consist of at least one gene transcript from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0115] In some embodiments, the set of gene transcripts (B) may consist of at least two, three, four, five, six, seven, or eight gene transcripts from the set of genes.

[0116] In some embodiments, the set of gene transcripts (B) may consist of at least two gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0117] In some embodiments, the set of gene transcripts (B) may consist of a set of genes comprising at least two gene transcripts, one of which is a gene transcript from the gene DLC1, and at least the second gene transcript is derived from a set of genes comprising AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0118] In some embodiments, the set of gene transcripts (B) may consist of a set of genes comprising DLC1 and AKAP2, and optionally a set of gene transcripts comprising at least one gene selected from CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0119] In some embodiments, the set of gene transcripts (B) may consist of a set of genes comprising DLC1, AKAP2, and CANX, and optionally a set of gene transcripts comprising at least one gene selected from SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0120] In some embodiments, the set of gene transcripts (B) may consist of a set of genes comprising DLC1, AKAP2, CANX, and SAFB2, and optionally at least one gene selected from EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0121] In some embodiments, the set of gene transcripts (B) may consist of a set of genes comprising DLC1, AKAP2, CANX, SAFB2, and EIF4H, and optionally a set of gene transcripts comprising at least one gene selected from NDUFS5, SEPT9, and EIF4A1.

[0122] In some embodiments, the set of gene transcripts (B) may consist of a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, and NDUFS5, and optionally a set of gene transcripts from at least one gene selected from SEPT9 and EIF4A1.

[0123] In some embodiments, the set of gene transcripts (B) may consist of a set of gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, and SEPT9, and optionally a set of genes comprising EIF4A1.

[0124] In some embodiments, the set of gene transcripts (B) may consist of gene transcripts from the gene DLC1.

[0125] In some embodiments, the set of gene transcripts (B) may consist of a set of gene transcripts from a set of genes comprising DLC1 and AKAP2.

[0126] In some embodiments, the set of gene transcripts (B) may consist of a set of gene transcripts from a set of genes comprising DLC1, AKAP2, and CANX.

[0127] In some embodiments, the set of gene transcripts (B) may consist of a set of gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, and SAFB2.

[0128] In some embodiments, the set of gene transcripts (B) may consist of a set of gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, and EIF4H.

[0129] In some embodiments, the set of gene transcripts (B) may consist of a set of gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, and NDUFS5.

[0130] In some embodiments, the set of gene transcripts (B) may consist of a set of gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, and SEPT9.

[0131] In some embodiments, the set of gene transcripts (B) may consist of a set of gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

[0132] The gene transcript may be an isolated or purified gene transcript.

[0133] In some embodiments, the set of gene transcripts may be an isolated set or a purified set of gene transcripts.

[0134] In some embodiments, gene transcripts can be RNA molecules.

[0135] Assay / RNA sequencing In some embodiments, gene transcript levels or gene transcript levels can be obtained.

[0136] In some embodiments, the gene transcript level may be a nucleic acid expression level, such as an RNA level, e.g., an mRNA level, or a DNA expression level. Any suitable method can be used to determine the nucleic acid expression level (or gene transcript level).

[0137] In some embodiments, gene transcript levels 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, sequential gene expression analysis (SAGE), whole-genome sequencing (WGS), and MassARRAY techniques.

[0138] For example, gene transcript levels can be determined using the RNA ACCESS protocol or the TRUSEQ RIBO-ZERO00 protocol (ILLUMINA), RT-qPCR, qPCR, multiplex qPCR or RT-qPCR, microarray analysis, SAGE, MassARRAY technology, or a combination thereof.

[0139] In addition, such methods may include one or more steps that allow for the determination of the level of target RNA in a biological sample by simultaneously examining the levels of comparison control RNA sequences of "housekeeping" genes, such as actin family members. In some embodiments, the sequence of the amplified target cDNA can be determined.

[0140] In some embodiments, the gene transcript is an RNA molecule, and its level is determined using RNA-seq.

[0141] In embodiment e, the gene transcript is an mRNA molecule.

[0142] RNA-seq enables the 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. Various RNA-seq methods are publicly known in the art (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).

[0143] RNA-seq typically involves the preparation of an RNA sequencing library. Standard protocols can be used to prepare the RNA sequencing library. For example, RNA can be fragmented and reverse transcribed into cDNA. Adapters can also be added to the cDNA, and the library can be amplified by PCR. The adapter is a short DNA sequence that enables fragment amplification by PCR and provides a barcode or identifier for the sequencing platform. The RNA library can then be sequenced using any known method in the art, such as next-generation sequencing, to obtain RNA-seq data.

[0144] Typically, RNA-seq data analysis involves the following steps: trimming to remove adapter sequences and low-quality nucleotides, alignment to a reference genome or transcriptome, counting or quantification by assigning reads to genes or transcripts, normalization of sequencing reads, and, very frequently, differential expression (DE) analysis between conditions.

[0145] Gene transcript levels can be quantified using software such as Cufflinks or RSEM. Differential expression analysis can be performed using software such as DESeq2 or edgeR.

[0146] Any method may include protocols for examining or detecting RNA, such as target RNA, in a sample using microarray technology.

[0147] Using nucleic acid microarrays, test and control RNA samples from test tissue samples and control samples are reverse transcribed and labeled to generate cDNA probes. The probes are then hybridized to an array of nucleic acids immobilized on a solid support. The array is configured such that the sequence and position of each member of the array are known. For example, any of the gene transcripts described herein can be arrayed on a solid support. Hybridization of a labeled probe with a specific array member indicates that the sample from which the probe originated expresses that gene.

[0148] The level of gene transcripts can be measured and quantified using the various methods mentioned above, such as microarray analysis or RNA sequencing (RNA-seq). The level of gene transcripts can also be expressed using FPKM (Fragments Per Kilobase of transcript per Million mapped reads), which is a normalized measure that takes into account the length of the transcript and the total number of reads generated by RNA sequencing.

[0149] FPKM is calculated by dividing the number of fragments (or reads) mapped to a specific gene transcript by the length of that transcript (in kilobases), and then normalizing this value by the total number of mapped fragments in the sample (in millions). This allows for comparison of expression levels between different genes and different samples.

[0150] Other appropriate measures of gene transcript levels include TPM (Transcripts Per Million) and RPKM (Reads Per Kilobase of transcripts per Million mapped reads).

[0151] TPM (transcripts per million) is a method for normalizing the levels of gene transcripts in RNA sequencing data. TPM considers both the length of the gene transcript and the total number of reads generated by RNA sequencing, but normalizes the gene transcript level by dividing the number of reads mapping to a specific gene transcript by the total number of reads in the sample, and then multiplying this value by 1 million. This allows for comparison of levels between different gene transcripts and different samples, taking into account differences in gene transcript length and sequencing depth.

[0152] RPKM (Reads per Kilobase of Transcript per Mapped Reads per Million Mapped Reads) is a method for quantifying the level of gene transcripts in RNA sequencing data. Similar to FPKM and TPM, RPKM takes into account both the length of the gene transcript and the total number of reads generated by RNA sequencing.

[0153] RPKM is calculated by dividing the number of reads mapped to a specific gene transcript by the length of that gene transcript (in kilobases), and then dividing this value by the total number of mapped reads in the sample (in millions). The resulting value represents the level of the gene transcript in kilobases of reads per million mapped reads. This allows for the comparison of levels between different gene transcripts and different samples, while taking into account differences in transcript length and sequencing depth.

[0154] In some embodiments, the levels of gene transcripts of the present disclosure are measured by RNA sequencing (RNA-seq) and expressed / quantified as FPKM (Fragments Per Kilobase of transcript per Million mapped reads).

[0155] Biological samples In some embodiments, gene transcripts can be obtained from a biological sample or a fraction thereof. A fraction of a biological sample suitable for this disclosure may be an extract of such a sample.

[0156] Biological samples can be obtained from subjects requiring them, cell cultures, or animal models, such as animal models of cancer.

[0157] Cells suitable for this disclosure may be TEAD-active cells. Examples of TEAD-active cells suitable for cell culture according to this disclosure include mesothelioma cell lines, NCI-H226, Mero-14, and SPC212. An example of TEAD-inactive cells suitable as a control according to this disclosure is the non-responsive colon cell line HCT116.

[0158] Examples of animal models of TEAD-activated cancers may include mouse models of colorectal cancer, lung cancer, liver cancer, and breast cancer that have gene deletions or mutations in TEAD or its cofactors, or that have overexpression or knockdown of TEAD or its cofactors.

[0159] The biological sample may be a body fluid sample or a cell culture supernatant.

[0160] Body fluid samples can be isolated from the subject for which they are needed.

[0161] Body fluid samples may 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.

[0162] In some embodiments, the bodily fluid sample may be a blood sample.

[0163] In some embodiments, the body fluid sample may be a plasma sample.

[0164] In some embodiments, the bodily fluid sample may be a urine sample.

[0165] In some embodiments, the bodily fluid sample may be a saliva sample.

[0166] In some embodiments, the biological sample or fraction thereof may contain extracellular vesicles.

[0167] Body fluid samples can be obtained by any known method in the art. Examples include needle puncture for obtaining blood samples, passive drainage or aspiration for obtaining saliva samples, lumbar puncture for obtaining CSF samples, midstream clean capture or catheterization for obtaining urine samples, and bronchoalveolar lavage for obtaining bronchoalveolar samples.

[0168] The biological sample is taken in an amount sufficient to contain the biological material necessary to obtain the gene transcripts of this disclosure. Those skilled in the art know how to adapt the amount of the biological sample according to the properties and source of the biological sample.

[0169] For example, depending on the fluid sample, the sample volume can range from approximately 1 ml for saliva samples to approximately 250 ml for bronchoalveolar samples.

[0170] A biological sample or fraction thereof suitable for this disclosure may contain extracellular vesicles (EVs).

[0171] Extracellular vesicles In some embodiments, the gene transcripts of this disclosure can be obtained from isolated extracellular vesicles. EVs can be obtained from biological specimens disclosed herein.

[0172] In some embodiments, gene transcripts can be extracted from extracellular vesicles.

[0173] Extracellular vesicles (EVs) are small, membrane-bound vesicles released from cells into the extracellular environment. Based on their biosynthesis, size, and contents, they can be classified into different subtypes, including exosomes, microvesicles (or ectosomes), apoptotic bodies, oncosomes, or exosome-like structures. EVs are known to play a crucial role in intercellular communication because they can transport various biomolecules, such as proteins, lipids, and nucleic acids, between cells.

[0174] In some embodiments, the present disclosure relates to isolated extracellular vesicles comprising a set of gene transcripts disclosed herein.

[0175] In some embodiments, the disclosure relates to an isolated extracellular vesicle comprising a set of gene transcripts (A) obtained from a set of genes, the set of genes comprising a first gene subset (1) and a second gene subset (2), the first gene subset (1) comprising 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) comprising CTSB, FTH1, SQSTM1, TCF25 and UBC.

[0176] In some embodiments, the disclosure relates to an isolated extracellular vesicle comprising 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.

[0177] In some embodiments, each extracellular vesicle in a set of extracellular vesicles may contain all of the gene transcripts in the set of gene transcripts described herein.

[0178] In some embodiments, each extracellular vesicle in a set of extracellular vesicles may contain at least a portion of the gene transcripts of the set of gene transcripts described herein, and all extracellular vesicles together with the set of EVs may contain all of the gene transcripts of the set of gene transcripts.

[0179] In some embodiments, extracellular vesicles may be selected from exosomes, microvesicles (or ectosomes), apoptotic bodies, oncosomes, exosome-like structures, and combinations thereof.

[0180] In some embodiments, extracellular vesicles can be isolated from biological samples, such as body fluid samples.

[0181] In some embodiments, extracellular vesicles can be purified from biological samples, such as body fluid samples.

[0182] Biological samples can be as detailed above.

[0183] Various methods for isolating and purifying extracellular vesicles (EVs) from suitable biological samples are known in the art. Examples of such methods include:

[0184] Ultracentrifugation: This method involves rapidly centrifuging the sample to separate extracellular fibers (EVs) from other cellular components.

[0185] Size Exclusion Chromatography (SEC): This method involves separating EVs based on size using a bead-filled column that allows for particle separation based on hydrodynamic diameter. Another type of SEC may be ultracentrifugation using a sucrose gradient.

[0186] 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 is the membrane-based affinity column method.

[0187] Membrane-based affinity columns: This method involves chromatography columns that use membranes with affinity for extracellular molecules (EVs) to selectively capture and purify them from a sample. The membranes contain ligands that bind to specific EV surface markers, such as tetraspanin CD63 or CD9, enabling the isolation of EVs, such as exosomes and microvesicles, with high purity and specificity.

[0188] The workflow for using a membrane-based affinity column typically involves binding extracellular viable cells (EVs) from the sample to the membrane, washing away unwanted material, and then eluting the purified EVs from the membrane using a buffer.

[0189] Microfluidic-based methods: These methods involve the use of microfluidic devices to isolate and purify EVs based on their size, shape, and other physical properties.

[0190] EVs can be isolated and the contents of gene transcripts extracted using various methods known in the art. Examples include differential ultracentrifugation, methods using size-based filters (e.g., EXOMIR, BIOOSCIENTIFIC), antibody-based capture methods (e.g., IMMUNOBEADS, HANSABIOMED), polymer-based precipitation reagent methods (e.g., LIFE TECHNOLOGIES, SYSTEM BIOSCIENCES INC.), or spin column-based methods (membrane-based affinity columns).

[0191] In some embodiments, EVs can be isolated from biological samples, such as blood or urine samples or supernatants of cultured cells, using membrane-based affinity columns (EXOEASY, MAXI KIT, QIAGEN). Membrane-based affinity column methods may include the following steps: The process involves preparing a membrane-based affinity column by immobilizing a specific ligand onto a column matrix. Suitable ligands include antibodies or aptamers that specifically bind to extracellular viable cells (EVs). A biological sample is prepared by removing cells and cell debris by centrifugation or filtration to obtain a supernatant or filtrate containing the target EV. Load the EV-containing sample into a prepared column under conditions suitable for EV binding to the immobilized ligand. Wash the column with an appropriate buffer to remove any unbound substances. Elute the extracellular matrix (EV) by appropriately changing buffer conditions, such as pH or salt concentration.

[0192] In some embodiments, extracellular vesicles can be passed through a membrane-based affinity column by centrifugation.

[0193] Isolated EVs can be quantified and characterized using various techniques known in the art, such as electron microscopy, nanoparticle tracking analysis, or Western blotting.

[0194] From isolated extracellular viable cells (EVs), gene transcripts can be extracted, separated, and sequenced by any method well known to those skilled in the art.

[0195] A suitable method for extracting and isolating the gene transcripts of this disclosure includes at least the following steps: The process involves lysing EV and obtaining the released gene transcripts from the lysate, The process involves packing the dissolved material into an affinity column, such as a spin column, Wash the affinity column with an appropriate buffer to remove any unbound substances, This includes eluting gene transcripts.

[0196] The quality and quantity of gene transcripts can be evaluated using any known method in the art, such as a bioanalyzer or spectrophotometer.

[0197] Usage and Method Purpose In some embodiments, the set of gene transcripts disclosed herein may be used as a biomarker for TEAD activity.

[0198] In some embodiments, the set of gene transcripts disclosed herein may be used to measure or characterize the TEAD activity of cancer.

[0199] In some embodiments, the set of gene transcripts disclosed herein may be used to measure or characterize the TEAD activity of a biological sample.

[0200] In some embodiments, the set of gene transcripts disclosed herein may be used for a TEAD inhibitor candidate compound screening method.

[0201] In some embodiments, the sets of gene transcripts disclosed herein may be for use in cancer diagnostic methods.

[0202] Cancer diagnostic methods can be selected from among 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 treatment, methods for monitoring the response of cancer to TEAD inhibitor treatment, methods for predicting the progression or regression of TEAD-active cancer, and methods for monitoring the progression or regression of TEAD-active cancer.

[0203] In some embodiments, this disclosure relates to the use of a set of gene transcripts disclosed herein to characterize the TEAD activity state of a biological sample.

[0204] In some embodiments, the present disclosure relates to the use of a set of gene transcripts disclosed herein to characterize the TEAD activity status of cancer in subjects requiring it.

[0205] In some embodiments, this disclosure relates to the use of a set of gene transcripts disclosed herein for measuring TEAD activity in biological samples.

[0206] In some embodiments, the present disclosure relates to the use of a set of gene transcripts disclosed herein for measuring TEAD activity in bodily fluid samples derived from subjects requiring it.

[0207] In some embodiments, the present disclosure relates to the use of a set of gene transcripts disclosed herein for measuring TEAD activity in cancer cell samples of a target cancer where such activity is required.

[0208] In some embodiments, the present disclosure relates to the use of a set of gene transcripts disclosed herein for predicting cancer responses characterized by TEAD activity to TEAD inhibitor therapy in subjects requiring such use.

[0209] In some embodiments, the disclosure relates to the use of a set of gene transcripts disclosed herein for monitoring cancer responses characterized by TEAD activity to TEAD inhibitor therapy in subjects where such monitoring is required.

[0210] In some embodiments, the disclosure relates to the use of a set of gene transcripts disclosed herein for predicting the progression or regression of cancer in subjects requiring such prediction.

[0211] In some embodiments, the disclosure relates to the use of a set of gene transcripts disclosed herein for monitoring the progression or regression of cancer in subjects requiring such monitoring.

[0212] The subjects that require this are those who are known to have TEAD-activated cancer, or who are suspected to have TEAD-activated cancer.

[0213] The cancers considered in this disclosure may be TEAD-activated cancers.

[0214] In some embodiments, the present disclosure relates to the use of a set of gene transcripts disclosed herein for screening candidate compounds for TEAD inhibitors.

[0215] In some embodiments, the set of gene transcripts can be obtained from a biological sample, as detailed above, for example.

[0216] In some embodiments, the set of gene transcripts can be obtained from extracellular vesicles, as detailed above, for example.

[0217] In some embodiments, an extracellular vesicle containing the set of gene transcripts of this disclosure may be used for the applications described above for gene transcripts from which the gene transcripts are extracted from the extracellular vesicle.

[0218] In some embodiments, the level of each gene transcript in the set of genes is obtained, for example, as described above.

[0219] The obtained gene transcript levels can characterize the TEAD activity state of cancer or biological samples.

[0220] The obtained gene transcript levels may be a measure of TEAD activity in cancer or biological samples.

[0221] In some embodiments, the obtained gene transcript levels can be compared to a reference value.

[0222] In some embodiments, the deviation observed with respect to the reference values may indicate that the cancer is TEAD-active or TEAD-inactive, or that the cancer is responsive or non-responsive to TEAD inhibitor treatment, or an effective or ineffective TEAD inhibitor treatment, or a progressive or regressing cancer, or that a TEAD inhibitor candidate compound is effective or ineffective.

[0223] In some embodiments, the transcription signature can be obtained from gene transcript levels.

[0224] The obtained transcription signature can be compared to a reference transcription signature.

[0225] In some embodiments, the observed deviation between the obtained transcription signature and the reference transcription signature indicates a cancer that is TEAD-active or TEAD-inactive, or a cancer that is responsive or non-responsive to TEAD inhibitor treatment, or an effective or ineffective TEAD inhibitor treatment, or a progressive or regressing cancer, or an effective or ineffective TEAD inhibitor candidate compound.

[0226] In some embodiments, the obtained gene transcript levels can be subject to mathematical normalization.

[0227] When using a set (A) of gene transcripts, a deR score can be calculated using mathematical normalization. The deR score involves the following steps: a) For each gene transcript of the set of genes, converting the level of the gene having the level of the gene transcript to a fractional rank by dividing the rank of the gene by the number of genes in the set of genes; b) Separating the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step a) and calculating their mean fractional rank (MFR subset (1) or MFR positive); c) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step a), and calculate their average fractional ranks (MFR subset (2) or MFR negative), d) The deR score is calculated by subtracting MFR subset (2) from MFR subset (1), It can be calculated according to a method that includes [a specific method].

[0228] The rank of a gene within a set is determined by assigning rank 1 to the gene with the lowest level of gene transcript, rank 2 to the gene with the next highest level of gene transcript, and so on, until the gene with the highest level of gene transcript is given the highest rank. Genes with equal levels of gene transcript expression are given an average rank. For example, two genes with identical gene transcripts and an expression level of 0 are given a rank of 1.5.

[0229] If a set of gene transcripts (B) is used, mathematical normalization calculates the (S) score. The (S) score is calculated in the following steps: a) Multiply each level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, b) The product Pgene obtained in step a) i (ΣPgene i (S) score: (ΣPgene) i To obtain (a constant), It may be calculated according to a method that includes, The coefficients and constants used in steps a) and b) are obtained in advance by stepwise multiple linear regression analysis that correlates (i) the level of the gene transcript previously obtained in the first biological sample with (ii) the level of the gene transcript of the TEAD-500 signature obtained in the second biological sample, where the TEAD-500 signature includes gene transcripts of a set of genes consisting of any 220 to 249 of the gene subset (1) disclosed in Table 2 below, and any 210 to 233 of the genes of gene subset (2).

[0230] In some embodiments, the correlation performed using stepwise multiple linear regression analysis in the methods and uses of the present disclosure may be between (i) the expression level of a gene transcript and (ii) the TEAD score obtained from the expression level of a gene transcript of the TEAD-500 signature.

[0231] The coefficients and constants are specific to a given set of gene transcripts (B), a given cancer, a given method used to determine the levels of the gene transcripts, and therefore a given mode used to determine / quantify the levels. Thus, for a given cancer, the coefficients and constants must be calculated for each embodiment of the set of gene transcripts (B), for example, to include any combination of 1 to 8 gene transcripts from the set of gene transcripts (B). For each cancer type, the coefficients and constants must be calculated for the set of gene transcripts (B).

[0232] The calculated coefficients and constants depend on the number of gene transcripts used in the set of gene transcripts (B) and the method used to obtain the levels of the gene transcripts, and therefore the mode used to determine / quantify the levels of the transcripts. The method used to obtain the levels of gene transcripts in the set of genes (B) does not need to be the same as the method used to measure the levels of gene transcripts in the TEAD-500 signature used for correlation.

[0233] Table 1 below shows examples of coefficients and constants that can be used for a set of gene transcripts (B) including mesothelioma and eight gene transcripts whose levels are determined by FKPM:

[0234] [Table 1]

[0235] The first and second biological samples can be obtained from subjects, cultured cells, or animal models of TEAD cancer.

[0236] The first and second biological samples are representative of the same TEAD-active cancer, the same cell culture, or the same TEAD-active cancer animal model. The first and second biological samples are representative of the cancer or the TEAD-active cancer cell culture or animal model for which use according to this disclosure is considered. Cells suitable for cell culture according to this disclosure may be TEAD-active cells.

[0237] The first sample may be a bodily fluid sample or a fraction thereof. The second sample may be a cancer cell sample.

[0238] The first and second biological samples may be taken from the same subject.

[0239] In some embodiments, the subjects from which the first and second biological samples used to calculate the coefficients and constants by stepwise multiple linear regression analysis are taken may be the same as or different from the subjects from which the (S) score is calculated.

[0240] If the subjects from which the coefficients and constants are calculated differ, those subjects may be a reference subject or a group of reference subjects known to have TEAD-active cancer. Since the coefficients and constants are specific to a given cancer, the subjects from which the (S) score is calculated must have, or be presumed to have, the same type of cancer as the reference subject or group of reference subjects (i.e., subjects from which biological and cancer cell samples were previously taken).

[0241] There are two types of biological samples collected from a reference subject: (a) a first biological sample known or presumed to contain EVs, such as a body fluid sample or a part thereof, and (a) a second biological sample from cancer cells of cancer.

[0242] As described hereinafter, the level of the gene transcript of the TEAD-500 signature is obtained from a biological sample of cancer cells of cancer, and the level of the gene transcript of the gene set (B) is obtained from a biological sample known to contain EVs.

[0243] The TEAD-500 signature may include gene transcripts of a gene set including any one of 220 to 249 genes of gene subset (1) and any one of 210 to 233 genes of gene subset (2), as disclosed in Table 2 hereinafter.

[0244] Perform a stepwise multiple regression analysis to correlate (i) the level of the gene transcript of the gene set (B) and (ii) the level of the gene transcript of the TEAD-500 signature, and obtain the coefficients and constants for the cancer and the gene set (B) of the gene transcripts. In some embodiments, the correlation performed using stepwise multiple linear regression analysis can be performed between (i) the expression level of the gene transcript and (ii) the TEAD score obtained from the expression level of the gene transcript of the TEAD-500 signature.

[0245] In some embodiments, the subject from which the first and second biological samples are collected for calculating the coefficients and constants by stepwise multiple linear regression analysis is the same subject having the calculated (S) score. In such a situation, the coefficients and constants can be calculated as described above. The score calculated using the (S) coefficients and constants can be calculated for different biological samples collected from the subject, for example, at different subsequent times. Such an (S) score can be used to track the progression or regression of TEAD-active cancer or the response of TEAD-active cancer to TEAD inhibitor treatment.

[0246] In some embodiments, the first biological sample from which gene transcript levels are obtained may be a body fluid sample or a portion thereof, as described above. The second biological sample may be a cancer cell sample.

[0247] In some embodiments, the first and second biological samples can be obtained from cultured cells.

[0248] The first biological sample derived from cultured cells may be the cell supernatant.

[0249] The second biological sample derived from cultured cells may be cultured cells themselves.

[0250] In some embodiments, the cell cultures from which the first and second biological samples are taken, i.e., the previously taken biological samples used to calculate the coefficients and constants by stepwise multiple linear regression analysis, may be the same as or different from the cell cultures from which the (S) score is calculated.

[0251] Cells suitable for cell culture according to this disclosure may be TEAD-active cells.

[0252] In some embodiments, the first and second biological samples can be obtained from an animal model of TEAD-activated cancer.

[0253] The first biological sample may be a fluid sample or a part thereof, as described above.

[0254] The second biological sample could be a cancer cell sample.

[0255] In some embodiments, the first and second biological samples, i.e., the previously collected biological samples, used to calculate coefficients and constants by stepwise multiple linear regression analysis, are animal models of TEAD-active cancer from which the biological samples are taken, and may be the same as or different from the animals from which the (S) score is calculated.

[0256] In some embodiments, the deR or (S) score is calculated directly.

[0257] The calculated deR or (S) score may be used to characterize the TEAD activity status of the target cancer or biological sample, or to measure the TEAD activity of a biological sample, for example, the target.

[0258] In some embodiments, the calculated deR or (S) score is compared to a baseline value, for example, a baseline deR or (S) score.

[0259] The reference deR or (S) score can be obtained from the target group or the reference target group, from reference cell cultures, or from reference TEAD-active animal models.

[0260] Comparison of the calculated deR or (S) score with a baseline deR or (S) score may be used to predict the progression or regression of a target TEAD-active cancer, or to predict the response of a target TEAD-active cancer to TEAD inhibitor therapy, or to characterize the TEAD activity status of a target cancer or biological sample requiring such treatment, or to measure the TEAD activity of a biological sample, or to screen candidate TEAD inhibitor compounds.

[0261] In some embodiments, a calculated deR or (S) score lower or higher than a baseline deR or (S) score 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 TEAD-active cancer is progressing or regressing, or that the candidate TEAD inhibitor is effective or ineffective.

[0262] In some embodiments, a calculated deR or (S) score lower than a baseline deR or (S) score may indicate that the cancer is TEAD-inactive, or that the cancer is responsive to TEAD inhibitor therapy, or that TEAD inhibitor therapy is effective, or that the cancer is regressing TEAD-active, or that the candidate TEAD inhibitor compound is effective.

[0263] In some embodiments, a calculated deR or (S) score higher than a baseline deR or (S) score may indicate that the cancer is TEAD active, or that the cancer is not responding to TEAD inhibitor treatment, or that TEAD inhibitor treatment is ineffective, or that the cancer is progressively TEAD active, or that the candidate TEAD inhibitor compound is ineffective.

[0264] In some embodiments, cancer may be characterized as TEAD active if the (S) score is greater than a baseline of approximately 0.055, and cancer may be characterized as TEAD inactive if the (S) score is less than or equal to a baseline of approximately 0.055.

[0265] In some embodiments, if the (S) score may be greater than a baseline of approximately 0.055, the cancer may be predicted to be responsive to TEAD inhibitor treatment, and if the (S) score may be less than or equal to a baseline of approximately 0.055, the cancer may be characterized as unresponsive to TEAD inhibitor treatment.

[0266] In some embodiments, if the (S) score can be greater than a reference value of approximately 0.055, TEAD inhibitor therapy may be observed to be ineffective against TEAD-active cancer, and if the (S) score can be less than or equal to a reference value of approximately 0.055, TEAD inhibitor therapy may be observed to be effective against TEAD-active cancer.

[0267] In some embodiments, if the (S) score may be greater than a baseline of approximately 0.055, TEAD-activated cancer may be predicted or detected as progressing, and if the (S) score may be less than or equal to a baseline of approximately 0.055, TEAD-activated cancer may be predicted or detected as regressing.

[0268] In some embodiments, if the (S) score can be greater than a baseline of approximately 0.055, the candidate TEAD inhibitor compound may be ineffective in inhibiting TEAD activity, and if the (S) score can be less than or equal to the baseline of approximately 0.055, the candidate TEAD inhibitor compound may be effective in inhibiting TEAD activity.

[0269] In some embodiments, the reference value may be a first deR or (S) score, and the deR or (S) score compared to the reference value may be a second deR or (S) score measured subsequently to the first deR or (S) score.

[0270] The first and second deR or (S) scores may be calculated from biological samples taken from the same subject or the same cell culture.

[0271] Comparison of a first deR or (S) score with a second deR or (S) score, or further comparison with subsequent deR or (S) scores, may be used to monitor the progression or regression of TEAD-active cancer in a subject, to monitor the response of a subject's TEAD-active cancer to TEAD inhibitor treatment, or to screen candidate TEAD inhibitor compounds.

[0272] In some embodiments, a second deR or (S) score lower than a first deR or (S) score may indicate TEAD activity inhibition.

[0273] In some embodiments, a first deR or (S) score may be measured in a biological sample obtained from a subject known or presumed to have TEAD-active cancer, and a second deR or (S) score may be measured in a second biological sample obtained from the subject following the first biological sample, and a second deR or (S) score lower than the first deR or (S) score may indicate regression of the cancer in the subject.

[0274] In some embodiments, a first deR or (S) score may be measured in a biological sample obtained from a subject known or presumed to have TEAD-active cancer, and a second deR or (S) score may be measured in a second biological sample obtained from the subject following the first biological sample, and a second deR or (S) score higher than the first deR or (S) score may indicate cancer progression in the subject.

[0275] In some embodiments, a first deR or (S) score may be measured in a first biological sample obtained from a subject known to or presumed to have TEAD-active cancer before administration of TEAD inhibitor therapy, and a second deR or (S) score may be measured in a second biological sample obtained from the subject after administration of the TEAD inhibitor therapy, and a second deR or (S) score lower than the first deR or (S) score may indicate effective TEAD inhibitor therapy in the subject.

[0276] In some embodiments, a first deR or (S) score may be measured in a first biological sample obtained from a subject known to or presumed to have TEAD-active cancer prior to administration of TEAD inhibitor therapy, and a second deR or (S) score may be measured in a second biological sample obtained from the subject after administration of the TEAD inhibitor therapy, and a second deR or (S) score higher than the first deR or (S) score may indicate ineffective TEAD inhibitor therapy in the subject.

[0277] In some embodiments, a first deR or (S) score can be measured in a first biological sample obtained from an animal model of TEAD-active cancer or the supernatant of a cell culture, the first biological sample being obtained before contacting the cell culture with the candidate TEAD inhibitor compound or before administering the animal model of TEAD-active cancer with the candidate TEAD inhibitor compound; a second deR or (S) score can be measured in a second biological sample obtained from an 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 candidate TEAD inhibitor or after administering the animal model of TEAD-active cancer with the candidate TEAD inhibitor; a second deR or (S) score lower than the first deR or (S) score may indicate an effective candidate TEAD inhibitor.

[0278] In some embodiments, a first deR or (S) score can be measured in a first biological sample obtained from an animal model of TEAD-active cancer or the supernatant of a cell culture, the first biological sample being obtained before contacting the cell culture with the candidate TEAD inhibitor compound or before administering the animal model of TEAD-active cancer with the candidate TEAD inhibitor compound; a second deR or (S) score can be measured in a second biological sample obtained from an 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 candidate TEAD inhibitor or after administering the animal model of TEAD-active cancer with the candidate TEAD inhibitor; a second deR or (S) score higher than the first deR or (S) score may indicate an ineffective candidate TEAD inhibitor compound.

[0279] Cells suitable for cell culture according to this disclosure may be TEAD-active cells.

[0280] method In some embodiments, the present disclosure relates to a method for characterizing or measuring the TEAD activity status of cancer in subjects requiring characterization or measurement of the TEAD activity status of cancer, wherein the method includes the use of a set of gene transcripts from a set of genes (A), and the method includes at least the following steps: a) Obtaining the level of each gene transcript in the set of genes from the biological sample obtained from the subject, b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The deR score is calculated by subtracting MFR subset (2) from MFR subset (1), Includes.

[0281] In some embodiments, the present disclosure relates to a method for characterizing or measuring the TEAD activity status of cancer in subjects requiring characterization or measurement of the TEAD activity status of cancer, the method comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) Obtaining the level of each gene transcript in the set of genes from the biological sample obtained from the subject, b) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene) i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, c) The product Pgene obtained in step b) i (ΣPgene i (S) score: (ΣPgene) i To obtain (a constant), Includes, The coefficients and constants used in steps b) and c) may be obtained in advance by stepwise multiple linear regression analysis that correlates (i) the level of gene transcripts obtained in advance from a first biological sample obtained from a subject with cancer with (ii) the level of gene transcripts of the TEAD-500 signature obtained from a second biological sample obtained from the subject, the second biological sample being derived from cancer cells from the subject, and the TEAD-500 signature containing gene transcripts of a set of genes including any of 220 to 249 of the gene subset (1) disclosed in Table 2 and any of 210 to 233 of the genes of gene subset (2).

[0282] In some embodiments, the correlation performed using stepwise multiple linear regression analysis may be between (i) the expression level of the gene transcript and (ii) the TEAD score obtained from the expression level of the gene transcript of the TEAD-500 signature.

[0283] As shown above, the subjects from which the first and second biological samples are taken may be the same as, or different from, the subjects from which the biological samples are taken to calculate the (S) score. The first and second biological samples represent the cancers covered by the method according to this disclosure.

[0284] In some embodiments, if the deR or (S) score is greater than a reference value, for example, the (S) score may be about 0.055, the cancer may be characterized as TEAD active, and if the deR or (S) score is less than or equal to a reference value, for example, the (S) score may be about 0.055, the cancer may be characterized as TEAD inactive.

[0285] In some embodiments, the present disclosure relates to a method for monitoring the progression or regression of cancer in subjects suspected or known to have TEAD-activated cancer, the method comprising the use of a set of gene transcripts from a set of genes (A), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the gene set from a first biological sample obtained from the subject at a first time point, b) For each gene transcript of the set of genes, the level of each gene transcript obtained in step a) is converted into a fractional rank by dividing the rank of the gene by the number of genes from the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The first deR score is calculated by subtracting MFR subset (2) from MFR subset (1), f) Obtaining the level of each gene transcript of the gene set from a second biological sample obtained from the subject at a second time point following the first time point, g) For each gene transcript in the set of genes, the level of each gene transcript obtained in step f) 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 in the set of genes, h) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (1) or MFR positive), i) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The second deR score is calculated by subtracting MFR subset (2) from MFR subset (1), k) Comparing 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. Includes.

[0286] In some embodiments, the second deR score may be greater than the first deR score, in which case the cancer may be observed to be progressing, while the second deR score may be lower than the first deR score, in which case the cancer may be observed to be regressing.

[0287] In some embodiments, the present disclosure relates to a method for monitoring the progression or regression of cancer in subjects suspected or known to have TEAD-activated cancer, the method comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the gene set from a first biological sample obtained from the subject at a first time point, b) Multiply each level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, c) The product Pgene obtained in step b) i (ΣPgene i The first (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), d) Obtaining the level of each gene transcript of the gene set from a second biological sample obtained from the subject at a second time point following the first time point, e) Multiply each level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene i Here genei (referring to the genes listed in the aforementioned set of genes) and f) The product Pgene obtained in step e) i (ΣPgene i The second (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), g) Comparing 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. Includes, The coefficients and constants used in steps b), c), e), and f) are obtained in advance by stepwise multiple linear regression analysis correlating (i) the levels of potentially pre-obtained gene transcripts in a third biological sample obtained from a subject with cancer with (ii) the levels of gene transcripts of the TEAD-500 signature obtained in a fourth biological sample derived from the subject, the fourth biological sample being derived from cancer cells from the subject, and the TEAD-500 signature containing gene transcripts of a set of genes including any of 220 to 249 of the gene subset (1) and any of 210 to 233 of the genes in gene subset (2) disclosed in Table 2.

[0288] In some embodiments, the correlation performed using stepwise multiple linear regression analysis may be between (i) the expression level of the gene transcript and (ii) the TEAD score obtained from the expression level of the gene transcript of the TEAD-500 signature.

[0289] The subjects from which the third and fourth biological samples were collected may be the same as, or different from, the subjects from which the first and second biological samples were collected for the calculation of the (S) score. The third and fourth biological samples represent the cancers covered by the method according to this disclosure.

[0290] In some embodiments, the second (S) score may be greater than the first (S) score, in which case the cancer may be observed to be progressing, while the second (S) score may be lower than the first (S) score, in which case the cancer may be observed to be regressing.

[0291] In some embodiments, the present disclosure relates to a method for diagnosing and treating a subject in need thereof, comprising the steps of characterizing the TEAD activity status of the cancer in the subject, and, if the cancer is characterized as TEAD activity, administering TEAD inhibitor therapy to the subject. Here, A method for characterizing the TEAD activity status of cancer, comprising the use of a set of gene transcripts from a set of genes (A), wherein the method comprises at least the following steps: a) Obtaining the level of each gene transcript in the set of genes from the biological sample obtained from the subject, b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The deR score is calculated by subtracting MFR subset (2) from MFR subset (1), f) A step of comparing 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-activated cancer, g) If the cancer is characterized as having TEAD activity, administer TEAD inhibitor therapy to the subject, Includes.

[0292] In some embodiments, the present disclosure relates to a method for diagnosing and treating a subject in need thereof, comprising the steps of characterizing the TEAD activity status of the subject's cancer, and, if the cancer is characterized as TEAD activity, administering TEAD inhibitor therapy to the subject. Here, A method for characterizing the TEAD activity status of cancer, comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) Obtaining the level of each gene transcript in the set of genes from the biological sample obtained from the subject, b) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene) i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, c) The product Pgene obtained in step b) i (ΣPgene i (S) score: (ΣPgene) i To obtain (a constant), d) A step of comparing 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 may indicate TEAD-active cancer, e) If the cancer is characterized as having TEAD activity, administer TEAD inhibitor therapy to the subject. Includes, The coefficients and constants used in steps b) and c) may be obtained in advance by stepwise multiple linear regression analysis that correlates (i) the level of gene transcripts obtained in advance from a first biological sample obtained from a subject with cancer with (ii) the level of gene transcripts of the TEAD-500 signature obtained from a second biological sample obtained from the subject, the second biological sample being derived from cancer cells from the subject, and the TEAD-500 signature containing gene transcripts of a set of genes including any of 220 to 249 of the gene subset (1) disclosed in Table 2 and any of 210 to 233 of the genes of gene subset (2).

[0293] In some embodiments, the correlation performed using stepwise multiple linear regression analysis may be between (i) the expression level of the gene transcript and (ii) the TEAD score obtained from the expression level of the gene transcript of the TEAD-500 signature.

[0294] The subjects from which the first and second biological samples are taken may be the same as, or different from, the subjects from which the biological samples are taken to calculate the (S) score. The first and second biological samples represent the cancers covered by the method according to this disclosure.

[0295] In some embodiments, the deR or (S) score may be greater than a reference value, for example, about 0.055 for the (S) score, the cancer may be characterized as having TEAD activity, and TEAD inhibitor therapy is administered to the subject.

[0296] In some embodiments, the deR or (S) score may be less than or equal to a reference value, for example, about 0.055 for the (S) score, the cancer may be characterized as TEAD-inactive, and TEAD inhibitor therapy is not administered to the subject.

[0297] In some embodiments, the present disclosure relates to a method for predicting the response to TEAD inhibitor therapy for subjects who are presumed to have TEAD-active cancer or who are known to have TEAD-active cancer. This method involves using a set of gene transcripts from a set of genes (A), and the method comprises at least the following steps: a) Obtaining the level of each gene transcript in the set of genes from the biological sample obtained from the subject, b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The deR score is calculated by subtracting MFR subset (2) from MFR subset (1), d) Comparing 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 can predict whether a subject is responsive or unresponsive to TEAD inhibitor treatment. Includes.

[0298] In some embodiments, the present disclosure relates to a method for predicting the response to TEAD inhibitor therapy for subjects who are presumed to have TEAD-active cancer or who are known to have TEAD-active cancer. This method involves using a set of gene transcripts from a set of genes (B), and the method comprises at least the following steps: a) Obtaining the level of each gene transcript in the set of genes from the biological sample obtained from the subject, b) Multiply each level obtained from each gene transcript of the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene) i Here gene i (referring to the genes listed in the aforementioned set of genes) and c) The product Pgene obtained in step b) i (ΣPgene i (S) score: (ΣPgene) i To obtain (a constant), d) Comparing the calculated (S) score obtained in step e) with a baseline (S) score, wherein the observed deviation between the calculated (S) score and the baseline (S) score can predict whether a subject is responsive or unresponsive to TEAD inhibitor treatment. Includes, The coefficients and constants used in steps b) and c) may be obtained in advance by stepwise multiple linear regression analysis that correlates (i) the level of gene transcripts obtained in advance from a first biological sample obtained from a subject with cancer with (ii) the level of gene transcripts of the TEAD-500 signature obtained from a second biological sample obtained from the subject, the second biological sample being derived from cancer cells from the subject, and the TEAD-500 signature containing gene transcripts of a set of genes including any of 220 to 249 of the gene subset (1) disclosed in Table 2 and any of 210 to 233 of the genes of gene subset (2).

[0299] The subjects from which the first and second biological samples are taken may be the same as, or different from, the subjects from which the biological samples are taken to calculate the (S) score. The first and second biological samples represent the cancers covered by the method according to this disclosure.

[0300] In some embodiments, the deR or (S) score may be greater than the reference value, for example, the (S) score may be about 0.055, in which case the cancer may be predicted to be responsive to TEAD inhibitor treatment, while if the deR or (S) score is below the reference value, for example, the (S) score may be about 0.055, the cancer may be characterized as unresponsive to TEAD inhibitor treatment.

[0301] In some embodiments, the present disclosure relates to a method for monitoring the response of a subject suspected of having or known to have TEAD-active cancer to treatment with a TEAD inhibitor, the method comprising the use of a set of gene transcripts from a set of genes (A), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the set of genes from a first biological sample collected from the subject before administration of TEAD inhibitor therapy, b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The first deR score is calculated by subtracting MFR subset (2) from MFR subset (1), f) Obtaining the level of each gene transcript of the gene set from a second biological sample collected from the subject before administration of TEAD inhibitor therapy, g) For each gene transcript in the set of genes, the level of each gene transcript obtained in step f) 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 in the set of genes, h) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (1) or MFR positive), i) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The second deR score is calculated by subtracting MFR subset (2) from MFR subset (1), k) Comparing the first deR score with the second deR score, wherein the observed deviation between the first and second deR scores may indicate effective or ineffective TEAD inhibitor treatment. Includes.

[0302] In some embodiments, if the second deR score is greater than the first deR score, TEAD inhibitor therapy may be observed to be ineffective against cancer, and if the second deR score is lower than the first deR score, TEAD inhibitor therapy may be observed to be effective against cancer.

[0303] In some embodiments, the present disclosure relates to a method for monitoring the response of a subject suspected of having or known to have TEAD-active cancer to treatment with a TEAD inhibitor, the method comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the set of genes from a first biological sample collected from the subject before administration of TEAD inhibitor therapy, b) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene) i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, c) The product Pgene obtained in step b) i (ΣPgene i The first (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), d) Obtaining the level of each gene transcript of the set of genes from a second biological sample collected from the subject before administration of TEAD inhibitor therapy, e) Multiply each level obtained from each gene transcript of the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene) i Here gene i (referring to the genes listed in the aforementioned set of genes) and f) The product Pgene obtained in step e) i (ΣPgene i The second (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), g) Comparing the first (S) score with the second (S) score, the observed deviation between the first and second (S) scores may indicate effective or ineffective TEAD inhibitor treatment. Includes, The coefficients and constants used in steps b), c), e), and f) are obtained in advance by stepwise multiple linear regression analysis that correlates (i) the level of gene transcripts obtained in a third biological sample obtained from a subject with cancer with (ii) the level of gene transcripts of the TEAD-500 signature obtained from a fourth biological sample obtained from the subject, the fourth biological sample being derived from cancer cells from the subject, and the TEAD-500 signature may include gene transcripts of a set of genes that include any of 220 to 249 of the genes in gene subset (1) and any of 210 to 233 of the genes in gene subset (2) disclosed in Table 2.

[0304] In some embodiments, the correlation performed using stepwise multiple linear regression analysis may be between (i) the expression level of the gene transcript and (ii) the TEAD score obtained from the expression level of the gene transcript of the TEAD-500 signature.

[0305] The subjects from which the third and fourth biological samples were taken may be the same as, or different from, the subjects from which the biological samples were taken to calculate the (S) score. The third and fourth biological samples represent the cancers covered by the method according to this disclosure.

[0306] In some embodiments, if the second (S) score is greater than the first (S) score, TEAD inhibitor therapy may be observed to be ineffective against cancer, and if the second (S) score is lower than the first (S) score, TEAD inhibitor therapy may be observed to be effective against cancer.

[0307] In the method disclosed above, the gene transcripts of set (A) or (B) can be obtained from a biological sample as shown above.

[0308] In the method disclosed above, the gene transcripts of set (A) or (B) can be obtained from a bodily fluid sample as shown above.

[0309] In the method disclosed above, the gene transcripts of set (A) or (B) can be obtained from the EV sample as shown above.

[0310] In some embodiments, the present disclosure relates to a method for screening candidate TEAD inhibitors that inhibit TEAD activity, the method comprising the use of a set of gene transcripts from a set of genes (A), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the gene set from a first biological sample obtained from the supernatant of the cell culture, which was obtained before contacting the cell culture with the candidate TEAD inhibitor compound, b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The first deR score is calculated by subtracting MFR subset (2) from MFR subset (1), f) Obtaining the level of each gene transcript in the set of genes in the second biological sample obtained from the supernatant of the cell culture after contacting the cell culture with the candidate TEAD inhibitor compound, g) For each gene transcript in the set of genes, the level of each gene transcript obtained in step f) 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 in the set of genes, h) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (1) or MFR positive), i) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The second deR score is calculated by subtracting MFR subset (2) from MFR subset (1), k) Comparing the first deR score with the second deR score, the observed deviation between the first and second deR scores may indicate a candidate compound that is effective or ineffective in inhibiting TEAD activity. Includes.

[0311] In some embodiments, the second deR score may be greater than the first deR score, in which case the candidate TEAD inhibitor compound may be observed to have no effect in inhibiting TEAD activity. Conversely, if the second deR score is smaller than the first deR score, the candidate TEAD inhibitor compound may be observed to have an effect in inhibiting TEAD activity.

[0312] In some embodiments, the present disclosure relates to a method for screening candidate TEAD inhibitors that inhibit TEAD activity, the method comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the gene set from a first biological sample obtained from the supernatant of the cell culture, which was obtained before contacting the cell culture with the candidate TEAD inhibitor compound, b) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene) i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, c) The product Pgene obtained in step b) i (ΣPgene i ) is summed, and a constant is added to obtain the first (S) score: (ΣPgene i + constant), and d) From a second biological sample obtained from the supernatant of the cell culture, in the second biological sample obtained after contacting the cell culture with the TEAD inhibitor candidate compound, obtaining the level of each gene transcript of the set of genes e) Multiply each obtained level of each gene transcript of the set of genes by a coefficient associated with each gene to obtain, for each gene, a product (Pgene i ), where gene i refers to the genes listed in the set of genes, and f) The products Pgene i (ΣPgene i ) are summed, and a constant is added to obtain the second (S) score: (ΣPgene i + constant), and g) Comparing the first (S) score with the second (S) score, wherein the deviation observed between the first and the second (S) scores can indicate an effective or ineffective TEAD inhibitor treatment and The coefficients and constants used in steps b), c), e) and f) can be obtained in advance by stepwise multiple linear regression analysis that correlates (i) the levels of gene transcripts previously obtained in a third biological sample obtained from the supernatant of the cell culture and (ii) the levels of gene transcripts of the TEAD-500 signature obtained in a fourth biological sample obtained from the cell culture. The fourth biological sample is derived from cancer cells from a subject, and the TEAD-500 signature includes gene transcripts of a set of genes including any 220 to 249 of the genes of gene subset (1) disclosed in Table 2 and any 210 to 233 of the genes of gene subset (2).

[0313] In some embodiments, the correlation performed using stepwise multiple linear regression analysis may be between (i) the expression level of the gene transcript and (ii) the TEAD score obtained from the expression level of the gene transcript of the TEAD-500 signature.

[0314] The cell cultures from which the third and fourth biological samples are taken may be the same as, or different from, the cell cultures from which the biological samples are taken for the calculation of the (S) score.

[0315] Cells suitable for cell culture according to this disclosure may be TEAD-active cells.

[0316] In some embodiments, the second (S) score may be greater than the first (S) score, in which case the candidate TEAD inhibitor compound may be observed to have no effect in inhibiting TEAD activity. Conversely, if the second (S) score is smaller than the first (S) score, the candidate TEAD inhibitor compound may be observed to have an effect in inhibiting TEAD activity.

[0317] In some embodiments, the biological sample may be as described above.

[0318] In some embodiments, a set of gene transcripts may be obtained from extracellular vesicles.

[0319] The contact between the cell culture and the candidate TEAD inhibitor compound is carried out under conditions suitable for the TEAD pathway to be inhibited by the compound.

[0320] In some embodiments, the present disclosure relates to a method for screening candidate TEAD inhibitors that inhibit TEAD activity, the method comprising the use of a set of gene transcripts from a set of genes (A), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the set of genes from a first biological sample obtained from an animal model of TEAD-active cancer, wherein the first biological sample is obtained before administering the candidate TEAD inhibitor compound to the animal model of TEAD-active cancer. b) For each gene transcript in the set of genes, 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 in the set of genes, c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR subset (1) or MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step c), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The first deR score is calculated by subtracting MFR subset (2) from MFR subset (1), f) Obtaining the level of each gene transcript of the set of genes from a second biological sample obtained from an animal model of TEAD-active cancer, wherein the second biological sample is obtained after administering the candidate TEAD inhibitor compound to the animal model of TEAD-active cancer. g) For each gene transcript in the set of genes, the level of each gene transcript obtained in step f) 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 in the set of genes, h) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (1) or MFR positive), i) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step g), and calculate their average fractional ranks (MFR subset (2) or MFR negative), e) The second deR score is calculated by subtracting MFR subset (2) from MFR subset (1), k) Comparing the first deR score with the second deR score, the observed deviation between the first and second deR scores may indicate a candidate compound that is effective or ineffective in inhibiting TEAD activity. Includes.

[0321] In some embodiments, the second deR score may be greater than the first deR score, in which case the candidate TEAD inhibitor compound may be observed to have no effect in inhibiting TEAD activity. Conversely, if the second deR score is smaller than the first deR score, the candidate TEAD inhibitor compound may be observed to have an effect in inhibiting TEAD activity.

[0322] In some embodiments, the present disclosure relates to a method for screening candidate TEAD inhibitors that inhibit TEAD activity, the method comprising the use of a set of gene transcripts from a set of genes (B), the method comprising at least the following steps: a) Obtaining the level of each gene transcript of the set of genes from a first biological sample obtained from an animal model of TEAD-active cancer, wherein the first biological sample is obtained before administering the candidate TEAD inhibitor compound to the animal model of TEAD-active cancer. b) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene) i ) to obtain, and here gene i This refers to the genes listed in the aforementioned set of genes, c) The product Pgene obtained in step b) i (ΣPgene i The first (S) score is calculated by summing the values ​​of (ΣPgene) and adding a constant: (ΣPgene i To obtain (a constant), d) Obtaining the level of each gene transcript of the set of genes from a second biological sample obtained from an animal model of TEAD-active cancer, wherein the second biological sample is obtained after administering the TEAD inhibitor candidate compound to the animal model of the TEAD-active cancer; e) Multiplying each obtained level of each gene transcript of the set of genes by a coefficient associated with each gene to obtain, for each gene, a product (Pgene i ), where gene i refers to the genes listed in the set of genes; f) Summing the products Pgene i (ΣPgene i ), adding a constant, and obtaining a second (S) score: (ΣPgene i + constant); g) Comparing the first (S) score with the second (S) score, wherein the deviation observed between the first and second (S) scores can indicate an effective or ineffective TEAD inhibitor treatment and The coefficients and constants used in steps b), c), e) and f) may be obtained in advance by stepwise multiple linear regression analysis to correlate (i) the levels of the gene transcripts previously obtained in a third biological sample obtained from an animal model of TEAD-active cancer and (ii) the levels of the gene transcripts of the TEAD-500 signature obtained in a fourth biological sample obtained from the animal model, wherein the fourth biological sample is derived from cancer cells from the animal model and the TEAD-500 signature comprises gene transcripts of a set of genes comprising any 22 of the genes of gene subset (1) disclosed in Table 2.0 to 249 and any 210 to 233 of the genes of gene subset (2).

[0323] In some embodiments, the correlation operated by stepwise multiple linear regression analysis can be performed between (i) the levels of the gene transcripts and (ii) the TEAD scores obtained from the levels of the gene transcripts of the TEAD-500 signature.

[0324] The animal models from which the third and fourth biological samples are collected may be the same as or different from the animal models from which the biological samples are collected for calculating the (S) score.

[0325] The third and fourth biological samples are representative of the cancers targeted by the methods described herein.

[0326] In some embodiments, the second (S) score may be greater than the first (S) score, in which case the candidate TEAD inhibitor compound may be observed to have no effect in inhibiting TEAD activity. Conversely, if the second (S) score is smaller than the first (S) score, the candidate TEAD inhibitor compound may be observed to have an effect in inhibiting TEAD activity.

[0327] In some embodiments, the first, second, and third biological samples may be as described above.

[0328] In some embodiments, a set of gene transcripts may be obtained from extracellular vesicles.

[0329] In some embodiments, the correlation manipulated by stepwise multiple linear regression analysis may be between (i) the level of the gene transcript and (ii) the TEAD score obtained at the level of the gene transcript of the TEAD-500 signature.

[0330] cancer In some embodiments, the cancers include mesothelioma, adrenocortical carcinoma, urothelial carcinoma of the bladder, invasive breast carcinoma, squamous cell carcinoma of the cervix and intracervical adenocarcinoma, cholangiocarcinoma, colorectal cancer, consensus molecule subtype 1 of colorectal cancer, consensus molecule subtype 2 of colorectal cancer, consensus molecule subtype 3 of colorectal cancer, consensus molecule subtype 4 of colorectal cancer, colonic adenocarcinoma, and diffuse large cell type B lymphoid neoplasm. The following can be selected: cellular lymphoma, esophageal cancer, glioblastoma multiforme, squamous cell carcinoma of the head and neck, renal pellucid cell carcinoma, renal papillary cell carcinoma, low-grade brain glioma, hepatocellular carcinoma of the liver, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid cancer, thymoma, endometrial cancer, and uterine carcinosarcoma.

[0331] In some embodiments, the cancer may be mesothelioma.

[0332] In some embodiments, the cancer may be colorectal cancer.

[0333] Kits and manufactured articles In some embodiments, kits or products comprising probes such as nucleic acids for detecting and quantifying any of the gene transcripts of the set of genes disclosed herein are provided herein.

[0334] In some embodiments, the present disclosure relates to a kit comprising a solid support containing a panel of nucleic acids for obtaining levels of gene transcripts of a set of genes disclosed herein.

[0335] The kits of this disclosure are suitable for use in the uses and methods disclosed herein.

[0336] In some embodiments, the kit or product may include one or more reagents for preparing a biological sample or fraction thereof for RNA sequencing analysis.

[0337] In some embodiments, the kit or product further comprises one or more reagents for determining the level of gene transcripts disclosed herein from a biological sample.

[0338] In some embodiments, the kit or manufactured product may include instructions for using the kit for any of the methods / uses described herein.

[0339] In some embodiments, a kit or product may include a container, a label on the container, and a composition contained within the container, the composition comprising one or more polynucleotides that hybridize to gene transcripts listed herein under stringent conditions, the label on the container indicating that the composition can be used to obtain levels of gene transcripts listed herein, and the kit may include instructions for using the polynucleotides to assess the presence of gene transcripts of this disclosure in a biological sample or fraction thereof and to quantify their levels.

[0340] In some embodiments, the kit or product is an oligonucleotide system and may include, for example, (1) an oligonucleotide that hybridizes to a gene transcript, such as a detectably labeled oligonucleotide, or (2) a primer pair useful for amplifying the gene transcript. In some embodiments, the kit or product may also include a buffer, preservative, or stabilizer. In some embodiments, the kit or product may further include components necessary for detecting the detectable label, such as an enzyme or substrate. In some embodiments, the kit or product may also include a control sample or a set of control samples that can be assayed and compared to the test sample. In some embodiments, each component of the kit or product may be sealed in an individual container, and all of the various containers may be placed in a single package along with instructions for interpreting the results of assays performed using the kit or product.

[0341] TEAD pathway inhibitors In some embodiments, the TEAD pathway inhibitor (or TEAD inhibitor) is administered to subjects in need. Subjects in need may be subjects identified using the methods and sets of gene transcripts described herein as having (or being known to have) TEAD-active cancer, or as likely to respond to (or presumed to have) TEAD-active cancer, or as likely to respond to a TEAD pathway inhibitor.

[0342] Any TEAD pathway inhibitor known in the art may be administered.

[0343] As used herein, "TEAD pathway inhibitor" may be a small or large molecule that inhibits the HIPPO-YAP / WWTR1 / TEAD pathway.

[0344] Examples of appropriate TEAD inhibitors include:

[0345] Statins: Statins have been reported to inhibit TEAD activity by reducing YAP expression.

[0346] Epigallocatechin gallate (EGCG): EGCG has been shown to inhibit TEAD activity by disrupting the interaction between TEAD and its co-activator YAP.

[0347] Cucurbitacin B: Cucurbitacin B has been shown to inhibit TEAD activity by disrupting the interaction between TEAD and its co-activator YAP.

[0348] CA3:CA3 is a synthetic compound that has been shown to inhibit TEAD activity by disrupting the interaction between TEAD and its co-activator, YAP.

[0349] CA-170: CA-170 is a small molecule inhibitor of both PD-L1 and VISTA (a V-domain Ig inhibitor of T cell activation) and has been shown to have TEAD inhibitory activity. A Phase I clinical trial of CA-170 is underway in patients with progressive solid tumors.

[0350] YAPi: YAPi is a monoclonal antibody that targets the YAP protein, a co-activator of TEAD. Phase I clinical trials of YAPi are underway in patients with progressive solid tumors.

[0351] CYT-0851: CYT-0851 is a small molecule inhibitor of the bromodomain and extraembryonic domain (BET) proteins that has been shown to have TEAD inhibitory activity. Phase I clinical trials of CYT-0851 are underway in patients with progressive solid tumors.

[0352] CB-839: CB-839 is a small molecule glutaminase inhibitor that has been shown to inhibit TEAD activity in preclinical studies. Phase I / II clinical trials of CB-839 in combination with a PD-L1 inhibitor are currently underway in patients with advanced solid tumors.

[0353] Compounds suitable for this disclosure are those of formula (I) disclosed in International Publication No. 2021 / 204823. [ka] It may be a 1H-indolylacrylamide derivative, in which, n is an integer selected from 0 and 1. R1 is selected from single bonds and (C1-C4) alkenyl groups. R2 is selected from (C1-C4) alkyl groups or groups substituted with one or more fluorine atoms, (C1-C3) alkoxy groups or groups substituted with one or more fluorine atoms, phenyl groups or unsubstituted groups substituted with one or more R3 groups, (C4-C8) cycloalkyl groups or unsubstituted groups substituted with one or more R5 groups, (C4-C8) heterocyclyl groups or groups substituted with one or more R6 groups, and NR9R10 groups. R3 is selected from an unsubstituted (C1-C4) alkyl group, a cyclopropyl group, a halogen atom, an unsubstituted (C1-C3) alkoxy group, a pentafluorosulfanyl group, a nitrile group, a (C1-C3) trialkylsilyl group, a (C1-C3) alkylsulfonyl group, and an unsubstituted or trifluoromethyl phenyl group. R4 is selected from a hydrogen atom and (C1-C4) alkyl groups. R5 is selected from a fluorine atom and a trifluoromethyl group. R6 is either unsubstituted, or selected from one or more fluorine atoms, one or more phenyl groups substituted with CF3 groups, one or more fluorine atoms substituted with (C1-C4) alkyl groups, and fluorine atoms. R7 is selected from a hydrogen atom, a nitrile group, and a (C1-C4) alkyl group. R8 is selected from a hydrogen atom and an unsubstituted or di(C1-C4)alkyl group (C1-C4) alkyl group. R9 and R10 are the same or different, selected from unsubstituted or (C1-C3) alkyl groups substituted with one or more fluorine atoms. This includes compounds having the structure of formula (IVA) or pharmaceutically acceptable salts thereof.

[0354] A suitable 1H-indolylacrylamide derivative of formula (I) is N-(1-(3-(trifluoromethyl)benzyl-1H-indole-5-yl)acrylamide.

[0355] TEAD-500 Signature A transcription signature suitable for this disclosure ("TEAD signature" or "TEAD-500 signature") can be obtained by measuring the level of gene transcripts of a set of genes, including any of the 220 to 249 genes (positive effector genes) of gene subset (1) and any of the 210 to 233 genes (negative effector genes) of gene subset (2), as shown in Table 2.

[0356] [Table 2]

[0357] [Table 3]

[0358] [Table 4]

[0359] The level of the TEAD signature gene transcript can be measured as described above for the level of the gene transcript in the set of gene transcripts of this disclosure.

[0360] The levels of gene transcripts of the TEAD-Signature are used herein in a stepwise multiple linear regression analysis to correlate (i) the levels of gene transcripts of a set of gene transcripts from a set of genes (B) with the levels of gene transcripts of the TEAD-500 Signature.

[0361] A transcriptional signature ("TEAD signature") can be obtained by measuring the level of gene transcripts of a set of genes including any of the 430–482 genes listed above, or any of the 435–482, 440–482, 445–482, 450–482, 455–482, 460–482, 465–482, 470–482, 475–482, or any of the 480–482 genes listed above.

[0362] The transcription signature ("TEAD signature") can be obtained by measuring the level of gene transcripts of a set of genes that include any of the 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, or 482 genes listed above.

[0363] The TEAD score can be determined using the transcription signature provided in this disclosure ("TEAD-signature").

[0364] In some embodiments, the TEAD score may be used in a stepwise multiple linear regression analysis to correlate (i) the levels of gene transcripts in a set of gene transcripts (B) with a TEAD score calculated using (ii) the levels of gene transcripts of the TEAD-500 signature.

[0365] In some embodiments, the TEAD score is calculated in the following way: a) Measuring the level of each gene in the set of genes in a biological sample. b) For each gene in the set of genes, convert the gene expression level obtained in step a) into a fractional rank by dividing the rank of the gene by the number of genes in the set of genes. c) Separate the fractional ranks obtained for the genes of gene subset (1) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR positive), d) Separate the fractional ranks obtained for the genes of gene subset (2) from the fractional ranks obtained in step b), and calculate their average fractional ranks (MFR negative), e) The deR score is calculated as MFR positive minus MFR negative, and is calculated according to the formula.

[0366] The rank of a gene within a set is determined by assigning rank 1 to the gene with the lowest level of gene transcript, rank 2 to the gene with the next highest level of gene transcript, and so on, until the gene with the highest level of gene transcript is given the highest rank. Genes with equal levels of gene transcript expression are given an average rank. For example, two genes with identical gene transcripts and an expression level of 0 are given a rank of 1.5.

[0367] If the deR score is greater than approximately 0.055, the cancer is TEAD active; if the deR score is approximately 0.055 or less, the cancer is TEAD inactive. [Examples]

[0368] The following examples illustrate the most well-known embodiments of the present invention. However, it should be understood that these are merely illustrative or illustrative examples of the application of the principles of the present invention. Numerous variations and alternative compositions, methods, and systems can be envisioned by those skilled in the art without departing from the spirit and scope of the present invention. Thus, although the present invention has been specifically described above, the following examples provide further details in relation to what is currently considered to be the most practical and preferred embodiments of the present invention.

[0369] Example 1: Materials and Method Cell and culture conditions All cell lines were grown at 37°C under 5% CO2 conditions. NCI-H226 (H226) (#CRL-5826) and HCT116 (#CCL-247) were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA) and cultured according to the supplier's recommendations. SPC212 (No. 11120717) and Mero-14 (No. 09100101) were purchased from the European Collection of Authenticated Cell Cultures (ECACC, Public Health England, Salisbury, UK) and cultured according to the supplier's recommendations. Short tandem repeat assays were performed on all cell lines at MICROSYNTH AG (Balgach, Switzerland). Mycoplasma infection was ruled out by PCR using the VENOR® GEM kit (BIOVALLEY, Nanterre, France).

[0370] Cell processing: Cells were treated for 24 hours at four concentrations: 0.0 μM, 0.3 μM, 1.0 μM, and 3.0 μM, with and without the addition of a TEAD inhibitor (TEADi) (control cells).

[0371] EVs and cellular RNA were isolated and profiled using the RNA-seq method detailed below.

[0372] Extracellular vesicle (EV) preparation and RNA extraction Extracellular vesicles (EVs) were separated 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).

[0373] EV RNA was extracted using the EXORNEASY SERUM / PLASMA MAXI PLASMA KIT (QIAGEN).

[0374] Total cellular RNA was extracted and purified using MIRNEASY MINI KIT (QIAGEN, 217, 004). RNA concentration and purity were evaluated using an ND-1000 spectrophotometer (NANODROP, THERMO FISHER SCIENTIFIC, Wilmington, DE, USA). Quality metrics included RNA integrity number (RIN) and DV200 value (percentage of RNA fragments 200 nt or longer). 100 ng of total RNA per sample was used as input material for library preparation.

[0375] RNA sequencing RNA-seq libraries were prepared from mRNA using SMARTER® STRANDED TOTAL RNA-SEQ KIT V2-PICO INPUT MAMMALIAN from TAKARA BIO USA, INC. Cell line mRNA sequencing was performed using the KAPPA LIBRARY AMPLIFICATION KIT (ILLUMINA). The libraries were sequenced using ILLUMINA NOVASEQ 6000.

[0376] The following steps were performed to prepare the RNA-seq library.

[0377] Step 1: DNAse digestion • DNA removal step using the following method: Using DNAseI amplification grade INVITROGEN catalog number 18068-015 under the following conditions: Add 8 μl of RNA / EV sample, 1 μl of 10X DNAseI buffer, and 1 μl of DNAseI (Amp grade, 1 U / μl), and let stand at room temperature for 15 minutes. Add 1 μl of 25 mM EDTA solution and incubate at 65°C for 10 minutes to inactivate the DNAse. EDTA was removed using RNAESY MINELUTE (QIAGEN COLUMN).

[0378] Step 2: RNA sequencing sample preparation was performed using TAKARA BIO USA, INC. SMARTER® STRANDED TOTAL RNA-SEQ KIT V2-PICO INPUT MAMMALIAN #634413, following the vendor's recommendations and using the same specifications. A) Protocol: cDNA synthesis a. Using option 2 (no fragmentation): Start with highly degraded RNA (EV) and follow the steps recommended by TAKARA: B) Protocol: Addition of ILLUMINA ADAPTERS / INDEX C) Protocol: Purification of RNA-seq libraries using AMPURE BEADS D) Protocol: Ribosomal cDNA removal using ZAPR V2 and R-PROBES V2 E) Protocol: Final amplification of PCR2-RNA-seq library F) Protocol: Purification of the final RNA-seq library using AMPURE BEADS

[0379] TEAD Active Signature To estimate cellular TEAD activity, transcriptome signatures (TEAD-500 signatures) from whole-cell RNA were used (Calvet, L., Dos-Santos, O., Spankis, 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 as a difference from RNA-seq data of a single sample. deR=Rp-Rn

[0380] Rp is the mean partial rank of positive YAP1 effectors, and Rn is the mean fractional rank of negative effectors. The deR score is calculated using only the levels of the genes that make up the signature.

[0381] A novel signature consisting of 28 gene transcripts was identified from gene transcripts isolated from EVs and used to calculate the TEAD-activity score according to the method used for the "TEAD-500" signature, as detailed in 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 in PCT / EP2023 / 057332.

[0382] Stepwise multiple regression analysis was used to correlate the levels of all gene transcripts readily detectable in extravasation (EVs) (Log(FPKM+0.1) > 4 at baseline) with the TEAD-500 signature score measured in cells that obtained EVs. This analysis yielded a set of eight genes (each with a specific coefficient) and a constant coefficient.

[0383] This second set of signatures, comprising 1 to 8 gene transcripts, was identified from gene transcripts isolated from extracellular viable cells (EVs). The second set of signatures was used to predict the TEAD activity score of parental tumor cells. The TEAD activity score was calculated from 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. BMC Cancer 22, 639. Doi.org / 10.1186 / s12885-022-09686, or PCT / EP2023 / 057332).

[0384] For a second set of this signature, which includes a set of gene transcripts from a set of genes, the predicted TEAD activity score was calculated as follows: a) Multiply each obtained level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene) i ) is obtained here gene i This refers to the genes listed in the set of genes. b) The product Pgene obtained in step a) i (ΣPgene i (S) score: (ΣPgene) i (+ constant) is obtained. The coefficients and constants used in steps a) and b) are obtained in advance by stepwise multiple linear regression analysis, which correlates (i) the level of the EV gene transcript with (ii) the TEAD-500 signature score measured in the cells from which the EV was obtained.

[0385] The SPSS statistical program (IBM; Armonk, NY) was used for stepwise multiple linear regression analysis to select a set of genes expressed in extracellular genes and predict the TEAD-500 values ​​of parental cells. All other statistical work was performed similarly.

[0386] Example 2: Results The effects of TEAD inhibition on intracellular and extracellular RNA isolated from the supernatants of three responsive mesothelioma cell lines, NCI-H226, Mero-14, and SPC212, and one non-responsive colon cell line, HCT116, were compared. The response was determined by the effect of TEAD inhibitor (TEADi) IC50. 50 The assay was defined based on the CELL TITER GLO®-PROMEGA assay. 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. EVs and tumor RNA were isolated and profiled by RNA-seq.

[0387] Figure 1 shows the process from cell processing to EV isolation and analysis.

[0388] As shown in Figure 2, the non-responsive colon cell line HCT116 has lower endogenous TEAD activity than the responsive mesothelioma cell lines H226, Mero14, and SPC112. A significant decrease was observed in all cell lines upon treatment, but the effect was more pronounced in responsive cells.

[0389] As shown in Figure 3, approximately 50% of the genes in the TEAD-500 signature were detected in the RNA of each EV across cell lines. Positive effectors indicate genes whose expression levels are positively correlated with TEAD activity, while negative effectors indicate genes whose expression levels are negatively correlated.

[0390] As shown in Figure 4, using the publicly available signatures of approximately 500 genes, lower scores for TEAD activity from EV RNA were generally obtained compared to the scores of parental cells. The correlation between EV and parental cell values ​​was H226(r 2 Except for (=0.66), all others were defective (Mero14 r 2 =0.00;Spc212 r 2 =0.50, HCT116 r 2 (=0.03).

[0391] TEAD inhibitors caused a sharp decline in TEAD activity scores, as measured by EV, only in the H266 cell line, but did not affect EV values ​​from the other two responsive or non-responsive cell lines.

[0392] As shown in Figure 5, focusing on a subset of 28 TEAD effectors (out of approximately 500) readily detected in EVs across three mesothelioma cell lines improved the correlation between TEAD activity observed in parental cells and the score predicted from EV RNA content. The TEADi dose effect was observed in EV RNA from two of the three responding cell lines (H226 and Mero14). In the negative control (HCT116), no correlation was observed between EV and parental cell measurements.

[0393] As shown in Figure 6, a genome-wide stepwise regression analysis was performed to identify a set of EV transcripts that successfully predict the TEAD activity score of parental cells. Only transcripts readily detected with log2FPKM > 4 in three response cell lines at baseline were included in this analysis. From this analysis, four transcript predictors were selected. Using these two signatures, high correlations were observed for all response cell lines (r2=0.96 for H226, Mero14=0.95, Spc212=0.89), but lower correlations were observed for the non-responding cell line HCT116 (r2=0.63).

[0394] Example 3: Conclusion Despite having a relatively lower yield compared to parental cells, EV RNA is suitable for transcriptome analysis.

[0395] By using a limited list of effector genes consisting of a rich array of EV transcripts, it becomes possible to estimate TEAD activity more accurately.

[0396] Novel biomarkers can be identified by genome-wide analysis of EV gene transcripts.

[0397] Signatures derived from EV gene transcripts identified for TEAD inhibition may be usable as peripheral markers of target involvement in plasma or other fluid biopsies.

Claims

1. A set of isolated gene transcripts from a set of genes, wherein the set of genes comprises a 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 a gene subset (2) consisting of CTSB, FTH1, SQSTM1, TCF25, and UBC.

2. An isolated set of gene transcripts consisting of at least two gene transcripts from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1.

3. The set of gene transcripts according to claim 2, comprising a set of gene transcripts from a set of genes consisting of DLC1, AKAP2, CANX and SAFB2, and optionally from at least one gene selected from 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. An isolated extracellular vesicle comprising the set of gene transcripts 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. A set of gene transcripts or a set of gene transcripts according to claim 1, comprising at least one gene transcript from a set of genes consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9 and EIF4A1, for use as a biomarker for 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. Use of the set of gene transcripts according to claim 1, or a set of gene transcripts consisting of at least one gene transcript from the set of genes consisting of DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9 and EIF4A1, for characterizing the TEAD activity status of a biological sample, or measuring TEAD activity in a biological sample, or predicting the cancer response to TEAD inhibitor therapy in subjects known or suspected to have TEAD-active cancer, or monitoring the cancer response to TEAD inhibitor therapy in subjects known or suspected to have TEAD-active cancer, or predicting the progression or regression of cancer in subjects known or suspected to have TEAD-active cancer, or for screening candidate TEAD inhibitor 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 transcription signature is obtained from the gene transcript level.

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

12. The use according to claim 9 or claim 10, wherein the gene transcript level is subject to mathematical normalization.

13. The use described in claim 12, When the set of gene transcripts described in claim 1 is used, the mathematical normalization is performed in the following steps: a) For each gene transcript in the set of genes, the rank of the gene having the level of the gene transcript is converted into a fractional rank by dividing it by the number of genes in the set of genes, b) Separate the fractional ranks obtained for the genes of the gene subset (1) from the fractional ranks obtained in step a), and calculate their average fractional ranks (MFR subset (1) or MFR positive), c) Separate the fractional ranks obtained for the genes of the gene subset (2) from the fractional ranks obtained in step a), and calculate their average fractional ranks (MFR subset (2) or MFR negative), d) The deR score is calculated by subtracting MFR subset (2) from MFR subset (1), The deR score is calculated according to a method including, Or, When a set of gene transcripts is used, consisting of at least one gene transcript from a set of genes comprising DLC1, AKAP2, CANX, SAFB2, EIF4H, NDUFS5, SEPT9, and EIF4A1, the mathematical normalization is performed in the following steps: a) Multiply each level of each gene transcript in the set of genes by a coefficient associated with each gene, and for each gene, the product (Pgene ｉ ) to obtain, here gene ｉ This refers to the genes listed in the aforementioned set of genes, b) The product Pgene obtained in step a) ｉ (ΣPgene ｉ The sum of the values ​​and the constant are added to get the (S) score: (ΣPgene ｉ Obtaining a constant (+) Includes, The coefficients and constants used in steps a) and b) are used to calculate the (S) score according to a method which includes (i) the level of the gene transcript previously obtained in a first biological sample and (ii) the level of the gene transcript of the TEAD-500 signature previously measured in a second biological sample, wherein the TEAD-500 signature includes the gene transcript of a set of genes which includes any of the 220 to 249 genes of gene subset (1) and any of the 210 to 233 genes of gene subset (2) disclosed in Table 2, and the first and second biological samples represent the same TEAD-activated 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 compared to the reference value is a second deR or (S) score measured following the first deR or (S) score.

15. The use according to any one of claims 9 to 14, wherein the aforementioned level of each gene transcript is obtained by RNA sequencing (RNA-sequence) and quantified as FPKM (fragment per kilobase of transcript per 1 million mapped reads).

16. The aforementioned cancers include mesothelioma, adrenocortical carcinoma, urothelial carcinoma of the bladder, invasive breast carcinoma, squamous cell carcinoma of the cervix and intracervical adenocarcinoma, cholangiocarcinoma, colorectal cancer, consensus molecule subtype 1 of colorectal cancer, consensus molecule subtype 2 of colorectal cancer, consensus molecule subtype 3 of colorectal cancer, consensus molecule subtype 4 of colorectal cancer, colonic adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal cancer, and polycystic cancer. Use according to any one of claims 7 to 15, selected from glioblastoma, head and neck squamous cell carcinoma, renal pellucid cell carcinoma, renal papillary cell carcinoma, brain glioma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid cancer, thymoma, uterine endometrial cancer, and uterine carcinosarcoma.

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

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