WO2021063972A1 - Cthrc1 as biomarker for a tgfbeta-activated tumor microenvironment - Google Patents

Cthrc1 as biomarker for a tgfbeta-activated tumor microenvironment Download PDF

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WO2021063972A1
WO2021063972A1 PCT/EP2020/077272 EP2020077272W WO2021063972A1 WO 2021063972 A1 WO2021063972 A1 WO 2021063972A1 EP 2020077272 W EP2020077272 W EP 2020077272W WO 2021063972 A1 WO2021063972 A1 WO 2021063972A1
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cancer
tgfp
tumor
patient
cthrc1
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Alexandre CALON
Mar IGLESIAS COMA
Eduard BATLLE GÓMEZ
Elena Sancho Suils
Antonio BERENGUER LLERGO
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Fundació Institut De Recerca Biomèdica (Irb Barcelona)
Fundació Institut Mar D'investigacions Mèdiques (Fimim)
Institució Catalana De Recerca I Estudis Avançats
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57419—Specifically defined cancers of colon
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present invention relates to a new biomarker useful to identify cancer patients with a TGFp-activated microenvironment in colorectal cancer and other tumor types. It further relates to methods of predicting response to inhibitors of the TGFp signaling pathway and methods of classification and selection of cancer patients for treatment with inhibitors of the TGFp signaling pathway, to prognostic methods, to methods of monitoring or evaluating response to inhibitors of the TGFp signaling pathway and to related second medical uses, diagnostic kits and uses thereof.
  • Colorectal cancer is the third most common type of cancer globally in men and the second in women and represents 8.5% of cancer deaths. In Europe, the overall survival is 43% while in the United States it is 62%. These values decrease to 5-10% in case of metastatic dissemination to other organs.
  • the initial treatment is the surgical resection of the primary tumor (whenever possible) and the decision of a post-operative or adjuvant treatment depends on the staging of the disease at the time of diagnosis.
  • the current staging system depending on the clinical-pathological criteria (I to IV) is based on the classification of the TNM (Tumor / Lymph node / Metastasis, of the American Joint Committee on Cancer (AJCC)).
  • This system has limited power when predicting tumor recurrence that reaches, after treatment, about 20% in stages II and 40% in stages III.
  • the cases of colorectal cancer that have recurred usually do so in the form of metastases and are associated with worse prognosis. For this reason, a more precise method is necessary to select the appropriate treatment of patients with CRC, based on aggressiveness and the likelihood of tumor recurrence.
  • Other variables such as lymphatic vascular invasion, peri neural invasion or the presence of tumoral budding are also considered prognostic factors; however, they present a great intrinsic variability in their detection (Odze & Goldblum, Surgical Pathology of the Gl Tract, Liver, Biliary Tract and Pancreas, 3e (2014)).
  • molecular classifications may have some prognostic value and improve the stratification of patients, thus helping to define most appropriate treatment.
  • the desirable objective is the identification of specific biomarkers that provide useful information on prognosis and/or about likelihood of response to a specific treatment.
  • CMS4 group with the worst prognosis
  • T ⁇ Rb response signatures from here onwards named as T ⁇ Rb response signatures or TBRS
  • T-TBRS normal tissue derived T cells
  • Ma-TBRS macrophages
  • F-TBRS fibroblasts
  • Each signature reflects the transcriptional activity of T ⁇ Rb signaling pathway in fibroblasts, macrophages and T cells respectively.
  • CTFIRC1 Collagen Triple Helix Repeat Containing 1
  • CTFIRC1 is a secreted glycoprotein that has multiple functions associated with wound repair, bone remodeling, hepatocyte fibrosis and adipose tissue formation among others (Jiang et al., Journal of Cancer, 7:715 (2016) 2213- 2220).
  • CTFIRC1 expression has been associated with poor prognosis in multiple tumor types.
  • CTFIRC1 overexpression of CTFIRC1 has been associated with increased aggressiveness in lung cancer (Ke et al., Oncotarget, 5:19 (2014) 9410-9424), with EMT in ovarian cancer (Hou et al., 2015), as well as with a poor prognosis in pancreatic adenocarcinoma (Liu et al., American Journal of Cancer Research, 6:8 (2016) 1820-7).
  • most of these works do not reflected if the expression of CTHRC1 is mainly stromal or epithelial and do not use immunohistochemical (IHC) analysis.
  • IHC immunohistochemical
  • q-PCR quantitative polymerase chain reaction
  • RNA-based tool of molecular classification The different variables of time, preservation protocol and sample transport logistics may have a profound effect in the quality of RNA of a fresh or frozen sample. This becomes an important limiting factor in order to build a robust, reproducible and feasible technical RNA- based tool of molecular classification.
  • RNA-based prognostic signatures In light of the technical challenges posed by use of mRNA-based technology and the minimal overlap among the genes that comprise the RNA-based prognostic signatures, a prediction approach based on the determination of the expression levels of proteins, for instance detected by immunohistochemistry (IHC), would be more accurate, reproducible and easy to apply to clinical management than methods based on RNA. To date, there is no biomarker used in clinical practice that can be determined by immunohistochemical study for stratification of CRC patients.
  • IHC immunohistochemistry
  • TGFp transforming growth factor-beta
  • the TME is a complex structure composed of extracellular matrix proteins (mainly, type I collagen) and various cell types including mesenchymal cells (cancer-associated fibroblasts [CAF]), endothelial cells and pericytes, nerve cells, immune cells, and bone marrow-derived stem cells. These cell types can express TQRb receptors and respond to elevated TQRb levels present in the TME. TQRb has been reported to regulate fibrosis, angiogenesis, and immune cell function in the context of tumors (Neuzillet et al., Pharmacology & Therapeutics 147 (2015) 22-31 ; Batlle and Massague, Immunity 50 (2019), 924-940).
  • CAF cancer-associated fibroblasts
  • TGF b activity in the TME was found to be associated with lack of response to PD-1-PD-L1 immune checkpoint inhibitors in CRC (Tauriello et al., Nature, Vol. 554 (2016) 539 - 543) and in human metastatic urothelial cancer (mUC) samples (Mariathasan et al., Nature, Vol. 554 (2016) 544 - 548).
  • TGF b signaling pathway There are many clinical challenges to developing inhibitors of TGF b signaling pathway, notably timing of treatment and predictive biomarkers for patient selection, in order to define in what kind of tumor microenvironment TGF b signaling inhibition may be more beneficial (Neuzillet et al., Pharmacology & Therapeutics 147 (2015) 22-31). As pointed out in deGramont et al. (Oncoimmunology 6:1 (2017) e1257453), due to the complex nature of the T ⁇ Rb pathway, its role in cell fate and their differential activity in tumor cells and their microenvironment, predictive biomarkers may be challenging to identify.
  • TGFp is secreted as a pro-hormone that is stored in the extracellular matrix in an inactive form and that can be subsequently mobilized and activated by sophisticated mechanisms (Lyons et al. J. Cell. Biol. (1990) 110(4), 1361-1367, (Batlle and Massague. Immunity 50 (2019), 924-940).
  • each cell type present in the TME may display different degrees of TGFp activity depending on the expression of TGFp receptors, presence of inhibitory molecules and other contextual molecules that modify their susceptibility to TGFp accumulated in the TME (David and Massague. Nat Rev Mol Cell Biol. 2018).
  • TGFp-activated TME has been previously measured in transcriptomic cancer datasets using as surrogate the fibroblast TGFp response signature (F-TBRS), the T-cell (T-TBRS) and the macrophage (Ma-TBRS) response signatures described in Calon et al. 2012 (Cancer Cell 22, (2012) 571-584).
  • F-TBRS fibroblast TGFp response signature
  • T-TBRS T-cell
  • Ma-TBRS macrophage response signatures described in Calon et al. 2012 (Cancer Cell 22, (2012) 571-584).
  • CTFIRC1 expression levels can be used as a surrogate of any or all of F-TBRS, T-TBRS or Ma-TBRS, preferably of F-TBRS.
  • the present invention provides a method for determining whether a patient’s tumor has a TGFp-activated microenvironment, wherein said method comprises or consists of: a) determining the expression levels of CTFIRC1 (Collagen Triple Helix Repeat Containing 1) gene in a biological sample isolated from said patient; b) comparing the expression levels of CTFIRC1 in the patient’s sample with a reference value; wherein an increase of the value in the patient’s sample with regard to said reference value is indicative that said patient’s tumor has a TGFp -activated microenvironment.
  • CTFIRC1 Collagen Triple Helix Repeat Containing 1
  • CTHRC1 as a biomarker which expression levels associate with general non-cell-specific activation of the tumor microenvironment by TGFp, more specifically, it associates with a tumor microenvironment comprising TGFp activated fibroblasts, T-cells and macrophages. Accordingly, in particular embodiments, CTHRC1 enables to capture the activation of the microenvironment by TGFp using a single biomarker instead of three distinct signatures (i.e., F-TBRS, T-TBRS, Ma-TBRS).
  • CMSs consensus molecular subtypes
  • CTHRC1 expression was also able to capture tumors with a TGFp-activated TME classified under other CMS groups (Example 2, Figure 2B and C).
  • the provision of a method identifying tumors presenting a TGFp-activated TME enables to select for treatment with an inhibitor of the TGFp signaling pathway, tumors belonging to CMS1-3 subgroups (typically not associated with a TGFp activated TME) which would thus otherwise likely not had been selected for treatment with a TGFp signaling pathway inhibitor.
  • the biomarker of the invention was capable of identifying tumors presenting a TGFp-activated TME, and thus likely to benefit from a therapy inhibiting the TGFp signaling pathway, independently of microsatellite stability status, that is, both in microsatellite stable (MSS) and microsatellite instable (MSI) phenotypes (Example 2, Figure 3).
  • MSI-high (MSI-H) status has been previously associated with better prognosis in early-stage CRC and has emerged as a predictor of sensitivity to check-point inhibitors immunotherapy treatments (Battaglin et al., Advances in Hematology and Oncology 2018, 16(11), 735-747). Nevertheless, as above-mentioned, immune checkpoint inhibitors were found by Tauriello et al. (Tauriello et al., Nature, Vol. 554 (2016) 539 - 543) to lack efficacy in CRC tumors with a TGFp activated microenvironment. Thus, the signature of the invention enables to identify from patients (e.g.
  • the method of the invention further enables selecting cancer patients having tumors (e.g. CRC or gastric cancer patients) which would likely benefit from a TGFp inhibition therapy for which such therapy would not had been selected on the basis of MSI status or CMS molecular classification methods.
  • TGFp signaling in the TME has been reported to operate as the main mechanism of immune evasion during metastasis formation. Elevated TGFp triggers T cell exclusion, a phenomenon associated with poor outcome in CRC and other tumor types, and blocks anti tumor Th1 effector phenotype (Tauriello et al., Nature, Vol. 554 (2016) 539 - 543; Galon et al., Science 313 (2006) 1960-1964; Mariathasan et al., Nature, Vol. 554 (2018) 544 - 548). In Tauriello et al., Galunisertib (GAL), which is a small molecule TGFBR1 inhibitor, was used to treat mice with TGFp high metastatic CRCs.
  • GAL Galunisertib
  • GAL treatment unleashed the immune system against CRC, which exerted a potent therapeutic response that prevented the formation of metastasis.
  • the combination of GAL with anti-PD-L1 treatment eradicated most metastases and prolonged recurrence-free survival for over a year after treatment cessation.
  • This striking response was characterized by disruption of the T-cell exclusion phenotype characteristic of progressed metastatic disease, and by prominent Th1 immune activation (Tauriello et al., Nature, Vol. 554 (2016) 539 - 543). When these same tumors were allowed to develop established metastasis, GAL alone had no effect.
  • Calon et al (Calon et al., Nature Genetics 47:4 (2015) 320-332) showed that in metastasis initiation assays performed in mice with human derived organoids, treatment with the TGFp inhibitor Galunisertib (GAL) greatly reduced metastasis initiation in this experimental setting ( Figure 8C in Calon et al., Nature Genetics 47:4 (2015) 320-332). Moreover, Tauriello et al. (Nature, Vol.
  • mice orthotopically transplanted with LAKTP mutant mice organoids showed that treatment with the TGFp inhibitor Galunisertib (GAL) of mice orthotopically transplanted with LAKTP mutant mice organoids was associated to a strong reduction/prevention of liver metastasis, as well as a reduction of primary tumor (and local carcinomatosis) size ( Figures 2 and 4 from Tauriello et al., Nature, Vol. 554 (2018) 539 - 543).
  • the inventors measured CTHRC1 protein or mRNA expression levels in the developed primary CRC tumors, which resemble the metastatic intestinal tumors of origin and exhibit a TGFb-activated TME, before and after treatment of mice with GAL.
  • CTFIRC1 expression levels were found to be lower after therapy with GAL, thus supporting the utility of CTFIRC1 expression as predictive biomarker of response to TGFp inhibition therapies and as biomarker for monitoring or evaluating response to treatment with TGFp inhibitors.
  • the invention also pertains to a method for predicting the efficacy of (or the likelihood of response to) a treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. predicting a higher efficacy or likelihood of response to a treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
  • the present invention also relates to a method for selecting a cancer patient which is likely to benefit from a treatment with an inhibitor of the TGFp signaling pathway, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. selecting said cancer patient for the treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
  • the invention pertains to an inhibitor of the TGFp signaling pathway for use in a method for treatment of a cancer patient, wherein said patient’s tumor has been determined to have a TGFp-activated microenvironment according to a method as described herein.
  • the invention provides the use of an inhibitor of the TGFp signaling pathway in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected as having a tumor with a TGFp-activated microenvironment by a method as described herein.
  • the invention relates to a method of treating a cancer patient by administering a therapeutically effective amount of an inhibitor of the TGFp signaling pathway, wherein said patient has been selected as having a tumor with a TGFp-activated microenvironment by a method as described herein
  • the present invention relates to an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy for use in a method for treatment of a cancer patient in combination with an inhibitor of the TGFp signaling pathway, wherein said patient’s tumor has a TGFp-activated microenvironment according to a method as described herein.
  • an inhibitor of the TGFp signaling pathway e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy for use in a method for treatment of a cancer patient in combination with an inhibitor of the TGFp signaling pathway, wherein said patient’s tumor has a TGFp-activated microenvironment according to a method as described herein.
  • the invention provides the use of an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in combination with an inhibitor of the TGFp signaling pathway in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
  • an anti-cancer agent other than an inhibitor of the TGFp signaling pathway e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy in combination with an inhibitor of the TGFp signaling pathway in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
  • the invention relates to a method of treating a cancer patient by administering a therapeutically effective amount of an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in combination with a therapeutically effective amount of an inhibitor of the TGFp signaling pathway, wherein said patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
  • an anti-cancer agent other than an inhibitor of the TGFp signaling pathway e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy e.g., cytotoxic agent or immune checkpoint inhibitor
  • the present invention relates to an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy for use in a method for treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said other anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway.
  • an anti-cancer agent other than an inhibitor of the TGFp signaling pathway e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy for use in a method for treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said other anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway.
  • the invention provides the use of an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in the manufacturing of a medicament for the treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said other anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway.
  • an anti-cancer agent other than an inhibitor of the TGFp signaling pathway e.g., cytotoxic agent or immune checkpoint inhibitor
  • the invention relates to a method of treating a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp -activated microenvironment, by administering a therapeutically effective amount of an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy, wherein said other anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway.
  • an anti-cancer agent other than an inhibitor of the TGFp signaling pathway e.g., cytotoxic agent or immune checkpoint inhibitor
  • said other anti-cancer agent or radiotherapy is administered as single agent or therapy.
  • the invention relates to a method for determining the prognosis of a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. classifying the patient as having poor prognosis when said patient’s tumor has a TGFp-activated microenvironment.
  • the present invention relates to a method for monitoring or evaluating the response to treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises determining whether a patient’s tumor has a TGFp- activated microenvironment as described herein, wherein a TGFp-activated microenvironment is associated to lack of response.
  • the invention concerns a kit suitable for determining the expression levels of the CTFIRC1 gene in an isolated tumor sample, wherein said kit comprises: i. a reagent for the quantification of the CTFIRC1 gene expression levels; and ii. optionally, a reagent for the quantification of a control gene expression levels; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
  • the invention refers to the use of a kit as described herein, in a method for determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein, in a method for selecting a cancer patient which is likely to benefit from a treatment with an inhibitor of the TGFp signaling pathway as described herein, in a method for monitoring or evaluating the response to an inhibitor of the TGFp signaling pathway as described herein, or in a method for determining the prognosis of a cancer patient as described herein.
  • CTHRC1 detects CRC stromal activated tumors.
  • Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles.
  • Patients with high CTHRC1 mRNA expression present tumors characterized by a higher degree of TGFp -activated stroma using as surrogates F-, T- and Ma-TBRSs.
  • CTHRC1 captures CMS4 subtype CRCs.
  • A Box-Plot graphs depicting overall CTHRC1 mRNA expression (z-score) in patients classified as CMS1-4. Patients with CMS4 tumors show overall highest mRNA expression levels of CTHRC1. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles.
  • B CTHRC1 captures 92.2% of CMS4 patients. As CTHRC1 identifies tumors with a TGFp active TME, these patients are likely to benefit from therapies directed towards inhibition of TGFp signaling. In addition, CTHRC1 also captures patients with high activated stroma in the CMS 1-3 subtypes, particularly CMS1.
  • CMS1 patients will be misclassified as good prognosis patients, likely to benefit from checkpoint immunotherapies, yet patients with high TGFp-activated stroma are unlikely to respond.
  • C Patients from the pooled transcriptomic cohort (GEO +TCGA) with tumors classified in each CMS1 , CMS2 and CMS3 subtypes have been divided in three subgroups (i.e., Low / Medium / High) according to CTHRC1 mRNA expression levels. Box Plots show association with overall expression levels of TGFb-activated stromal signatures for each CMS subtype of patient’s tumors. Results are shown for all samples. Each dot is one sample.
  • Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs.
  • Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles.
  • Patients with high CTHRC1 mRNA expression present tumors characterized by a higher degree of TGFp -activated stroma using as surrogates F-, T- and Ma-TBRSs.
  • FIG. 1 A. CTHRC1 mRNA expression identifies patients susceptible for treatment with TGF-beta inhibitors in previously used classifications for CRC prognosis such as
  • Kaplan-Meier graphs show disease free survival (DFS) in years per MSS or MSI CRCs in the above subgroups (i.e., Low / Medium / High) according to CTHRC1 mRNA expression.
  • CTHRC1 expression can identify patients with a TGFp-activated TME, that have bad prognosis. These patients may not respond to monotherapy with immune checkpoint inhibitors.
  • they are susceptible of benefitting from therapies that inhibit TGFp signaling pathway, and combination therapies including TGFp signaling pathway inhibitors and immune checkpoint inhibitors.
  • CTHRC1 mRNA expression shows inverse correlation with the expression of a signature of CD45 positive cells.
  • Heatmaps illustrating the correlations between CTHRC1 mRNA expression with average gene expression of F-TBRS, T-TBRS, Ma-TBRS and distribution of Consensus Molecular Subtypes (CMS) in CRC transcriptomic cohorts (TCGA+GEO: 1705 patients), including patients from stages I to IV.
  • CMS Consensus Molecular Subtypes
  • MSI (and CMS1) patients are evenly distributed throughout the range of CTHFtCI expression values indicating, as previously described, that a high % of these theoretically good prognosis patients exhibit a high degree of TGFb-activated TME and, therefore, should be considered as bad prognosis patients, likely candidates to receive TGFp signaling inhibitory therapies, and unlikely to respond to immunecheckpoint therapies alone.
  • CTHRC1 protein expression can be used to identify CRC with TGFp stromal activated tumors that have poor prognosis.
  • A. Box Plots show distribution of patients of CTHFtCI protein expression within stage I, II and III CRC patients. Each dot is one sample. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. CTHRC1 expression is significantly higher in more advanced cancers (stage II and III versus stage I; *** p ⁇ 0.0001).
  • B-G Box Plots show distribution of patients of CTHFtCI protein expression within stage I, II and III CRC patients. Each dot is one sample. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. CTHRC1 expression is significantly higher in more advanced cancers (stage II and III versus stage I; *** p ⁇ 0.0001).
  • CTHRC1 expression identifies the microenvironment of tumors that respond to TGFp inhibitory therapies.
  • CTHRC1 expression levels measured by IHC (A, in human tumors) or mRNA (B, in mouse tumors) were found to be clearly reduced in mice treated with LY2157299 (Galunisertib), indicating the inhibitor is effectively diminishing TQRb signaling in the tumor TME.
  • LY2157299 Gibunisertib
  • These mice showed a reduction in tumor burden upon treatment ( Figure 8C in Calon et al., Nature Genetics 47:4 (2015) 320-332); and Figures 2 and 4 from Tauriello et al., Nature, Vol. 554 (2018) 539 - 543).
  • CTHRC1 is a good biomarker of response to T ⁇ Rb inhibition therapies and reinforces its value as predictive biomarker of T ⁇ Rb signaling inhibition therapies (Example 3).
  • FIG. 7 CTHRC1 detects stromal activated tumors in gastric cancers. Box-Plot graphs depicting overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in 3 groups of patients according to their CTHRC1 mRNA expression levels (Low, Medium and High) in stomach adenocarcinoma (STAD) patients from the TCGA cohort. Results are presented for all samples, and according to the MSI/MSS status of the patients (second and third rows). Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. Patients with high CTHRC1 mRNA expression present tumors characterized by a higher degree of T ⁇ Rb -activated TME using as surrogates F-, T- and Ma-TBRSs.
  • CTHRC1 detects stromal activated tumors in breast cancer. Box-Plot graphs depicting overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in 3 groups of patients according to their CTHRC1 mRNA expression (Low, Medium and High) in the various subtypes of breast cancer patients (LumA; LumB; Her2, Basal; and Normal-like) in the Metabric cohort. Results are shown for all samples in each subtype. Each dot is a sample. Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. Patients with high CTHRC1 mRNA expression present tumors characterized by a higher degree of TGFb-activated TME using as surrogates F-, T- and Ma-TBRSs.
  • FIG. 9 CTHRC1 detects stromal activated tumors across various tumor types (panmarker). Box-Plot graphs depicting overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in 3 groups of patients according to their CTHRC1 mRNA expression (Low, Medium and High) in Bladder cancer and pancreas adenocarcinoma patients from the TCGA cohort, prostate carcinoma patients from the GSE21034 cohort, and lung cancer patients from the GSE31210 cohort.
  • Figure 10. IL11RS or IL11 capacity to predict relapse in colorectal cancer patients is weaker in comparison with CTHRC1.
  • Kaplan-Meier graphs show disease free survival (DFS) in years per all colorectal cancer patients or for patients classified as MSS or MSI in the above subgroups (i.e., Low / Medium / High) according to IL11 expression (A) or (B) to the expression of a signature containing more than 1000 genes responsive to IL11 (IL11 RS; Calon et al., 2012). Identification of patients at high risk of relapse is superior when using CTFIRC1 as a marker (please see Fig 3B for comparison). Statistical significance was assessed by means of Likelihood Ratio Tests (LRT).
  • LRT Likelihood Ratio Tests
  • subject or “individual”' are used herein interchangeably to refer to all the animals classified as mammals and includes but is not limited to domestic and farm animals, primates and humans, for example, human beings, non-human primates, cows, horses, pigs, sheep, goats, dogs, cats, or rodents.
  • the subject is a male or female human being of any age or race.
  • cancer patient and “subject suffering from cancer” are used herein interchangeably. It may refer to those subjects diagnosed after a confirmatory test (e.g., biopsy and/or histology) and subjects suspected of having cancer.
  • a confirmatory test e.g., biopsy and/or histology
  • subject suspected of having cancer refers to a subject that presents one or more signs or symptoms indicative of a cancer and is being screened for cancer.
  • a subject suspected of having cancer encompasses for instance an individual who has received a preliminary diagnosis (e.g., an X-ray computed tomography scan showing a mass) but for whom a confirmatory test (e.g., biopsy and/or histology) has not been done or for whom the stage of cancer is not known.
  • cancer and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Included in this definition are benign and malignant cancers or tumors as well as dormant tumors or micrometastases. Examples of cancer include but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia.
  • cancers include breast cancer, squamous cell cancer, lung cancer (including small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung, and squamous carcinoma of the lung), cancer of the peritoneum, hepatocellular cancer, gastric or stomach cancer (including gastrointestinal cancer), pancreatic cancer, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bladder cancer, hepatoma, colon cancer, colorectal cancer, rectal cancer, endometrial or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, liver cancer, prostate cancer, vulval cancer, thyroid cancer, hepatic carcinoma and various types of head and neck cancer, as well as B-cell lymphoma (including low grade/follicular non-Hodgkin's lymphoma (NHL); small lymphocytic (SL) NHL; intermediate grade/follicular NHL; intermediate grade diffuse NHL; high grade immunoblastic NHL; high grade lymphoblastic NHL; high grade small cell lymphom
  • tumor is an abnormal mass of tissue that may be benign, premalignant, or cancerous. Preferably, this “tumor” is a cancerous tumor.
  • Metastasis refers to distant metastasis affecting organs other than the primary tumor site. Metastasis may be defined as the process by which cancer spreads or transfers from the primary site to other regions of the body with the development of a similar cancerous lesion at the new location (see for instance: Chambers AF et al., Nat Rev Cancer 2002; 2: 563-72). For instance, in colorectal cancer, metastasis in another organ (e.g., the liver) typically shows an enteroid adenocarcinoma pattern.
  • a “metastatic” or “metastasizing” cell is typically one that loses adhesive contacts with neighboring cells and migrates via the bloodstream or lymph from the primary site of disease to invade neighboring body structures.
  • tumor microenvironment refers to a complex structure composed of extracellular matrix proteins (mainly, type I collagen) and various cell types including mesenchymal cells (cancer-associated fibroblasts [CAFs]), endothelial cells and pericytes, nerve cells, immune cells, and bone marrow-derived stem cells that generally surround and feed a tumor.
  • CAFs cancer-associated fibroblasts
  • a tumor can change its microenvironment, and the microenvironment can affect how a tumor grows and spreads.
  • TGFb-activated microenvironment refers to a tumor microenvironment wherein the cells surrounding the tumor express T ⁇ Rb receptors and respond to elevated T ⁇ Rb levels present in the TME.
  • T ⁇ Rb has been reported to regulate fibrosis, angiogenesis, and immune cell function in the context of tumors (Neuzillet et al., Pharmacology & Therapeutics 147 (2015) 22-31 ; Batlle and Massague, Immunity 50 (2019), 924-940).
  • TQRb response signature refers to the gene expression program induced by addition of TQRb1 in cultures of normal tissue derived T cells (T-TBRS), macrophages (Ma-TBRS) or fibroblasts (F-TBRS) It was selected as TBRS the full set of genes upregulated by TQRb signaling in these cell cultures (>2 fold, p ⁇ 0.05).
  • F-TBRS is composed of 165 genes, T-TBRS of 76 and Ma-TBRS of 1125 genes. Each signature reflects the transcriptional activity of TQBb signaling in fibroblasts, macrophages and T cells respectively.
  • F-TBRS, T-TBRS and Ma-TBRS were previously shown by the inventors to act as surrogates of the degree of activation of the most abundant cell types in the TME of CRCs by TGFp (Calon et al., Cancer Cell 22 (2012) 571-584; Calon et al.,
  • TGFp activation of fibroblasts, T-cells and macrophages can be assessed by determining expression of F-TBRS, T-TBRS and Ma- TBRS, respectively.
  • organoid or “cancer organoid” as used herein refers to small, self-organized three dimensional tissue cultures derived from primary tumors or metastasis. They can be maintained indefinitely in the appropriate cell culture conditions. When inoculated in experimental models, they faithfully recapitulate many of the traits of the tumor of origin.
  • treating includes the amelioration, cure, and/or maintenance of a cure (i.e., the prevention or delay of relapse) of a disease or disorder.
  • Treatment after a disorder has started aims to reduce, alleviate, ameliorate or altogether eliminate the disorder, and/or its associated symptoms, to prevent it from becoming worse, to slow the rate of progression, or to prevent the disorder from re-occurring once it has been initially eliminated (i.e., to prevent a relapse).
  • treatment refers to the act of “treating”.
  • terapéuticaally effective amount refers to an amount that is effective, upon single or multiple dose administration to a subject (such as a human patient) in the treatment of a disease, disorder or pathological condition.
  • combination therapy or “combination treatment” are used herein indistinctively, and is meant to comprise the administration of the referred therapeutic agents to a subject suffering from cancer, in the same or separate pharmaceutical formulations, and at the same time or at different times. If the therapeutic agents are administered at different times they should be administered sufficiently close in time to provide for the combined effect (e.g. potentiating or synergistic response) to occur.
  • the particular combination of therapies to employ in a combination regimen will take into account compatibility of the desired therapeutics and/or procedures and/or the desired therapeutic effect to be achieved. It will be appreciated that the therapies employed may achieve a desired effect for the same disorder (for example, anti-cancer effects), and/or they may achieve different effects (e.g., control of any adverse effects).
  • single agent as used herein relates to the use of an active ingredient sufficiently separate in time from another active ingredient to prevent for the potentiating or synergistic response to occur. More specifically, the use as “single agent” does not encompass the use as a “combination therapy”.
  • anti-cancer treatment may include any treatment to stop or prevent cancer, including but not limited to surgery, radiotherapy, anti-cancer agents and any other existing therapies or to be developed.
  • anti-cancer agent refers to any therapeutic agents useful in treating cancer.
  • anti-cancer therapeutic agents include, but are limited to, e.g., an inhibitor of the TGFp signaling pathway, chemotherapeutic agents, growth inhibitory agents, cytotoxic agents, anti-hormonal agents, agents used in radiation therapy, anti angiogenesis agents, apoptotic agents, anti-tubulin agents, and other agents to treat cancer, such as anti-HER-2 antibodies (e.g., Herceptin®), anti-CD20 antibodies, an epidermal growth factor receptor (EGFR) antagonist (e.g., a tyrosine kinase inhibitor), FIER1/EGFR inhibitor (e.g., erlotinib (Tarceva ⁇ ®>)), platelet derived growth factor inhibitors (e.g., GleevecTM (Imatinib Mesylate)), a COX-2 inhibitor (e.g., celecoxib), interferons, cytokines, antagonists
  • EGFR epiderma
  • TGFp inhibitor or “TGFp (signaling) pathway inhibitor” or “inhibitor of the TGFp signaling pathway” are used herein indistinctively and refer to an agent inhibiting TGFp signaling by any means.
  • TGFp inhibitors are those inhibiting TGFp signaling pathway at any of the (i) ligand level, (ii) the ligand-receptor level or the (ii) intracellular level.
  • Agents inhibiting the TGF b pathway at the ligand level include antisense oligonucleotides which may be delivered directly intravenously or engineered into immune cells to prevent TGFp synthesis (for example, trabedersen [AP12009], an antisense oligonucleotide targeting TGFP2; and Lucanix® [belagenpumatucel-L], a TGFP2 antisense gene-modified allogeneic cancer cell vaccine);
  • Agents inhibiting the TGF b pathway at the ligand-receptor level include ligand-traps (e.g., TGFp-neutralizing affinity ligands [e.g., monoclonal antibodies] and soluble receptors), antibodies or molecules that prevent TGFp release from latent complexes, like antibodies against GARP (Cuende et al., Science Translational Medicine 7 (2015), issue 284, pp. 284ra56) or antibodies against anbd integrin that blocks the release of active TGFp by cancer cells (N. Takasaka et al., JCI Insight.
  • ligand-traps e.g., TGFp-neutralizing affinity ligands [e.g., monoclonal antibodies] and soluble receptors
  • antibodies or molecules that prevent TGFp release from latent complexes like antibodies against GARP (Cuende et al., Science Translational Medicine 7 (2015), issue 284, pp. 284ra56) or antibodies against anbd integrin
  • anti-TGFp-receptor affinity ligands to prevent ligand- receptor interaction
  • fresolimumab a pan-TGFp antibody
  • disitertide [P144] a peptidic TGFpi inhibitor specifically designed to block the interaction with its receptor
  • IMCTR1 LY3022859], a monoclonal antibody against TGFpRII
  • TGFp receptor kinase inhibitors to prevent signal transduction for example, galunisertib [LY2157299], a small molecule inhibitor of TGFpRI.
  • TGFp pathway inhibitors in development in cancer are provided in Table 1 of Neuzillet et al. (Pharmacology & Therapeutics 147 (2015) 22-31):
  • reduce or inhibit may refer to the ability to cause an overall decrease preferably of 20% or greater, more preferably of 50% or greater, and most preferably of 75%, 85%, 90%, 95%, or greater. Reduce or inhibit can refer for instance, to the biological activity of an active ingredient or a ligand (e.g. TGF b activity), to the symptoms of the disorder being treated, the presence or size of metastases, the size of the primary tumor, etc.
  • cytotoxic agent refers to a substance that inhibits or prevents the function of cells and/or causes destruction of cells. The term is intended to include radioactive isotopes (e.g.
  • chemotherapeutic agent refers to a chemical compound useful in the treatment of cancer.
  • examples of chemotherapeutic agents include alkylating agents such as thiotepa and CYTOXAN® cyclosphosphamide; alkyl sulfonates such as busulfan, improsulfan and piposulfan; aziridines such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylamelamines including altretamine, triethylenemelamine, trietylenephosphoramide, triethiylenethiophosphoramide and trimethylolomelamine; acetogenins (especially bullatacin and bullatacinone); a camptothecin (including the synthetic analogue topotecan); bryostatin; callystatin; CC-1065 (including its adozelesin, carzelesin and bizelesin synthetic ana
  • calicheamicin especially calicheamicin gammal and calicheamicin omegaH ; dynemicin, including dynemicin A; bisphosphonates, such as clodronate; an esperamicin; as well as neocarzinostatin chromophore and related chromoprotein enediyne antiobiotic chromophores), aclacinomysins, actinomycin, authramycin, azaserine, bleomycins, cactinomycin, carabicin, carminomycin, carzinophilin, chromomycinis, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, ADRIAMYCIN® doxorubicin (including morpholino-doxorubicin, cyanomorpholino- doxorubicin, 2-pyrrolino-doxorubicin
  • anti-hormonal agents refers to agents that act to regulate or inhibit hormone action on tumors such as anti-estrogens and selective estrogen receptor modulators (SERMs), including, for example, tamoxifen (including NOLVADEX® tamoxifen), raloxifene, droloxifene, 4-hydroxytamoxifen, trioxifene, keoxifene, LY 117018, onapristone, and FARESTON- toremifene; aromatase inhibitors that inhibit the enzyme aromatase, which regulates estrogen production in the adrenal glands, such as, for example, 4(5)-imidazoles, aminoglutethimide, MEGASE® megestrol acetate, AROMASIN® exemestane, formestanie, fadrozole, RIVISOR® vorozole, FEMARA® letrozole, and ARIMIDEX® anastrozole; and anti-androgens such as flu
  • SERMs selective estrogen
  • cytokine is a generic term for proteins released by one cell population which act on another cell as intercellular mediators.
  • cytokines are lymphokines, monokines, and traditional polypeptide hormones. Included among the cytokines are growth hormones such as human growth hormone, N-methionyl human growth hormone, and bovine growth hormone; parathyroid hormone; thyroxine; insulin; proinsulin; relaxin; prorelaxin; glycoprotein hormones such as follicle stimulating hormone (FSH), thyroid stimulating hormone (TSH), and luteinizing hormone (LH); epidermal growth factor; hepatic growth factor; fibroblast growth factor; prolactin; placental lactogen; tumor necrosis factor- alpha and -beta; mullerian-inhibiting substance; mouse gonadotropin-associated peptide; inhibin; activin; vascular endothelial growth factor; integrin; thrombopoietin (TPO); nerve growth factors such as N
  • growth inhibitory agent refers to a compound or composition which inhibits growth of a cell in vitro and/or in vivo.
  • the growth inhibitory agent may be one which significantly reduces the percentage of cells in S phase.
  • growth inhibitory agents include agents that block cell cycle progression (at a place other than S phase), such as agents that induce G1 arrest and M-phase arrest.
  • Classical M-phase blockers include the vincas (vincristine and vinblastine), TAXOL®, and topo II inhibitors such as doxorubicin, epirubicin, daunorubicin, etoposide, and bleomycin.
  • DNA alkylating agents such as tamoxifen, prednisone, dacarbazine, mechlorethamine, cisplatin, methotrexate, 5- fluorouracil, and ara-C.
  • DNA alkylating agents such as tamoxifen, prednisone, dacarbazine, mechlorethamine, cisplatin, methotrexate, 5- fluorouracil, and ara-C.
  • radiation therapy refers to the use of directed gamma rays or beta rays to induce sufficient damage to a cell so as to limit its ability to function normally or to destroy the cell altogether. It will be appreciated that there will be many ways known in the art to determine the dosage and duration of treatment. Typical treatments are given as a one-time administration and typical dosages range from 10 to 200 units (Grays) per day.
  • biomarker may refer to markers of disease, prognostic or predictive markers which are typically substances found in a bodily sample that can be easily measured.
  • Said bodily sample can be for instance a blood, plasma or feces sample.
  • biomarker encompasses biophysical and biochemical determinations, including genetic and serological markers.
  • prognosis refers to predicting disease progression or outcome. More specifically, “prognostic markers” may refer to patient or tumor characteristics that predict outcome (usually survival) independent of the treatment. Thus, they are usually identified and validated in patients who receive no therapy or surgical therapy only. The goal of identifying prognostic markers is to define patient subpopulations with significantly different anticipated outcomes, which might benefit from different therapies. Good prognostic patients may not require additional treatment beyond the primary surgical resection, while poor prognostic patients may derive improved survival benefit from adjuvant therapy or other closer clinical follow up or therapeutic strategy.
  • Predictive markers may refer to patient or tumor characteristics that predict benefit from specific treatments (either in terms of tumor shrinkage or survival). In other words, the differences in tumor response or survival benefit between treated versus untreated patients will be significantly different in those positive or negative for the predictive marker (Zhu CQ and Tsao MS, 2014).
  • substantially identical sequence refers to a sequence which is at least about 95%, preferably at least about 96%, 97%, 98%, or 99% identical to a reference sequence. Identity percentage between the two sequences can be determined by any means known in the art, for example the Needleman and Wunsch global alignment algorithm.
  • probe refers to synthetic or biologically produced nucleic acids, between 10 and 285 base pairs in length which contain specific nucleotide sequences that allow specific and preferential hybridization under predetermined conditions to target nucleic acid sequences, and optionally contain a moiety for detection or for enhancing assay performance.
  • Probes may optionally contain certain constituents that contribute to their proper or optimal functioning under certain assay conditions. For example, probes may be modified to improve their resistance to nuclease degradation (e.g., by end capping), to carry detection ligands (e.g., fluorescein), to carry ligands for purification or enrichment purposes (e.g. biotin) or to facilitate their capture onto a solid support (e.g., poly-deoxyadenosine "tails").
  • detection ligands e.g., fluorescein
  • biotin ligands for purification or enrichment purposes
  • solid support e.g., poly-deoxyadenosine "tails"
  • primers refers to oligonucleotides that can be used in an amplification method, such as a polymerase chain reaction (“PCR”), to amplify a nucleotide sequence. Primers are designed based on the polynucleotide sequence of a particular target sequence.
  • PCR polymerase chain reaction
  • nucleotide sequence will hybridize to/amplify a predetermined target sequence and will not substantially hybridize to/amplify a non-target sequence under the assay conditions, generally stringent conditions are used.
  • hybridization refers to a process by which, under predetermined reaction conditions, two partially or completely complementary strands of nucleic acid are allowed to come together in an antiparallel fashion to form a double-stranded nucleic acid with specific and stable hydrogen bonds, following explicit rules pertaining to which nucleic acid bases may pair with one another.
  • substantially hybridization means that the amount of hybridization observed will be such that one observing the results would consider the result positive with respect to hybridization data in positive and negative controls. Data which is considered “background noise” is not substantial hybridization.
  • stringent hybridization conditions means approximately 35°C to 65°C in a salt solution of approximately 0.9 molar NaCI. Stringency may also be governed by such reaction parameters as the concentration and type of ionic species present in the hybridization solution, the types and concentrations of denaturing agents present, and the temperature of hybridization. Generally as hybridization conditions become more stringent, longer probes are preferred if stable hybrids are to be formed. As a rule, the stringency of the conditions under which hybridization is to take place will dictate certain characteristics of the preferred probes to be employed.
  • affinity reagent may refer to a ligand (e.g., antibody, peptide, protein, nucleic acid or small molecule) that selectively captures (binds to) a target molecule through specific molecular recognition, typically with a binding affinity in the nanomolar to sub-nanomolar range.
  • the affinity reagent may be an aptamer, antibody or antibody-mimetic.
  • affinity may refer to the equilibrium constant for the dissociation of an antigen with an antigen-binding molecule (KD), and is considered a measure for the binding strength between an antigenic determinant and an antigen-binding site on the antigen -binding molecule: the lesser the value of the KD, the stronger the binding strength between an antigenic determinant and the antigen-binding molecule (alternatively, the affinity can also be expressed as the association constant (KA), which is 1/KD).
  • KA association constant
  • the dissociation constant may be the actual or apparent dissociation constant.
  • aptamer or “nucleic acid aptamer” as used herein may refer to an isolated or purified single-stranded nucleic acid (RNA or DNA) that binds with high specificity and affinity to a target through interactions other than Watson-Crick base pairing.
  • An aptamer has a three dimensional structure that provides chemical contacts to specifically bind to a target. Unlike traditional nucleic acid binding, aptamer binding is not dependent upon a conserved linear base sequence, but rather a particular secondary or tertiary structure. That is, the nucleic acid sequences of aptamers are non-coding sequences.
  • any coding potential that an aptamer may possess is entirely fortuitous and plays no role whatsoever in the binding of an aptamer to a target.
  • a typical minimized aptamer is 5-15 kDa in size (15-45 nucleotides), binds to a target with nanomolar to sub-nanomolar affinity, and discriminates against closely related targets (e.g., aptamers will typically not bind to other proteins from the same gene or functional family).
  • the term “antibody” as used herein may refer to an immunoglobulin or an antigen-binding fragment thereof.
  • the term includes, but is not limited to, polyclonal, monoclonal, monospecific, multispecific, humanized, human, chimeric, synthetic, recombinant, hybrid, mutated, grafted, and in vitro generated antibodies.
  • the antibody can include a constant region, or a portion thereof, such as the kappa, lambda, alpha, gamma, delta, epsilon and mu constant region genes.
  • heavy chain constant regions of the various isotypes can be used, including: IgGi, lgG 2 , lgG 3 , lgG 4 , IgM, IgAi, lgA 2 , IgD, and IgE.
  • the light chain constant region can be kappa or lambda.
  • the term "antibody” may also refer to antibody derivatives, such as antibody- based fusion proteins or antibodies further modified to contain additional non-proteinaceous moieties, such as water-soluble polymers, e.g. polyethylene glycol (PEG).
  • PEG polyethylene glycol
  • antigen-binding domain and “antigen-binding fragment” refer to a part of an antibody molecule that comprises amino acids responsible for the specific binding between antibody and antigen.
  • the antigen-binding domain or antigen-binding fragment may only bind to a part of the antigen.
  • Antigen-binding domains and antigen-binding fragments include Fab; a F(ab') 2 fragment (a bivalent fragment having two Fab fragments linked by a disulfide bridge at the hinge region); a Fv fragment; a single chain Fv fragment (scFv); a Fd fragment (having the two V H and CH1 domains); single domain antibodies (sdAbs; consisting of a single V H domain), and other antibody fragments that retain antigen-binding function.
  • the Fab fragment has V H -CH1 and V L -Ci_ domains covalently linked by a disulfide bond between the constant regions.
  • the F v fragment is smaller and has V H and V L domains non-covalently linked.
  • the scF v contains a flexible polypeptide that links (1) the C-terminus of V H to the N-terminus of Vi_, or (2) the C-terminus of Vi_ to the N-terminus of VH .
  • the sdAbs include heavy chain antibodies naturally devoid of light chains and single-domain antibodies derived from conventional four chain antibodies. These antigen-binding domains and fragments are obtained using conventional techniques known to those with skill in the art, and are evaluated for function in the same manner as are intact immunoglobulins.
  • recombinant antibody refers to an antibody produced or expressed using a recombinant expression vector, where the expression vector comprises a nucleic acid encoding the recombinant antibody, such that introduction of the expression vector into an appropriate host cell results in the production or expression of the recombinant antibody.
  • Recombinant antibodies may be chimeric or humanized antibodies, mono- or multi-specific antibodies.
  • an antibody mimetic as used herein refers to single-domain scaffolds, which have been engineered to bind therapeutic targets with affinity and specificity that match that of natural antibodies. Antibody mimetics have been developed utilizing an immunoglobulin-like fold, for example, fibronectin type III, NCAM and CTLA-4.
  • the present invention provides a method for determining whether a patient’s tumor has a TGFp-activated microenvironment, wherein said method comprises or consists of: a) determining the expression levels of CTHRC1 (Collagen Triple Helix Repeat Containing 1) gene in a biological sample isolated from said patient; b) comparing the expression levels of CTHRC1 in the patient’s sample with a reference value; wherein an increase of the value in the patient’s sample with regard to said reference value is indicative that said patient’s tumor has a TGFp -activated microenvironment.
  • CTHRC1 Collagen Triple Helix Repeat Containing 1
  • the present invention concerns a method for identifying cancer patients having a tumor with a high TGFp-activated microenvironment and poor prognosis, wherein said method comprises steps a) to c) as described herein, wherein in step c) an increase of the value in the patient’s sample with regard to said reference value is indicative that said patient’s tumor has a TGFp-activated microenvironment and poor prognosis.
  • Step (a) of the methods of the invention comprises or consists of determining in said biological sample the expression levels of CTFIRC1 .
  • CTFIRC1 Collagen Triple Helix Repeat Containing 1
  • This gene has been described in humans (Gene ID: 115908, updated on 11 -Sep- 2019). Mutations at this locus have been associated with Barrett esophagus and esophageal adenocarcinoma.
  • CTHRC1 gene is located in chromosome 8 (8q22.3; Reference GRCh38.p13 Primary Assembly) and has 5 exons.
  • Alternative splicing transcript variants have been described:
  • the canonical sequence of the mRNA expression product of human CTHRC1 gene corresponds to NM_138455.4 transcript variant 1 , mRNA and is referred as SEQ ID NO:1 :
  • CTHRC1 mRNA may refer to any of CTHRC1 gene transcript variants, preferably, to any transcript variants corresponding to isoform 1 (e.g., transcript variants 1), more preferably, said sequence is SEQ ID NO: 1 .
  • the canonical sequence of the protein expression product of human CTHRC1 gene corresponds to CTHRC1 isoform 1 (NCBI NP 001243028.1/ Q96CG8) and is referred as SEQ ID NO:2:
  • CTHRC1 protein as used herein may refer to any of the protein isoforms, preferably it refers to SEQ ID NO: 2.
  • Step (a) of the methods of the invention requires the determination of the expression levels of CTHRC1 gene in a biological sample isolated from a subject suffering from cancer also referred herein as a “cancer patient”. It may comprise the determination of the expression levels of 900 or less genes, 800 or less genes, 700 or less genes, 600 or less gens, 500 or less genes, 400 or less genes, 300 or less genes, 200 or less genes, 100 or less genes, 90 or less genes, 80 or less genes, 70 or less genes, 60 or less genes, 50 or less genes, 40 or less genes, 30 or less genes, 20 or less genes, 10 or less genes and 5 or less genes.
  • the group of genes which expression is determined in step a) consists of less than 19 genes, preferably step (a) consists of determining the expression levels of CTHRC1 gene.
  • the methods of the invention can be applied to any type of biological sample from a patient, such as a biopsy sample, tissue, cell or fluid (serum, saliva, semen, sputum, cerebral spinal fluid (CSF), tears, mucus, sweat, milk, brain extracts and the like).
  • said biological sample from the cancer patient is preferably a sample containing tumor cells. Tumors or portions thereof may be surgically resected from the patient or obtained by routine biopsy.
  • a tumor sample is obtained from the primary tumor.
  • said biological sample isolated from the subject is a tumor biopsy sample, preferably obtained from a resected tumor.
  • sample samples are routinely used in the clinical practice and a person skilled in the art will know how to identify the most appropriate means for their obtaining and preservation.
  • a sample Once a sample has been obtained, it may be used fresh, it may be frozen or preserved using appropriate means (e.g., as a formalin-fixed, paraffin-embedded tissue sample).
  • appropriate means e.g., as a formalin-fixed, paraffin-embedded tissue sample.
  • Such biological samples can be taken around the time of diagnosis, before, during or after treatment (e.g. surgical resection).
  • the determination of the expression of the CTHRC1 gene is carried out at protein level.
  • Suitable methods for determining the levels of a given protein include, without limitation, those described herein below.
  • Preferred methods for determining the protein expression levels in the methods of the present invention are immunoassays.
  • Various types of immunoassays are known to one skilled in the art for the quantitation of proteins of interest. These methods are based on the use of affinity reagents, which may be any antibody or ligand specifically binding to the target protein or to a fragment thereof, wherein said affinity reagent is preferably labeled.
  • labels include radioactive isotopes, enzymes, fluorophores, chemoluminescent reagents, enzyme cofactors or substrates, enzyme inhibitors, particles, dyes, etc.
  • Affinity reagents may be any antibody or ligand specifically binding to the target protein or to a fragment thereof.
  • Affinity ligands may include proteins, peptides, peptide aptamers, affimers and other target specific protein scaffolds, like antibody-mimetics.
  • Specific antibodies against the protein markers used in the methods of the invention may be produced for example by immunizing a host with a protein of the present invention or a fragment thereof.
  • peptides specific against the protein markers used in the methods of the invention may be produced by screening synthetic peptide libraries.
  • Western blot or immunoblotting techniques allow comparison of relative abundance of proteins separated by an electrophoretic gel (e.g., native proteins by 3-D structure or denatured proteins by the length of the polypeptide).
  • Immunoblotting techniques use antibodies (or other specific ligands in related techniques) to identify target proteins among a number of unrelated protein species. They involve identification of protein target via antigen- antibody (or protein-ligand) specific reactions. Proteins are typically separated by electrophoresis and transferred onto a sheet of polymeric material (generally nitrocellulose, nylon, or polyvinylidene difluoride). Dot and slot blots are simplified procedures in which protein samples are not separated by electrophoresis but immobilized directly onto a membrane.
  • Said immunoassay may be for example an enzyme-linked immunosorbent assay (ELISA), a fluorescent immunosorbent assay (FIA), a chemiluminescence immunoassay (CIA), or a radioimmunoassay (RIA), an enzyme multiplied immunoassay, a solid phase radioimmunoassay (SPROA), a fluorescence polarization (FP) assay, a fluorescence resonance energy transfer (FRET) assay, a time-resolved fluorescence resonance energy transfer (TR-FRET) assay, a surface plasmon resonance (SPR) assay.
  • ELISA enzyme-linked immunosorbent assay
  • FFA fluorescent immunosorbent assay
  • CIA chemiluminescence immunoassay
  • RIA radioimmunoassay
  • an enzyme multiplied immunoassay a solid phase radioimmunoassay (SPROA)
  • FP fluorescence polarization
  • FRET fluorescence resonance energy transfer
  • any next generation versions of any of the above such as bead-based flow- cytometry immunoassays (e.g., based on the Luminex xMAP technology) are specifically encompassed.
  • said immunoassay is an ELISA assay or any multiplex version thereof.
  • MS mass spectrometry
  • LC / MS liquid chromatography coupled to mass spectrometry
  • Immunohistochemistry analysis is typically conducted using thin sections of the biological sample immobilized on coated slides. These sections, when derived from paraffin- embedded tissue samples, are deparaffinised and preferably treated so as to retrieve the antigen. The detection can be carried out in individual samples or in tissue microarrays. This procedure, although is subjectively determined by the pathologist, is the standard method of measurement of IHC results, and well known in the art.
  • the use of this technique entails the determination of the Histological score value.
  • the Histological score (H-Score) value may be determined per biological sample according to (i) staining intensity and (ii) the percentage of positive staining tumor cells by using the following formula:
  • H-Score ⁇ (intensity grade x % stained cells)
  • the staining intensity of tumor cells may be scored in different intensity grades, for example the following 4 grades:
  • the percentage of positive staining cells for each intensity grade may be scored from 0 to 100.
  • the final score is preferably calculated by adding the products of the percentage cells stained with a given intensity grade (0-100) by the corresponding staining intensity grade value (0-3).
  • the following formula may be applied:
  • Preferred embodiments do not require determining the percentage of cells stained with a given intensity grade in stromal cells. As described above, the obtained results indicate that the predictive power of this biomarker does not depend on the percentage of cells expressing these markers within the tumor area, but on a semi-quantitative estimation of their positivity (distinguishing between negative (0) / low (1) and moderate (2) / high (3) intensity) in stromal cells surrounding tumor epithelial cells.
  • the assessment of the staining intensity and the percentage of positive staining tumor cells can be determined by any means known to the skilled person including but not limited to one expert pathologist, or a panel of at least two independent pathologists, with no knowledge about clinical data scoring all immunohistochemical stainings. In case, the panel of pathologist were to disagree in the scores it is convenient to expand the panel of independent pathologists to at least 3, 4, or 5.
  • the value of the H-Score may be obtained by applying the above formula.
  • the resulting value of the H-Score determines the level of expression of the protein marker.
  • the mRNA expression level of these genes is determined.
  • Molecular biology methods for measuring quantities of target nucleic acid sequences include but are not limited to end point PCR, competitive PCR, reverse transcriptase-PCR (RT-PCR), quantitative PCR (qPCR), reverse transcriptase qPCR (RT-qPCR), PCR-pyrosequencing, PCR-ELISA, DNA microarrays, gene expression panels (e.g.
  • nucleic acid sequencing such as next generation sequencing methods, in situ hybridization assays (such as dot-blot, Fluorescence In Situ Hybridization assay (FISH), RNA-ISH, automated quantitative RNA ISH (RNAscope®)), mass spectrometry, branched DNA (Nolte, Adv. Clin. Chem. 1998,33:201-235) and to multiplex versions of said methods (see for instance, Andoh et al., Current Pharmaceutical Design, 2009;15,2066- 2073) and the next generation of any of the techniques listed and combinations thereof, all of which are within the scope of the present invention.
  • in situ hybridization assays such as dot-blot, Fluorescence In Situ Hybridization assay (FISH), RNA-ISH, automated quantitative RNA ISH (RNAscope®)
  • mass spectrometry mass spectrometry
  • branched DNA Nolte, Adv. Clin. Chem. 1998,33:201-235
  • Such methods may also include the pre-conversion of mRNA into cDNA by the reaction with a reverse transcriptase (RT), for example the PCR or qPCR reaction is usually preceded by conversion of mRNA into cDNA and referred to as RT-PCR or RT-qPCR, respectively.
  • RT reverse transcriptase
  • next-generation sequencing methods have been described and are well known to a person skilled in the art. These include for instance sequencing by synthesis with cyclic reversible termination approaches (e.g., Illumina, SEQLL, Qiagen), sequencing by synthesis with single-nucleotide addition approaches (e.g., Roche-454, Thermo Fisher-Ion Torrent), sequencing by ligation (e.g., Thermo Fisher SOLiD and BGI-Complete Genomics), real-time long-read sequencing (e.g., Pacific Biosciences, Oxford Nanopore Technologies), synthetic long-read sequencing (e.g., Illumina, 10X Genomics, iGenomeX), see for instance Goodwin S, et al., Nat Rev Genet. 2016, 17(6):333-51 ).
  • cyclic reversible termination approaches e.g., Illumina, SEQLL, Qiagen
  • sequencing by synthesis with single-nucleotide addition approaches e.g., Roche-454, Ther
  • said molecular biology quantification methods are based on sequence specific amplification.
  • Such an amplification based assay comprises an amplification step which comprises contacting a sample (preferably an isolated DNA sample) with two or more amplification oligonucleotides specific for a target sequence in a target nucleic acid to produce an amplified product if the target nucleic sequence is present in the sample.
  • Suitable amplification methods include for example, replicase-mediated amplification, ligase chain reaction (LCR), strand-displacement amplification (SDA), transcription mediated amplification (TMA) and polymerase chain reaction (PCR), which includes quantitative PCR.
  • qPCR quantitative PCR
  • real-time PCR PCR
  • qPCR quantitative PCR
  • RT reverse transcriptase
  • ABI Prism 7700 SDS GeneAmp 5700 SDS
  • ABI Prism 7900 HT SDS from Applied Biosystems
  • iCycler iQ from Bio-Rad
  • Smart Cycler from Cepheid
  • Rotor-Gene from Corbett Research
  • LightCycler from Roche Molecular Biochemicals and Mx4000 Multiplex from Stratagene.
  • the qPCR process enables accurate quantification of the PCR product in real time by measuring PCR product accumulation very early in the exponential phase of the reaction, thus reducing bias in the quantification linked to the PCR amplification efficiency occurring in end-point PCR.
  • Real-time PCR is well known in the art and is thus not described in detail herein.
  • the quantification method is a multiplex qPCR.
  • detection chemistry refers to a method to report amplification of specific PCR product in real-time PCR. These detecting chemistries may be classified into two main groups; the first group comprises double-stranded DNA intercalating molecules, such as SYBR Green I and EvaGreen, whereas the second includes fluorophore-labeled oligonucleotides.
  • Said probes may be dual-labeled oligonucleotides, such as hydrolysis probes or molecular beacons.
  • the 5’ end of the oligonucleotide is typically labelled with a fluorescent reporter molecule while the 3’ end is labeled with a quencher molecule.
  • the sequence of the probe is specific for a region of interest in the amplified target molecule.
  • said probe is a hydrolysis probe which is designed so that the length of the sequence places the 5’ fluorophore and the 3’ quencher in close enough proximity so as to suppress fluorescence.
  • reporter molecules and quenchers for use in qPCR probes are well known in the art.
  • oligonucleotides such as probes and / or primers are used.
  • the term "a primer and / or a probe” specifically includes “primers and / or probes". Both expressions are used interchangeably herein and encompass for example a primer; a probe; a primer and a probe; a pair of primers; and a pair of primers and a probe.
  • Design and validation of primers and probes is well known in the art. For the design of primers and probes in quantitative real-time PCR methods, see for instance Rodriguez A et al. (Methods Mol Biol., 2015, 1275:31-56).
  • Preferred primers and/or probes which may be used in the methods of the invention are described herein below under the kits of the invention.
  • oligonucleotides useful in the methods of the invention are about 5 to about 50 nucleotides in length, about 10 to about 30 nucleotides in length, or about 20 to about 25 nucleotides in length.
  • oligonucleotides specifically hybridizing with the target sequence are about 19 to about 21 nucleotides in length.
  • said oligonucleotides have been modified for detection or to enhance assay performance.
  • These oligonucleotides may be ribonucleotides or deoxyribonucleotides.
  • the oligonucleotides may have at least one chemical modification.
  • suitable oligonucleotides may be comprised of one or more “conformationally constrained” or bicyclic sugar nucleoside modifications, for example, “locked nucleic acids.”
  • “Locked nucleic acids” LNAs
  • LNAs Locked nucleic acids
  • the oligonucleotides may comprise peptide nucleic acids (PNAs), which contain a peptide-based backbone rather than a sugar-phosphate backbone.
  • oligonucleotides may contain, but are not limited to, sugar modifications, such as 2’-0-alkyl ( e.g . 2’-0-methyl, 2’-0-methoxyethyl), 2’-fluoro, and 4’ thio modifications, and backbone modifications, such as one or more phosphorothioate, morpholino, or phosphonocarboxylate linkages.
  • sugar modifications such as 2’-0-alkyl ( e.g . 2’-0-methyl, 2’-0-methoxyethyl), 2’-fluoro, and 4’ thio modifications
  • backbone modifications such as one or more phosphorothioate, morpholino, or phosphonocarboxylate linkages.
  • these oligonucleotides can comprise one or more affinity enhancing modifications, such as, but not limited to, LNAs, bicyclic nucleosides, phosphonoformates, 2’ O-alkyl and the like.
  • the oligonucleotides may be chemically modified, for instance to improve their resistance to nuclease degradation (e.g., by end capping), to carry detection ligands (e.g., fluorescein) or to facilitate their capture onto a solid support (e.g., poly-deoxyadenosine "tails").
  • RNA isolated from frozen or fresh samples is extracted from the cells by any of the methods typical in the art, for example, Sambrook, Fischer and Maniatis, Molecular Cloning, a laboratory manual, (2nd ed.), Cold Spring Flarbor Laboratory Press, New York, (1989). Preferably, care is taken to avoid degradation of the RNA during the extraction process.
  • the expression level is determined using mRNA obtained from a formalin-fixed, paraffin-embedded tissue sample.
  • An exemplary deparaffinization method involves washing the paraffinized sample with an organic solvent, such as xylene, for example. Deparaffinized samples can be rehydrated with an aqueous solution of a lower alcohol.
  • Suitable lower alcohols for example include, methanol, ethanol, propanols, and butanols.
  • Deparaffinized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example. Alternatively, the sample is simultaneously deparaffinised and rehydrated. The sample is then lysed and RNA is extracted from the sample.
  • kits may be used for RNA extraction from paraffin samples, such as PureLinkTM FFPE Total RNA Isolation Kit (Thermofisher Scientific Inc., US).
  • the expression "determining the expression levels” as used herein, refers to ascertaining the absolute or relative amount or concentration of the biomarker in the sample. Techniques to assay levels of individual biomarkers from test samples are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed.
  • Expression levels may be absolute or relative. When the expression levels are normalized, normalization can be performed with respect to different measures in the sample. These procedures are well known to one skilled in the art. Typically, expression levels are normalized with respect to an "endogenous control".
  • An "endogenous control” as used herein may relate to a gene expression product whose expression levels do not change or change only in limited amounts in tumor cells with respect to non-tumorigenic cells.
  • Endogenous control also referred herein as “control gene” or “normalizing gene” is usually the expression product from a housekeeping gene and which codes for a protein which is constitutively expressed and carries out essential cellular functions.
  • Housekeeping genes that can be used as endogenous control include for example b-2-microglobulin, ubiquitin, 18- S ribosomal protein, cyclophilin, GAPDH, actin and HPRT.
  • control genes which can be used for normalization purposes is not particularly limited. For illustrative, but not limiting, purposes, this may be conducted with one, two, three, four, five, six, seven eight, nine, ten, eleven, twelve, fifteen, twenty, or as many genes as desired. For instance, when next generation sequencing methods are used to quantify transcript expression, normalization may be conducted against the whole genome.
  • step (b) of the methods of the invention comprises comparing the expression levels of CTHRC1 in the patient’s sample with a reference value and classifying/selecting the cancer patient as having a TGFp activated microenvironment said patient’s tumor has a TGFp -activated microenvironment.
  • step c) said method comprises comparing the CTHRC1 gene expression levels in the subject sample with a reference value; and an increase of the levels in the subject sample with regard to said reference value is indicative of a TGFp activated microenvironment; whereas a decrease of the levels in the subject sample with regard to said reference value is indicative of a tumor which fails to have a TGFp activated microenvironment.
  • step (b) of the methods of the invention comprises: b) calculating a score from the expression levels of the genes determined in step a); and c) determining whether a patient’s tumor has a TGFp-activated microenvironment according to the score obtained in b) by comparison with a reference value; wherein an increase of the value in the patient’s sample with respect to said reference value is indicative that said patient’s tumor has a TGFp -activated microenvironment.
  • the score is a value obtained according to a given mathematical algorithm wherein the expression values of each of the gene markers used in the methods of the invention are variables of said mathematical algorithm.
  • the score is proportional to the expression levels of CTFIRC1 ; wherein the higher the score, the higher the TGFp activation of the TME.
  • said score may be calculated as the sum of the product between the gene expression values and their estimated regression coefficients obtained in a regression analysis.
  • the coefficient is positive for CTFIRC1 and the higher the score, the higher the TGFp activation of the TME.
  • a TGFp activated microenvironment is defined as a tumor microenvironment comprising TGFp activated fibroblasts, T-cells and macrophages.
  • reference value relates to a predetermined criteria used as a reference for evaluating the values or data obtained from the samples collected from a subject. This “reference value” may also be referred as “cut-off value” or “threshold value”.
  • the reference value can be an absolute value, a relative value, a value that has an upper or a lower limit, a range of values, an average value, a median value, a mean value, a z-score (e.g. mean value + or - 1 standard deviation (SD)), a tertile value, or a value as compared to a particular control or baseline value.
  • said reference value is the mean value or the tertile value.
  • threshold or cut-off level of expression may be selected, for example, based on data from Receiver Operating Characteristic (ROC) plots.
  • ROC Receiver Operating Characteristic
  • Sensitivity, specificity, and/or accuracy are parameters typically used to describe the validity or performance of a test. In particular, they are used to quantify how good and reliable the discrimination method is.
  • a test is usually calibrated in terms of the desired specificity and sensitivity according to the target use of the test in clinical practice. High sensitivity corresponds to high negative predictive value and is considered generally a desired property for a “rule out” test, such as a screening test which typically will be followed by a confirmatory test. High specificity corresponds to high positive predictive value and is considered generally a desired property for a “rule in” test, such as a companion diagnostic test.
  • the methods of the invention have sensitivity, specificity and/or accuracy values of at least about 60 %, preferably of at least about 70 %, and can be, for example, at least 75 %, at least 80 %, at least 85 %, at least 90 %, at least 95 %, at least 96 %, at least 97 %, at least 98 %, at least 99 % or 100% in at least 60 % of the group or population assayed, or preferably in at least 65 %, 70 %, 75 %, 80 %, 85 %, 90 %, 95 % or 100 % of the group or population assayed.
  • a reference value can be based on an individual sample value but is generally based on a large number of samples, including or excluding the sample to be tested.
  • this reference value may be derived from a collection of tumor tissue samples from a reference cancer patients’ population for whom historical information relating to the actual clinical outcome for the corresponding cancer patient is available.
  • Said reference cancer patient’s population may for instance be from subjects suffering from one or more of the cancer types referred herein, including particular subgroups therefrom (e.g. patients belonging to a particular tumor-node-metastasis (TNM) stage(s)).
  • TPM tumor-node-metastasis
  • the expression levels of a gene are considered “decreased” with regard to a reference value when its value is lower than said reference value.
  • the expression levels of a gene are considered to be lower than a reference value when these are at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more lower than the reference value.
  • the expression levels of a gene are considered “increased” with regard to a reference value when its value is higher than a reference value.
  • the score is considered to be higher than a reference value when it is at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more higher than a reference value.
  • the method of the invention does not claim to be correct in 100% of the analyzed samples. However, it requires that a statistically significant amount of the analyzed samples are classified correctly.
  • the amount that is statistically significant can be established by a person skilled in the art by means of using different statistical significance measures obtained by statistical tests; illustrative, non limiting examples of said statistical significance measures include determining confidence intervals, determining the p-value, etc.
  • Preferred confidence intervals are at least 90%, at least 95%, at least 97%, at least 98%, at least 99%.
  • the p-values are, preferably less than 0.1 , less than 0.05, less than 0.01 , less than 0.005 or less than 0.0001 .
  • the teachings of the present invention preferably allow correctly classifying at least 60%, at least 70%, at least 80%, or at least 90% of the subjects of a determining group or population analyzed.
  • the accuracy of the method of the invention can be further increased by additionally considering other gene markers, biochemical parameters and/or clinical characteristics of the patients (e.g. age, sex, tobacco and/or other risk factors). Determination of these other markers, parameters and/or characteristics (including the characterization of the tumor subtype according to these) can be sequential or simultaneous to any or all of the method steps as described herein above.
  • the methods of the invention may be applied or common to several cancer types, including, but not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia. More particular examples of such cancers include breast cancer, squamous cell cancer, small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung, squamous carcinoma of the lung, cancer of the peritoneum, hepatocellular cancer, gastrointestinal cancer, pancreatic cancer, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bladder cancer, hepatoma, colon cancer, colorectal cancer, endometrial or uterine carcinoma, salivary gland carcinoma, kidney cancer, liver cancer, prostate cancer, renal cancer, vulval cancer, thyroid cancer, hepatic carcinoma, gastric cancer, melanoma, and various types of head and neck cancer.
  • cancers include breast cancer, squamous cell cancer, small-cell lung cancer, non-small cell lung cancer, adenocar
  • said method is not tissue-specific.
  • said method is useful in the determination of a TGFp-activated microenvironment in at least two, preferably three, four, five, six or seven different cancer types.
  • this cancer is selected from the group consisting of colorectal cancer, stomach adenocarcinoma, bladder cancer, pancreas adenocarcinoma, prostate cancer, lung cancer, breast cancer.
  • said cancer is selected from the group consisting of colorectal cancer, stomach adenocarcinoma, bladder cancer, pancreas adenocarcinoma, breast cancer and bladder cancer.
  • the cancer is colorectal cancer.
  • the subject has a solid tumor.
  • This solid may be previously treated or untreated.
  • the subject has a previously untreated solid tumor. Tumors can be further stratified according to its stage of development.
  • the tumor-node-metastasis (TNM) staging system is the standard method used for treatment selection and clinically predicting survival of patients with cancer.
  • the TNM staging system was developed and is maintained by the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC). It was developed as a tool for doctors to stage different types of cancer based on certain, standardized criteria (www.cancerstaging.org).
  • AJCC American Joint Committee on Cancer
  • UICC Union for International Cancer Control
  • the TNM staging system is based on the extent of the tumor (T), the extent of spread to the lymph nodes (N), and the presence of metastasis (M).
  • the T category describes the original (primary) tumor.
  • the N category describes whether or not the cancer has reached nearby lymph nodes
  • the M category tells whether there are distant metastases (spread of cancer to other parts of the body).
  • non-anatomic factors are used in groupings, there is a definition of the groupings provided for cases where the non-anatomic factor is not available (X) or where it is desired to assign a group ignoring the non-anatomic factor.
  • Stage I cancers are the least advanced and often have a better prognosis. Higher stage cancers are often more advanced but, in many cases, can still be treated successfully.
  • stage classification For instance, for CRC the following stage classification may be used:
  • Stage I T1 NO MO T11 Tumor invades submucosa Stage I T2 NO MO T2: Tumor invades muscularis muscularis
  • Stage ll-A T3 NO MO T3 Tumor invades subserosa or beyond (without other organs involved)
  • Stage ll-B T4 NO MO T4 Tumor invades adjacent organs or perforates the visceral peritoneum
  • Stage lll-A T 1 -2 N1 MO N1 Metastasis to 1 to 3 regional lymph nodes. T 1 or T2.
  • Stage lll-B T3-4 N1 MO N1 Metastasis to 1 to 3 regional lymph nodes. T3 or T4.
  • Stage lll-C any T, N2 MO N2 Metastasis to 4 or more regional lymph nodes. Any T.
  • Stage IV any T, any N,M1 M1 : Distant metastases present. Any T, any N.
  • the methods of the invention comprise further to step a):
  • A1) determining in said sample the stage according to the TNM classification of tumors; and b) calculating a score from the expression levels of the markers determined in the biological sample as defined in step a) and the stage of TNM classification as defined in step A1); and c) determining whether a patient’s tumor has a TGFp-activated microenvironment according to the score obtained in b).
  • the methods of the invention do not comprise determining the stage according to the TNM classification of tumors as defined in stage A1) above.
  • tumors e.g. CRC, gastric cancer, etc.
  • MSI microsatellite instable tumors
  • MSS microsatellite stable tumors
  • MSS tumors are characterized by changes in chromosomal copy number and show worse prognosis, on the contrary the less common MSI tumors (about 15% in colon tumors) are characterized by the accumulation of a high number of mutations and show predominance in females, proximal colonic localization, poor differentiation, tumor-infiltrating lymphocytes and better prognosis.
  • these subtypes have been described to exhibit different responses to chemotherapeutic agents, and it is also well established that they arise from a distinctive molecular mechanism.
  • MSI tumors result from the inactivation of DNA mismatch repair genes like MLH-1.
  • MSI status is generally determined by immunohistochemistry (IHC) methods that detect protein MLH1 , MSH2, MSH6 and/or PMS2 (Manavis J., et al. Appl Immunohistochem Mol Morphol. 2003 Mar;11(1) :73-7).
  • IHC immunohistochemistry
  • PMS2 Manavis J., et al. Appl Immunohistochem Mol Morphol. 2003 Mar;11(1) :73-7.
  • other techniques such as molecular biology techniques to evaluate microsatellites or even newly developed Next generation sequencing methods can also be used (Nowak J.A., et al. J Mol Diagn. (2017) 19(1): 84-91). Bethesda guidelines are typically followed to determine MSI/MSH status (Umar, A. et al. J. Natl. Cancer Inst. (2004) 96, 261-268).
  • the biomarker described herein was shown by the inventors to enable identifying tumors (e.g., CRC or stomach cancer) presenting a TGFp-activated microenvironment independently of microsatellite stability status, that is, both in microsatellite stable (MSS) and microsatellite instable (MSI) phenotypes (Examples 2 and 5; and Figures 3 and 7).
  • MSS microsatellite stable
  • MSI microsatellite instable
  • the method of the invention is characterized by identifying a TGFp-activated microenvironment in any of microsatellite instable (MSI) or microsatellite stable (MSS) tumors.
  • MSI microsatellite instable
  • MSS microsatellite stable
  • MSI-high (MSI-H) status has been associated with a better prognosis in early-stage CRC, and has emerged as a predictor of sensitivity to immunotherapy treatments with checkpoint inhibitors (Battaglin et al., Advances in Flematology and Oncology 2018, 16(11), 735-747). Nevertheless, as above-mentioned, immune checkpoint inhibitors were found by Tauriello et al. (Nature, Vol. 554 (2016) 539 - 543) to lack efficacy in CRC tumors with a TGFp activated microenvironment.
  • the signature of the invention enables to identify from patients having a MSI tumor (e.g., CRC or stomach cancer) those which would likely not benefit from an immune check point inhibitor treatment alone and select the same for a combination treatment with an inhibitor of the TGFp signaling pathway.
  • a MSI tumor e.g., CRC or stomach cancer
  • said patient’s tumor is a MSI tumor.
  • said tumor is a MSI colorectal cancer tumor.
  • the methods of the invention do not comprise determining the tumor MSI/MSS status.
  • the methods of the invention comprise further to step a): A2) determining in said sample the tumor MSI/MSS status; and b) calculating a score from the expression levels of the markers determined in the biological sample as defined in step a) and the tumor MSI/MSS status as defined in step A2), optionally the stage of TNM classification as defined in step A1); and c) determining whether a patient’s tumor has a TGFp-activated microenvironment according to the score obtained in b).
  • CMS consensus molecular subtype
  • CMSs consensus molecular subtypes
  • the method of the invention was also able to capture tumors (e.g. CRC or stomach cancer) with a TGFp activated TME classified under other CMS groups (Example 2), which would otherwise likely not had been selected for treatment with an inhibitor of the TGFp signaling pathway.
  • the method of the invention is capable of identifying a tumor with a TGFp- activated microenvironment in any of CMS1 , CMS2, CMS3 or CMS4 molecular subtype tumors, preferably in any of CMS1 , CMS3 or CMS4.
  • the methods of the invention do not comprise determining the tumor CMS subtype.
  • the tumor CMS subtype is determined and said tumor is of any of the CMS1 , CMS3 or CMS4 subtypes.
  • said tumor is CMS1 , CMS3 or CMS4 CRC.
  • the methods of the invention comprise further to step a):
  • A3) determining in said sample the tumor CMS subtype; and b) calculating a score from the expression levels of the markers determined in the biological sample as defined in step a) and the tumor CMS subtype as defined in A3), optionally the stage of TNM classification as defined in step A1) and/or the tumor MSI/MSS status defined in step A2); and c) determining whether a patient’s tumor has a TGF p -activated microenvironment according to the score obtained in b).
  • the invention also pertains to a method for predicting the efficacy of (or the likelihood of response to) a treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. predicting a higher efficacy or likelihood of response to a treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
  • the present invention also relates to a method for selecting a cancer patient which is likely to benefit from a treatment with an inhibitor of the TGFp signaling pathway, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. selecting said cancer patient for the treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
  • This treatment with an inhibitor of the TGFp signaling pathway may be a neoadjuvant treatment administered prior to the surgical removal of the tumor and/or an adjuvant treatment after the surgical intervention.
  • said treatment is an adjuvant treatment.
  • a treatment with an inhibitor of the TGFp signaling pathway may be as described herein below.
  • step b) is calculated using a computer; and/or the selection, classification and/or determination of step c) is conducted using a computer.
  • a further aspect of the invention refers to a computer implemented method, wherein the method is any of the methods disclosed herein or any combination thereof.
  • This computer program is typically directly loadable into the internal memory of a digital computer, comprising software code portions for performing the steps of comparing the score (e.g., obtained from the level of one or more of the target markers as described in the invention), from the one or more biological samples of a subject with a reference value and determining the prognosis of said subject or whether it would benefit from adjuvant therapy, when said product is run on a computer.
  • the score e.g., obtained from the level of one or more of the target markers as described in the invention
  • any device or apparatus comprising means for carrying out the steps of any of the methods of the present invention or any combination thereof, or carrying a computer program capable of, or for implementing any of the methods of the present invention or any combination thereof, is included as forming part of the present specification.
  • the methods of the invention may also comprise the storing of the method results in a data carrier, preferably wherein said data carrier is a computer readable medium.
  • the present invention further relates to a computer-readable storage medium having stored thereon a computer program of the invention or the results of any of the methods of the invention.
  • a computer readable medium can be any apparatus that may include, store, communicate, propagate, or transport the results of the determination of the method of the invention.
  • the medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium.
  • the invention pertains to an inhibitor of the TGFp signaling pathway for use in a method for treatment of a cancer patient, wherein said patient’s tumor has been determined to have a TGFp-activated microenvironment according to a method as described herein.
  • the invention provides the use of an inhibitor of the TGFp signaling pathway in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected as having a tumor with a TGFp-activated microenvironment by a method as described herein.
  • the invention relates to a method of treating a cancer patient by administering a therapeutically effective amount of an inhibitor of the TGFp signaling pathway, wherein said patient has been selected as having a tumor with a TGFp-activated microenvironment by a method as described herein.
  • the present invention further provides any of the methods as described herein above, which further comprises the step of administering to said patient a therapeutically effective amount of a TGFp signaling pathway inhibitor.
  • Said TGFp signaling pathway inhibitor may be administered as single agent or in combination with another therapy or drug.
  • the inhibitor of the TGFp signaling pathway is administered to said patient in combination (i.e., as a combination treatment) with another anti-cancer treatment, preferably with another anti-cancer agent or radiotherapy.
  • anti-cancer agent has been defined herein above and may include but is not limited to chemotherapeutic agents, growth inhibitory agents, cytotoxic agents, anti-hormonal agents, agents used in radiation therapy, anti-angiogenesis agents, apoptotic agents, anti tubulin agents, etc. and any combinations thereof.
  • Said anti-cancer agent may be administered prior, concomitantly or after the TGFp inhibitor administration.
  • the two drugs may form part of the same composition or be provided as a separate composition for administration at the same time or at a different time.
  • said TGFp signaling pathway inhibitor is administered to said patient in combination with a cytotoxic agent or immune checkpoint inhibitor.
  • the present invention relates to an anti-cancer agent other than a TGFp signaling pathway inhibitor (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy for use in a method for treatment of a cancer patient in combination with a TGFp signaling pathway inhibitor, wherein said patient’s tumor has a TGFp-activated microenvironment according to a method as described herein.
  • a TGFp signaling pathway inhibitor e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy for use in a method for treatment of a cancer patient in combination with a TGFp signaling pathway inhibitor, wherein said patient’s tumor has a TGFp-activated microenvironment according to a method as described herein.
  • the invention provides the use of an anti-cancer agent other than a TGFp signaling pathway inhibitor (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in combination with a TGFp signaling pathway inhibitor in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
  • a TGFp signaling pathway inhibitor e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy in combination with a TGFp signaling pathway inhibitor in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
  • the invention relates to a method of treating a cancer patient by administering a therapeutically effective amount of an anti-cancer agent other than a TGFp signaling pathway inhibitor (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in combination with a therapeutically effective amount of a TGFp signaling pathway inhibitor, wherein said patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
  • said other anti-cancer agent is a cytotoxic agent or immune checkpoint inhibitor.
  • T cells play a central role in cell-mediated immunity.
  • Checkpoint proteins interact with specific ligands which send a signal into the T cell and essentially switch off or inhibit T cell function.
  • Cancer cells take advantage of this system by driving high levels of expression of checkpoint proteins on their surface which results in control of the T cells expressing checkpoint proteins on the surface of T cells that enter the tumor microenvironment, thus suppressing the anti-cancer immune response. As such, inhibition of checkpoint proteins would result in restoration of T cell function and an immune response to the cancer cells.
  • checkpoint proteins include, but are not limited to CTLA-4, PDL1 , PDL2, PD1, B7-H3, B7- H4, BTLA, HVEM, TIM3, GAL9, LAG3, VISTA, KIR, 2B4 (belongs to the CD2 family of molecules and is expressed on all NK, gd, and memory CD8 + (ab) T cells), CD 160 (also referred to as BY55), CGEN-15049, CHK 1 and CHK2 kinases, A2aR and various B-7 family ligands.
  • PD-1 Programmed cell death protein 1
  • PD-1 has two ligands, PD-L1 and PD-L2, which are members of the B7 family.
  • PD-L1 protein is upregulated on macrophages and dendritic cells (DC) in response to LPS and GM- CSF treatment, and on T cells and B cells upon TCR and B cell receptor signaling, whereas in resting mice, PD-L1 mRNA can be detected in the heart, lung, thymus, spleen, and kidney.
  • DC macrophages and dendritic cells
  • PD-L1 mRNA can be detected in the heart, lung, thymus, spleen, and kidney.
  • PD-1 negatively regulates T cell responses.
  • PD-1 has been shown to play a role in tumor-specific escape from immune surveillance. It has been demonstrated that PD-1 is highly expressed in tumor-specific cytotoxic T lymphocytes (CTLs) in both chronic myelogenous leukemia (CML) and acute myelogenous leukemia (AML). PD-1 is also up-regulated in melanoma infiltrating T lymphocytes (TILs) (Dotti, Blood (2009) 114 (8): 1457-58).
  • CTLs tumor-specific cytotoxic T lymphocytes
  • CML chronic myelogenous leukemia
  • AML acute myelogenous leukemia
  • TILs melanoma infiltrating T lymphocytes
  • Tumors have been found to express the PD-1 ligand (PDL-1 and PDL-2) which, when combined with the up-regulation of PD-1 in CTLs, may be a contributory factor in the loss in T cell functionality and the inability of CTLs to mediate an effective anti-tumor response.
  • PDL-1 and PDL-2 lymphocytic choriomeningitis virus
  • said method comprises administering to the cancer patient an inhibitor of the TGFp signaling pathway in combination with an agent that is an immune checkpoint inhibitor.
  • This checkpoint inhibitor may be a biologic therapeutic or a small molecule.
  • the checkpoint inhibitor is an antibody.
  • the checkpoint inhibitor inhibits a checkpoint protein which may be CTLA-4, PDL1 , PDL2, PD1 , B7-H3, B7-H4, BTLA, HVEM, TIM3, GAL9, LAG3, VISTA, KIR, 2B4, CD160, CGEN-15049, CHK 1 , CHK2, A2aR, B-7 family ligands or a combination thereof.
  • this immune-check point inhibitor is an anti-PD1/PDL1 inhibitor, including combinations of any thereof.
  • anti-PD1/PDL1 inhibitors are the following PD-1 inhibitors currently being tested in clinical trials:
  • CT-011 is a humanized lgG1 monoclonal antibody against PD-1.
  • a phase II clinical trial in subjects with diffuse large B-cell lymphoma (DLBCL) who have undergone autologous stem cell transplantation was recently completed. Preliminary results demonstrated that 70% of subjects were progression-free at the end of the follow-up period, compared with 47% in the control group, and 82% of subjects were alive, compared with 62% in the control group.
  • This trial determined that CT-011 not only blocks PD-1 function, but it also augments the activity of natural killer cells, thus intensifying the antitumor immune response.
  • BMS 936558 is a fully human lgG4 monoclonal antibody targeting PD-1 agents under a phase I trial, biweekly administration of BMS-936558 in subjects with advanced, treatment- refractory malignancies showed durable partial or complete regressions. The most significant response rate was observed in subjects with melanoma (28%) and renal cell carcinoma (27%), but substantial clinical activity was also observed in subjects with non small cell lung cancer (NSCLC), and some responses persisted for more than a year. It was also relatively well tolerated; grade >3 adverse events occurred in 14% of subjects.
  • BMS 936559 is a fully human lgG4 monoclonal antibody that targets the PD-1 ligand PD-L1.
  • Phase I results showed that biweekly administration of this drug led to durable responses, especially in subjects with melanoma.
  • Objective response rates ranged from 6% to 17%) depending on the cancer type in subjects with advanced-stage NSCLC, melanoma, RCC, or ovarian cancer, with some subjects experiencing responses lasting a year or longer.
  • MK 3475 is a humanized lgG4 anti-PD-1 monoclonal antibody in phase I development in a five-part study evaluating the dosing, safety, and tolerability of the drug in subjects with progressive, locally advanced, or metastatic carcinoma, melanoma, or NSCLC.
  • MPDL 3280A is a monoclonal antibody, which also targets PD-L1 , undergoing phase I testing in combination with the BRAF inhibitor vemurafenib in subjects with BRAF V600- mutant metastatic melanoma and in combination with bevacizumab, which targets vascular endothelial growth factor receptor (VEGFR), with or without chemotherapy in subjects with advanced solid tumors.
  • VAGFR vascular endothelial growth factor receptor
  • AMP 224 is a fusion protein of the extracellular domain of the second PD-1 ligand, PD-L2, and IgGI, which has the potential to block the PD-L2/PD-1 interaction.
  • AMP-224 is currently undergoing phase I testing as monotherapy in subjects with advanced cancer.
  • Medi 4736 is an anti-PD-L1 antibody in phase I clinical testing in subjects with advanced malignant melanoma, renal cell carcinoma, NSCLC, and colorectal cancer.
  • CTLA4 cytotoxic T-lymphocyte-associated protein
  • CTLA4 is a protein receptor that down regulates the immune system.
  • CTLA4 is found on the surface of T cells, which lead the cellular immune attack on antigens.
  • the T cell attack can be turned on by stimulating the CD28 receptor on the T cell.
  • the T cell attack can be turned off by stimulating the CTLA4 receptor.
  • this immune-check point inhibitor is an anti- CTLA4 inhibitor, such as ipilimumab (YervoyTM), or any combination thereof.
  • the inhibitor of the TGFp signaling pathway alone or in combination with an anti-cancer agent may be formulated as a pharmaceutical composition, wherein said pharmaceutical composition further comprises a pharmaceutically acceptable excipient, vehicle or carrier.
  • Said pharmaceutical composition may be a “dosage form” devised to enable administration of the drug medication in the prescribed dosage amounts. Depending on the method/route of administration different dosage forms will be used.
  • the present invention relates to an anti-cancer agent (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy for use in a method for treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said anti-cancer agent or radiotherapy is not administered in combination with a TGFp signaling pathway inhibitor.
  • an anti-cancer agent e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy for use in a method for treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said anti-cancer agent or radiotherapy is not administered in combination with a TGFp signaling pathway inhibitor.
  • the invention provides the use of an anti-cancer agent (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in the manufacturing of a medicament for the treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said anti-cancer agent or radiotherapy is not administered in combination with a TGF p signaling pathway inhibitor.
  • an anti-cancer agent e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy in the manufacturing of a medicament for the treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said anti-cancer agent or radiotherapy is not administered in combination with a TGF p signaling pathway inhibitor.
  • the invention relates to a method of treating a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, by administering a therapeutically effective amount of an anti-cancer agent (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy, wherein said anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway.
  • an anti-cancer agent e.g., cytotoxic agent or immune checkpoint inhibitor
  • radiotherapy e.g., cytotoxic agent or immune checkpoint inhibitor
  • the invention relates to a method for determining the prognosis of a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. classifying the patient as having poor prognosis when said patient’s tumor has a TGFp-activated microenvironment.
  • a method for determining the prognosis of a cancer patient comprises: a) determining the expression levels of CTHRC1 (Collagen Triple Helix Repeat Containing 1) gene in a tumor sample isolated from said patient; b) comparing the expression levels of CTHRC1 in the patient’s sample with a reference value; c) wherein an increase of the value in the patient’s sample with regard to said reference value is indicative of poor prognosis.
  • Poor prognosis in CRC has been associated to the presence of a TGFb-activated stroma (Calon et al., Cancer cell 22 (2012) 571-584; Calon et al., Nature genetics 47:4 (2015) 320- 332).
  • the method of the invention is used to determine those patients with poor prognosis/outcome which have been classified under CMS1-4 subgroups when using the consensus molecular classification (Guinney et al. 2015), especially CMS4 patients.
  • CTHRC1 expression enables to identify patients with poor prognosis independently from their MSS/MSI status (Fig. 3).
  • the method of the invention is used to determine those patients with poor prognosis/outcome from those which have been classified as MSI (typically associated with better prognosis) further to microsatellite stability determination.
  • Example 3 in a multivariate analysis the inventors found a statistically significant correlation between CTHRC1 levels (as a continuous variable) and prognosis, for the global group of CRC patients analyzed (stage I, II and III) and in the differentiated analysis by tumor stage (II and III separately).
  • the method of the invention is used to determine those CRC patients with poor prognosis/outcome from those which have been classified according to the AJCC criteria as stage II or III, preferably from those classified under stage II.
  • the method of the invention is used for stratifying CRC patients classified under stage II or III, preferably stage II, and selecting those having a TGFp- activated TME and poor prognosis for an adjuvant and/or neoadjuvant treatment (e.g. a treatment with a TGFp inhibitor as described herein).
  • an adjuvant and/or neoadjuvant treatment e.g. a treatment with a TGFp inhibitor as described herein.
  • Disease progression or outcome may be measured using different parameters, including but not limited to, tumor growth, tumor growth delay, increase/decrease of tumor size, increase/decrease in tumor markers, and patient’s survival.
  • the clinical outcome of a subject is expressed as overall survival and/or disease-free survival.
  • Survival of cancer patients is generally suitably expressed by Kaplan-Meier curves, named after Edward L. Kaplan and Paul Meier who first described it (Kaplan, Meier: Amer. Statist. Assn. 53:457-481).
  • the Kaplan-Meier estimator is also known as the product limit estimator. It serves for estimating the survival function from life-time data.
  • a plot of the Kaplan-Meier estimate of the survival function is a series of horizontal steps of declining magnitude which, when a large enough sample is taken, approaches the true survival function for that population. The value of the survival function between successive distinct sampled observations is assumed to be constant.
  • the Kaplan-Meier estimator may be used to measure the fraction of patients living for a certain amount of time after beginning a therapy (e.g. after tumor resection).
  • the clinical outcome predicted may be the (overall/disease-free) survival in months/years from the time point of taking the sample. It may be survival for a certain period from taking the sample, such as of six months or more, one year or more, two years or more, three years or more, four years or more, five years or more, six years or more.
  • “survival” may refer to “overall survival” or “disease free survival”.
  • disease free survival is defined as the interval of time from start of treatment (e.g., date of surgery) to the first measurement of cancer growth.
  • all survival is defined as the interval of time from the start of treatment (e.g., date of surgery) to death from any cause.
  • the term “poor prognosis” as used herein may refer to a high risk of recurrence, relapse, metastasis and/or death.
  • the term “poor prognosis” means a survival (i.e. DFS and/or OS) of six months or less, one year or less, two years or less, three years or less, four years or less, five years or less, six years or less, etc.
  • the term poor prognosis refers to a DFS and/or OS of less than 5 years.
  • the present invention relates to a method for monitoring or evaluating the response to treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises determining whether a patient’s tumor has a TGFp- activated microenvironment as described herein, wherein a TGFp-activated microenvironment is associated to lack of response.
  • a method for monitoring or evaluating the response to treatment with a TGFp signaling pathway inhibitor in a cancer patient comprises: a) determining the expression levels of CTHRC1 (Collagen Triple Helix Repeat Containing 1) gene in a tumor sample isolated from said patient; b) comparing the expression levels of CTHRC1 in the patient’s sample with a reference value; wherein a decrease of the value in the patient’s sample with regard to said reference value is indicative of response to the treatment.
  • said treatment is an adjuvant treatment administered to the patient after surgical resection.
  • said reference value is the value of the expression levels of CTHRC1 in this patient at an earlier time point.
  • the invention concerns a kit suitable for determining the expression levels of the CTHRC1 gene in an isolated tumor sample, wherein said kit comprises: i. a reagent for the quantification of the CTHRC1 gene expression levels; and ii. optionally, a reagent for the quantification of a control gene expression levels; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
  • kit or "testing kit” denotes combinations of reagents and adjuvants required for an analysis. Although a test kit consists in most cases of several units, one-piece analysis elements are also available, which must likewise be regarded as testing kits.
  • said kit comprises: i. an oligonucleotide specific to CTHRC1 mRNA or an affinity reagent for the CTHRC1 gene encoded protein; and ii. optionally, an oligonucleotide specific to a normalizing gene or an affinity reagent for the control gene encoded protein; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
  • said kit comprises: i. an oligonucleotide specific to SEQ ID NO:1 or an affinity reagent for SEQ ID NO:2; ii. optionally, an oligonucleotide specific to a normalizing gene or an affinity reagent for a control gene encoded protein; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
  • the various oligonucleotides/affinity reagents may be labelled with the same or different tags. Preferably, these will be labelled with different tags for multiplex analysis.
  • Said reagents in points i) to iii) may be as described herein above for the methods of nucleic acid and protein quantification in step (a) of the methods of the invention.
  • said oligonucleotide specific to CTHRC1 mRNA in i) is a primer and/or probe specific to CTHRC1 mRNA.
  • said oligonucleotide specific to CTHRC1 gene mRNA is a pair of primers (e.g., one forward and one reverse) and a probe, for instance a hydrolysis probe (e.g. a Taqman probe) that aligns within the fragment amplified by the two primers.
  • a probe for instance a hydrolysis probe (e.g. a Taqman probe) that aligns within the fragment amplified by the two primers.
  • a hydrolysis probe e.g. a Taqman probe
  • said kit may further comprise an oligonucleotide specific to a control gene.
  • said oligonucleotide is a primer and/or probe specific to said control gene. This is typically a housekeeping gene as defined herein above.
  • kits may comprise additional reagents.
  • said kit comprises reagents to perform a real-time PCR reaction, which typically contain a DNA polymerase, such as Taq DNA polymerase ⁇ e.g., hot- start Taq DNA polymerase), buffer, magnesium, dNTPs, and optionally other agents (e.g., stabilizing agents such as gelatin and bovine serum albumin).
  • a DNA polymerase such as Taq DNA polymerase ⁇ e.g., hot- start Taq DNA polymerase
  • buffer such as buffer, magnesium, dNTPs, and optionally other agents (e.g., stabilizing agents such as gelatin and bovine serum albumin).
  • agents e.g., stabilizing agents such as gelatin and bovine serum albumin.
  • real-time PCR reaction mixtures also contain reagents for real time detection and quantification of amplification products as described above herein.
  • said affinity reagent is an antibody (i.e., an anti- CTHRC1 antibody), preferably a monoclonal antibody.
  • the affinity reagent may bind to any linear or conformational region (e.g. epitope) specific for CTHRC1 protein.
  • the antibody Vli-55 used in the Examples was raised against a synthetic peptide corresponding to the conserved C terminus of CTHRC1.
  • said affinity reagent specific to CTHRC1 is the rabbit monoclonal antibody Vli-55 (MMCRI, 2019) or antibodies binding to the same antigenic region and/or epitope in the marker protein.
  • Monoclonal antibodies may also be produced by preparing immortalized cell lines capable of producing antibodies having the desired specificity. Such immortalized cell lines may be produced in a variety of ways. Conveniently, a small non-human animal, such as a mouse, is hyperimmunized with the desired immunogen.
  • the vertebrate is then sacrificed, usually several days after the final immunization, the spleen cells removed, and the spleen cells immortalized.
  • the most common technique is fusion with a myeloma cell fusion partner, as first described by Kohler and Milstein (1975) Nature 256:495-497.
  • Other techniques including EBV transformation, transformation with bare DNA, e.g., oncogenes, retroviruses, etc., or any other method which provides for stable maintenance of the cell line and production of monoclonal antibodies are also well known. Specific techniques for preparing monoclonal antibodies are described in Antibodies; A Laboratory Manual, Harlow and Lane, eds.. Cold Spring Harbor Laboratory, 1988, the full disclosure of which is incorporated herein by reference.
  • Non-limiting examples of commercially available antibodies specifically binding to UNR protein are ab96124 (Abeam, Cambridge, UK), HP A018846 (Sigma-Aldrich) and HPA052221 (Sigma-Aldrich).
  • said kit comprises reagents to perform an immunohistochemistry (ICH) assay.
  • ICH immunohistochemistry
  • it may contain interalia : an enzyme- conjugated secondary antibody (e.g. conjugated to horseradish peroxidase or alkaline phosphatase), an enzyme substrate, and a counterstain such as hematoxylin.
  • Kits for ICH are well known in the art and commercially available (http://www.sigmaaldrich.com/life- science/cell-biology/antibodies/antibodies- application/protocols/immunohistochemistry.html#reagents_equipment).
  • ICH assay kits for use in a method of the invention also contain reagents for quantification of the target protein markers, as described herein above.
  • the invention relates to the use of a kit as described above in any of the methods as described herein, such as for selecting a cancer patient which is likely to benefit from a treatment with a TGFp signaling pathway inhibitor, for determining whether a patient ' s tumor microenvironment has a TGFp -activated microenvironment, for predicting the tumor prognosis or for monitoring or evaluating the response to treatment with a TGFp signaling pathway inhibitor.
  • FFPE colorectal cancer samples were obtained retrospectively from patients with colon or rectum adenocarcinoma who underwent surgical resection at Hospital del Mar. The study was approved by the Hospital del Mar MarBioBanc and by Hospital Clinic Ethical Committee for Clinical Investigation which acts as the reference Ethical Committee for research carried out at IRB Barcelona. Samples used were from patients that had provided written informed consent, and the study was conducted in accordance with the Declaration of Helsinki.
  • Retrospective CRC cases were collected according to the following guidelines: a) patients with stage 1-3 colon or rectum adenocarcinoma with no residual disease (with an emphasis on stage 2 and 3), b) patients aged 45 years or older at time of primary surgery with no family history of colorectal cancer in order to exclude potential hereditary cases; c) No preoperative cancer therapy.
  • Table 1 Demographics for patients included in the 3 TMAs from the FFPE cohort.
  • TGFp tumor growth factor
  • T-TBRS cancer fibroblasts
  • T-TBRS T-cells
  • Ma- TBRS macrophages
  • CMS Consensus Molecular Subtypes
  • RNA-Seq version 2 data from the The Cancer Genome Atlas (TCGA) project was retrieved from the legacy version of the GDC commons repository (Grossman et al., New England Journal of Medicine (2016), 375: 1109-1112). Clinical and follow-up information was retrieved from the TCGA-Clinical Data Resource (CDR) [https://gdc.cancer.gov/about- data/publications/PanCan-Clinical-2018]. Expression measures were expressed in RSEM in the legacy version, which were log2-transformed and quantile normalized. Samples from different tumors (colon and rectum) and from different platforms (Genome Analyzer and HiSeq) were processed separately.
  • CDR TCGA-Clinical Data Resource
  • Non-primary tumor samples were filtered out and duplicated samples measured in different platforms were excluded from the HiSeq subset. Only primary tumors from patients with no previous cancer diagnosis were kept for analyses. An exploratory analysis was carried out using Principal Component Analyses in order to identify samples with abnormal expression values in each dataset. Samples TCGA-A6-2679- 01 A and TCGA-AA-A004-01 A were excluded as their gene expression showed an anomalous distribution compared to the rest of samples in their dataset, even after quantile normalization. Finally, each dataset was corrected by technical variation using a mixed-effect linear model, in which sample’s center of origin and plate identifiers were modeled as a fixed and a random effect, respectively. Coefficients from the fixed effects and random effects' imputations provided by the models were then used to correct these technical effects.
  • Microarray datasets were separately processed using R packages affy (Gautier et al., Bioinformatics (2004), 3: 307-315) and affyPLM (Bolstad et al., Gentleman R, Carey V, Huber W, Irizarry R, and Dudoit S. (Eds.), (2005) Springer, New York) from Bioconductor (Gentleman et al., Genome Biology (2004), 5: R80).
  • Raw cel files data were processed using RMA (Irizarry et al., Biostatistics (2003), 4:249-264) and annotated using the information available in the Affymetrix web page (Affmetrix - Thermofisher web page. https ://www.thermofisher.com/es/en/home/life-science/microarrav-analvsis/affvmetrix.html).
  • Standard quality controls were performed in order to identify abnormal samples (Gentleman et al., Bioinformatics and Computational Biology Solutions Using R and Bioconductor (2005) (Springer, New York)) regarding: a) spatial artefacts in the hybridization process (scan images and pseudo-images from probe level models); b) intensity dependences of differences between chips (MvA plots); c) RNA quality (RNA digest plot); d) global intensity levels (boxplot of perfect match log-intensity distributions before and after normalization and RLE plots); e) anomalous intensity profile compared to the rest of samples (NUSE plots, Principal Component Analyses).
  • each microarray dataset was corrected a priori by Eklund metrics (Eklund and Szallasi, Genome Biology (2008), 9, R26) effects estimated from a standard linear model.
  • Eklund metrics Eklund and Szallasi, Genome Biology (2008), 9, R26
  • mixed-effect linear models were used to correct microarray expression by the sample’s center of origin (if more than one) and date of scanning (microarrays). Expression values were corrected using the coefficients from the fixed effects and the imputations of the random effects provided by the models.
  • MSI status was imputed in each dataset separately based on the expression of genes included in a published transcriptomic signature (Jorissen et al., Clinical Cancer Research (2008), 14, 8061-8069). For doing so, Pearson’s correlation coefficients were computed between each sample’s profile and an artificial MSI profile consisting on “one” values for genes included in the signature that were over-expressed in MSI samples and a “zero” values for genes over-expressed in MSS samples in (Jorissen et al., Clinical Cancer Research (2008), 14, 8061-8069).
  • GSE35602 contains transcriptomic data for epithelial and stromal cells microdissected from 13 CRC tissue samples and 4 adjacent morphologically normal colorectal mucosae (>5 cm from the tumor).
  • GSE39395 and GSE39396 were processed using RMA [Irizarry RA, et al., Biostatistics (2003) 4, 249-64] (see section Pre processing of microarray CRC datasets) while the series matrix version of GSE35602 was used in the analyses.
  • TCGA RNASeq data for stomach (STAD), bladder (BLCA) and pancreatic (PAAD) adenocarcinoma were downloaded from the GDC commons repository (Grossman et al., New England Journal of Medicine (2016), 375: 1109-1112) and processed in an analogous way to the colon and rectum TCGA datasets.
  • microarray datasets of prostate - GSE21034 (Taylor et al., Cancer Cell (2010), 18: 11-22) and Lung - GSE31210 (Okayama et al., Cancer Research (2011), 72: 100-111) were downloaded from the NCBI GEO repository and processed as described previously (see sections on Pre-processing of microarray CRC datasets).
  • dataset GSE21034 was processed using the oligo R package [Carvalho BS, Irizarry RA (2010). “A Framework for Oligonucleotide Microarray Preprocessing.” Bioinformatics, 26(19), 2363-7. ISSN 1367-4803, doi: 10.1093/bioinformatics/btq431], as their samples were hybridized in a different Affymetrix platform. (Human Exon 1.0 ST Array).
  • Metabric breast cancer data (Curtis et al., Nature (2012), 486, 346-352) was downloaded from the cBioportal for Cancer Genomics [http://cancerdiscovery.aacrjournals.Org/content/2/5/401.abstract]; Metabric's expression values were a-priori corrected using a linear model in which PAM50 subtype was included as a covariate.
  • association with relapse was evaluated using a Cox proportional hazards model. For the rest of association analyses with gene expression levels, linear models were carried out. In all cases, sample’s dataset of origin was included in the models for statistical adjustment. In addition, and for signatures evaluation, a global signature was computed using all the genes in the expression matrix and used as adjusting variable in the models. This strategy has been proved useful to avoid systematic biases due to the gene-correlation structure present in the data and to adjust by the expectation under gene randomization, i.e., the association expected for a signature whose genes have been chosen at random [Adjusting for systematic technical biases in risk assessment of gene signatures in transcriptomic cancer cohorts.
  • Cox proportional hazard ratios smoothed by continuous expression levels were graphically represented using smoothing splines with a p- spline basis (Eilers, Paul H. and Marx, Brian D., Statistical Science 11 (1996) 89-121) as implemented in the R package phenoTest (Evarist Planet (2016). phenoTest: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. R package versionl .30.0.).
  • a heatmap was built to graphically show the correlation between multiple gene expression signatures simultaneously. In this heatmap, centered and scaled signature values were showed using a white to black color gradation, where black indicated the highest expression and white corresponded to the lowest expression values. For clarity, the most extreme expression values were truncated to -1.5 and 1.5.
  • CRC stem cells were purified from patient derived xenografts or fresh human biopsies using the protocol developed to obtain normal colonic mucosa (CoSC) stem cells applied to colorectal cancer samples (Jung et al., 2011 ; Merlos Suarez et al., 2011 ; Calon et al., Nature Genetics 47:4 (2015) 320-332). Briefly, cells were purified from CRC samples that had elevated EpHB2 receptor levels in a Fluorescence Activated Cell Sorter (FACS). They were grown in Matrigel (Basement Membrane Matrix Low Concentration, BD) with a simplified version of the specific CoSCs medium described by Jung et al.
  • FACS Fluorescence Activated Cell Sorter
  • EpHB2-elevated cells expand three-dimensionally forming tumor organoids indefinitely, while cells with medium or low levels of EphB2 do not. When implanted into immunodeficient mice, these organoids generate heterogeneous tumors similar to the original tumor.
  • Fluman tumor organoids were implanted subcutaneously in NSG or nude females (Jackson Labs), 5-6 weeks old. Mice were treated with LY2157299 twice a day, at a dose of 160mg / kg, orally, beginning the third day after inoculation of the tumor cells, continuing until the end of the experiment. All animal experiments were approved by the committee of the Barcelona Science Park (CEEA-PCB) and the Catalan Government, regarding the use and care of experimental animals. The general conditions of the animals were monitored throughout the experiment.
  • Mouse tumor organoids (MTOs) mutant in four pathways involved in colorectal cancer progression were obtained from genetically modified mice (described in Tauriello et al., Nature, Vol. 554 (2016) 539 - 543). Briefly, we crossed mouse strains bearing engineered alleles for four of the most common genetic alterations found in human CRC - Ape fl/fl , Kras LSL G12D , Tgfbr2 fl/fl and p53 fl/fl - and recombined these mutations in intestinal stem cells by means of the Lgr5-creERT2 driver (LAKTP mice).
  • LAKTP mice Lgr5-creERT2 driver
  • Quadruple mutant mice developed metastatic intestinal tumors that reproduced several key features of human poor prognosis microsatellite stable CRCs including a stromal rich TGFb-activated TME and T cell exclusion. From these mouse tumors, we derived a biobank of 3D MTOs (described in Tauriello et al., Nature, Vol. 554 (2016) 539 - 543). Upon transplantation into the caecum of wild-type syngeneic fully immunocompetent C57BL/6 mice, these tumor organoids (MTOs) generated primary CRCs that resemble those from the tumor of origin.
  • MTOs tumor organoids
  • MTOs derived from compound LAKTP mutant mice were orthotopically transplanted into syngeneic C57BL/6J mice.
  • MT01 organoids grown for 3 days were harvested from their 3D growth matrix (basement membrane extract, BME), counted and injected as intact spheroids in 30% BME below the serosa of the caecum of an anaesthetized mouse.
  • Parental MTOs 2 and 3 were first injected subcutaneously and 1 mm 3 pieces of resulting tumors were transplanted onto the tip of the caecum of syngeneic mice; the caecum tip was then folded over to mitigate carcinomatosis.
  • mice were treated with 10mg of Galunisertib (GAL) (or placebo) twice per day by gavage, starting at day 11 after tumor implantation and lasting until sacrifice at 5 weeks. This treatment was associated to a strong reduction/prevention of liver metastasis, as well as a reduction of primary tumor (and local carcinomatosis) size (Tauriello et al., Nature, Vol. 554 (2016) 539 - 543).
  • GAL Galunisertib
  • Gene expression data obtained from mouse tumor samples (Tauriello et al., Nature (2018), 554,538-543) were processed similarly to Affymetrix human samples (Pre processing of microarray CRC datasets).
  • expression values were corrected a priori by metrics RMA.IQR and RNA.DEG described in Eklund AC et al., (Genome Biol. (2008), 9(2):R26).
  • genes were translated to their corresponding human homologous using the Mouse Genome Informatics Database. (14.353 genes) (Blake JA, et al., Nucleic Acids Res. (2017) 45(D1):D723-D729). Association of gene expression with treatment was assessed using a linear model in which sample’s organoid of origin was included as a covariate.
  • Example 2 Elevated CTHRC1 mRNA expression levels identify a subset of patients with stromal activation by TGF-beta in transcriptomic cohorts of colorectal cancer patients
  • the inventors obtained good data demonstrating that the group of patients with higher CTHRC1 mRNA expression are characterized by a higher degree of TGF-beta-activated TME, shown by higher levels of TGFp response signatures from fibroblasts (F-TBRS), T- cells (T-TBRS) and macrophages (Ma-TBRS) in transcriptomic cohorts from various types of cancer compared to patients with medium and low CTHRC1 mRNA expression.
  • F-TBRS fibroblasts
  • T-TBRS T- cells
  • Mo-TBRS macrophages
  • CTHRC1 mRNA expression was compared versus the F-/TVMa-TBRS (from Calon et al., Cancer Cell 22 (2012) 571-584). Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs.
  • CRC colorectal cancer
  • CTHRC1 mRNA expression captures a large proportion of CMS4 tumors as well as tumors in other CMS that show a TGFb-activated TME and are also likely candidates for therapies inhibiting the TGF-beta signaling pathway.
  • the inventors identified that high CTHRC1 expression levels associated with tumors exhibiting elevated F-TBRS, T-TBRS and Ma-TBRS in both MSS (microsatellite stable) and MSI (microsatellite instable) subgroups of patients ( Figure 3). Accordingly, CTHRC1 expression has been found to be independent from MSI status in the identification of a TGFb-activated stroma.
  • MSI patients are considered as good prognosis patients and are therefore typically not selected for neoadjuvant and/or adjuvant treatments.
  • CTFIRC1 expression would thus be capable of capturing a subset of MSI patients that have high levels of TGF-b (Figure 3A), poor prognosis ( Figure 3B) and would likely benefit from anti-TGFp therapies.
  • current guidelines propose that MSI patients are candidates to checkpoint immunotherapies. According to Tauriello et al., (Nature, Vol. 554 (2018) 539 - 543), patients with tumors with a high degree of TGFp-activated TME, will likely not respond to such therapies alone. Therefore, administration of TGFp signaling pathway inhibitors would be particularly useful in combination with immune checkpoint inhibitors since these patients are likely to be no-responders to checkpoint immunotherapy alone.
  • the inventors further show heatmaps (Figure 4) illustrating the correlations between CTFIRC1 mRNA expression with average gene expression of overall F-TBRS, T-TBRS and Ma-TRBRS (from Calon et al., Nature Genetics 47:4 (2015) 320-332) and distribution of Consensus Molecular Subtypes (CMS) in CRC transcriptomic cohorts (TCGA+GEO: 1705 patients), including patients from stages I to IV.
  • Figure 4 illustrate the correlations between CTFIRC1 mRNA expression with average gene expression of overall F-TBRS, T-TBRS and Ma-TRBRS (from Calon et al., Nature Genetics 47:4 (2015) 320-332) and distribution of Consensus Molecular Subtypes (CMS) in CRC transcriptomic cohorts (TCGA+GEO: 1705 patients), including patients from stages I to IV.
  • CTFIRC1 mRNA expression shows a footprint of tumors that have a TGFp-activated TME, as well as immune-excluded (see reverse pattern for expression of a signature of CD45 positive cells that captures the relative abundance of leukocytes compared to the other cell types present in the TME).
  • CMS4 patients cluster with high CTFIRC1 mRNA expression and TGFp- activated stromal signatures, whereas CMS2 and CMS3 patients show the reverse pattern.
  • CMS1 and MSI patients are distributed along the axis indicating that those with high TGFp- active TME which segregates with bad prognosis (Figure 5B) could also potentially benefit form therapies inhibiting TGFp signaling pathway.
  • TGF-beta- activated TME in patients with CMS1 or MSI tumors will likely predict no-response to these therapies alone, unless in combination with TGFp signaling inhibitory therapies.
  • Tukey box plots have whiskers of maximum 1 .5 times the interquartile range; the boxes represent first, second (median) and third quartiles.
  • stromal biomarkers which were in principle equally suitable prognostic biomarkers (i.e. have an increased expression in cancer-associated fibroblasts (CAF) in the dataset from Calon et al. (Calon et al., Nature Genetics 47:4 (2015) 320-332), namely CLIC4, DPYSL3, PTRF, SULF1 , ZEB1 , RAB31 and LUMICAN) in relation to prognosis in CRC were also tested by the inventors, but a statistically significant association was not found when evaluated in FFPE samples of CRC patients by IHC (data not shown).
  • CAF cancer-associated fibroblasts
  • the obtained results indicate that the predictive power of this biomarker does not depend on the percentage of cells expressing these markers within the tumor area, but on a semi-quantitative estimation of their positivity (distinguishing between negative (0) / low (1) and moderate (2) / high (3) intensity) in stromal cells surrounding tumor epithelial cells.
  • the inventors identified a biomarker whose expression can be determined semi-quantitatively by immunohistochemistry, without the need to establish a strict cut-off point, and that robustly predict the risk of recurrence can be very useful in clinical practice, although it is evident the need to standardize the detection method and the reading criteria that may vary between different centers and specialists. An interesting possibility would be to establish automatic processing and reading protocols.
  • Figure 5B shows Cox proportional hazard ratios smoothed by continuous CTHRC1 expression levels.
  • Kaplan Meier curves evaluating disease-free survival invariably show that patients with tumors exhibiting high CTHRC1 protein levels had worse prognosis. This was true for all patients (Stage 1-3; Figure 5C) and also for patients when further stratified in stage II or stage III ( Figure 5E and 5G).
  • CTHRC1 levels (as a continuous variable) and prognosis, for the global group of patients analyzed (stage I, II and III) and in the differentiated analysis by tumor stage (II and III separately; see table below).
  • Correlation with prognosis was independent of clinical variables such as age, gender, staging, and neoadjuvant treatment.
  • CTHRC1 expression as a categorical variable by segregating patients into three groups characterized by low, medium and high expression of CTHRC1 .
  • determining the expression levels of this biomarker may help to stratify and select patients with stage II CRC having poor prognosis which may benefit of an adjuvant and/or neoadjuvant treatment.
  • Example 4.- CTHRC1 is a biomarker of response to therapies inhibitinq TGFBsiqnalinq pathway.
  • Galunisertib GAL; LY2157299
  • TGFBR1 small molecule inhibitor a TGFBR1 small molecule inhibitor
  • CTHRC1 is a good marker to select patients with high TGFp activated stroma (Figure 6A and 6B, before treatment), likely to respond to therapies inhibiting the TGF-beta pathway (Figure 8C in Calon et al., Nature Genetics 47:4 (2015) 320- 332); and Figures 2 and 4 from Tauriello et al., Nature, Vol. 554 (2016) 539 - 543).
  • Example 5 Elevated CTHRC1 mRNA expression levels identify a subset of patients with high F-TBRS, T-TBRS and Ma-TBRS expression in transcriptomic cohorts across various types of cancers
  • CTHRC1 measurement captures the essence of a TGFp-activated microenvironment in the transcriptomic cohorts of STAD (from TCGA), as shown in Figure 7.
  • CTHRC1 expression levels are shown for all patients or for patients according to their MSS or MSI status.
  • the inventors analyzed CTHRC1 expression in the Metabric cohort of breast cancer patients (Curtis et al., Nature (2012) 486(7403): 346-52. From the data in Figure 8, association with TBRSs is good for all breast cancer subtypes.
  • CTHRC1 expression captured the essence of a TGFp-activated TME in the TCGA transcriptomic cohorts of bladder and pancreatic adenocarcinoma, in the GSE21034 cohort of prostate cancer, and in the GSE31210 cohort of lung cancer as shown Figure 9.
  • TGFp- activated tumor microenvironment TGFp- activated tumor microenvironment
  • IL-11 RS IL-11 response signature
  • IL-11 RS is a gene signature comprising 1139 genes which was described in Calon et al. 2012 to be associated with TGFp levels and TGFp activation in fibroblasts (F-TBRS) in colorectal cancer samples. Besides, IL-11 mRNA expression levels were described to be reduced further to treating a xenograft model bearing CRC stem cell-derived tumors with a TFGp-inhibitor.
  • the inventors determined the association of each of the above- mentioned gene expressions and signatures with F-TBRS, T- TBRS, or Ma-TBRS by using Partial Pearson Correlations (R).
  • F-TBRS, T- TBRS, or Ma-TBRS signatures are used as surrogates of TGFp activation in fibroblasts, T-cells and macrophages respectively, the latter being cell types from the TME.
  • Table 2 The results are shown in Table 2 below.
  • the correlation coefficient (R value) of CTHRC1 gene expression levels with the three TGFp-activation signatures is substantially superior to that of IL-11 , presenting levels of correlation with a TGFp-activated TME approaching those of IL-11 RS.
  • IL-11 RS was found to be superior to CTHRC1 , it is rather surprising and unexpected that a single gene could capture 90% of the power of the >1000 gene IL-11 RS signature.
  • Table 3 shows results for Pancreas Adenocarcinoma (TCGA), Table 4 for Breast Cancer patients from the Metabric cohort, and Table 5 for Bladder Cancer.
  • TCGA Pancreas Adenocarcinoma
  • Table 4 for Breast Cancer patients from the Metabric cohort
  • Table 5 for Bladder Cancer.
  • the inventors further compared the ability of CTFIRC1 gene expression levels to predict relapse in colorectal cancer patients (Pooled cohort GEO + TCGA) with respect to IL-11 gene expression and IL-11 response signature (IL-11 RS) described in Calon et al. 2012.
  • CTHRC1 is a better predictor of relapse than IL11-RS or IL11 both in MSS and MSI CRC patients.
  • the Kaplan-Meier graphs on relapse for the MSS and MSI subgroups of CRC patients are shown in Fig. 3B and those relating to IL-11 RS and IL11 in Figs. 10A and 10B.

Abstract

The present invention relates to a new biomarker useful to identify cancer patients with a TGFβ-activated microenvironment in colorectal cancer and other tumor types. It further relates to methods of predicting response to inhibitors of the TGFβ signaling pathway and methods of classification and selection of cancer patients for treatment with inhibitors of the TGFβ signaling pathway, to prognostic methods, to methods of monitoring or evaluating response to inhibitors of the TGFβ signaling pathway and to related second medical uses, diagnostic kits and uses thereof.

Description

CTHRC1 AS BIOMARKER FOR A TGFBETA-ACTIVATED TUMOR MICROENVIRONMENT
FIELD OF THE INVENTION
The present invention relates to a new biomarker useful to identify cancer patients with a TGFp-activated microenvironment in colorectal cancer and other tumor types. It further relates to methods of predicting response to inhibitors of the TGFp signaling pathway and methods of classification and selection of cancer patients for treatment with inhibitors of the TGFp signaling pathway, to prognostic methods, to methods of monitoring or evaluating response to inhibitors of the TGFp signaling pathway and to related second medical uses, diagnostic kits and uses thereof.
BACKGROUND OF THE INVENTION
Colorectal cancer (CRC) is the third most common type of cancer globally in men and the second in women and represents 8.5% of cancer deaths. In Europe, the overall survival is 43% while in the United States it is 62%. These values decrease to 5-10% in case of metastatic dissemination to other organs. The initial treatment is the surgical resection of the primary tumor (whenever possible) and the decision of a post-operative or adjuvant treatment depends on the staging of the disease at the time of diagnosis. The current staging system depending on the clinical-pathological criteria (I to IV) is based on the classification of the TNM (Tumor / Lymph node / Metastasis, of the American Joint Committee on Cancer (AJCC)). This system has limited power when predicting tumor recurrence that reaches, after treatment, about 20% in stages II and 40% in stages III. The cases of colorectal cancer that have recurred usually do so in the form of metastases and are associated with worse prognosis. For this reason, a more precise method is necessary to select the appropriate treatment of patients with CRC, based on aggressiveness and the likelihood of tumor recurrence. Other variables such as lymphatic vascular invasion, peri neural invasion or the presence of tumoral budding are also considered prognostic factors; however, they present a great intrinsic variability in their detection (Odze & Goldblum, Surgical Pathology of the Gl Tract, Liver, Biliary Tract and Pancreas, 3e (2014)).
Faced with this challenge, the scientific community has focused on establishing molecular classifications based on biological patterns with prognostic value. Thus, several molecular classifications of the CRC have recently been proposed (De Sousa E Melo et al., Nature Medicine 19:5 (2013) 614-618; Marisa et al.,PLoS Medicine 10:5 (2013); Sadanandam et al., Nature Medicine, 19:5 (2013) 619-625) which have finally been collected in a publication in which a European consortium agreed on a consensus molecular classification (Guinney et al., Nature Medicine, 21 :11(2013) 1350-1356). From the point of view of their applicability, molecular classifications may have some prognostic value and improve the stratification of patients, thus helping to define most appropriate treatment. In this context, the desirable objective is the identification of specific biomarkers that provide useful information on prognosis and/or about likelihood of response to a specific treatment.
Original molecular classifications suggested that poor-prognosis subtypes in colorectal cancer were associated to an epithelial-mesenchymal transition (EMT) phenotype in the tumor (Sadanandam et al., Nature Medicine, 19:5 (2013) 619-625; Marisa et al.,PLoS Medicine 10:5 (2013) ; De Sousa E Melo et al., Nature Medicine 19:5 (2013) 614-618 Guinney et al., Nature Medicine, 21 :11(2013) 1350-1356). In particular, in the consensus classification published by Guinney et al., the group with the worst prognosis (CMS4) was reported to be characterized by activation of pathways involved in EMT and the TGF-beta program, angiogenesis, inflammatory system and remodeling of the extracellular matrix.
Besides, the inventors previously described that the activation of fibroblasts, T lymphocytes and macrophages in the TME by TΰRb is associated with CRC progression and disease relapse (Calon et al., Cancer Cell 22 (2012) 571-584; Calon et al., Nature Genetics 47:4 (2015) 320-332). More specifically, Calon et al. used of the gene expression programs induced by addition of TΰRb1 (from here onwards named as TΰRb response signatures or TBRS) in cultures of normal tissue derived T cells (T-TBRS), macrophages (Ma-TBRS) or fibroblasts (F-TBRS) as surrogates of the degree of activation of the most abundant cell types in the TME of CRCs by TGFp. It was selected as TBRS the full set of genes upregulated by TΰRb signaling in these cell cultures (>2 fold, p<0.05). F-TBRS is composed of 165 genes, T-TBRS of 76 and Ma-TBRS of 1125 genes. Each signature reflects the transcriptional activity of TΰRb signaling pathway in fibroblasts, macrophages and T cells respectively. Authors followed to show that the average expression of each of these TBRS predict very significantly poor prognosis in CRC patient transcriptomic cohorts (Calon et al., Cancer Cell 22 (2012) 571-584; Calon et al., Nature Genetics 47:4 (2015) 320-332).
CTFIRC1 (Collagen Triple Helix Repeat Containing 1) is a secreted glycoprotein that has multiple functions associated with wound repair, bone remodeling, hepatocyte fibrosis and adipose tissue formation among others (Jiang et al., Journal of Cancer, 7:715 (2016) 2213- 2220). CTFIRC1 expression has been associated with poor prognosis in multiple tumor types. For example, overexpression of CTFIRC1 has been associated with increased aggressiveness in lung cancer (Ke et al., Oncotarget, 5:19 (2014) 9410-9424), with EMT in ovarian cancer (Hou et al., 2015), as well as with a poor prognosis in pancreatic adenocarcinoma (Liu et al., American Journal of Cancer Research, 6:8 (2016) 1820-7). However, most of these works do not reflected if the expression of CTHRC1 is mainly stromal or epithelial and do not use immunohistochemical (IHC) analysis. In the work of Yang et al. (Yang et al., Int J Clin Exp Pathol, 8:10 (2015) 12793-12801)) and Kim et al. (Kim et al., Oncotarget, 5:2 (2014) 519-529), the high expression of CTHRC1 in CRC is related to poor prognosis at the transcriptional level and IHC study is also carried out, however, they mention the expression in both epithelial cells and fibroblasts, but do not relate this expression to prognosis or selection of adequate treatment. It should be noted that in the image shown in this publication the expression is weak and located in the cytoplasm.
In the last decades, efforts have focused on developing quantitative polymerase chain reaction (q-PCR)-based molecular signatures. It is unlikely that every gene in the molecular profiles obtained by microarray analysis has equal relevance with respect to prognosis. Ideally, a small number of genes could predict survival with the same precision as microarray-based gene signatures. These genes could be analyzed by q-PCR, the gold standard assay for gene expression. Q-PCR has significant advantages to microarray-based assays, including widespread availability, cost and reproducibility. However, one of the major logistic problems in order to implement q-PCR-based signatures in daily clinical management is the need of good quality RNA extracted from fresh frozen resected tissue. The different variables of time, preservation protocol and sample transport logistics may have a profound effect in the quality of RNA of a fresh or frozen sample. This becomes an important limiting factor in order to build a robust, reproducible and feasible technical RNA- based tool of molecular classification.
In light of the technical challenges posed by use of mRNA-based technology and the minimal overlap among the genes that comprise the RNA-based prognostic signatures, a prediction approach based on the determination of the expression levels of proteins, for instance detected by immunohistochemistry (IHC), would be more accurate, reproducible and easy to apply to clinical management than methods based on RNA. To date, there is no biomarker used in clinical practice that can be determined by immunohistochemical study for stratification of CRC patients.
The transforming growth factor-beta (TGFp) signaling inhibition is an emerging strategy for cancer therapy. The role of the TGFp pathway as a tumor promoter or suppressor at the cancer cell level is still a matter of debate, due to its differential effects at the early and late stages of carcinogenesis. Conversely, the TQRb signaling pathway has been described to exert pro-tumoral activities at the microenvironment level. Thus, TQRb pathway inhibition strategies in cancer are primarily targeting the tumor microenvironment (TME).
The TME is a complex structure composed of extracellular matrix proteins (mainly, type I collagen) and various cell types including mesenchymal cells (cancer-associated fibroblasts [CAF]), endothelial cells and pericytes, nerve cells, immune cells, and bone marrow-derived stem cells. These cell types can express TQRb receptors and respond to elevated TQRb levels present in the TME. TQRb has been reported to regulate fibrosis, angiogenesis, and immune cell function in the context of tumors (Neuzillet et al., Pharmacology & Therapeutics 147 (2015) 22-31 ; Batlle and Massague, Immunity 50 (2019), 924-940).
Recently, increased TGF b activity in the TME was found to be associated with lack of response to PD-1-PD-L1 immune checkpoint inhibitors in CRC (Tauriello et al., Nature, Vol. 554 (2018) 539 - 543) and in human metastatic urothelial cancer (mUC) samples (Mariathasan et al., Nature, Vol. 554 (2018) 544 - 548).
There are many clinical challenges to developing inhibitors of TGF b signaling pathway, notably timing of treatment and predictive biomarkers for patient selection, in order to define in what kind of tumor microenvironment TGF b signaling inhibition may be more beneficial (Neuzillet et al., Pharmacology & Therapeutics 147 (2015) 22-31). As pointed out in deGramont et al. (Oncoimmunology 6:1 (2017) e1257453), due to the complex nature of the TΰRb pathway, its role in cell fate and their differential activity in tumor cells and their microenvironment, predictive biomarkers may be challenging to identify.
Despite recent advances, there is still a need to find simple, accurate and reproducible methods which are easy to apply in the clinical practice for predicting the outcome of cancer and/or help clinicians in the selection of the more appropriate treatment strategies. In particular, there is a need for identifying new predictive biomarkers determining those cancer patients having tumors with a TGFb-activated TME and thus presenting a higher likelihood to respond to a TΰRb pathway inhibition therapy. These patients are also likely not to respond to checkpoint immunotherapies. To this end, the assessment of F-TBRS, T-TBRS and Ma- TBRS described above represents an unfeasible approach as each TBRS is composed of a very large number of genes (>1000 in total). Therefore, a simple method to identify tumors that exhibit an overall TGF^-activated TME could be of great utility for personalized medicine and cancer treatment, especially when this method is of general application to a variety of tumor types.
SUMMARY OF THE INVENTION
TGFp is secreted as a pro-hormone that is stored in the extracellular matrix in an inactive form and that can be subsequently mobilized and activated by sophisticated mechanisms (Lyons et al. J. Cell. Biol. (1990) 110(4), 1361-1367, (Batlle and Massague. Immunity 50 (2019), 924-940). In addition, each cell type present in the TME may display different degrees of TGFp activity depending on the expression of TGFp receptors, presence of inhibitory molecules and other contextual molecules that modify their susceptibility to TGFp accumulated in the TME (David and Massague. Nat Rev Mol Cell Biol. 2018). The degree of TGFp-activated TME has been previously measured in transcriptomic cancer datasets using as surrogate the fibroblast TGFp response signature (F-TBRS), the T-cell (T-TBRS) and the macrophage (Ma-TBRS) response signatures described in Calon et al. 2012 (Cancer Cell 22, (2012) 571-584).
In the present application the inventors showed that the group of cancer patient samples with highest CTFIRC1 mRNA expression also exhibited elevated levels of the three, cell type-specific, TBRS signatures previously disclosed in Calon et al. 2012 (Cancer Cell 22, (2012) 571-584), see Example 2. As described herein, the inventors have surprisingly found that CTFIRC1 expression levels can be used as a surrogate of any or all of F-TBRS, T-TBRS or Ma-TBRS, preferably of F-TBRS.
In a first aspect, the present invention provides a method for determining whether a patient’s tumor has a TGFp-activated microenvironment, wherein said method comprises or consists of: a) determining the expression levels of CTFIRC1 (Collagen Triple Helix Repeat Containing 1) gene in a biological sample isolated from said patient; b) comparing the expression levels of CTFIRC1 in the patient’s sample with a reference value; wherein an increase of the value in the patient’s sample with regard to said reference value is indicative that said patient’s tumor has a TGFp -activated microenvironment.
Therefore, the inventors have identified CTHRC1 as a biomarker which expression levels associate with general non-cell-specific activation of the tumor microenvironment by TGFp, more specifically, it associates with a tumor microenvironment comprising TGFp activated fibroblasts, T-cells and macrophages. Accordingly, in particular embodiments, CTHRC1 enables to capture the activation of the microenvironment by TGFp using a single biomarker instead of three distinct signatures (i.e., F-TBRS, T-TBRS, Ma-TBRS). This association was found in several tumors when analyzing the transcriptomic cohorts of various cancer types (Examples 2 and 5), namely in colorectal carcinoma, stomach adenocarcinoma, bladder cancer, pancreas adenocarcinoma, prostate carcinoma, lung cancer and breast cancer.
Interestingly, a high CTHRC1 score was shown to identify 92.2% of CRC tumors classified under CMS4, a CRC molecular subtype associated with prominent TGFp activation according to the consensus molecular subtypes (CMSs) classification (Guinney et al. Nat Med. 2015, 21(11), 1350-6). Therefore, determining CTHRC1 expression could be a simple method to identify patients bearing CMS4 tumors, instead of using complex molecular signatures. Guinney’s et al. CMS classification uses a signature with 693 genes which relative expression is used to classify cancer patients in CMS1 to CMS4 groups. MOMTGFB has been shown to be advantageous in identifying CMS4 patients (those associated with high TGFp expression and a worst prognosis). This 3-gene signature is surprisingly capturing the essence of the more than 200 genes within this signature that are differentially expressed in CMS4 patients.
Importantly, CTHRC1 expression was also able to capture tumors with a TGFp-activated TME classified under other CMS groups (Example 2, Figure 2B and C). The provision of a method identifying tumors presenting a TGFp-activated TME enables to select for treatment with an inhibitor of the TGFp signaling pathway, tumors belonging to CMS1-3 subgroups (typically not associated with a TGFp activated TME) which would thus otherwise likely not had been selected for treatment with a TGFp signaling pathway inhibitor.
In addition, the inventors showed that the biomarker of the invention was capable of identifying tumors presenting a TGFp-activated TME, and thus likely to benefit from a therapy inhibiting the TGFp signaling pathway, independently of microsatellite stability status, that is, both in microsatellite stable (MSS) and microsatellite instable (MSI) phenotypes (Example 2, Figure 3).
MSI-high (MSI-H) status has been previously associated with better prognosis in early-stage CRC and has emerged as a predictor of sensitivity to check-point inhibitors immunotherapy treatments (Battaglin et al., Advances in Hematology and Oncology 2018, 16(11), 735-747). Nevertheless, as above-mentioned, immune checkpoint inhibitors were found by Tauriello et al. (Tauriello et al., Nature, Vol. 554 (2018) 539 - 543) to lack efficacy in CRC tumors with a TGFp activated microenvironment. Thus, the signature of the invention enables to identify from patients (e.g. CRC or gastric cancer patients) having a MSI tumor, those which would likely not benefit from an immune check point inhibitor treatment alone and select the same patients for a combination treatment with an inhibitor of the TGFp signaling pathway. Thus, the method of the invention further enables selecting cancer patients having tumors (e.g. CRC or gastric cancer patients) which would likely benefit from a TGFp inhibition therapy for which such therapy would not had been selected on the basis of MSI status or CMS molecular classification methods.
TGFp signaling in the TME has been reported to operate as the main mechanism of immune evasion during metastasis formation. Elevated TGFp triggers T cell exclusion, a phenomenon associated with poor outcome in CRC and other tumor types, and blocks anti tumor Th1 effector phenotype (Tauriello et al., Nature, Vol. 554 (2018) 539 - 543; Galon et al., Science 313 (2006) 1960-1964; Mariathasan et al., Nature, Vol. 554 (2018) 544 - 548). In Tauriello et al., Galunisertib (GAL), which is a small molecule TGFBR1 inhibitor, was used to treat mice with TGFp high metastatic CRCs. GAL treatment unleashed the immune system against CRC, which exerted a potent therapeutic response that prevented the formation of metastasis. In mice bearing overt metastatic disease, the combination of GAL with anti-PD-L1 treatment eradicated most metastases and prolonged recurrence-free survival for over a year after treatment cessation. This striking response was characterized by disruption of the T-cell exclusion phenotype characteristic of progressed metastatic disease, and by prominent Th1 immune activation (Tauriello et al., Nature, Vol. 554 (2018) 539 - 543). When these same tumors were allowed to develop established metastasis, GAL alone had no effect. Yet, a combination of GAL plus immune checkpoint therapies cured overt metastatic disease (data from Tauriello et al., Nature, Vol. 554 (2018) 539 - 543). Similar observations were made in models of urothelial and breast carcinomas (Mariathasan et al., Nature, Vol. 554 (2018) 544 - 548).
Calon et al (Calon et al., Nature Genetics 47:4 (2015) 320-332) showed that in metastasis initiation assays performed in mice with human derived organoids, treatment with the TGFp inhibitor Galunisertib (GAL) greatly reduced metastasis initiation in this experimental setting (Figure 8C in Calon et al., Nature Genetics 47:4 (2015) 320-332). Moreover, Tauriello et al. (Nature, Vol. 554 (2018) 539 - 543) showed that treatment with the TGFp inhibitor Galunisertib (GAL) of mice orthotopically transplanted with LAKTP mutant mice organoids was associated to a strong reduction/prevention of liver metastasis, as well as a reduction of primary tumor (and local carcinomatosis) size (Figures 2 and 4 from Tauriello et al., Nature, Vol. 554 (2018) 539 - 543). As described in Example 4, the inventors measured CTHRC1 protein or mRNA expression levels in the developed primary CRC tumors, which resemble the metastatic intestinal tumors of origin and exhibit a TGFb-activated TME, before and after treatment of mice with GAL. As shown in Figure 6A and 6B, CTFIRC1 expression levels were found to be lower after therapy with GAL, thus supporting the utility of CTFIRC1 expression as predictive biomarker of response to TGFp inhibition therapies and as biomarker for monitoring or evaluating response to treatment with TGFp inhibitors.
In a second aspect, the invention also pertains to a method for predicting the efficacy of (or the likelihood of response to) a treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. predicting a higher efficacy or likelihood of response to a treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
In a third aspect, the present invention also relates to a method for selecting a cancer patient which is likely to benefit from a treatment with an inhibitor of the TGFp signaling pathway, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. selecting said cancer patient for the treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
In a fourth aspect, the invention pertains to an inhibitor of the TGFp signaling pathway for use in a method for treatment of a cancer patient, wherein said patient’s tumor has been determined to have a TGFp-activated microenvironment according to a method as described herein.
In an alternative aspect, the invention provides the use of an inhibitor of the TGFp signaling pathway in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected as having a tumor with a TGFp-activated microenvironment by a method as described herein.
In a further alternative aspect, the invention relates to a method of treating a cancer patient by administering a therapeutically effective amount of an inhibitor of the TGFp signaling pathway, wherein said patient has been selected as having a tumor with a TGFp-activated microenvironment by a method as described herein
In a fifth aspect, the present invention relates to an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy for use in a method for treatment of a cancer patient in combination with an inhibitor of the TGFp signaling pathway, wherein said patient’s tumor has a TGFp-activated microenvironment according to a method as described herein.
In an alternative aspect, the invention provides the use of an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in combination with an inhibitor of the TGFp signaling pathway in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
In a further alternative aspect, the invention relates to a method of treating a cancer patient by administering a therapeutically effective amount of an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in combination with a therapeutically effective amount of an inhibitor of the TGFp signaling pathway, wherein said patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
In a sixth aspect, the present invention relates to an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy for use in a method for treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said other anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway. In an alternative aspect, the invention provides the use of an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in the manufacturing of a medicament for the treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said other anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway.
In a further alternative aspect, the invention relates to a method of treating a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp -activated microenvironment, by administering a therapeutically effective amount of an anti-cancer agent other than an inhibitor of the TGFp signaling pathway (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy, wherein said other anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway.
In a particular embodiment of any thereof, said other anti-cancer agent or radiotherapy is administered as single agent or therapy.
In a seventh aspect, the invention relates to a method for determining the prognosis of a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. classifying the patient as having poor prognosis when said patient’s tumor has a TGFp-activated microenvironment.
In an eighth aspect, the present invention relates to a method for monitoring or evaluating the response to treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises determining whether a patient’s tumor has a TGFp- activated microenvironment as described herein, wherein a TGFp-activated microenvironment is associated to lack of response.
In a ninth aspect, the invention concerns a kit suitable for determining the expression levels of the CTFIRC1 gene in an isolated tumor sample, wherein said kit comprises: i. a reagent for the quantification of the CTFIRC1 gene expression levels; and ii. optionally, a reagent for the quantification of a control gene expression levels; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
In a tenth aspect, the invention refers to the use of a kit as described herein, in a method for determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein, in a method for selecting a cancer patient which is likely to benefit from a treatment with an inhibitor of the TGFp signaling pathway as described herein, in a method for monitoring or evaluating the response to an inhibitor of the TGFp signaling pathway as described herein, or in a method for determining the prognosis of a cancer patient as described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1. CTHRC1 detects CRC stromal activated tumors. Transcriptomic data from the GEO + TCGA cohort of CRC patients (n=1705). Box-Plot graphs depicting overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in 3 groups of patients defined according to CTHRC1 mRNA expression (Low, Medium and High). Results are shown for all samples. Each dot is one sample. Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. Patients with high CTHRC1 mRNA expression present tumors characterized by a higher degree of TGFp -activated stroma using as surrogates F-, T- and Ma-TBRSs.
Figure 2. CTHRC1 captures CMS4 subtype CRCs. A. Box-Plot graphs depicting overall CTHRC1 mRNA expression (z-score) in patients classified as CMS1-4. Patients with CMS4 tumors show overall highest mRNA expression levels of CTHRC1. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. B. CTHRC1 captures 92.2% of CMS4 patients. As CTHRC1 identifies tumors with a TGFp active TME, these patients are likely to benefit from therapies directed towards inhibition of TGFp signaling. In addition, CTHRC1 also captures patients with high activated stroma in the CMS 1-3 subtypes, particularly CMS1. According to current stratification methods, CMS1 patients will be misclassified as good prognosis patients, likely to benefit from checkpoint immunotherapies, yet patients with high TGFp-activated stroma are unlikely to respond. C Patients from the pooled transcriptomic cohort (GEO +TCGA) with tumors classified in each CMS1 , CMS2 and CMS3 subtypes have been divided in three subgroups (i.e., Low / Medium / High) according to CTHRC1 mRNA expression levels. Box Plots show association with overall expression levels of TGFb-activated stromal signatures for each CMS subtype of patient’s tumors. Results are shown for all samples. Each dot is one sample. Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. Patients with high CTHRC1 mRNA expression present tumors characterized by a higher degree of TGFp -activated stroma using as surrogates F-, T- and Ma-TBRSs.
Figure 3. A. CTHRC1 mRNA expression identifies patients susceptible for treatment with TGF-beta inhibitors in previously used classifications for CRC prognosis such as
MSS and MSI patients. Box Plots show association of CTHRC1 mRNA expression with overall expression levels of TGFb-activated stromal signatures for MSS or MSI CRCs. Results are shown for all samples. Each dot is one sample. Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. Patients with high CTHRC1 expression present tumors characterized by a higher degree of TGFp -activated stroma using as surrogates F-, T- and Ma-TBRSs. B. Kaplan-Meier graphs show disease free survival (DFS) in years per MSS or MSI CRCs in the above subgroups (i.e., Low / Medium / High) according to CTHRC1 mRNA expression. As shown, within patients with MSI, considered as patients with good prognosis, and candidates for therapies with immune checkpoint inhibitors, CTHRC1 expression can identify patients with a TGFp-activated TME, that have bad prognosis. These patients may not respond to monotherapy with immune checkpoint inhibitors. In addition, they are susceptible of benefitting from therapies that inhibit TGFp signaling pathway, and combination therapies including TGFp signaling pathway inhibitors and immune checkpoint inhibitors. Statistical significance for HRs were assessed using a Wald test (LRT p-value = 0,010185 and 0,0065846 for HRs in MSS and MSI, respectively).
Figure 4. CTHRC1 mRNA expression shows inverse correlation with the expression of a signature of CD45 positive cells. Heatmaps illustrating the correlations between CTHRC1 mRNA expression with average gene expression of F-TBRS, T-TBRS, Ma-TBRS and distribution of Consensus Molecular Subtypes (CMS) in CRC transcriptomic cohorts (TCGA+GEO: 1705 patients), including patients from stages I to IV. Please note the inverse correlation with the expression of a signature of CD45 positive cells that captures the relative abundance of leukocytes compared to the other cell types present in the TME. Furthermore, as previously showed (Figure 2), most patients classified as CMS4 cluster in the CTHRC1- High area, CD45 low area, further indicating they are immune excluded. MSI (and CMS1) patients are evenly distributed throughout the range of CTHFtCI expression values indicating, as previously described, that a high % of these theoretically good prognosis patients exhibit a high degree of TGFb-activated TME and, therefore, should be considered as bad prognosis patients, likely candidates to receive TGFp signaling inhibitory therapies, and unlikely to respond to immunecheckpoint therapies alone.
Figure 5. CTHRC1 protein expression can be used to identify CRC with TGFp stromal activated tumors that have poor prognosis. A. Box Plots show distribution of patients of CTHFtCI protein expression within stage I, II and III CRC patients. Each dot is one sample. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. CTHRC1 expression is significantly higher in more advanced cancers (stage II and III versus stage I; *** p <0.0001). B-G. Smoothed estimates of log-Hazard Ratio (B,D,F) analyzing the risk of relapse associated with increased protein expression (IU; intensity Units) in the TMAs; and Kaplan-Meier graphs (C,E,G) showing disease free survival in months in patients classified in three groups according to CTHRC1 protein expression values (Low, Medium, High) in a FFPE cohort in all patients (stage 1-3; B and C), in stage II (D and E) and in stage III patients (F and G). Hazard Ratio (HR) and p values are indicated. Dashed lines indicate a 95% confidence interval. As mentioned before, patients with high CTHRC1 protein expression levels bear tumors with a TGFp-activated TME and their prognosis is likely to benefit from treatments aimed to inhibit TGFp signaling.
Figure 6. CTHRC1 expression identifies the microenvironment of tumors that respond to TGFp inhibitory therapies. A. Representative CTHRC1 protein expression in histological sections from human tumors implanted subcutaneously in immunodeficient mice. Staining is reduced in tumors treated with small molecule TGFp receptor 1 inhibitor LY2157299 (Galunisertib). B. Tukey box plots depicting CTHRC1 mRNA expression levels in tumors derived from mouse tumor organoids treated or not with small molecule TGFp receptor 1 inhibitor LY2157299 (Galunisertib). Box Plots have whiskers that cover the maximum and minimum values of the scores; the boxes represent first, second (median) and third quartiles. CTHRC1 expression levels measured by IHC (A, in human tumors) or mRNA (B, in mouse tumors) were found to be clearly reduced in mice treated with LY2157299 (Galunisertib), indicating the inhibitor is effectively diminishing TQRb signaling in the tumor TME. These mice showed a reduction in tumor burden upon treatment (Figure 8C in Calon et al., Nature Genetics 47:4 (2015) 320-332); and Figures 2 and 4 from Tauriello et al., Nature, Vol. 554 (2018) 539 - 543). Thus, CTHRC1 is a good biomarker of response to TΰRb inhibition therapies and reinforces its value as predictive biomarker of TΰRb signaling inhibition therapies (Example 3).
Figure 7: CTHRC1 detects stromal activated tumors in gastric cancers. Box-Plot graphs depicting overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in 3 groups of patients according to their CTHRC1 mRNA expression levels (Low, Medium and High) in stomach adenocarcinoma (STAD) patients from the TCGA cohort. Results are presented for all samples, and according to the MSI/MSS status of the patients (second and third rows). Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. Patients with high CTHRC1 mRNA expression present tumors characterized by a higher degree of TΰRb -activated TME using as surrogates F-, T- and Ma-TBRSs.
Figure 8. CTHRC1 detects stromal activated tumors in breast cancer. Box-Plot graphs depicting overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in 3 groups of patients according to their CTHRC1 mRNA expression (Low, Medium and High) in the various subtypes of breast cancer patients (LumA; LumB; Her2, Basal; and Normal-like) in the Metabric cohort. Results are shown for all samples in each subtype. Each dot is a sample. Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs. Tukey box plots have whiskers of maximum 1.5 times the interquartile range; the boxes represent first, second (median) and third quartiles. Patients with high CTHRC1 mRNA expression present tumors characterized by a higher degree of TGFb-activated TME using as surrogates F-, T- and Ma-TBRSs.
Figure 9. CTHRC1 detects stromal activated tumors across various tumor types (panmarker). Box-Plot graphs depicting overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in 3 groups of patients according to their CTHRC1 mRNA expression (Low, Medium and High) in Bladder cancer and pancreas adenocarcinoma patients from the TCGA cohort, prostate carcinoma patients from the GSE21034 cohort, and lung cancer patients from the GSE31210 cohort. Figure 10. IL11RS or IL11 capacity to predict relapse in colorectal cancer patients is weaker in comparison with CTHRC1. Kaplan-Meier graphs show disease free survival (DFS) in years per all colorectal cancer patients or for patients classified as MSS or MSI in the above subgroups (i.e., Low / Medium / High) according to IL11 expression (A) or (B) to the expression of a signature containing more than 1000 genes responsive to IL11 (IL11 RS; Calon et al., 2012). Identification of patients at high risk of relapse is superior when using CTFIRC1 as a marker (please see Fig 3B for comparison). Statistical significance was assessed by means of Likelihood Ratio Tests (LRT).
DETAILED DESCRIPTION OF THE INVENTION a. Definitions
The terms "subject", or "individual"' are used herein interchangeably to refer to all the animals classified as mammals and includes but is not limited to domestic and farm animals, primates and humans, for example, human beings, non-human primates, cows, horses, pigs, sheep, goats, dogs, cats, or rodents. Preferably, the subject is a male or female human being of any age or race.
The term “cancer patient” and “subject suffering from cancer” are used herein interchangeably. It may refer to those subjects diagnosed after a confirmatory test (e.g., biopsy and/or histology) and subjects suspected of having cancer. The term “subject suspected of having cancer” as used herein, refers to a subject that presents one or more signs or symptoms indicative of a cancer and is being screened for cancer. A subject suspected of having cancer encompasses for instance an individual who has received a preliminary diagnosis (e.g., an X-ray computed tomography scan showing a mass) but for whom a confirmatory test (e.g., biopsy and/or histology) has not been done or for whom the stage of cancer is not known.
The terms "cancer" and "cancerous" refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Included in this definition are benign and malignant cancers or tumors as well as dormant tumors or micrometastases. Examples of cancer include but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia. More particular examples of such cancers include breast cancer, squamous cell cancer, lung cancer (including small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung, and squamous carcinoma of the lung), cancer of the peritoneum, hepatocellular cancer, gastric or stomach cancer (including gastrointestinal cancer), pancreatic cancer, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bladder cancer, hepatoma, colon cancer, colorectal cancer, rectal cancer, endometrial or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, liver cancer, prostate cancer, vulval cancer, thyroid cancer, hepatic carcinoma and various types of head and neck cancer, as well as B-cell lymphoma (including low grade/follicular non-Hodgkin's lymphoma (NHL); small lymphocytic (SL) NHL; intermediate grade/follicular NHL; intermediate grade diffuse NHL; high grade immunoblastic NHL; high grade lymphoblastic NHL; high grade small non-cleaved cell NHL; bulky disease NHL; mantle cell lymphoma; AIDS-related lymphoma; and Waldenstrom's Macroglobulinemia); chronic lymphocytic leukemia (CLL); acute lymphoblastic leukemia (ALL); Hairy cell leukemia; chronic myeloblastic leukemia; and post-transplant lymphoproliferative disorder (PTLD), as well as abnormal vascular proliferation associated with phakomatoses, edema (such as that associated with brain tumors), and Meigs' syndrome.
The term “tumor” is an abnormal mass of tissue that may be benign, premalignant, or cancerous. Preferably, this “tumor” is a cancerous tumor.
The term “metastasis” as used herein refers to distant metastasis affecting organs other than the primary tumor site. Metastasis may be defined as the process by which cancer spreads or transfers from the primary site to other regions of the body with the development of a similar cancerous lesion at the new location (see for instance: Chambers AF et al., Nat Rev Cancer 2002; 2: 563-72). For instance, in colorectal cancer, metastasis in another organ (e.g., the liver) typically shows an enteroid adenocarcinoma pattern. A “metastatic” or “metastasizing” cell is typically one that loses adhesive contacts with neighboring cells and migrates via the bloodstream or lymph from the primary site of disease to invade neighboring body structures.
The term “tumor microenvironment” or “TME” as used herein refers to a complex structure composed of extracellular matrix proteins (mainly, type I collagen) and various cell types including mesenchymal cells (cancer-associated fibroblasts [CAFs]), endothelial cells and pericytes, nerve cells, immune cells, and bone marrow-derived stem cells that generally surround and feed a tumor. A tumor can change its microenvironment, and the microenvironment can affect how a tumor grows and spreads.
The term “TGFb-activated microenvironment” or “TGFb-activated TME” as used herein, refers to a tumor microenvironment wherein the cells surrounding the tumor express TΰRb receptors and respond to elevated TΰRb levels present in the TME. TΰRb has been reported to regulate fibrosis, angiogenesis, and immune cell function in the context of tumors (Neuzillet et al., Pharmacology & Therapeutics 147 (2015) 22-31 ; Batlle and Massague, Immunity 50 (2019), 924-940).
The term “TQRb response signature” or “TBRS” as used herein refers to the gene expression program induced by addition of TQRb1 in cultures of normal tissue derived T cells (T-TBRS), macrophages (Ma-TBRS) or fibroblasts (F-TBRS) It was selected as TBRS the full set of genes upregulated by TQRb signaling in these cell cultures (>2 fold, p<0.05). F-TBRS is composed of 165 genes, T-TBRS of 76 and Ma-TBRS of 1125 genes. Each signature reflects the transcriptional activity of TQBb signaling in fibroblasts, macrophages and T cells respectively. F-TBRS, T-TBRS and Ma-TBRS were previously shown by the inventors to act as surrogates of the degree of activation of the most abundant cell types in the TME of CRCs by TGFp (Calon et al., Cancer Cell 22 (2012) 571-584; Calon et al.,
Nature Genetics 47:4 (2015) 320-332). Accordingly, TGFp activation of fibroblasts, T-cells and macrophages can be assessed by determining expression of F-TBRS, T-TBRS and Ma- TBRS, respectively.
The term “organoid” or “cancer organoid” as used herein refers to small, self-organized three dimensional tissue cultures derived from primary tumors or metastasis. They can be maintained indefinitely in the appropriate cell culture conditions. When inoculated in experimental models, they faithfully recapitulate many of the traits of the tumor of origin.
The term "treating", as used herein, unless otherwise indicated, includes the amelioration, cure, and/or maintenance of a cure (i.e., the prevention or delay of relapse) of a disease or disorder. Treatment after a disorder has started aims to reduce, alleviate, ameliorate or altogether eliminate the disorder, and/or its associated symptoms, to prevent it from becoming worse, to slow the rate of progression, or to prevent the disorder from re-occurring once it has been initially eliminated (i.e., to prevent a relapse). The term "treatment", as used herein, unless otherwise indicated, refers to the act of “treating”.
The term “therapeutically effective amount” as used herein refers to an amount that is effective, upon single or multiple dose administration to a subject (such as a human patient) in the treatment of a disease, disorder or pathological condition.
The term “combination therapy” or “combination treatment” are used herein indistinctively, and is meant to comprise the administration of the referred therapeutic agents to a subject suffering from cancer, in the same or separate pharmaceutical formulations, and at the same time or at different times. If the therapeutic agents are administered at different times they should be administered sufficiently close in time to provide for the combined effect (e.g. potentiating or synergistic response) to occur. The particular combination of therapies to employ in a combination regimen will take into account compatibility of the desired therapeutics and/or procedures and/or the desired therapeutic effect to be achieved. It will be appreciated that the therapies employed may achieve a desired effect for the same disorder (for example, anti-cancer effects), and/or they may achieve different effects (e.g., control of any adverse effects).
The term “single agent” as used herein relates to the use of an active ingredient sufficiently separate in time from another active ingredient to prevent for the potentiating or synergistic response to occur. More specifically, the use as “single agent” does not encompass the use as a “combination therapy”.
The term “anti-cancer treatment” as used herein may include any treatment to stop or prevent cancer, including but not limited to surgery, radiotherapy, anti-cancer agents and any other existing therapies or to be developed.
The term "anti-cancer agent" as used herein refers to any therapeutic agents useful in treating cancer. Examples of anti-cancer therapeutic agents include, but are limited to, e.g., an inhibitor of the TGFp signaling pathway, chemotherapeutic agents, growth inhibitory agents, cytotoxic agents, anti-hormonal agents, agents used in radiation therapy, anti angiogenesis agents, apoptotic agents, anti-tubulin agents, and other agents to treat cancer, such as anti-HER-2 antibodies (e.g., Herceptin®), anti-CD20 antibodies, an epidermal growth factor receptor (EGFR) antagonist (e.g., a tyrosine kinase inhibitor), FIER1/EGFR inhibitor (e.g., erlotinib (Tarceva<®>)), platelet derived growth factor inhibitors (e.g., Gleevec™ (Imatinib Mesylate)), a COX-2 inhibitor (e.g., celecoxib), interferons, cytokines, antagonists (e.g., neutralizing antibodies) that bind to one or more of the following targets ErbB2, ErbB3, ErbB4, PDGFR-beta, BlyS, APRIL, BCMA or VEGF receptor(s), TRAIL/Apo2, and other bioactive and organic chemical agents, etc. Combinations thereof are also included in the invention.
The terms “TGFp inhibitor” or “TGFp (signaling) pathway inhibitor” or “inhibitor of the TGFp signaling pathway” are used herein indistinctively and refer to an agent inhibiting TGFp signaling by any means. Illustrative, non-limiting examples of “TGFp inhibitors” are those inhibiting TGFp signaling pathway at any of the (i) ligand level, (ii) the ligand-receptor level or the (ii) intracellular level.
(i) Agents inhibiting the TGF b pathway at the ligand level include antisense oligonucleotides which may be delivered directly intravenously or engineered into immune cells to prevent TGFp synthesis (for example, trabedersen [AP12009], an antisense oligonucleotide targeting TGFP2; and Lucanix® [belagenpumatucel-L], a TGFP2 antisense gene-modified allogeneic cancer cell vaccine);
(ii) Agents inhibiting the TGF b pathway at the ligand-receptor level include ligand-traps (e.g., TGFp-neutralizing affinity ligands [e.g., monoclonal antibodies] and soluble receptors), antibodies or molecules that prevent TGFp release from latent complexes, like antibodies against GARP (Cuende et al., Science Translational Medicine 7 (2015), issue 284, pp. 284ra56) or antibodies against anbd integrin that blocks the release of active TGFp by cancer cells (N. Takasaka et al., JCI Insight. 3 (2018), doi:10.1172/jci.insight.122591); and anti-TGFp-receptor affinity ligands to prevent ligand- receptor interaction (for example, fresolimumab, a pan-TGFp antibody; disitertide [P144], a peptidic TGFpi inhibitor specifically designed to block the interaction with its receptor; and IMCTR1 [LY3022859], a monoclonal antibody against TGFpRII) ; and
(iii) Agents inhibiting the TGF b pathway intracellular level include TGFp receptor kinase inhibitors to prevent signal transduction (for example, galunisertib [LY2157299], a small molecule inhibitor of TGFpRI.
Particular examples of TGFp pathway inhibitors in development in cancer are provided in Table 1 of Neuzillet et al. (Pharmacology & Therapeutics 147 (2015) 22-31):
Figure imgf000021_0001
35 and in Table 1 of Batlle and Massague (Immunity (2019) 50(4):924-940):
Figure imgf000022_0001
Figure imgf000023_0001
The term "reduce or inhibit" as used herein may refer to the ability to cause an overall decrease preferably of 20% or greater, more preferably of 50% or greater, and most preferably of 75%, 85%, 90%, 95%, or greater. Reduce or inhibit can refer for instance, to the biological activity of an active ingredient or a ligand (e.g. TGF b activity), to the symptoms of the disorder being treated, the presence or size of metastases, the size of the primary tumor, etc. The term "cytotoxic agent" as used herein refers to a substance that inhibits or prevents the function of cells and/or causes destruction of cells. The term is intended to include radioactive isotopes (e.g. At<211>, l<131 >, l<125>, Y<90>, Re<186>, Re<188>, Sm<153>, Bi<212>, P<32>and radioactive isotopes of Lu), chemotherapeutic agents, and toxins such as small molecule toxins or enzymatically active toxins of bacterial, fungal, plant or animal origin, including fragments and/or variants thereof.
The term "chemotherapeutic agent" as used herein refers to a chemical compound useful in the treatment of cancer. Examples of chemotherapeutic agents include alkylating agents such as thiotepa and CYTOXAN® cyclosphosphamide; alkyl sulfonates such as busulfan, improsulfan and piposulfan; aziridines such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylamelamines including altretamine, triethylenemelamine, trietylenephosphoramide, triethiylenethiophosphoramide and trimethylolomelamine; acetogenins (especially bullatacin and bullatacinone); a camptothecin (including the synthetic analogue topotecan); bryostatin; callystatin; CC-1065 (including its adozelesin, carzelesin and bizelesin synthetic analogues); cryptophycins (particularly cryptophycin 1 and cryptophycin 8); dolastatin; duocarmycin (including the synthetic analogues, KW-2189 and CB1-TM1); eleutherobin; pancratistatin; a sarcodictyin; spongistatin; nitrogen mustards such as chlorambucil, chlornaphazine, cholophosphamide, estramustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, novembichin, phenesterine, prednimustine, trofosfamide, uracil mustard; nitrosureas such as carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimnustine; antibiotics such as the enediyne antibiotics (e. g., calicheamicin, especially calicheamicin gammal and calicheamicin omegaH ; dynemicin, including dynemicin A; bisphosphonates, such as clodronate; an esperamicin; as well as neocarzinostatin chromophore and related chromoprotein enediyne antiobiotic chromophores), aclacinomysins, actinomycin, authramycin, azaserine, bleomycins, cactinomycin, carabicin, carminomycin, carzinophilin, chromomycinis, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, ADRIAMYCIN® doxorubicin (including morpholino-doxorubicin, cyanomorpholino- doxorubicin, 2-pyrrolino-doxorubicin and deoxydoxorubicin), epirubicin, esorubicin, idarubicin, marcellomycin, mitomycins such as mitomycin C, mycophenolic acid, nogalamycin, olivomycins, peplomycin, potfiromycin, puromycin, quelamycin, rodorubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, zorubicin; anti-metabolites such as methotrexate and 5-fluorouracil (5-FU); folic acid analogues such as denopterin, methotrexate, pteropterin, trimetrexate; purine analogs such as fludarabine, 6- mercaptopurine, thiamiprine, thioguanine; pyrimidine analogs such as ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, floxuridine; androgens such as calusterone, dromostanolone propionate, epitiostanol, mepitiostane, testolactone; anti-adrenals such as aminoglutethimide, mitotane, trilostane; folic acid replenisher such as frolinic acid; aceglatone; aldophosphamide glycoside; aminolevulinic acid; eniluracil; amsacrine; bestrabucil; bisantrene; edatraxate; defofamine; demecolcine; diaziquone; elfornithine; elliptinium acetate; an epothilone; etoglucid; gallium nitrate; hydroxyurea; lentinan; lonidainine; maytansinoids such as maytansine and ansamitocins; mitoguazone; mitoxantrone; mopidanmol; nitraerine; pentostatin; phenamet; pirarubicin; losoxantrone; podophyllinic acid; 2- ethylhydrazide; procarbazine; PSK® polysaccharide complex (JHS Natural Products, Eugene, OR); razoxane; rhizoxin; sizofiran; spirogermanium; tenuazonic acid; triaziquone; 2,2',2"-trichlorotriethylamine; trichothecenes (especially T-2 toxin, verracurin A, roridin A and anguidine); urethan; vindesine; dacarbazine; mannomustine; mitobronitol; mitolactol; pipobroman; gacytosine; arabinoside ("Ara-C"); cyclophosphamide; thiotepa; taxoids, e.g., TAXOL® paclitaxel, ABRAXANE® Cremophor- free, albumin-engineered nanoparticle formulation of paclitaxel, and TAXOTERE® doxetaxel; chloranbucil; GEMZAR® gemcitabine; 6-thioguanine; mercaptopurine; methotrexate; platinum analogs such as cisplatin, oxaliplatin and carboplatin; vinblastine; platinum; etoposide (VP-16); ifosfamide; mitoxantrone; vincristine; NAVELBINE® vinorelbine; novantrone; teniposide; edatrexate; daunomycin; aminopterin; xeloda; ibandronate; irinotecan (Camptosar, CPT-11) (including the treatment regimen of irinotecan with 5-FU and leucovorin); topoisomerase inhibitor RFS 2000; difluorometlhylornithine (DMFO); retinoids such as retinoic acid; capecitabine; combretastatin; leucovorin (LV); oxaliplatin, including the oxaliplatin treatment regimen (FOLFOX); lapatinib (Tykerb®); inhibitors of PKC-alpha, Raf, H-Ras, EGFR (e.g., erlotinib (Tarceva<®>)) and VEGF-A that reduce cell proliferation and pharmaceutically acceptable salts, acids or derivatives of any of the above.
The term “anti-hormonal agents” as used herein refers to agents that act to regulate or inhibit hormone action on tumors such as anti-estrogens and selective estrogen receptor modulators (SERMs), including, for example, tamoxifen (including NOLVADEX® tamoxifen), raloxifene, droloxifene, 4-hydroxytamoxifen, trioxifene, keoxifene, LY 117018, onapristone, and FARESTON- toremifene; aromatase inhibitors that inhibit the enzyme aromatase, which regulates estrogen production in the adrenal glands, such as, for example, 4(5)-imidazoles, aminoglutethimide, MEGASE® megestrol acetate, AROMASIN® exemestane, formestanie, fadrozole, RIVISOR® vorozole, FEMARA® letrozole, and ARIMIDEX® anastrozole; and anti-androgens such as flutamide, nilutamide, bicalutamide, leuprolide, and goserelin; as well as troxacitabine (a 1 ,3-dioxolane nucleoside cytosine analog); antisense oligonucleotides, particularly those which inhibit expression of genes in signaling pathways implicated in abherant cell proliferation, such as, for example, PKC-alpha, Ralf and H-Ras; ribozymes such as a VEGF expression inhibitor (e.g., ANGIOZYME® ribozyme) and a HER2 expression inhibitor; vaccines such as gene therapy vaccines, for example, ALLOVECTIN® vaccine, LEUVECTIN® vaccine, and VAXID® vaccine; PROLEUKIN® rlL-2; LURTOTECAN® topoisomerase 1 inhibitor; ABARELIX® rmRH; and pharmaceutically acceptable salts, acids or derivatives of any of the above.
The term "cytokine" is a generic term for proteins released by one cell population which act on another cell as intercellular mediators. Examples of such cytokines are lymphokines, monokines, and traditional polypeptide hormones. Included among the cytokines are growth hormones such as human growth hormone, N-methionyl human growth hormone, and bovine growth hormone; parathyroid hormone; thyroxine; insulin; proinsulin; relaxin; prorelaxin; glycoprotein hormones such as follicle stimulating hormone (FSH), thyroid stimulating hormone (TSH), and luteinizing hormone (LH); epidermal growth factor; hepatic growth factor; fibroblast growth factor; prolactin; placental lactogen; tumor necrosis factor- alpha and -beta; mullerian-inhibiting substance; mouse gonadotropin-associated peptide; inhibin; activin; vascular endothelial growth factor; integrin; thrombopoietin (TPO); nerve growth factors such as NGF-alpha; platelet-growth factor; transforming growth factors (TGFs) such as TGF-alpha and TGF-b ; insulin-like growth factor-l and -II; erythropoietin (EPO); osteoinductive factors; interferons such as interferon-alpha, -beta and -gamma colony stimulating factors (CSFs) such as macrophage-CSF (M-CSF); granulocyte- macrophage-CSF (GM-CSF); and granulocyte-CSF (G-CSF); interleukins (ILs) such as IL-1 , IL-1 alpha, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-11 , IL-12; a tumor necrosis factor such as TNF-alpha or TNF-beta; and other polypeptide factors including LIF and kit ligand (KL). As used herein, the term cytokine includes proteins from natural sources or from recombinant cell culture and biologically active equivalents of the native sequence cytokines.
The term "growth inhibitory agent" as used herein refers to a compound or composition which inhibits growth of a cell in vitro and/or in vivo. Thus, the growth inhibitory agent may be one which significantly reduces the percentage of cells in S phase. Examples of growth inhibitory agents include agents that block cell cycle progression (at a place other than S phase), such as agents that induce G1 arrest and M-phase arrest. Classical M-phase blockers include the vincas (vincristine and vinblastine), TAXOL®, and topo II inhibitors such as doxorubicin, epirubicin, daunorubicin, etoposide, and bleomycin. Those agents that arrest G1 also spill over into S-phase arrest, for example, DNA alkylating agents such as tamoxifen, prednisone, dacarbazine, mechlorethamine, cisplatin, methotrexate, 5- fluorouracil, and ara-C. Further information can be found in The Molecular Basis of Cancer, Mendelsohn and Israel, eds., Chapter 1 , entitled "Cell cycle regulation, oncogenes, and antineoplastic drugs" by Murakami et al. (WB Saunders: Philadelphia, 1995), especially p. 13 .
The term “radiation therapy” or “radiotherapy” as used herein refers to the use of directed gamma rays or beta rays to induce sufficient damage to a cell so as to limit its ability to function normally or to destroy the cell altogether. It will be appreciated that there will be many ways known in the art to determine the dosage and duration of treatment. Typical treatments are given as a one-time administration and typical dosages range from 10 to 200 units (Grays) per day.
The term “marker” or “biomarker” as used herein may refer to markers of disease, prognostic or predictive markers which are typically substances found in a bodily sample that can be easily measured. Said bodily sample can be for instance a blood, plasma or feces sample. The term biomarker encompasses biophysical and biochemical determinations, including genetic and serological markers.
The term “prognosis” as used herein refers to predicting disease progression or outcome. More specifically, “prognostic markers” may refer to patient or tumor characteristics that predict outcome (usually survival) independent of the treatment. Thus, they are usually identified and validated in patients who receive no therapy or surgical therapy only. The goal of identifying prognostic markers is to define patient subpopulations with significantly different anticipated outcomes, which might benefit from different therapies. Good prognostic patients may not require additional treatment beyond the primary surgical resection, while poor prognostic patients may derive improved survival benefit from adjuvant therapy or other closer clinical follow up or therapeutic strategy.
“Predictive markers”, on the other hand, may refer to patient or tumor characteristics that predict benefit from specific treatments (either in terms of tumor shrinkage or survival). In other words, the differences in tumor response or survival benefit between treated versus untreated patients will be significantly different in those positive or negative for the predictive marker (Zhu CQ and Tsao MS, 2014).
The term “substantially identical” sequence as used herein refers to a sequence which is at least about 95%, preferably at least about 96%, 97%, 98%, or 99% identical to a reference sequence. Identity percentage between the two sequences can be determined by any means known in the art, for example the Needleman and Wunsch global alignment algorithm. The term "probe" as used herein refers to synthetic or biologically produced nucleic acids, between 10 and 285 base pairs in length which contain specific nucleotide sequences that allow specific and preferential hybridization under predetermined conditions to target nucleic acid sequences, and optionally contain a moiety for detection or for enhancing assay performance. A minimum of ten nucleotides is generally necessary in order to statistically obtain specificity and to form stable hybridization products, and a maximum of 285 nucleotides generally represents an upper limit for length in which reaction parameters can be easily adjusted to determine mismatched sequences and preferential hybridization. Probes may optionally contain certain constituents that contribute to their proper or optimal functioning under certain assay conditions. For example, probes may be modified to improve their resistance to nuclease degradation (e.g., by end capping), to carry detection ligands (e.g., fluorescein), to carry ligands for purification or enrichment purposes (e.g. biotin) or to facilitate their capture onto a solid support (e.g., poly-deoxyadenosine "tails").
The term "primers" as used herein refers to oligonucleotides that can be used in an amplification method, such as a polymerase chain reaction ("PCR"), to amplify a nucleotide sequence. Primers are designed based on the polynucleotide sequence of a particular target sequence.
The term “specific" as used herein in connection with a nucleotide sequence means that a nucleotide sequence will hybridize to/amplify a predetermined target sequence and will not substantially hybridize to/amplify a non-target sequence under the assay conditions, generally stringent conditions are used.
The term "hybridization" as used herein refers to a process by which, under predetermined reaction conditions, two partially or completely complementary strands of nucleic acid are allowed to come together in an antiparallel fashion to form a double-stranded nucleic acid with specific and stable hydrogen bonds, following explicit rules pertaining to which nucleic acid bases may pair with one another.
The term "substantial hybridization" means that the amount of hybridization observed will be such that one observing the results would consider the result positive with respect to hybridization data in positive and negative controls. Data which is considered "background noise" is not substantial hybridization. The term "stringent hybridization conditions" means approximately 35°C to 65°C in a salt solution of approximately 0.9 molar NaCI. Stringency may also be governed by such reaction parameters as the concentration and type of ionic species present in the hybridization solution, the types and concentrations of denaturing agents present, and the temperature of hybridization. Generally as hybridization conditions become more stringent, longer probes are preferred if stable hybrids are to be formed. As a rule, the stringency of the conditions under which hybridization is to take place will dictate certain characteristics of the preferred probes to be employed.
The term "affinity reagent" may refer to a ligand (e.g., antibody, peptide, protein, nucleic acid or small molecule) that selectively captures (binds to) a target molecule through specific molecular recognition, typically with a binding affinity in the nanomolar to sub-nanomolar range. For example, the affinity reagent may be an aptamer, antibody or antibody-mimetic.
The term “affinity” as used herein may refer to the equilibrium constant for the dissociation of an antigen with an antigen-binding molecule (KD), and is considered a measure for the binding strength between an antigenic determinant and an antigen-binding site on the antigen -binding molecule: the lesser the value of the KD, the stronger the binding strength between an antigenic determinant and the antigen-binding molecule (alternatively, the affinity can also be expressed as the association constant (KA), which is 1/KD). It will be clear to the skilled person that the dissociation constant may be the actual or apparent dissociation constant.
The term “aptamer” or “nucleic acid aptamer” as used herein may refer to an isolated or purified single-stranded nucleic acid (RNA or DNA) that binds with high specificity and affinity to a target through interactions other than Watson-Crick base pairing. An aptamer has a three dimensional structure that provides chemical contacts to specifically bind to a target. Unlike traditional nucleic acid binding, aptamer binding is not dependent upon a conserved linear base sequence, but rather a particular secondary or tertiary structure. That is, the nucleic acid sequences of aptamers are non-coding sequences. Any coding potential that an aptamer may possess is entirely fortuitous and plays no role whatsoever in the binding of an aptamer to a target. A typical minimized aptamer is 5-15 kDa in size (15-45 nucleotides), binds to a target with nanomolar to sub-nanomolar affinity, and discriminates against closely related targets (e.g., aptamers will typically not bind to other proteins from the same gene or functional family). The term “antibody” as used herein may refer to an immunoglobulin or an antigen-binding fragment thereof. Unless otherwise specified, the term includes, but is not limited to, polyclonal, monoclonal, monospecific, multispecific, humanized, human, chimeric, synthetic, recombinant, hybrid, mutated, grafted, and in vitro generated antibodies. The antibody can include a constant region, or a portion thereof, such as the kappa, lambda, alpha, gamma, delta, epsilon and mu constant region genes. For example, heavy chain constant regions of the various isotypes can be used, including: IgGi, lgG2, lgG3, lgG4, IgM, IgAi, lgA2, IgD, and IgE. By way of example, the light chain constant region can be kappa or lambda. In certain embodiments, the term "antibody" may also refer to antibody derivatives, such as antibody- based fusion proteins or antibodies further modified to contain additional non-proteinaceous moieties, such as water-soluble polymers, e.g. polyethylene glycol (PEG).
The terms “antigen-binding domain” and “antigen-binding fragment” refer to a part of an antibody molecule that comprises amino acids responsible for the specific binding between antibody and antigen. For certain antigens, the antigen-binding domain or antigen-binding fragment may only bind to a part of the antigen. The part of the antigen that is specifically recognized and bound by the antibody is referred to as the “epitope” or “antigenic determinant.” Antigen-binding domains and antigen-binding fragments include Fab; a F(ab')2 fragment (a bivalent fragment having two Fab fragments linked by a disulfide bridge at the hinge region); a Fv fragment; a single chain Fv fragment (scFv); a Fd fragment (having the two VH and CH1 domains); single domain antibodies (sdAbs; consisting of a single VH domain), and other antibody fragments that retain antigen-binding function. The Fab fragment has VH-CH1 and VL-Ci_ domains covalently linked by a disulfide bond between the constant regions. The Fv fragment is smaller and has VH and VL domains non-covalently linked. The scFv contains a flexible polypeptide that links (1) the C-terminus of VH to the N-terminus of Vi_, or (2) the C-terminus of Vi_ to the N-terminus of VH. The sdAbs include heavy chain antibodies naturally devoid of light chains and single-domain antibodies derived from conventional four chain antibodies. These antigen-binding domains and fragments are obtained using conventional techniques known to those with skill in the art, and are evaluated for function in the same manner as are intact immunoglobulins.
The term "recombinant antibody" as used herein refers to an antibody produced or expressed using a recombinant expression vector, where the expression vector comprises a nucleic acid encoding the recombinant antibody, such that introduction of the expression vector into an appropriate host cell results in the production or expression of the recombinant antibody. Recombinant antibodies may be chimeric or humanized antibodies, mono- or multi-specific antibodies. The term “an antibody mimetic” (AbM) as used herein refers to single-domain scaffolds, which have been engineered to bind therapeutic targets with affinity and specificity that match that of natural antibodies. Antibody mimetics have been developed utilizing an immunoglobulin-like fold, for example, fibronectin type III, NCAM and CTLA-4. Other mimetics scaffolds bearing no similarity to immunoglobulin folds have also been obtained. Non-limiting examples of said scaffolds are DARPins, anticalins, affibodies, adnectins, fynomers, etc. (see for instance, Weidle et al. Cancer Genomics & Proteomics. 2013, 10:1 - 18; Lofblom, J. et al., Curr. Opin. Biotechnol. 2011 , 22: 843-848; Banta, S. et al., Annu. Rev. Biomed. Eng., 2010, 15: 93-113). b. Method for identifying tumors with a TGFB-activated TME
In a first aspect, the present invention provides a method for determining whether a patient’s tumor has a TGFp-activated microenvironment, wherein said method comprises or consists of: a) determining the expression levels of CTHRC1 (Collagen Triple Helix Repeat Containing 1) gene in a biological sample isolated from said patient; b) comparing the expression levels of CTHRC1 in the patient’s sample with a reference value; wherein an increase of the value in the patient’s sample with regard to said reference value is indicative that said patient’s tumor has a TGFp -activated microenvironment.
In a related aspect, the present invention concerns a method for identifying cancer patients having a tumor with a high TGFp-activated microenvironment and poor prognosis, wherein said method comprises steps a) to c) as described herein, wherein in step c) an increase of the value in the patient’s sample with regard to said reference value is indicative that said patient’s tumor has a TGFp-activated microenvironment and poor prognosis.
Step (a) of the methods of the invention comprises or consists of determining in said biological sample the expression levels of CTFIRC1 .
CTFIRC1 (Collagen Triple Helix Repeat Containing 1) is a secreted glycoprotein that has multiple functions associated with wound repair, bone remodeling, hepatocyte fibrosis and adipose tissue formation among others (Jiang et al., Journal of Cancer, 7:(15) (2016) 2213- 2220). This gene has been described in humans (Gene ID: 115908, updated on 11 -Sep- 2019). Mutations at this locus have been associated with Barrett esophagus and esophageal adenocarcinoma.
CTHRC1 gene is located in chromosome 8 (8q22.3; Reference GRCh38.p13 Primary Assembly) and has 5 exons. Alternative splicing transcript variants have been described:
Figure imgf000032_0001
The canonical sequence of the mRNA expression product of human CTHRC1 gene corresponds to NM_138455.4 transcript variant 1 , mRNA and is referred as SEQ ID NO:1 :
1 gaaaggcgca ttgatgcagc ctgcggcggc ctcggagcgc ggcggagcca gacgctgacc
61 acgttcctct cctcggtctc ctccgcctcc agctccgcgc tgcccggcag ccgggagcca
121 tgcgacccca gggccccgcc gcctccccgc agcggctccg cggcctcctg ctgctcctgc
181 tgctgcagct gcccgcgccg tcgagcgcct ctgagatccc caaggggaag caaaaggcgc
241 agctccggca gagggaggtg gtggacctgt ataatggaat gtgcttacaa gggccagcag
301 gagtgcctgg tcgagacggg agccctgggg ccaatggcat tccgggtaca cctgggatcc
361 caggtcggga tggattcaaa ggagaaaagg gggaatgtct gagggaaagc tttgaggagt
421 cctggacacc caactacaag cagtgttcat ggagttcatt gaattatggc atagatcttg
481 ggaaaattgc ggagtgtaca tttacaaaga tgcgttcaaa tagtgctcta agagttttgt
541 tcagtggctc acttcggcta aaatgcagaa atgcatgctg tcagcgttgg tatttcacat
601 tcaatggagc tgaatgttca ggacctcttc ccattgaagc tataatttat ttggaccaag
661 gaagccctga aatgaattca acaattaata ttcatcgcac ttcttctgtg gaaggacttt
721 gtgaaggaat tggtgctgga ttagtggatg ttgctatctg ggttggtact tgttcagatt
781 acccaaaagg agatgcttct actggatgga attcagtttc tcgcatcatt attgaagaac
841 taccaaaata aatgctttaa ttttcatttg ctacctcttt ttttattatg ccttggaatg 901 gttcacttaa atgacatttt aaataagttt atgtatacat ctgaatgaaa agcaaagcta
961 aatatgttta cagaccaaag tgtgatttca cactgttttt aaatctagca ttattcattt
1021 tgcttcaatc aaaagtggtt tcaatatttt ttttagttgg ttagaatact ttcttcatag
1081 tcacattctc tcaacctata atttggaata ttgttgtggt cttttgtttt ttctcttagt
1141 atagcatttt taaaaaaata taaaagctac caatctttgt acaatttgta aatgttaaga
1201 atttttttta tatctgttaa ataaaaatta tttccaacaa
The term “CTHRC1 mRNA” as used herein may refer to any of CTHRC1 gene transcript variants, preferably, to any transcript variants corresponding to isoform 1 (e.g., transcript variants 1), more preferably, said sequence is SEQ ID NO: 1 .
The canonical sequence of the protein expression product of human CTHRC1 gene corresponds to CTHRC1 isoform 1 (NCBI NP 001243028.1/ Q96CG8) and is referred as SEQ ID NO:2:
MRPQGPAASPQRLRGLLLLLLLQLPAPSSASEIPKGKQKAQLRQREVVDLYNGMCLQGPA
GVPGRDGSPGANGIPGTPGIPGRDGFKGEKGECLRESFEESWTPNYKQCSWSSLNYGIDL
GKIAECTFTKMRSNSALRVLFSGSLRLKCRNACCQRWYFTFNGAECSGPLPIEAIIYLDQ
GSPEMNSTINIHRTSSVEGLCEGIGAGLVDVAIWVGTCSDYPKGDASTGWNSVSRIIIEE
LPK
The term “CTHRC1 protein” as used herein may refer to any of the protein isoforms, preferably it refers to SEQ ID NO: 2.
Step (a) of the methods of the invention requires the determination of the expression levels of CTHRC1 gene in a biological sample isolated from a subject suffering from cancer also referred herein as a “cancer patient”. It may comprise the determination of the expression levels of 900 or less genes, 800 or less genes, 700 or less genes, 600 or less gens, 500 or less genes, 400 or less genes, 300 or less genes, 200 or less genes, 100 or less genes, 90 or less genes, 80 or less genes, 70 or less genes, 60 or less genes, 50 or less genes, 40 or less genes, 30 or less genes, 20 or less genes, 10 or less genes and 5 or less genes. In preferred embodiments, the group of genes which expression is determined in step a) consists of less than 19 genes, preferably step (a) consists of determining the expression levels of CTHRC1 gene. The methods of the invention can be applied to any type of biological sample from a patient, such as a biopsy sample, tissue, cell or fluid (serum, saliva, semen, sputum, cerebral spinal fluid (CSF), tears, mucus, sweat, milk, brain extracts and the like). In performing the method of the present invention, said biological sample from the cancer patient is preferably a sample containing tumor cells. Tumors or portions thereof may be surgically resected from the patient or obtained by routine biopsy. Preferably, a tumor sample is obtained from the primary tumor. In a particular embodiment, optionally in combination with any of the features or embodiments described above or below, said biological sample isolated from the subject is a tumor biopsy sample, preferably obtained from a resected tumor.
These types of samples are routinely used in the clinical practice and a person skilled in the art will know how to identify the most appropriate means for their obtaining and preservation. Once a sample has been obtained, it may be used fresh, it may be frozen or preserved using appropriate means (e.g., as a formalin-fixed, paraffin-embedded tissue sample). Such biological samples can be taken around the time of diagnosis, before, during or after treatment (e.g. surgical resection).
Methods for quantifying gene expression are well known in the art. Any current or future means for quantifying gene expression can be used in the methods of the invention.
In a particular embodiment, optionally in combination with one or more of the features or embodiments described herein, the determination of the expression of the CTHRC1 gene is carried out at protein level. Suitable methods for determining the levels of a given protein include, without limitation, those described herein below. Preferred methods for determining the protein expression levels in the methods of the present invention are immunoassays. Various types of immunoassays are known to one skilled in the art for the quantitation of proteins of interest. These methods are based on the use of affinity reagents, which may be any antibody or ligand specifically binding to the target protein or to a fragment thereof, wherein said affinity reagent is preferably labeled. Illustrative, but non-exclusive, examples of labels that can be used include radioactive isotopes, enzymes, fluorophores, chemoluminescent reagents, enzyme cofactors or substrates, enzyme inhibitors, particles, dyes, etc.
Affinity reagents may be any antibody or ligand specifically binding to the target protein or to a fragment thereof. Affinity ligands may include proteins, peptides, peptide aptamers, affimers and other target specific protein scaffolds, like antibody-mimetics. Specific antibodies against the protein markers used in the methods of the invention may be produced for example by immunizing a host with a protein of the present invention or a fragment thereof. Likewise, peptides specific against the protein markers used in the methods of the invention may be produced by screening synthetic peptide libraries.
Western blot or immunoblotting techniques allow comparison of relative abundance of proteins separated by an electrophoretic gel (e.g., native proteins by 3-D structure or denatured proteins by the length of the polypeptide). Immunoblotting techniques use antibodies (or other specific ligands in related techniques) to identify target proteins among a number of unrelated protein species. They involve identification of protein target via antigen- antibody (or protein-ligand) specific reactions. Proteins are typically separated by electrophoresis and transferred onto a sheet of polymeric material (generally nitrocellulose, nylon, or polyvinylidene difluoride). Dot and slot blots are simplified procedures in which protein samples are not separated by electrophoresis but immobilized directly onto a membrane.
Traditionally, quantification of proteins in solution has been carried out by immunoassays on a solid support. Said immunoassay may be for example an enzyme-linked immunosorbent assay (ELISA), a fluorescent immunosorbent assay (FIA), a chemiluminescence immunoassay (CIA), or a radioimmunoassay (RIA), an enzyme multiplied immunoassay, a solid phase radioimmunoassay (SPROA), a fluorescence polarization (FP) assay, a fluorescence resonance energy transfer (FRET) assay, a time-resolved fluorescence resonance energy transfer (TR-FRET) assay, a surface plasmon resonance (SPR) assay. Multiplex and any next generation versions of any of the above, such as bead-based flow- cytometry immunoassays (e.g., based on the Luminex xMAP technology) are specifically encompassed. In a particular embodiment, said immunoassay is an ELISA assay or any multiplex version thereof.
Other methods that can be used for quantification of proteins in solution are techniques based on mass spectrometry (MS) such as liquid chromatography coupled to mass spectrometry (LC / MS), described for example in US2010/0173786, or tandem LC-MS / MS (W02012/155019, US2011/0039287, M. Rauh, J Chromatogr B Analyt Technol Biomed Life Sci 2012 February 1 , 883-884. 59-67) and multiplex versions of the above techniques, as well as the next generation of such techniques and combinations thereof.
For determining protein expression and location, immunohistochemical and in-situ hybridization analysis are usually preferred. Immunohistochemistry (IHC) analysis is typically conducted using thin sections of the biological sample immobilized on coated slides. These sections, when derived from paraffin- embedded tissue samples, are deparaffinised and preferably treated so as to retrieve the antigen. The detection can be carried out in individual samples or in tissue microarrays. This procedure, although is subjectively determined by the pathologist, is the standard method of measurement of IHC results, and well known in the art.
Generally, the use of this technique entails the determination of the Histological score value. For instance, the Histological score (H-Score) value may be determined per biological sample according to (i) staining intensity and (ii) the percentage of positive staining tumor cells by using the following formula:
H-Score = å (intensity grade x % stained cells)
The staining intensity of tumor cells may be scored in different intensity grades, for example the following 4 grades:
1 . no staining or very weak staining (intensity = 0);
2. positive weak staining (intensity= 1 or +);
3. positive moderate staining (intensity=, 2 or ++); and
4. positive strong staining (intensity=3 or +++).
The percentage of positive staining cells for each intensity grade may be scored from 0 to 100.
In some embodiments, the final score, called Ή-Score”, is preferably calculated by adding the products of the percentage cells stained with a given intensity grade (0-100) by the corresponding staining intensity grade value (0-3). The following formula may be applied:
H-Score = å intensity grade x % stained cells = 1 x (% of cells with weak staining) + 2 x (% of cells with moderate staining) + 3 x (% of cells with strong staining).
Preferred embodiments do not require determining the percentage of cells stained with a given intensity grade in stromal cells. As described above, the obtained results indicate that the predictive power of this biomarker does not depend on the percentage of cells expressing these markers within the tumor area, but on a semi-quantitative estimation of their positivity (distinguishing between negative (0) / low (1) and moderate (2) / high (3) intensity) in stromal cells surrounding tumor epithelial cells.
The assessment of the staining intensity and the percentage of positive staining tumor cells can be determined by any means known to the skilled person including but not limited to one expert pathologist, or a panel of at least two independent pathologists, with no knowledge about clinical data scoring all immunohistochemical stainings. In case, the panel of pathologist were to disagree in the scores it is convenient to expand the panel of independent pathologists to at least 3, 4, or 5.
Once the staining intensity and the percentage of positive staining tumor cells have been determined, the value of the H-Score may be obtained by applying the above formula. The resulting value of the H-Score determines the level of expression of the protein marker.
In another embodiment, the mRNA expression level of these genes is determined. Molecular biology methods for measuring quantities of target nucleic acid sequences are well known in the art. These methods include but are not limited to end point PCR, competitive PCR, reverse transcriptase-PCR (RT-PCR), quantitative PCR (qPCR), reverse transcriptase qPCR (RT-qPCR), PCR-pyrosequencing, PCR-ELISA, DNA microarrays, gene expression panels (e.g. nanoString™), nucleic acid sequencing, such as next generation sequencing methods, in situ hybridization assays (such as dot-blot, Fluorescence In Situ Hybridization assay (FISH), RNA-ISH, automated quantitative RNA ISH (RNAscope®)), mass spectrometry, branched DNA (Nolte, Adv. Clin. Chem. 1998,33:201-235) and to multiplex versions of said methods (see for instance, Andoh et al., Current Pharmaceutical Design, 2009;15,2066- 2073) and the next generation of any of the techniques listed and combinations thereof, all of which are within the scope of the present invention. Such methods may also include the pre-conversion of mRNA into cDNA by the reaction with a reverse transcriptase (RT), for example the PCR or qPCR reaction is usually preceded by conversion of mRNA into cDNA and referred to as RT-PCR or RT-qPCR, respectively.
Diverse next-generation sequencing methods have been described and are well known to a person skilled in the art. These include for instance sequencing by synthesis with cyclic reversible termination approaches (e.g., Illumina, SEQLL, Qiagen), sequencing by synthesis with single-nucleotide addition approaches (e.g., Roche-454, Thermo Fisher-Ion Torrent), sequencing by ligation (e.g., Thermo Fisher SOLiD and BGI-Complete Genomics), real-time long-read sequencing (e.g., Pacific Biosciences, Oxford Nanopore Technologies), synthetic long-read sequencing (e.g., Illumina, 10X Genomics, iGenomeX), see for instance Goodwin S, et al., Nat Rev Genet. 2016, 17(6):333-51 ).
In some embodiments, said molecular biology quantification methods are based on sequence specific amplification. Such an amplification based assay comprises an amplification step which comprises contacting a sample (preferably an isolated DNA sample) with two or more amplification oligonucleotides specific for a target sequence in a target nucleic acid to produce an amplified product if the target nucleic sequence is present in the sample. Suitable amplification methods include for example, replicase-mediated amplification, ligase chain reaction (LCR), strand-displacement amplification (SDA), transcription mediated amplification (TMA) and polymerase chain reaction (PCR), which includes quantitative PCR.
One particularly preferred quantification method is quantitative PCR (qPCR), also known as real-time PCR. It relates to a type of PCR that amplifies and simultaneously quantifies a target DNA molecule. Its key feature is that the amplified DNA is detected as the reaction progresses in real time. Unless otherwise provided, the term qPCR as used herein encompasses reverse transcriptase (RT)-qPCR. Different instruments are available, such as ABI Prism 7700 SDS, GeneAmp 5700 SDS, ABI Prism 7900 HT SDS from Applied Biosystems; iCycler iQ from Bio-Rad; Smart Cycler from Cepheid; Rotor-Gene from Corbett Research; LightCycler from Roche Molecular Biochemicals and Mx4000 Multiplex from Stratagene. The qPCR process enables accurate quantification of the PCR product in real time by measuring PCR product accumulation very early in the exponential phase of the reaction, thus reducing bias in the quantification linked to the PCR amplification efficiency occurring in end-point PCR. Real-time PCR is well known in the art and is thus not described in detail herein. Technology overview and protocols for qPCR are available for instance from the above-mentioned vendors, e.g., http://www.sigmaaldrich.com/technical- documents/protocols/biology/sybr-green-qpcr.html or http://www.sigmaaldrich.com/life- science/molecular-biology/pcr/quantitative-pcr/qpcr-technical-guide.html. For a review of qPCR methods see Wong ML y Medrano JF, Biotechniques 2005, 39(1):75-85. In a particular embodiment, the quantification method is a multiplex qPCR.
Different detecting chemistries are available for qPCR. All of them can be used with the above-mentioned qPCR instruments. The term “detection chemistry” refers to a method to report amplification of specific PCR product in real-time PCR. These detecting chemistries may be classified into two main groups; the first group comprises double-stranded DNA intercalating molecules, such as SYBR Green I and EvaGreen, whereas the second includes fluorophore-labeled oligonucleotides. The latter, in turn, has been divided into three subgroups according to the type of fluorescent molecules used in the PCR reaction: (i) primer-probes (Scorpions, Amplifluor®, LUX™, Cyclicons, Angler®); (ii) probes; hydrolysis (TaqMan, MGB-TaqMan, Snake assay) and hybridization (Hybprobe or FRET, Molecular Beacons, HyBeacon™, MGB-Pleiades, MGB-Eclipse, ResonSense®, Yin-Yang or displacing); and (iii) analogues of nucleic acids (PNA, LNA®, ZNA™, non-natural bases: Plexor™ primer, Tiny-Molecular Beacon) see E. Navarro eta l.,Clinica Chimica Acta, Volume 439, 15 January 2015, Pages 231-250.
Said probes may be dual-labeled oligonucleotides, such as hydrolysis probes or molecular beacons. The 5’ end of the oligonucleotide is typically labelled with a fluorescent reporter molecule while the 3’ end is labeled with a quencher molecule. The sequence of the probe is specific for a region of interest in the amplified target molecule. In a more preferred embodiment, said probe is a hydrolysis probe which is designed so that the length of the sequence places the 5’ fluorophore and the 3’ quencher in close enough proximity so as to suppress fluorescence. Several reporter molecules and quenchers for use in qPCR probes are well known in the art.
Generally, for the quantification of nucleotide sequences specific oligonucleotides, such as probes and / or primers are used. The term "a primer and / or a probe" specifically includes "primers and / or probes". Both expressions are used interchangeably herein and encompass for example a primer; a probe; a primer and a probe; a pair of primers; and a pair of primers and a probe. Design and validation of primers and probes is well known in the art. For the design of primers and probes in quantitative real-time PCR methods, see for instance Rodriguez A et al. (Methods Mol Biol., 2015, 1275:31-56). Preferred primers and/or probes which may be used in the methods of the invention are described herein below under the kits of the invention.
Preferably, oligonucleotides useful in the methods of the invention are about 5 to about 50 nucleotides in length, about 10 to about 30 nucleotides in length, or about 20 to about 25 nucleotides in length. In certain embodiments, oligonucleotides specifically hybridizing with the target sequence are about 19 to about 21 nucleotides in length. In a particular embodiment, said oligonucleotides have been modified for detection or to enhance assay performance. These oligonucleotides may be ribonucleotides or deoxyribonucleotides. In particular embodiments, the oligonucleotides may have at least one chemical modification. For instance, suitable oligonucleotides may be comprised of one or more “conformationally constrained” or bicyclic sugar nucleoside modifications, for example, “locked nucleic acids.” “Locked nucleic acids” (LNAs) are modified ribonucleotides that contain an extra bridge between the 2’ and 4’ carbons of the ribose sugar moiety resulting in a “locked” conformation that confers enhanced thermal stability to oligonucleotides containing the LNAs. In other embodiments, the oligonucleotides may comprise peptide nucleic acids (PNAs), which contain a peptide-based backbone rather than a sugar-phosphate backbone. Other chemical modifications that the oligonucleotides may contain include, but are not limited to, sugar modifications, such as 2’-0-alkyl ( e.g . 2’-0-methyl, 2’-0-methoxyethyl), 2’-fluoro, and 4’ thio modifications, and backbone modifications, such as one or more phosphorothioate, morpholino, or phosphonocarboxylate linkages. For instance, these oligonucleotides, particularly those of shorter lengths {e.g., less than 15 nucleotides) can comprise one or more affinity enhancing modifications, such as, but not limited to, LNAs, bicyclic nucleosides, phosphonoformates, 2’ O-alkyl and the like. In some embodiments, the oligonucleotides may be chemically modified, for instance to improve their resistance to nuclease degradation (e.g., by end capping), to carry detection ligands (e.g., fluorescein) or to facilitate their capture onto a solid support (e.g., poly-deoxyadenosine "tails").
For mRNA determination, RNA isolated from frozen or fresh samples is extracted from the cells by any of the methods typical in the art, for example, Sambrook, Fischer and Maniatis, Molecular Cloning, a laboratory manual, (2nd ed.), Cold Spring Flarbor Laboratory Press, New York, (1989). Preferably, care is taken to avoid degradation of the RNA during the extraction process. In a particular embodiment, the expression level is determined using mRNA obtained from a formalin-fixed, paraffin-embedded tissue sample. An exemplary deparaffinization method involves washing the paraffinized sample with an organic solvent, such as xylene, for example. Deparaffinized samples can be rehydrated with an aqueous solution of a lower alcohol. Suitable lower alcohols, for example include, methanol, ethanol, propanols, and butanols. Deparaffinized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example. Alternatively, the sample is simultaneously deparaffinised and rehydrated. The sample is then lysed and RNA is extracted from the sample. Commercially available kits may be used for RNA extraction from paraffin samples, such as PureLink™ FFPE Total RNA Isolation Kit (Thermofisher Scientific Inc., US). The expression "determining the expression levels" as used herein, refers to ascertaining the absolute or relative amount or concentration of the biomarker in the sample. Techniques to assay levels of individual biomarkers from test samples are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed.
Expression levels may be absolute or relative. When the expression levels are normalized, normalization can be performed with respect to different measures in the sample. These procedures are well known to one skilled in the art. Typically, expression levels are normalized with respect to an "endogenous control". An "endogenous control" as used herein may relate to a gene expression product whose expression levels do not change or change only in limited amounts in tumor cells with respect to non-tumorigenic cells.
"Endogenous control", also referred herein as “control gene” or “normalizing gene” is usually the expression product from a housekeeping gene and which codes for a protein which is constitutively expressed and carries out essential cellular functions. Housekeeping genes that can be used as endogenous control include for example b-2-microglobulin, ubiquitin, 18- S ribosomal protein, cyclophilin, GAPDH, actin and HPRT.
Also, the number of control genes which can be used for normalization purposes is not particularly limited. For illustrative, but not limiting, purposes, this may be conducted with one, two, three, four, five, six, seven eight, nine, ten, eleven, twelve, fifteen, twenty, or as many genes as desired. For instance, when next generation sequencing methods are used to quantify transcript expression, normalization may be conducted against the whole genome.
Furthermore, step (b) of the methods of the invention comprises comparing the expression levels of CTHRC1 in the patient’s sample with a reference value and classifying/selecting the cancer patient as having a TGFp activated microenvironment said patient’s tumor has a TGFp -activated microenvironment.
Typically, in step c) said method comprises comparing the CTHRC1 gene expression levels in the subject sample with a reference value; and an increase of the levels in the subject sample with regard to said reference value is indicative of a TGFp activated microenvironment; whereas a decrease of the levels in the subject sample with regard to said reference value is indicative of a tumor which fails to have a TGFp activated microenvironment.
In some embodiments, when step (a) comprises the determination of the expression levels of other genes as described herein above, step (b) of the methods of the invention comprises: b) calculating a score from the expression levels of the genes determined in step a); and c) determining whether a patient’s tumor has a TGFp-activated microenvironment according to the score obtained in b) by comparison with a reference value; wherein an increase of the value in the patient’s sample with respect to said reference value is indicative that said patient’s tumor has a TGFp -activated microenvironment.
The score is a value obtained according to a given mathematical algorithm wherein the expression values of each of the gene markers used in the methods of the invention are variables of said mathematical algorithm. In a particular embodiment, the score is proportional to the expression levels of CTFIRC1 ; wherein the higher the score, the higher the TGFp activation of the TME.
For instance, said score may be calculated as the sum of the product between the gene expression values and their estimated regression coefficients obtained in a regression analysis. In such embodiment, the coefficient is positive for CTFIRC1 and the higher the score, the higher the TGFp activation of the TME.
As described above, the method of the invention enables to identify non-cell-specific TGFp activation of the microenvironment. In preferred embodiments, a TGFp activated microenvironment is defined as a tumor microenvironment comprising TGFp activated fibroblasts, T-cells and macrophages.
The term “reference value”, as used herein, relates to a predetermined criteria used as a reference for evaluating the values or data obtained from the samples collected from a subject. This “reference value” may also be referred as “cut-off value” or “threshold value”. The reference value can be an absolute value, a relative value, a value that has an upper or a lower limit, a range of values, an average value, a median value, a mean value, a z-score (e.g. mean value + or - 1 standard deviation (SD)), a tertile value, or a value as compared to a particular control or baseline value. In a particular embodiment, optionally in combination with one or more of the embodiments or features described above or below, said reference value is the mean value or the tertile value.
In addition, it is further noted that a variety of statistical and mathematical methods for establishing the threshold or cut-off level of expression are known in the prior art. A threshold or cut-off expression level for a particular biomarker may be selected, for example, based on data from Receiver Operating Characteristic (ROC) plots. One of skill in the art will appreciate that these threshold or cut-off expression levels can be varied, for example, by moving along the ROC plot for a particular biomarker or combinations thereof, to obtain different values for sensitivity or specificity thereby affecting overall assay performance.
Sensitivity, specificity, and/or accuracy are parameters typically used to describe the validity or performance of a test. In particular, they are used to quantify how good and reliable the discrimination method is. A test is usually calibrated in terms of the desired specificity and sensitivity according to the target use of the test in clinical practice. High sensitivity corresponds to high negative predictive value and is considered generally a desired property for a “rule out” test, such as a screening test which typically will be followed by a confirmatory test. High specificity corresponds to high positive predictive value and is considered generally a desired property for a “rule in” test, such as a companion diagnostic test.
In preferred embodiments, the methods of the invention have sensitivity, specificity and/or accuracy values of at least about 60 %, preferably of at least about 70 %, and can be, for example, at least 75 %, at least 80 %, at least 85 %, at least 90 %, at least 95 %, at least 96 %, at least 97 %, at least 98 %, at least 99 % or 100% in at least 60 % of the group or population assayed, or preferably in at least 65 %, 70 %, 75 %, 80 %, 85 %, 90 %, 95 % or 100 % of the group or population assayed.
A reference value can be based on an individual sample value but is generally based on a large number of samples, including or excluding the sample to be tested. For instance, this reference value may be derived from a collection of tumor tissue samples from a reference cancer patients’ population for whom historical information relating to the actual clinical outcome for the corresponding cancer patient is available. Said reference cancer patient’s population may for instance be from subjects suffering from one or more of the cancer types referred herein, including particular subgroups therefrom (e.g. patients belonging to a particular tumor-node-metastasis (TNM) stage(s)).
In the methods of the invention, the expression levels of a gene are considered “decreased” with regard to a reference value when its value is lower than said reference value. Preferably, the expression levels of a gene are considered to be lower than a reference value when these are at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more lower than the reference value.
Likewise, in the context of the methods of the invention, the expression levels of a gene are considered “increased” with regard to a reference value when its value is higher than a reference value. Preferably, the score is considered to be higher than a reference value when it is at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more higher than a reference value.
Alternatively or in addition, subjects having more than about 1.1 , 1.2, 1.3, 1.4, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 or 20 fold levels deviation (i.e., increase or decrease) than the reference value as described herein.
The method of the invention, as it is understood by a person skilled in the art, does not claim to be correct in 100% of the analyzed samples. However, it requires that a statistically significant amount of the analyzed samples are classified correctly. The amount that is statistically significant can be established by a person skilled in the art by means of using different statistical significance measures obtained by statistical tests; illustrative, non limiting examples of said statistical significance measures include determining confidence intervals, determining the p-value, etc. Preferred confidence intervals are at least 90%, at least 95%, at least 97%, at least 98%, at least 99%. The p-values are, preferably less than 0.1 , less than 0.05, less than 0.01 , less than 0.005 or less than 0.0001 . The teachings of the present invention preferably allow correctly classifying at least 60%, at least 70%, at least 80%, or at least 90% of the subjects of a determining group or population analyzed.
It is further noted that the accuracy of the method of the invention can be further increased by additionally considering other gene markers, biochemical parameters and/or clinical characteristics of the patients (e.g. age, sex, tobacco and/or other risk factors). Determination of these other markers, parameters and/or characteristics (including the characterization of the tumor subtype according to these) can be sequential or simultaneous to any or all of the method steps as described herein above.
In some embodiments, the methods of the invention may be applied or common to several cancer types, including, but not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia. More particular examples of such cancers include breast cancer, squamous cell cancer, small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung, squamous carcinoma of the lung, cancer of the peritoneum, hepatocellular cancer, gastrointestinal cancer, pancreatic cancer, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bladder cancer, hepatoma, colon cancer, colorectal cancer, endometrial or uterine carcinoma, salivary gland carcinoma, kidney cancer, liver cancer, prostate cancer, renal cancer, vulval cancer, thyroid cancer, hepatic carcinoma, gastric cancer, melanoma, and various types of head and neck cancer.
In a particular embodiment, said method is not tissue-specific. In related embodiments, said method is useful in the determination of a TGFp-activated microenvironment in at least two, preferably three, four, five, six or seven different cancer types. Preferably, this cancer is selected from the group consisting of colorectal cancer, stomach adenocarcinoma, bladder cancer, pancreas adenocarcinoma, prostate cancer, lung cancer, breast cancer. In some embodiments, said cancer is selected from the group consisting of colorectal cancer, stomach adenocarcinoma, bladder cancer, pancreas adenocarcinoma, breast cancer and bladder cancer. In other embodiments, the cancer is colorectal cancer.
As described in Example 5.2 (Fig. 8) and Example 6 (Table 4), association with TBRSs is good for all the tested breast cancer subtypes, including LumA, LumB, normal, basal and Her2 subtypes. Preferably, said breast cancer is selected from the LumA, LumB and Her2 subtypes. In some embodiments of the methods of the invention the subject has a solid tumor. This solid may be previously treated or untreated. In preferred embodiments, the subject has a previously untreated solid tumor. Tumors can be further stratified according to its stage of development. Nowadays, the tumor-node-metastasis (TNM) staging system is the standard method used for treatment selection and clinically predicting survival of patients with cancer. The TNM staging system was developed and is maintained by the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC). It was developed as a tool for doctors to stage different types of cancer based on certain, standardized criteria (www.cancerstaging.org). In particular, the TNM staging system is based on the extent of the tumor (T), the extent of spread to the lymph nodes (N), and the presence of metastasis (M).
The T category describes the original (primary) tumor.
Figure imgf000046_0001
The N category describes whether or not the cancer has reached nearby lymph nodes
Figure imgf000046_0002
The M category tells whether there are distant metastases (spread of cancer to other parts of the body).
Figure imgf000046_0003
Because each cancer type has its own classification system, letters and numbers do not always mean the same thing for every kind of cancer. Once the T, N, and M are determined, they are combined, and an overall stage of 0, I, II, III, IV is assigned. Sometimes these stages are subdivided as well, using letters such as IMA and NIB. In some cancer types, non-anatomic factors are required for assigning the anatomic stage/prognostic group. These are clearly defined in each chapter of the AJCC Cancer Staging Manual (e.g. Gleason Score in Prostate). These factors are collected separately from T, N, and M, which remain purely anatomic and are used to assign stage groups.
Where non-anatomic factors are used in groupings, there is a definition of the groupings provided for cases where the non-anatomic factor is not available (X) or where it is desired to assign a group ignoring the non-anatomic factor.
Stage I cancers are the least advanced and often have a better prognosis. Higher stage cancers are often more advanced but, in many cases, can still be treated successfully.
For instance, for CRC the following stage classification may be used:
AJCC TNM stage TNM stage criteria for stage colorectal cancer
Stage 0 Tis NO MO Tis: Tumor confined to mucosa; cancer- in situ
Stage I T1 NO MO T11: Tumor invades submucosa Stage I T2 NO MO T2: Tumor invades muscularis propria
Stage ll-A T3 NO MO T3: Tumor invades subserosa or beyond (without other organs involved)
Stage ll-B T4 NO MO T4: Tumor invades adjacent organs or perforates the visceral peritoneum
Stage lll-A T 1 -2 N1 MO N1 : Metastasis to 1 to 3 regional lymph nodes. T 1 or T2. Stage lll-B T3-4 N1 MO N1 : Metastasis to 1 to 3 regional lymph nodes. T3 or T4. Stage lll-C any T, N2 MO N2: Metastasis to 4 or more regional lymph nodes. Any T. Stage IV any T, any N,M1 M1 : Distant metastases present. Any T, any N.
With the continuous flow of new data and the increasing knowledge of the disease, the staging system requires continuous adjustment. The eight edition of the TNM classification of malignant tumors (James D. Brierley, Mary K. Gospodarowicz, Christian Wittekind, December 2016) provides the latest, internationally agreed-upon standards to describe and categorize cancer stages and progression.
In a particular embodiment, optionally in combination with any of the embodiments or features described herein, the methods of the invention comprise further to step a):
A1) determining in said sample the stage according to the TNM classification of tumors; and b) calculating a score from the expression levels of the markers determined in the biological sample as defined in step a) and the stage of TNM classification as defined in step A1); and c) determining whether a patient’s tumor has a TGFp-activated microenvironment according to the score obtained in b).
In other embodiments, optionally in combination with any of the embodiments or features described herein, the methods of the invention do not comprise determining the stage according to the TNM classification of tumors as defined in stage A1) above.
Alternatively, or in addition, other markers may be used for tumor stratification. It is currently accepted that tumors (e.g. CRC, gastric cancer, etc.) can be classified according to their global genomic status into two main types: microsatellite instable tumors (MSI) and microsatellite stable (MSS) tumors (also known as tumors with chromosomal instability), see Umar, A. et al. J. Natl. Cancer Inst. (2004) 96, 261-268; Ratti, M et al., Cell Mol Life Sci. (2018) 75(22): 4151-4162. This taxonomy may play a significant role in determining pathologic, clinical and biological characteristics of tumors: MSS tumors are characterized by changes in chromosomal copy number and show worse prognosis, on the contrary the less common MSI tumors (about 15% in colon tumors) are characterized by the accumulation of a high number of mutations and show predominance in females, proximal colonic localization, poor differentiation, tumor-infiltrating lymphocytes and better prognosis. In addition, these subtypes have been described to exhibit different responses to chemotherapeutic agents, and it is also well established that they arise from a distinctive molecular mechanism.
Whereas MSS tumors generally follow the classical adenoma-to-carcinoma progression described by Vogelstein and Fearon (Cell 1 (1990) 61(5):759-767), MSI tumors result from the inactivation of DNA mismatch repair genes like MLH-1. In clinical practice, MSI status is generally determined by immunohistochemistry (IHC) methods that detect protein MLH1 , MSH2, MSH6 and/or PMS2 (Manavis J., et al. Appl Immunohistochem Mol Morphol. 2003 Mar;11(1) :73-7). Nevertheless, other techniques such as molecular biology techniques to evaluate microsatellites or even newly developed Next generation sequencing methods can also be used (Nowak J.A., et al. J Mol Diagn. (2017) 19(1): 84-91). Bethesda guidelines are typically followed to determine MSI/MSH status (Umar, A. et al. J. Natl. Cancer Inst. (2004) 96, 261-268).
The biomarker described herein was shown by the inventors to enable identifying tumors (e.g., CRC or stomach cancer) presenting a TGFp-activated microenvironment independently of microsatellite stability status, that is, both in microsatellite stable (MSS) and microsatellite instable (MSI) phenotypes (Examples 2 and 5; and Figures 3 and 7).
Accordingly, in a particular embodiment, the method of the invention is characterized by identifying a TGFp-activated microenvironment in any of microsatellite instable (MSI) or microsatellite stable (MSS) tumors.
MSI-high (MSI-H) status has been associated with a better prognosis in early-stage CRC, and has emerged as a predictor of sensitivity to immunotherapy treatments with checkpoint inhibitors (Battaglin et al., Advances in Flematology and Oncology 2018, 16(11), 735-747). Nevertheless, as above-mentioned, immune checkpoint inhibitors were found by Tauriello et al. (Nature, Vol. 554 (2018) 539 - 543) to lack efficacy in CRC tumors with a TGFp activated microenvironment. Thus, the signature of the invention enables to identify from patients having a MSI tumor (e.g., CRC or stomach cancer) those which would likely not benefit from an immune check point inhibitor treatment alone and select the same for a combination treatment with an inhibitor of the TGFp signaling pathway. In preferred embodiments of the methods of the invention, said patient’s tumor is a MSI tumor. In a more preferred embodiment, said tumor is a MSI colorectal cancer tumor.
In some embodiments, optionally in combination with any of the embodiments or features described herein, the methods of the invention do not comprise determining the tumor MSI/MSS status.
In other embodiments, optionally in combination with any of the embodiments or features described herein, the methods of the invention comprise further to step a): A2) determining in said sample the tumor MSI/MSS status; and b) calculating a score from the expression levels of the markers determined in the biological sample as defined in step a) and the tumor MSI/MSS status as defined in step A2), optionally the stage of TNM classification as defined in step A1); and c) determining whether a patient’s tumor has a TGFp-activated microenvironment according to the score obtained in b).
A further classification system which has been described for tumor subtype determination and may be used in the methods of the invention is the consensus molecular subtype (CMS) classification system described by Guinney et al. (Nat Med. 2015, 21(11), 1350-6). The CMS classification comprises 4 CMSs with the following characterizing features:
- CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation;
- CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation;
- CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and
- CMS4 (mesenchymal, 23%), prominent transforming growth factor-b activation, stromal invasion and angiogenesis.
Interestingly, a high CTHRC1 score was shown to identify 92.2% of CRC tumors classified under CMS4 (associated with prominent TGF b activation) according to the consensus molecular subtypes (CMSs) classification. It thus provides a valuable tool for clinicians to identify tumors (e.g. CRC or stomach cancer) presenting CMS4 characteristics with a much simpler test (only requires determining the expression levels of one marker, i.e. CTHRC1). In particular embodiments, the methods of the invention are useful for determining whether a patient’s tumor has a phenotype corresponding to the CMS4 subgroup.
In addition, the method of the invention was also able to capture tumors (e.g. CRC or stomach cancer) with a TGFp activated TME classified under other CMS groups (Example 2), which would otherwise likely not had been selected for treatment with an inhibitor of the TGFp signaling pathway. Accordingly, the method of the invention is capable of identifying a tumor with a TGFp- activated microenvironment in any of CMS1 , CMS2, CMS3 or CMS4 molecular subtype tumors, preferably in any of CMS1 , CMS3 or CMS4. Thus, in some embodiments, optionally in combination with any of the embodiments or features described herein, the methods of the invention do not comprise determining the tumor CMS subtype. In other embodiments, the tumor CMS subtype is determined and said tumor is of any of the CMS1 , CMS3 or CMS4 subtypes. In preferred embodiments, said tumor is CMS1 , CMS3 or CMS4 CRC.
In other embodiments, optionally in combination with any of the embodiments or features described herein, the methods of the invention comprise further to step a):
A3) determining in said sample the tumor CMS subtype; and b) calculating a score from the expression levels of the markers determined in the biological sample as defined in step a) and the tumor CMS subtype as defined in A3), optionally the stage of TNM classification as defined in step A1) and/or the tumor MSI/MSS status defined in step A2); and c) determining whether a patient’s tumor has a TGF p -activated microenvironment according to the score obtained in b). c. Predictive methods of the invention
In a second aspect, the invention also pertains to a method for predicting the efficacy of (or the likelihood of response to) a treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. predicting a higher efficacy or likelihood of response to a treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
In a third aspect, the present invention also relates to a method for selecting a cancer patient which is likely to benefit from a treatment with an inhibitor of the TGFp signaling pathway, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. selecting said cancer patient for the treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
This treatment with an inhibitor of the TGFp signaling pathway may be a neoadjuvant treatment administered prior to the surgical removal of the tumor and/or an adjuvant treatment after the surgical intervention. Preferably, said treatment is an adjuvant treatment. A treatment with an inhibitor of the TGFp signaling pathway may be as described herein below.
The methods of the present invention or any of the steps thereof might be implemented by a computer. In a particular embodiment, optionally in combination with any of the features or embodiments as described herein, the score of step b) is calculated using a computer; and/or the selection, classification and/or determination of step c) is conducted using a computer.
Therefore, a further aspect of the invention refers to a computer implemented method, wherein the method is any of the methods disclosed herein or any combination thereof.
It is noted that any computer program capable of implementing any of the methods of the present invention or used to implement any of these methods or any combination thereof, also forms part of the present invention.
This computer program is typically directly loadable into the internal memory of a digital computer, comprising software code portions for performing the steps of comparing the score (e.g., obtained from the level of one or more of the target markers as described in the invention), from the one or more biological samples of a subject with a reference value and determining the prognosis of said subject or whether it would benefit from adjuvant therapy, when said product is run on a computer.
It is also noted that any device or apparatus comprising means for carrying out the steps of any of the methods of the present invention or any combination thereof, or carrying a computer program capable of, or for implementing any of the methods of the present invention or any combination thereof, is included as forming part of the present specification.
The methods of the invention may also comprise the storing of the method results in a data carrier, preferably wherein said data carrier is a computer readable medium. The present invention further relates to a computer-readable storage medium having stored thereon a computer program of the invention or the results of any of the methods of the invention.
As used herein, “a computer readable medium” can be any apparatus that may include, store, communicate, propagate, or transport the results of the determination of the method of the invention. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. d. Methods for treatment, related medical uses and compositions
In a fourth aspect, the invention pertains to an inhibitor of the TGFp signaling pathway for use in a method for treatment of a cancer patient, wherein said patient’s tumor has been determined to have a TGFp-activated microenvironment according to a method as described herein.
In an alternative aspect, the invention provides the use of an inhibitor of the TGFp signaling pathway in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected as having a tumor with a TGFp-activated microenvironment by a method as described herein.
In a further alternative aspect, the invention relates to a method of treating a cancer patient by administering a therapeutically effective amount of an inhibitor of the TGFp signaling pathway, wherein said patient has been selected as having a tumor with a TGFp-activated microenvironment by a method as described herein.
In a related aspect, the present invention further provides any of the methods as described herein above, which further comprises the step of administering to said patient a therapeutically effective amount of a TGFp signaling pathway inhibitor.
Said TGFp signaling pathway inhibitor may be administered as single agent or in combination with another therapy or drug. In a particular embodiment of any of these aspects, the inhibitor of the TGFp signaling pathway is administered to said patient in combination (i.e., as a combination treatment) with another anti-cancer treatment, preferably with another anti-cancer agent or radiotherapy.
The term anti-cancer agent has been defined herein above and may include but is not limited to chemotherapeutic agents, growth inhibitory agents, cytotoxic agents, anti-hormonal agents, agents used in radiation therapy, anti-angiogenesis agents, apoptotic agents, anti tubulin agents, etc. and any combinations thereof. Said anti-cancer agent may be administered prior, concomitantly or after the TGFp inhibitor administration. The two drugs may form part of the same composition or be provided as a separate composition for administration at the same time or at a different time.
Preferably, said TGFp signaling pathway inhibitor is administered to said patient in combination with a cytotoxic agent or immune checkpoint inhibitor.
In a fifth aspect, the present invention relates to an anti-cancer agent other than a TGFp signaling pathway inhibitor (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy for use in a method for treatment of a cancer patient in combination with a TGFp signaling pathway inhibitor, wherein said patient’s tumor has a TGFp-activated microenvironment according to a method as described herein.
In an alternative aspect, the invention provides the use of an anti-cancer agent other than a TGFp signaling pathway inhibitor (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in combination with a TGFp signaling pathway inhibitor in the manufacturing of a medicament for the treatment of a cancer patient, wherein said cancer patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment.
In a further alternative aspect, the invention relates to a method of treating a cancer patient by administering a therapeutically effective amount of an anti-cancer agent other than a TGFp signaling pathway inhibitor (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in combination with a therapeutically effective amount of a TGFp signaling pathway inhibitor, wherein said patient has been selected by a method as described herein as having a tumor with a TGFp-activated microenvironment. In preferred embodiments of any thereof, said other anti-cancer agent is a cytotoxic agent or immune checkpoint inhibitor.
Immune checkpoints regulate T cell function in the immune system. T cells play a central role in cell-mediated immunity. Checkpoint proteins interact with specific ligands which send a signal into the T cell and essentially switch off or inhibit T cell function. Cancer cells take advantage of this system by driving high levels of expression of checkpoint proteins on their surface which results in control of the T cells expressing checkpoint proteins on the surface of T cells that enter the tumor microenvironment, thus suppressing the anti-cancer immune response. As such, inhibition of checkpoint proteins would result in restoration of T cell function and an immune response to the cancer cells. Examples of checkpoint proteins include, but are not limited to CTLA-4, PDL1 , PDL2, PD1, B7-H3, B7- H4, BTLA, HVEM, TIM3, GAL9, LAG3, VISTA, KIR, 2B4 (belongs to the CD2 family of molecules and is expressed on all NK, gd, and memory CD8+ (ab) T cells), CD 160 (also referred to as BY55), CGEN-15049, CHK 1 and CHK2 kinases, A2aR and various B-7 family ligands.
Programmed cell death protein 1 (PD-1) is a 288 amino acid cell surface protein molecule is expressed on T cells and pro-B cells and plays a role in their fate/ differentiation. PD-1 has two ligands, PD-L1 and PD-L2, which are members of the B7 family. PD-L1 protein is upregulated on macrophages and dendritic cells (DC) in response to LPS and GM- CSF treatment, and on T cells and B cells upon TCR and B cell receptor signaling, whereas in resting mice, PD-L1 mRNA can be detected in the heart, lung, thymus, spleen, and kidney. PD-1 negatively regulates T cell responses.
PD-1 has been shown to play a role in tumor-specific escape from immune surveillance. It has been demonstrated that PD-1 is highly expressed in tumor-specific cytotoxic T lymphocytes (CTLs) in both chronic myelogenous leukemia (CML) and acute myelogenous leukemia (AML). PD-1 is also up-regulated in melanoma infiltrating T lymphocytes (TILs) (Dotti, Blood (2009) 114 (8): 1457-58). Tumors have been found to express the PD-1 ligand (PDL-1 and PDL-2) which, when combined with the up-regulation of PD-1 in CTLs, may be a contributory factor in the loss in T cell functionality and the inability of CTLs to mediate an effective anti-tumor response. Researchers have shown that in mice chronically infected with lymphocytic choriomeningitis virus (LCMV), administration of anti-PD-1 antibodies blocked PD-1-PDL interaction and was able to restore some T cell functionality (proliferation and cytokine secretion), and lead to a decrease in viral load (Barber et al (2006) Nature 439 (9): 682-687). Accordingly, in a particular embodiment, optionally in combination with one or more of the embodiments of features described herein, said method comprises administering to the cancer patient an inhibitor of the TGFp signaling pathway in combination with an agent that is an immune checkpoint inhibitor. This checkpoint inhibitor may be a biologic therapeutic or a small molecule. Preferably, the checkpoint inhibitor is an antibody. In preferred embodiments, the checkpoint inhibitor inhibits a checkpoint protein which may be CTLA-4, PDL1 , PDL2, PD1 , B7-H3, B7-H4, BTLA, HVEM, TIM3, GAL9, LAG3, VISTA, KIR, 2B4, CD160, CGEN-15049, CHK 1 , CHK2, A2aR, B-7 family ligands or a combination thereof. In a particular embodiment, this immune-check point inhibitor is an anti-PD1/PDL1 inhibitor, including combinations of any thereof. Illustrative, non-limiting examples of anti-PD1/PDL1 inhibitors are the following PD-1 inhibitors currently being tested in clinical trials:
CT-011 is a humanized lgG1 monoclonal antibody against PD-1. A phase II clinical trial in subjects with diffuse large B-cell lymphoma (DLBCL) who have undergone autologous stem cell transplantation was recently completed. Preliminary results demonstrated that 70% of subjects were progression-free at the end of the follow-up period, compared with 47% in the control group, and 82% of subjects were alive, compared with 62% in the control group. This trial determined that CT-011 not only blocks PD-1 function, but it also augments the activity of natural killer cells, thus intensifying the antitumor immune response.
BMS 936558 is a fully human lgG4 monoclonal antibody targeting PD-1 agents under a phase I trial, biweekly administration of BMS-936558 in subjects with advanced, treatment- refractory malignancies showed durable partial or complete regressions. The most significant response rate was observed in subjects with melanoma (28%) and renal cell carcinoma (27%), but substantial clinical activity was also observed in subjects with non small cell lung cancer (NSCLC), and some responses persisted for more than a year. It was also relatively well tolerated; grade >3 adverse events occurred in 14% of subjects.
BMS 936559 is a fully human lgG4 monoclonal antibody that targets the PD-1 ligand PD-L1. Phase I results showed that biweekly administration of this drug led to durable responses, especially in subjects with melanoma. Objective response rates ranged from 6% to 17%) depending on the cancer type in subjects with advanced-stage NSCLC, melanoma, RCC, or ovarian cancer, with some subjects experiencing responses lasting a year or longer.
MK 3475 is a humanized lgG4 anti-PD-1 monoclonal antibody in phase I development in a five-part study evaluating the dosing, safety, and tolerability of the drug in subjects with progressive, locally advanced, or metastatic carcinoma, melanoma, or NSCLC. MPDL 3280A is a monoclonal antibody, which also targets PD-L1 , undergoing phase I testing in combination with the BRAF inhibitor vemurafenib in subjects with BRAF V600- mutant metastatic melanoma and in combination with bevacizumab, which targets vascular endothelial growth factor receptor (VEGFR), with or without chemotherapy in subjects with advanced solid tumors.
AMP 224 is a fusion protein of the extracellular domain of the second PD-1 ligand, PD-L2, and IgGI, which has the potential to block the PD-L2/PD-1 interaction. AMP-224 is currently undergoing phase I testing as monotherapy in subjects with advanced cancer.
Medi 4736 is an anti-PD-L1 antibody in phase I clinical testing in subjects with advanced malignant melanoma, renal cell carcinoma, NSCLC, and colorectal cancer.
CTLA4 (cytotoxic T-lymphocyte-associated protein), is a protein receptor that down regulates the immune system. CTLA4 is found on the surface of T cells, which lead the cellular immune attack on antigens. The T cell attack can be turned on by stimulating the CD28 receptor on the T cell. The T cell attack can be turned off by stimulating the CTLA4 receptor. In another particular embodiment, this immune-check point inhibitor is an anti- CTLA4 inhibitor, such as ipilimumab (Yervoy™), or any combination thereof.
The inhibitor of the TGFp signaling pathway alone or in combination with an anti-cancer agent may be formulated as a pharmaceutical composition, wherein said pharmaceutical composition further comprises a pharmaceutically acceptable excipient, vehicle or carrier. Said pharmaceutical composition may be a “dosage form” devised to enable administration of the drug medication in the prescribed dosage amounts. Depending on the method/route of administration different dosage forms will be used.
Still in a sixth aspect, the present invention relates to an anti-cancer agent (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy for use in a method for treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said anti-cancer agent or radiotherapy is not administered in combination with a TGFp signaling pathway inhibitor. In an alternative aspect, the invention provides the use of an anti-cancer agent (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy in the manufacturing of a medicament for the treatment of a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, wherein said anti-cancer agent or radiotherapy is not administered in combination with a TGF p signaling pathway inhibitor.
In a further alternative aspect, the invention relates to a method of treating a cancer patient selected by a method as described herein as failing to have a tumor with a TGFp-activated microenvironment, by administering a therapeutically effective amount of an anti-cancer agent (e.g., cytotoxic agent or immune checkpoint inhibitor) or radiotherapy, wherein said anti-cancer agent or radiotherapy is not administered in combination with an inhibitor of the TGFp signaling pathway. e. Additional methods of the invention
In a seventh aspect, the invention relates to a method for determining the prognosis of a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method as described herein; and ii. classifying the patient as having poor prognosis when said patient’s tumor has a TGFp-activated microenvironment.
In a related aspect, it refers to a method for determining the prognosis of a cancer patient, wherein said method comprises: a) determining the expression levels of CTHRC1 (Collagen Triple Helix Repeat Containing 1) gene in a tumor sample isolated from said patient; b) comparing the expression levels of CTHRC1 in the patient’s sample with a reference value; c) wherein an increase of the value in the patient’s sample with regard to said reference value is indicative of poor prognosis. Poor prognosis in CRC has been associated to the presence of a TGFb-activated stroma (Calon et al., Cancer cell 22 (2012) 571-584; Calon et al., Nature genetics 47:4 (2015) 320- 332). In preferred embodiments, the method of the invention is used to determine those patients with poor prognosis/outcome which have been classified under CMS1-4 subgroups when using the consensus molecular classification (Guinney et al. 2015), especially CMS4 patients. Similarly, CTHRC1 expression enables to identify patients with poor prognosis independently from their MSS/MSI status (Fig. 3). In preferred embodiments, the method of the invention is used to determine those patients with poor prognosis/outcome from those which have been classified as MSI (typically associated with better prognosis) further to microsatellite stability determination.
Also, as shown in Example 3, in a multivariate analysis the inventors found a statistically significant correlation between CTHRC1 levels (as a continuous variable) and prognosis, for the global group of CRC patients analyzed (stage I, II and III) and in the differentiated analysis by tumor stage (II and III separately). In some embodiments, the method of the invention is used to determine those CRC patients with poor prognosis/outcome from those which have been classified according to the AJCC criteria as stage II or III, preferably from those classified under stage II.
In preferred embodiments, the method of the invention is used for stratifying CRC patients classified under stage II or III, preferably stage II, and selecting those having a TGFp- activated TME and poor prognosis for an adjuvant and/or neoadjuvant treatment (e.g. a treatment with a TGFp inhibitor as described herein).
Disease progression or outcome may be measured using different parameters, including but not limited to, tumor growth, tumor growth delay, increase/decrease of tumor size, increase/decrease in tumor markers, and patient’s survival.
Preferably, in the context of the present invention, the clinical outcome of a subject, is expressed as overall survival and/or disease-free survival. Survival of cancer patients is generally suitably expressed by Kaplan-Meier curves, named after Edward L. Kaplan and Paul Meier who first described it (Kaplan, Meier: Amer. Statist. Assn. 53:457-481). The Kaplan-Meier estimator is also known as the product limit estimator. It serves for estimating the survival function from life-time data. A plot of the Kaplan-Meier estimate of the survival function is a series of horizontal steps of declining magnitude which, when a large enough sample is taken, approaches the true survival function for that population. The value of the survival function between successive distinct sampled observations is assumed to be constant. With respect to the present invention, the Kaplan-Meier estimator may be used to measure the fraction of patients living for a certain amount of time after beginning a therapy (e.g. after tumor resection). The clinical outcome predicted may be the (overall/disease-free) survival in months/years from the time point of taking the sample. It may be survival for a certain period from taking the sample, such as of six months or more, one year or more, two years or more, three years or more, four years or more, five years or more, six years or more. In each case, “survival” may refer to “overall survival” or “disease free survival”.
The term "disease free survival", or “DFS” as used herein, is defined as the interval of time from start of treatment (e.g., date of surgery) to the first measurement of cancer growth. The term “overall survival” or “OS” as used herein, is defined as the interval of time from the start of treatment (e.g., date of surgery) to death from any cause.
The term “poor prognosis” as used herein may refer to a high risk of recurrence, relapse, metastasis and/or death. In preferred embodiments, the term “poor prognosis” means a survival (i.e. DFS and/or OS) of six months or less, one year or less, two years or less, three years or less, four years or less, five years or less, six years or less, etc. In one embodiment, the term poor prognosis refers to a DFS and/or OS of less than 5 years.
In an eighth aspect, the present invention relates to a method for monitoring or evaluating the response to treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises determining whether a patient’s tumor has a TGFp- activated microenvironment as described herein, wherein a TGFp-activated microenvironment is associated to lack of response.
In a related aspect, it concerns a method for monitoring or evaluating the response to treatment with a TGFp signaling pathway inhibitor in a cancer patient, wherein said method comprises: a) determining the expression levels of CTHRC1 (Collagen Triple Helix Repeat Containing 1) gene in a tumor sample isolated from said patient; b) comparing the expression levels of CTHRC1 in the patient’s sample with a reference value; wherein a decrease of the value in the patient’s sample with regard to said reference value is indicative of response to the treatment. Preferably said treatment is an adjuvant treatment administered to the patient after surgical resection. In preferred embodiments, said reference value is the value of the expression levels of CTHRC1 in this patient at an earlier time point. f. Kit and use of a kit in the methods of the invention
In a ninth aspect, the invention concerns a kit suitable for determining the expression levels of the CTHRC1 gene in an isolated tumor sample, wherein said kit comprises: i. a reagent for the quantification of the CTHRC1 gene expression levels; and ii. optionally, a reagent for the quantification of a control gene expression levels; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
The term "kit" or "testing kit" denotes combinations of reagents and adjuvants required for an analysis. Although a test kit consists in most cases of several units, one-piece analysis elements are also available, which must likewise be regarded as testing kits.
In preferred embodiments, said kit comprises: i. an oligonucleotide specific to CTHRC1 mRNA or an affinity reagent for the CTHRC1 gene encoded protein; and ii. optionally, an oligonucleotide specific to a normalizing gene or an affinity reagent for the control gene encoded protein; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
In further preferred embodiments, said kit comprises: i. an oligonucleotide specific to SEQ ID NO:1 or an affinity reagent for SEQ ID NO:2; ii. optionally, an oligonucleotide specific to a normalizing gene or an affinity reagent for a control gene encoded protein; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
The various oligonucleotides/affinity reagents may be labelled with the same or different tags. Preferably, these will be labelled with different tags for multiplex analysis.
Said reagents in points i) to iii) may be as described herein above for the methods of nucleic acid and protein quantification in step (a) of the methods of the invention. In one embodiment, said oligonucleotide specific to CTHRC1 mRNA in i) is a primer and/or probe specific to CTHRC1 mRNA.
In a preferred embodiment, said oligonucleotide specific to CTHRC1 gene mRNA is a pair of primers (e.g., one forward and one reverse) and a probe, for instance a hydrolysis probe (e.g. a Taqman probe) that aligns within the fragment amplified by the two primers. For instance, commercially available reagents from Taqman (Assay ID: Bt03254467_m1).
Also, said kit may further comprise an oligonucleotide specific to a control gene. Preferably, said oligonucleotide is a primer and/or probe specific to said control gene. This is typically a housekeeping gene as defined herein above.
Said kit may comprise additional reagents. In a particular embodiment, said kit comprises reagents to perform a real-time PCR reaction, which typically contain a DNA polymerase, such as Taq DNA polymerase {e.g., hot- start Taq DNA polymerase), buffer, magnesium, dNTPs, and optionally other agents (e.g., stabilizing agents such as gelatin and bovine serum albumin). In addition, real-time PCR reaction mixtures also contain reagents for real time detection and quantification of amplification products as described above herein.
Possible immunoassays and affinity reagents have been described herein. In a particular embodiment, said affinity reagent is an antibody (i.e., an anti- CTHRC1 antibody), preferably a monoclonal antibody. The affinity reagent may bind to any linear or conformational region (e.g. epitope) specific for CTHRC1 protein.
The antibody Vli-55 used in the Examples was raised against a synthetic peptide corresponding to the conserved C terminus of CTHRC1. In preferred embodiments, said affinity reagent specific to CTHRC1 is the rabbit monoclonal antibody Vli-55 (MMCRI, 2019) or antibodies binding to the same antigenic region and/or epitope in the marker protein.
A person skilled in the art will know how to obtain antibodies specific to CTHRC1 protein. Numerous methods known to those skilled in the art are available for obtaining antibodies or antigen-binding fragments thereof. Antibodies can be produced using recombinant DNA methods, see, e.g., Current Trends in Monoclonal Antibody Development (Steven Shire et al, Eds. Springer, 2010), the disclosures of which are incorporated herein by reference in their entirety. Monoclonal antibodies may also be produced by preparing immortalized cell lines capable of producing antibodies having the desired specificity. Such immortalized cell lines may be produced in a variety of ways. Conveniently, a small non-human animal, such as a mouse, is hyperimmunized with the desired immunogen. The vertebrate is then sacrificed, usually several days after the final immunization, the spleen cells removed, and the spleen cells immortalized. The most common technique is fusion with a myeloma cell fusion partner, as first described by Kohler and Milstein (1975) Nature 256:495-497. Other techniques, including EBV transformation, transformation with bare DNA, e.g., oncogenes, retroviruses, etc., or any other method which provides for stable maintenance of the cell line and production of monoclonal antibodies are also well known. Specific techniques for preparing monoclonal antibodies are described in Antibodies; A Laboratory Manual, Harlow and Lane, eds.. Cold Spring Harbor Laboratory, 1988, the full disclosure of which is incorporated herein by reference. Non-limiting examples of commercially available antibodies specifically binding to UNR protein are ab96124 (Abeam, Cambridge, UK), HP A018846 (Sigma-Aldrich) and HPA052221 (Sigma-Aldrich).
In a particular embodiment, optionally in combination with any of the embodiments or features described above or below, said kit comprises reagents to perform an immunohistochemistry (ICH) assay. For instance, it may contain interalia : an enzyme- conjugated secondary antibody (e.g. conjugated to horseradish peroxidase or alkaline phosphatase), an enzyme substrate, and a counterstain such as hematoxylin. Kits for ICH are well known in the art and commercially available (http://www.sigmaaldrich.com/life- science/cell-biology/antibodies/antibodies- application/protocols/immunohistochemistry.html#reagents_equipment). In addition, ICH assay kits for use in a method of the invention also contain reagents for quantification of the target protein markers, as described herein above.
Other preferred features and embodiments of the kit of the invention are as described herein throughout the specification.
In a tenth aspect, the invention relates to the use of a kit as described above in any of the methods as described herein, such as for selecting a cancer patient which is likely to benefit from a treatment with a TGFp signaling pathway inhibitor, for determining whether a patient's tumor microenvironment has a TGFp -activated microenvironment, for predicting the tumor prognosis or for monitoring or evaluating the response to treatment with a TGFp signaling pathway inhibitor.
EXAMPLES
Example 1.- Material and Methods
FFPE Cohort
FFPE colorectal cancer samples were obtained retrospectively from patients with colon or rectum adenocarcinoma who underwent surgical resection at Hospital del Mar. The study was approved by the Hospital del Mar MarBioBanc and by Hospital Clinic Ethical Committee for Clinical Investigation which acts as the reference Ethical Committee for research carried out at IRB Barcelona. Samples used were from patients that had provided written informed consent, and the study was conducted in accordance with the Declaration of Helsinki. Retrospective CRC cases were collected according to the following guidelines: a) patients with stage 1-3 colon or rectum adenocarcinoma with no residual disease (with an emphasis on stage 2 and 3), b) patients aged 45 years or older at time of primary surgery with no family history of colorectal cancer in order to exclude potential hereditary cases; c) No preoperative cancer therapy.
Tissue MicroArray (TMA) construction
A total of n=277 CRC patients were selected for the construction of n=3 TMAs for the IHC analysis of CTHRC1 in FFPE preserved tumors. Two different areas were marked in H&E histological sections from each selected patient. If a certain tumor exhibited histologically different microscopic tumor areas, representative areas were selected. If normal mucosa or adenomatous tissue was also present in the H&E section, appropriate representative areas were also labelled for further processing following TMA construction standard procedures.
Table 1 . Demographics for patients included in the 3 TMAs from the FFPE cohort.
Figure imgf000065_0001
* Tissue cores from 11 patients were not suitable for analysis by CTHRC1 IHC in these TMAs
Immunohistochemistry
Histological sections of 4 m m were obtained from the TMA paraffin blocks. For immunohistochemistry (IHC) briefly, after antigen retrieval, samples were blocked with Peroxidase-Blocking Solution (Dako: S202386) for 10 min at room temperature and incubated overnight at 4 eC with primary antibody CTHRC (Anti-Cthrd Antibody Catalog#:Vli55) diluted 1 :500. After incubation with secondary antibody for 45 minutes, at room temperature, slides were developed using DAB (Dako, K346811) and counterstained with hematoxylin prior to mounting with DPX (Panreac, 255254.1608). Washes were performed in between steps with EnVision FLEX Wash Buffer (Dako, K800721).
The intensity of staining obtained through IHC was analyzed in the TMAs by an expert pathologist and patient samples were categorized along a scale from 0-3 according to CTHRC1 protein expression. For 11 samples, the tissue cores could not be evaluated, leaving the analysis of the FFPE cohort to a total of n=266 patients.
Calculation of gene expression scores: F-/T-/Ma-TBRS, TGFB, IL-11 and IL-11 RSscores
Response to TGFp in cancer fibroblasts (F-TBRS), T-cells (T-TBRS) and macrophages (Ma- TBRS) was assessed in whole tumors samples using the gene expression signature previously published (Calon et al., Cancer Cell 2012, 22, 571-584). For doing so, expression values were centred and scaled gene-wise to produce z-scores, which were then averaged across all genes included in a given gene signature. The resulting scores were in turn centred and scaled across samples that were included in the dataset. The same procedure was carried out to obtain scores for TGFp family genes (TGFB1+TGFB2+TGFB3), as well as for the IL-11 gene and IL-11 response signature (IL-11 RS) previously published in Calon et al. 2012. These calculations were carried out after correcting the expression data by potential sources of bias due to technical variability (section Transcriptomic datasets of human CRC samples).
CTHRC1 score
Within each TMA, levels of expression of CTHFtCI were computed as the mean of intensity scores across staining blocks. Sample groups showing low, medium and high protein expression were defined using the tertiles of the CTHFtCI values distribution, after previous normalization of TMA global levels based on their respective median values. For 11 samples, tissue cores could not be evaluated for CTHRC1 protein expression, leaving the analysis of CTHRC1 in the FFPE cohort to a total of n=266 patients.
Transcriptomic datasets Colorectal Cancer
Seven public high-throughput datasets were used for transcriptomic analyses in human tumors [see summary in table below], including five Affymetrix microarray series publicly available in the NCBI GEO repository (Barrett et al., Nucleic Acids Res (2013), 41 (Database issue):D991-5) and the TCGA RNAseq datasets for Colon and Rectum tumors (TCGA
Research Network: http://canceraenome.nih.gov/)· Consensus Molecular Subtypes (CMS) information was available for most of these samples and was retrieved from (Guinney et al., Nature Medicine (2015), 21 :1350-1356).
Figure imgf000067_0001
Other Cancer Types The datasets below were used for the indicated cancer types.
Figure imgf000067_0002
Pre-processing of TCGA CRC data RNA-Seq (version 2) data from the The Cancer Genome Atlas (TCGA) project was retrieved from the legacy version of the GDC commons repository (Grossman et al., New England Journal of Medicine (2016), 375: 1109-1112). Clinical and follow-up information was retrieved from the TCGA-Clinical Data Resource (CDR) [https://gdc.cancer.gov/about- data/publications/PanCan-Clinical-2018]. Expression measures were expressed in RSEM in the legacy version, which were log2-transformed and quantile normalized. Samples from different tumors (colon and rectum) and from different platforms (Genome Analyzer and HiSeq) were processed separately. Non-primary tumor samples were filtered out and duplicated samples measured in different platforms were excluded from the HiSeq subset. Only primary tumors from patients with no previous cancer diagnosis were kept for analyses. An exploratory analysis was carried out using Principal Component Analyses in order to identify samples with abnormal expression values in each dataset. Samples TCGA-A6-2679- 01 A and TCGA-AA-A004-01 A were excluded as their gene expression showed an anomalous distribution compared to the rest of samples in their dataset, even after quantile normalization. Finally, each dataset was corrected by technical variation using a mixed-effect linear model, in which sample’s center of origin and plate identifiers were modeled as a fixed and a random effect, respectively. Coefficients from the fixed effects and random effects' imputations provided by the models were then used to correct these technical effects.
Pre-processing of microarray CRC datasets
Microarray datasets were separately processed using R packages affy (Gautier et al., Bioinformatics (2004), 3: 307-315) and affyPLM (Bolstad et al., Gentleman R, Carey V, Huber W, Irizarry R, and Dudoit S. (Eds.), (2005) Springer, New York) from Bioconductor (Gentleman et al., Genome Biology (2004), 5: R80). Raw cel files data were processed using RMA (Irizarry et al., Biostatistics (2003), 4:249-264) and annotated using the information available in the Affymetrix web page (Affmetrix - Thermofisher web page. https ://www.thermofisher.com/es/en/home/life-science/microarrav-analvsis/affvmetrix.html).
Standard quality controls were performed in order to identify abnormal samples (Gentleman et al., Bioinformatics and Computational Biology Solutions Using R and Bioconductor (2005) (Springer, New York)) regarding: a) spatial artefacts in the hybridization process (scan images and pseudo-images from probe level models); b) intensity dependences of differences between chips (MvA plots); c) RNA quality (RNA digest plot); d) global intensity levels (boxplot of perfect match log-intensity distributions before and after normalization and RLE plots); e) anomalous intensity profile compared to the rest of samples (NUSE plots, Principal Component Analyses). Technical information concerning samples processing and hybridization was retrieved from the original CEL files: date of scanning were collected in order to define scan batches in each dataset separately; technical metrics described by Eklund AC and Szallasi Z in (Eklund and Szallasi, Genome Biology (2008), 9, R26) were computed and recorded as additional features for each sample. Microarray expression values were summarized at the gene level (entrez) using the first principal component of the probesets mapping to the same gene. This component was centered and scaled to the weighted mean of the means and standard deviations of the probesets using the corresponding contribution to the component as weight. The sign of this gene-based expression summary was imputed so that it was congruent to the sign of the probeset contributing the most to the original first component.
Previously to gene-level summarization, each microarray dataset was corrected a priori by Eklund metrics (Eklund and Szallasi, Genome Biology (2008), 9, R26) effects estimated from a standard linear model. In addition, mixed-effect linear models were used to correct microarray expression by the sample’s center of origin (if more than one) and date of scanning (microarrays). Expression values were corrected using the coefficients from the fixed effects and the imputations of the random effects provided by the models.
Final CRC transcriptomic pooled cohort (TCGA + GEO)
Datasets were merged after gene-wise standardization by median and median absolute deviation to the GSE39582 dataset. As a result, this CRC pooled cohort (TCGA + GEO) included a total of 1 .705 primary tumor samples.
MSI imputation in transcriptomic CRC samples
When not available in the published clinical information, MSI status was imputed in each dataset separately based on the expression of genes included in a published transcriptomic signature (Jorissen et al., Clinical Cancer Research (2008), 14, 8061-8069). For doing so, Pearson’s correlation coefficients were computed between each sample’s profile and an artificial MSI profile consisting on “one” values for genes included in the signature that were over-expressed in MSI samples and a “zero” values for genes over-expressed in MSS samples in (Jorissen et al., Clinical Cancer Research (2008), 14, 8061-8069). Assignation to MSI or MSS was performed according to results of a cluster analysis based on non- parametric density estimation (Azzalini A., Torelli N.S., Statistics and Computing (2007), 17, 71-80; Azzalini A., Menardi G., Journal of Statistical Software (2014) , 57(11), 1 -26) on these correlation coefficients. This imputation procedure was performed at the gene level using the entrez identifier and Affymetrix annotation (Affmetrix - Thermofisher web page) before correction by tecnical effects (see section Transcriptomic datasets of human tumors); for doing so, not-corrected microarray datasets were summarized at the gene level by the most variable probeset mapping to the same gene as measured by the median absolute deviation. In each case, the same number of top MSI and MSS over-expressed genes were used in order to avoid biases towards the group that included more genes (MSI). Accuracy of this imputation was evaluated in dataset GSE39582 and TCGA colon dataset, which included annotation of microsatellite-stable (MSS) and -instable (MSI) samples in their clinical information (GSE39582: 97% and 77% accuracy for MSS and MSI samples, respectively; TCGA colon: 97% and 92% accuracy for MSS and MSI, respectively).
Cell Population Scores
Three data sets publicly available in the NCBI GEO repository (Barrett et al., Nucleic Acids Res (2013), 41 (Database issue):D991-5) were used to characterize the subtype gene profiles according to specific gene expression in tumor cell subpopulations: GSE39395, GSE39396 [Calon A, et al., Cancer Cell (2012) 22(5), 571-84] and GSE35602 [Nishida Net al., Clin Cancer Res (2012) 18, 3054-70]. In GSE39395 and GSE39396, FACS was used to separate the following populations from 14 fresh CRC samples: CD45+EpCAM-CD31-FAP- CD45-EpCAM+CD31-FAP- CD45-EpCAM-CD31+FAP- and CD45-EpCAM- CD31-FAP+. GSE35602 contains transcriptomic data for epithelial and stromal cells microdissected from 13 CRC tissue samples and 4 adjacent morphologically normal colorectal mucosae (>5 cm from the tumor). GSE39395 and GSE39396 were processed using RMA [Irizarry RA, et al., Biostatistics (2003) 4, 249-64] (see section Pre processing of microarray CRC datasets) while the series matrix version of GSE35602 was used in the analyses.
Cell population signatures were derived after differential expression analysis using moderated t-statistics by empirical Bayes shrinkage (limma) [Ritchie, M.E. et al., Nucleic Acids Research 43(7), e47.]. For doing so, stromal genes were selected as they showed differential expression (2 fold-change, FDR < 1%) between epithelial and stromal compartments of samples in dataset GSE35602, and simultaneously over-expressed in the target population compared to the rest of cell types (5 fold-change, FDR < 1%). The Benjamini-Flochberg method was used for multiple comparison adjustment [Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing Yoav Benjamini and Yosef Flochberg Journal of the Royal Statistical Society. Series B (Methodological) Vol. 57, No. 1 (1995), pp. 289-300]. Cell population signatures in the CRC transcriptomic meta-cohort were obtained as described in the previous section (F-/T-/Ma- TBRS and TGFB scores). Finally, estimations of the immune infiltration in the tumors of the CRC transcriptomic meta-cohort shown in Figure 7 were computed as the difference of the CD45 signature and the sum of the stromal signatures (CD45 + FAP + CD31) obtained in this way. The resulting score was centered and scaled across samples in the dataset.
Trancriptomic datasets - other cancer types
TCGA RNASeq data for stomach (STAD), bladder (BLCA) and pancreatic (PAAD) adenocarcinoma were downloaded from the GDC commons repository (Grossman et al., New England Journal of Medicine (2016), 375: 1109-1112) and processed in an analogous way to the colon and rectum TCGA datasets. Likewise, microarray datasets of prostate - GSE21034 (Taylor et al., Cancer Cell (2010), 18: 11-22) and Lung - GSE31210 (Okayama et al., Cancer Research (2011), 72: 100-111) were downloaded from the NCBI GEO repository and processed as described previously (see sections on Pre-processing of microarray CRC datasets). Unlike the rest of microarray data, dataset GSE21034 was processed using the oligo R package [Carvalho BS, Irizarry RA (2010). “A Framework for Oligonucleotide Microarray Preprocessing.” Bioinformatics, 26(19), 2363-7. ISSN 1367-4803, doi: 10.1093/bioinformatics/btq431], as their samples were hybridized in a different Affymetrix platform. (Human Exon 1.0 ST Array). Finally, Metabric breast cancer data (Curtis et al., Nature (2012), 486, 346-352) was downloaded from the cBioportal for Cancer Genomics [http://cancerdiscovery.aacrjournals.Org/content/2/5/401.abstract]; Metabric's expression values were a-priori corrected using a linear model in which PAM50 subtype was included as a covariate.
Association analyses with clinical outcome in human datasets
Association with relapse was evaluated using a Cox proportional hazards model. For the rest of association analyses with gene expression levels, linear models were carried out. In all cases, sample’s dataset of origin was included in the models for statistical adjustment. In addition, and for signatures evaluation, a global signature was computed using all the genes in the expression matrix and used as adjusting variable in the models. This strategy has been proved useful to avoid systematic biases due to the gene-correlation structure present in the data and to adjust by the expectation under gene randomization, i.e., the association expected for a signature whose genes have been chosen at random [Adjusting for systematic technical biases in risk assessment of gene signatures in transcriptomic cancer cohorts. Adria Caballe Mestres, Antonio Berenguer-Llergo, Camille Stephan-Otto Attolini doi: https://doi.org/10.1101/360495; On Testing the Significance of Sets of Genes. Bradley Efron and Robert Tibshirani. The Annals of Applied Statistics. Vol. 1 , No. 1 (Jun., 2007), pp. 107- 129]
Statistical significance was assessed by means of Log-likelihood Ratio Tests (LRT, Cox models), and Wald tests, as appropriate. Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation (transcriptomic data) or tertiles (IHC). Hazard Ratios (HR) were computed as measures of association in relapse analyses, while Partial Pearson Correlations were used for association between gene expression levels. To improve interpretability of results, gene expression levels were scaled and centered across samples previously to the association analyses in the transcriptomic datasets. For visualization purposes, Kaplan-Meier survival curves and boxplots were estimated for groups of samples showing low, medium and high gene or signature expression levels, while scatter plots were used to represent correlation between gene or gene signatures values. Also, Cox proportional hazard ratios smoothed by continuous expression levels were graphically represented using smoothing splines with a p- spline basis (Eilers, Paul H. and Marx, Brian D., Statistical Science 11 (1996) 89-121) as implemented in the R package phenoTest (Evarist Planet (2018). phenoTest: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. R package versionl .30.0.). In addition, a heatmap was built to graphically show the correlation between multiple gene expression signatures simultaneously. In this heatmap, centered and scaled signature values were showed using a white to black color gradation, where black indicated the highest expression and white corresponded to the lowest expression values. For clarity, the most extreme expression values were truncated to -1.5 and 1.5.
Only samples from patients diagnosed in stages I, II and III were taken into consideration for analyses of time to relapse, for a total of 955 CRC samples; out of them 768 samples were Microsatellite Stable tumors (MSS) while 186 were Microsatellite Instable (MSI); TCGA’s rectum samples were excluded from prognosis analyses accordingly to recommendations of the TCGA-Clinical Data Resource (CDR) [Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, Kovatich AJ, Benz CC, Levine DA, Lee AV, Omberg L, Wolf DM, Shriver CD, Thorsson V; Cancer Genome Atlas Research Network, Hu H. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018 Apr 5;173(2):400-416.e11. doi: 10.1016/j.cell.2018.02.052. PubMed PMID: 29625055; PubMed Central PMCID: PMC6066282.]. The threshold for statistical significance was set at 5%. All analyses were carried out using R (Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2018), at http://www.r-proiect.org).
Generation and Treatment of human Tumors
CRC stem cells were purified from patient derived xenografts or fresh human biopsies using the protocol developed to obtain normal colonic mucosa (CoSC) stem cells applied to colorectal cancer samples (Jung et al., 2011 ; Merlos Suarez et al., 2011 ; Calon et al., Nature Genetics 47:4 (2015) 320-332). Briefly, cells were purified from CRC samples that had elevated EpHB2 receptor levels in a Fluorescence Activated Cell Sorter (FACS). They were grown in Matrigel (Basement Membrane Matrix Low Concentration, BD) with a simplified version of the specific CoSCs medium described by Jung et al. (DMEM / F12; 10 mM FIEPES; 1x Glutamax; 1x B-27 with retinoic acid; 1x N-2; 20 ng mL-1 bFGF; 50 ng mL-1 EGF; 1 mM LY2157299 and 10 pM Y-27632). Under these conditions, EpHB2-elevated cells expand three-dimensionally forming tumor organoids indefinitely, while cells with medium or low levels of EphB2 do not. When implanted into immunodeficient mice, these organoids generate heterogeneous tumors similar to the original tumor.
Fluman tumor organoids were implanted subcutaneously in NSG or nude females (Jackson Labs), 5-6 weeks old. Mice were treated with LY2157299 twice a day, at a dose of 160mg / kg, orally, beginning the third day after inoculation of the tumor cells, continuing until the end of the experiment. All animal experiments were approved by the committee of the Barcelona Science Park (CEEA-PCB) and the Catalan Government, regarding the use and care of experimental animals. The general conditions of the animals were monitored throughout the experiment.
Generation and Treatment of Mouse Tumors
Mouse tumor organoids (MTOs) mutant in four pathways involved in colorectal cancer progression were obtained from genetically modified mice (described in Tauriello et al., Nature, Vol. 554 (2018) 539 - 543). Briefly, we crossed mouse strains bearing engineered alleles for four of the most common genetic alterations found in human CRC - Ape fl/fl, KrasLSL G12D, Tgfbr2fl/fl and p53 fl/fl- and recombined these mutations in intestinal stem cells by means of the Lgr5-creERT2 driver (LAKTP mice). Quadruple mutant mice developed metastatic intestinal tumors that reproduced several key features of human poor prognosis microsatellite stable CRCs including a stromal rich TGFb-activated TME and T cell exclusion. From these mouse tumors, we derived a biobank of 3D MTOs (described in Tauriello et al., Nature, Vol. 554 (2018) 539 - 543). Upon transplantation into the caecum of wild-type syngeneic fully immunocompetent C57BL/6 mice, these tumor organoids (MTOs) generated primary CRCs that resemble those from the tumor of origin.
Three different MTOs derived from compound LAKTP mutant mice were orthotopically transplanted into syngeneic C57BL/6J mice. MT01 organoids grown for 3 days were harvested from their 3D growth matrix (basement membrane extract, BME), counted and injected as intact spheroids in 30% BME below the serosa of the caecum of an anaesthetized mouse. Parental MTOs 2 and 3 were first injected subcutaneously and 1 mm3 pieces of resulting tumors were transplanted onto the tip of the caecum of syngeneic mice; the caecum tip was then folded over to mitigate carcinomatosis. Mice were treated with 10mg of Galunisertib (GAL) (or placebo) twice per day by gavage, starting at day 11 after tumor implantation and lasting until sacrifice at 5 weeks. This treatment was associated to a strong reduction/prevention of liver metastasis, as well as a reduction of primary tumor (and local carcinomatosis) size (Tauriello et al., Nature, Vol. 554 (2018) 539 - 543).
Analysis of Transcriptomic data from mouse tumors
Gene expression data (GSE103562) obtained from mouse tumor samples (Tauriello et al., Nature (2018), 554,538-543) were processed similarly to Affymetrix human samples (Pre processing of microarray CRC datasets). Previously to gene-level summarization, expression values were corrected a priori by metrics RMA.IQR and RNA.DEG described in Eklund AC et al., (Genome Biol. (2008), 9(2):R26). Next, genes were translated to their corresponding human homologous using the Mouse Genome Informatics Database. (14.353 genes) (Blake JA, et al., Nucleic Acids Res. (2017) 45(D1):D723-D729). Association of gene expression with treatment was assessed using a linear model in which sample’s organoid of origin was included as a covariate.
Example 2.- Elevated CTHRC1 mRNA expression levels identify a subset of patients with stromal activation by TGF-beta in transcriptomic cohorts of colorectal cancer patients
The inventors obtained good data demonstrating that the group of patients with higher CTHRC1 mRNA expression are characterized by a higher degree of TGF-beta-activated TME, shown by higher levels of TGFp response signatures from fibroblasts (F-TBRS), T- cells (T-TBRS) and macrophages (Ma-TBRS) in transcriptomic cohorts from various types of cancer compared to patients with medium and low CTHRC1 mRNA expression.
The results in Figure 1 show that CTHRC1 mRNA expression captured the essence of a TGFb-activated stroma in a pooled transcriptomic cohort of CRC. In particular, this cohort contains n=1705 patients and pools together data from TCGA, as well as the pooled cohort of GEO datasets: GSE14333, GSE33113, GSE39582, GSE38832, and GSE44076.
CTHRC1 mRNA expression was compared versus the F-/TVMa-TBRS (from Calon et al., Cancer Cell 22 (2012) 571-584). Sample groups of low, medium and high expression levels were defined using the mean and -1 standard deviation as cutoffs.
Molecular classifications of colorectal cancer (CRC) based on global gene expression profiles have defined the CMS4 subtype as displaying resistance to therapy and poor prognosis (Guinney et al. Nat Med. 2015, 21(11), 1350-6). Patients with CMS4 tumors show overall worse prognosis compared to CMS1-3 (Guinney et al. Nat Med. 2015, 21(11), 1350- 6). These tumors are characterized by high TGF-beta signaling and abundant stroma. The inventors found that CTHRC1 mRNA expression is higher in tumors classified as CMS4 subtype (Fig.2A). Therefore, only one biomarker can be used to identify CMS4 patients in the clinic. Actually, as shown in Figure 2B CTHRC1 mRNA expression captures 92.2% of CMS4 patients.
On the contrary, patients with tumors classified as CMS1 , 2 or 3 are considered of good prognosis. Importantly, amongst the patients with tumors classified as CMS1-3, we also identify a sub-group of patients with tumors that show a TΰRb activated TME defined as high expression of F-T-and Ma-TBRSs (Figure 2C), that can be captured by higher CTHRC1 mRNA expression levels (Figure 2B and C). All patients from the different CMS with high TΰRb activated TME are likely to benefit from therapies directed towards inhibition of TΰRb signaling. This is particularly important for CMS1 patients which are considered as good prognosis patients. Importantly, patients with CMS1 tumors are likely candidates to checkpoint immunotherapies. Many of these patients present tumors with a high degree of TGFp-activated TME, measured by CTHRC1 (Figure 2B and 2C) and will likely not respond to such therapies alone Tauriello et al., (Nature, Vol. 554 (2018) 539 - 543)
Therefore, CTHRC1 mRNA expression captures a large proportion of CMS4 tumors as well as tumors in other CMS that show a TGFb-activated TME and are also likely candidates for therapies inhibiting the TGF-beta signaling pathway. In addition, the inventors identified that high CTHRC1 expression levels associated with tumors exhibiting elevated F-TBRS, T-TBRS and Ma-TBRS in both MSS (microsatellite stable) and MSI (microsatellite instable) subgroups of patients (Figure 3). Accordingly, CTHRC1 expression has been found to be independent from MSI status in the identification of a TGFb-activated stroma. Generally, MSI patients are considered as good prognosis patients and are therefore typically not selected for neoadjuvant and/or adjuvant treatments. CTFIRC1 expression would thus be capable of capturing a subset of MSI patients that have high levels of TGF-b (Figure 3A), poor prognosis (Figure 3B) and would likely benefit from anti-TGFp therapies. Again, current guidelines propose that MSI patients are candidates to checkpoint immunotherapies. According to Tauriello et al., (Nature, Vol. 554 (2018) 539 - 543), patients with tumors with a high degree of TGFp-activated TME, will likely not respond to such therapies alone. Therefore, administration of TGFp signaling pathway inhibitors would be particularly useful in combination with immune checkpoint inhibitors since these patients are likely to be no-responders to checkpoint immunotherapy alone.
The inventors further show heatmaps (Figure 4) illustrating the correlations between CTFIRC1 mRNA expression with average gene expression of overall F-TBRS, T-TBRS and Ma-TRBRS (from Calon et al., Nature Genetics 47:4 (2015) 320-332) and distribution of Consensus Molecular Subtypes (CMS) in CRC transcriptomic cohorts (TCGA+GEO: 1705 patients), including patients from stages I to IV.
CTFIRC1 mRNA expression shows a footprint of tumors that have a TGFp-activated TME, as well as immune-excluded (see reverse pattern for expression of a signature of CD45 positive cells that captures the relative abundance of leukocytes compared to the other cell types present in the TME). With respect to the distribution of patients within CMS, it is observed that CMS4 patients cluster with high CTFIRC1 mRNA expression and TGFp- activated stromal signatures, whereas CMS2 and CMS3 patients show the reverse pattern. CMS1 and MSI patients are distributed along the axis indicating that those with high TGFp- active TME which segregates with bad prognosis (Figure 5B) could also potentially benefit form therapies inhibiting TGFp signaling pathway. Importantly, these patients are likely candidates to receive immune checkpoint inhibitors therapy. The presence of a TGF-beta- activated TME in patients with CMS1 or MSI tumors will likely predict no-response to these therapies alone, unless in combination with TGFp signaling inhibitory therapies. Example 3.- CTHRC1 protein expression in FFPE samples of colorectal cancer (CRC) patients
3.1 Distribution of CTHRC1 protein expression across stage I, and III CRC patients
Figure imgf000077_0001
evaluated bv IHC.
CTHRC1 protein expression levels were measured by IHC in TMAs, (see Figure 5; n=266) CTHRC1 protein expression is higher in samples from advanced CRC (stage II and III), in comparison with stage I. Figure 5A depicts CTHRC1 protein expression in patients with stage I, II, or III colorectal cancer in retrospective FFPE samples from patients diagnosed and treated at Hospital del Mar in Barcelona (n= 266). Each dot is one sample. Tukey box plots have whiskers of maximum 1 .5 times the interquartile range; the boxes represent first, second (median) and third quartiles.
It should be noted that other stromal biomarkers which were in principle equally suitable prognostic biomarkers (i.e. have an increased expression in cancer-associated fibroblasts (CAF) in the dataset from Calon et al. (Calon et al., Nature Genetics 47:4 (2015) 320-332), namely CLIC4, DPYSL3, PTRF, SULF1 , ZEB1 , RAB31 and LUMICAN) in relation to prognosis in CRC were also tested by the inventors, but a statistically significant association was not found when evaluated in FFPE samples of CRC patients by IHC (data not shown).
Also, the obtained results indicate that the predictive power of this biomarker does not depend on the percentage of cells expressing these markers within the tumor area, but on a semi-quantitative estimation of their positivity (distinguishing between negative (0) / low (1) and moderate (2) / high (3) intensity) in stromal cells surrounding tumor epithelial cells. Thus, the inventors identified a biomarker whose expression can be determined semi-quantitatively by immunohistochemistry, without the need to establish a strict cut-off point, and that robustly predict the risk of recurrence can be very useful in clinical practice, although it is evident the need to standardize the detection method and the reading criteria that may vary between different centers and specialists. An interesting possibility would be to establish automatic processing and reading protocols.
3.2. Association of CTHRC1 protein expression with disease free survival (DFS) in
FFPE samples of CRC patients Moreover, the inventors found a linear association between the average CTHRC1 protein expression levels and the risk of relapse after therapy. Figure 5B shows Cox proportional hazard ratios smoothed by continuous CTHRC1 expression levels. The risk of disease relapse (Hazard Ratio, HR) increases by 2.25 per unit of intensity (IU, intensity units) at the protein level expressed in the tumor stroma (p-value = 0.00016; n= 266, same as above). Based on this finding patients were stratified in 3 groups according to High (H), Medium (M) and Low (L) CTHRC1 expression as indicated (Figure 5B).
CTHRC1 protein expression in CRC patients was shown to segregate patients with poor prognosis in the FFPE cohort of CRC patients (n=266). Kaplan Meier curves evaluating disease-free survival invariably show that patients with tumors exhibiting high CTHRC1 protein levels had worse prognosis. This was true for all patients (Stage 1-3; Figure 5C) and also for patients when further stratified in stage II or stage III (Figure 5E and 5G).
In a multivariate analysis the inventors found a statistically significant correlation between CTHRC1 levels (as a continuous variable) and prognosis, for the global group of patients analyzed (stage I, II and III) and in the differentiated analysis by tumor stage (II and III separately; see table below). HRs were estimates from a Cox model in which TMA, age at diagnosis, gender, tumor location, stage and chemotherapy (Yes/No) were included as covariates for adjustment (n=264 patients; two patients lack suitable information). Correlation with prognosis was independent of clinical variables such as age, gender, staging, and neoadjuvant treatment.
In addition, when considering CTHRC1 expression as a categorical variable by segregating patients into three groups characterized by low, medium and high expression of CTHRC1 , the statistical significance was maintained (when comparing low and high levels), both in stage II and in stage III (HR: 3.22, p = 0.049 for stage II and HR: 2.56, p = 0.036 for stage III) (Table below). Again, this was independent of clinical variables such as age, gender, staging, and neoadjuvant treatment.
Figure imgf000078_0001
Figure imgf000079_0001
Figure imgf000079_0002
Figure imgf000079_0003
Thus, determining the expression levels of this biomarker may help to stratify and select patients with stage II CRC having poor prognosis which may benefit of an adjuvant and/or neoadjuvant treatment.
Example 4.- CTHRC1 is a biomarker of response to therapies inhibitinq TGFBsiqnalinq pathway. Galunisertib (GAL; LY2157299), a TGFBR1 small molecule inhibitor, was used to treat mice with TGFp high CRCs (Figure 6). CTHRC1 expression levels measured by IHC (Figure 6A, in human tumors) or mRNA (Figure 6B, in mouse tumors) were clearly reduced in mice treated with LY2157299, indicating the inhibitor is effectively diminishing TGFp signaling in the tumor TME. Thus, the presented data evidences that CTHRC1 is a good biomarker of response to TGFp inhibition therapies. In addition, it re-inforces the notion presented above (Examples 2 and 3) that CTHRC1 is a good marker to select patients with high TGFp activated stroma (Figure 6A and 6B, before treatment), likely to respond to therapies inhibiting the TGF-beta pathway (Figure 8C in Calon et al., Nature Genetics 47:4 (2015) 320- 332); and Figures 2 and 4 from Tauriello et al., Nature, Vol. 554 (2018) 539 - 543).
Example 5.- Elevated CTHRC1 mRNA expression levels identify a subset of patients with high F-TBRS, T-TBRS and Ma-TBRS expression in transcriptomic cohorts across various types of cancers
5.1 Association of CTHRC1 expression with overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in transcriptomic cohorts of stomach adenocarcinoma (ST AD)
CTHRC1 measurement captures the essence of a TGFp-activated microenvironment in the transcriptomic cohorts of STAD (from TCGA), as shown in Figure 7. CTHRC1 expression levels are shown for all patients or for patients according to their MSS or MSI status.
5.2. Association of CTHRC1 expression with with overall F-TBRS, T-TBRS and Ma- TRBRS expression levels in transcriptomic cohorts of breast cancer (Metabric)
The inventors analyzed CTHRC1 expression in the Metabric cohort of breast cancer patients (Curtis et al., Nature (2012) 486(7403): 346-52. From the data in Figure 8, association with TBRSs is good for all breast cancer subtypes.
5.3. Association of CTHRC1 expression with overall F-TBRS, T-TBRS and Ma-TRBRS expression levels in transcriptomic cohorts of Pancreatic adenocarcinoma, bladder, Prostate, and Lung Cancer CTHRC1 expression captured the essence of a TGFp-activated TME in the TCGA transcriptomic cohorts of bladder and pancreatic adenocarcinoma, in the GSE21034 cohort of prostate cancer, and in the GSE31210 cohort of lung cancer as shown Figure 9.
Thus, the presented data evidences that CTHRC1 is a good biomarker to identify patients with high TGFp-activated TME across various tumor types. As described extensively for CRC patients, in all these additional examples (5.1-5.3) patients with high CTHRC1 expression, and thus, TGFp-active TME could potentially benefit form therapies inhibiting TGFp signaling pathway. g. Example 6- Comparative Example 1
The inventors compared the ability of CTHRC1 gene expression levels to identify a TGFp- activated tumor microenvironment (TME) in colorectal cancer patients (Pooled cohort GEO + TCGA) with respect to IL-11 gene expression and IL-11 response signature (IL-11 RS) described in Calon et al. 2012 (Alexandre Calon et al., Cancer Cell 2012, 22(5) 571 -584).
IL-11 RS is a gene signature comprising 1139 genes which was described in Calon et al. 2012 to be associated with TGFp levels and TGFp activation in fibroblasts (F-TBRS) in colorectal cancer samples. Besides, IL-11 mRNA expression levels were described to be reduced further to treating a xenograft model bearing CRC stem cell-derived tumors with a TFGp-inhibitor.
In the present analysis, the inventors determined the association of each of the above- mentioned gene expressions and signatures with F-TBRS, T- TBRS, or Ma-TBRS by using Partial Pearson Correlations (R). F-TBRS, T- TBRS, or Ma-TBRS signatures are used as surrogates of TGFp activation in fibroblasts, T-cells and macrophages respectively, the latter being cell types from the TME. The results are shown in Table 2 below.
Table 2. Correlation coefficient (R) of CTHCR1 gene expression levels, IL-11 gene expression levels and IL-11 RS score, respectively, with the TGFp activation signatures F- TBRS, T-TBRS and Ma-TBRS in a cohort of colorectal cancer patients (Pooled cohort GEO + TCGA).
Figure imgf000081_0001
Figure imgf000082_0001
As shown in Table 2 above the correlation coefficient (R value) of CTHRC1 gene expression levels with the three TGFp-activation signatures (F-TBRS, T-TBRS and Ma-TBRS) is substantially superior to that of IL-11 , presenting levels of correlation with a TGFp-activated TME approaching those of IL-11 RS. Although, IL-11 RS was found to be superior to CTHRC1 , it is rather surprising and unexpected that a single gene could capture 90% of the power of the >1000 gene IL-11 RS signature.
Analyses with other tumor types showed equivalent correlations. As illustrative examples, Table 3 shows results for Pancreas Adenocarcinoma (TCGA), Table 4 for Breast Cancer patients from the Metabric cohort, and Table 5 for Bladder Cancer. Again, it is rather surprising and unexpected that a single gene could provide a prediction power which is equivalent and even in some instances better (e.g. pancreas) than the >1000 gene IL-11 RS signature.
Table 3. Correlation coefficient (R) of CTHRC1 gene expression levels, IL-11 gene expression levels and IL-11 RS score, respectively, with the TGFp activation signatures F- TBRS, T-TBRS and Ma-TBRS in a cohort of pancreas adenocarcinoma patients (TCGA).
Figure imgf000082_0002
Figure imgf000083_0001
Table 4. Correlation coefficient (R) of CTHRC1 gene expression levels and IL-11 RS score, respectively, with the TGFp activation signatures F-TBRS, T-TBRS and Ma-TBRS in a cohort of breast cancer patients (metabric). Correlation coefficients are shown for all patients and for patients within each breast cancer subtype (Fler-2, Lum A, LumB, Normal, Basal). No data for IL11 is available for this cohort of patients.
Figure imgf000083_0002
Figure imgf000084_0001
Table 5. Correlation coefficient (R) of CTHRC1 gene expression levels, IL-11 and IL-11 RS score, respectively, with the TGFp activation signatures F-TBRS, T-TBRS and Ma-TBRS in a cohort of bladder cancer patients (TCGA).
Figure imgf000084_0002
h. Example 7- Comparative Example 2
The inventors further compared the ability of CTFIRC1 gene expression levels to predict relapse in colorectal cancer patients (Pooled cohort GEO + TCGA) with respect to IL-11 gene expression and IL-11 response signature (IL-11 RS) described in Calon et al. 2012.
As shown in Table 5, it was surprisingly found that CTHRC1 is a better predictor of relapse than IL11-RS or IL11 both in MSS and MSI CRC patients. For illustrative purposes, the Kaplan-Meier graphs on relapse for the MSS and MSI subgroups of CRC patients are shown in Fig. 3B and those relating to IL-11 RS and IL11 in Figs. 10A and 10B.
Table 6. Flazard ratio (FIR) values regarding disease free survival (DFS) in years when comparing the High (FI) vs low (L) groups according to CTFIRC1 mRNA expression, IL-11 and IL-11 RS in CRC patients, and MSS or MSI CRC subgroups. A Likelihood Ratio test was applied. Confident intervals [Cl] and [p values] are also shown.
All HvsL MSS HvsL MSI HvsL
Relapse CRC
HR [Cl] (p value) HR [Cl] (p value) HR [Cl] (p value)
1.45 1.53 1.37
IL11RS [0.93,2.25] [0.96,2.44] [0.31,5.99]
(0.087749) (0.063789) (0.66103)
1.76 1.70 89014988.20 ( )
CTHRC1 [1.16,2.68] [1.11,2.61] [0.00, Inf]
(0.0051284) (0.010185) (0.0065846)
1.10 1.19 0.44
IL11 [0.60,2.02] [0.63,2.26] [0.06,3.41]
(0.75475) (0.58579) (0.48129)

Claims

1. A method for determining whether a patient’s tumor has a TGFp-activated microenvironment, wherein said method comprises: a) determining the expression levels of CTHRC1 (Collagen Triple Helix Repeat Containing 1) gene in a tumor sample isolated from said patient; b) comparing the expression levels of CTHRC1 in the patient’s sample with a reference value; wherein an increase of the value in the patient’s sample with regard to said reference value is indicative that said patient’s tumor has a TGFp -activated microenvironment; wherein step a) comprises determining the expression levels of 50 or less genes.
2. The method according to claim 1, wherein step a) comprises determining the expression levels of 30 or less genes, preferably 20 or less genes, more preferably less than 19 genes.
3. The method according to any of the preceding claims, wherein step a) consist of determining the expression levels of CTHRC1 gene.
4. The method according to any of the preceding claims, wherein the expression levels are protein expression levels determined by immunohistochemistry (IHC).
5. The method according to any of the preceding claims, wherein said method is not tissue-specific.
6. The method according to any of the preceding claims, wherein said tumor is selected from the group consisting of colorectal cancer, stomach adenocarcinoma, bladder cancer, pancreas adenocarcinoma, prostate cancer, lung cancer and breast cancer; preferably is selected from the group consisting of colorectal cancer, stomach adenocarcinoma, bladder cancer, pancreas adenocarcinoma and breast cancer.
7. The method according to any of the preceding claims, wherein said tumor is colorectal cancer.
8. The method according to any of the preceding claims, wherein said method identifies a TGFp-activated microenvironment in any of microsatellite instable (MSI) or microsatellite stable (MSS) tumors.
9. The method according to any of the preceding claims, wherein said method identifies a TGFp-activated microenvironment in any of CMS1, CMS2, CMS3 or CMS4 molecular subtype groups tumors.
10. The method according to any of the preceding claims, wherein said method is a computer-implemented method.
11. A method for selecting a cancer patient which is likely to benefit from a treatment with an inhibitor of the TGFp signaling pathway, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method of any of claims 1 to 10; and ii. selecting said cancer patient for the treatment with an inhibitor of the TGFp signaling pathway when said patient’s tumor has a TGFp-activated microenvironment.
12. An inhibitor of the TGFp signaling pathway for use in a method for treatment of a cancer patient, wherein said patient’s tumor has a TGFp-activated microenvironment according to a method of any of claims 1 to 10.
13. The inhibitor of the TGFp signaling pathway for use according to claim 12, wherein said inhibitor is administered to said patient in combination with another anti-cancer agent or radiotherapy, preferably with a cytotoxic agent or immune checkpoint inhibitor.
14. An anti-cancer agent other than a TGFp signaling pathway inhibitor, preferably a cytotoxic agent or immune checkpoint inhibitor, or radiotherapy for use in a method for treatment of a cancer patient in combination with an inhibitor of the TGFp signaling pathway, wherein said patient’s tumor has a TGFp-activated microenvironment according to a method of any of claims 1 to 10.
15. A method for determining the prognosis of a cancer patient, wherein said method comprises: i. determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method of any of claims 1 to 10; and ii. classifying the patient as having poor prognosis when said patient’s tumor has a TGF p -activated microenvironment.
16. A method for monitoring or evaluating the response to treatment with an inhibitor of the TGFp signaling pathway in a cancer patient, wherein said method comprises determining whether a patient’s tumor has a TGFp-activated microenvironment according to a method of any of claims 1 to 10, wherein a TGFp-activated microenvironment is associated to lack of response.
17. Use of a kit suitable for determining the expression levels of CTHRC1 gene in an isolated tumor sample, in a method for determining whether a patient tumor’s has a TGF p -activated microenvironment according to any of claims 1 to 10, in a method for selecting a cancer patient which is likely to benefit from a treatment with a TGFp signaling pathway inhibitor according to claim 11, in a method for determining the prognosis of a cancer patient according to claim 15 or in a method for monitoring or evaluating the response to treatment with a TGFp signaling pathway inhibitor according to claim 16; wherein said kit comprises: i. a reagent for the quantification of the CTHRC1 gene expression levels; and ii. optionally, a reagent for the quantification of a control gene expression levels; iii. optionally, further comprising tumor cells to be used as low and/or high expression controls; iv. optionally, further comprising instructions for the use of said reagents in determining the expression levels of said genes in a tumor sample isolated from a cancer patient.
18. The use of a kit according to claim 17, wherein said reagent in a) is an oligonucleotide specific to CTHRC1 mRNA or an affinity reagent for the CTHRC1 gene encoded protein.
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CN113736862B (en) * 2021-08-04 2024-03-22 上海健康医学院附属周浦医院 Application of detection reagent in preparation of kit for detecting CTHRC1 gene content in prostate cancer tissue by microdroplet digital PCR

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