In vitro method for predicting longevity in cancer survivors

Abstract

The present invention refers to an in vitro method for predicting longevity in cancer survivors.

Description

[0001] IN VITRO METHOD FOR PREDICTING LONGEVITY IN CANCER SURVIVORS

[0002] FIELD OF THE INVENTION

[0003] The present invention refers to the medical field. Particularly, the present invention refers to an in vitro method for predicting longevity in cancer survivors.

[0004] STATE OF THE ART

[0005] Cancer survival rates vary by the type of cancer, stage at diagnosis, treatment given and many other factors, including country.

[0006] Regarding breast cancer, there is an unmet medical need of finding a method able to identify patients suffering from breast cancer who have a longer overall survival (OS) or longevity and differentiate them from those patients suffering from breast cancer who have a shorter overall survival or longevity, regardless of whether the patient is suffering from cancer recurrence or relapse, and / or independent of patient's age.

[0007] Being able to identify those patients with longer longevity, and differentiate them from those patients with shorter longevity, regardless of whether the patient is suffering from recurrence or relapse and / or independent of patient's age, is of utmost importance to physicians. This way, once it is identified whether the patient may have a longer or shorter longevity, regardless of whether the patient is suffering from recurrence or relapse and / or independent of patient's age, physicians could adapt the intensity of treatment to be administered according to the patient's longevity and considering recurrence or relapse and / or age as secondary variables.

[0008] The present invention is focused on solving this problem and it is directed to a method for predicting longevity, in patients suffering from cancer, preferably early-stage breast cancer, independent of age and cancer recurrence or relapse. DESCRIPTION OF THE INVENTION

[0009] As explained above, the present invention refers to an in vitro method for predicting longevity in cancer survivors.

[0010] The inventors of the present invention have identified in a discovery phase (see Example 1 and Example 2) the association between the level of expression of immune genes (IGG signature), particularly the immune genes CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17, and OS or longevity, independent of the patient's age, in patients with breast cancer that did not experience a relapse.

[0011] So, the “special technical feature”, i.e., the technical feature that defines the contribution of the present invention over the prior art, and that confers unity to the present invention, is the use of immune genes for predicting longevity in patients suffering from cancer, preferably early- stage breast cancer, independent of age and cancer recurrence or relapse.

[0012] Particularly, the inventors of the present invention obtained, from two cohorts of early-stage breast cancer patients: SCAN-B (N=4470 patients without breast cancer relapse) and METABRIC (n=1133 without breast cancer relapse), gene expression and clinical data, including baseline characteristics and long-term follow-up. The association of the 14-gene IGG signature: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17, with OS in the absence of recurrence or relapse was assessed in both cohorts. Additionally, the core IGG genes were identified which are strongly associated with longevity and examined their relationship with age (Table 2 and Table 3). On the other hand, the performance of synergistic two-pair combinations of the genes was assayed through addition, subtraction, multiplication, and division to enhance the prediction of longevity, such as it can be seen in several two-gene combinations shown in Table 4 and Table 5.

[0013] Such as it is shown in Example 1 and Example 2 below, the continuous expression of the 14- gene IGG signature was significantly associated with OS without recurrence or relapse in both cohorts (p<0.0001), independent of hormone receptor expression, HER2 status, tumor stage, and nodal status. Among the 14 IGG signature genes, the continuous expression of the genes CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 or IGKC was significantly associated with longevity (p<0.0001) across both datasets, defining the Core IGG genes (CIGG). Moreover, combining the expression of two genes provided more prognostic information than a single gene (p<0.001).

[0014] So, immune genes are associated with improved longevity in patients who have experienced breast cancer, suggesting a potential role in predicting prolonged survival independent of cancer recurrence or relapse and age.

[0015] Of note, the inventors of the present invention indicate that there are specific mechanisms through which these genes contribute to improved longevity beyond breast cancer recurrence or relapse and suggest that they collectively create an immune microenvironment that supports prolonged overall survival. Understanding the intricate interplay between these genes and their downstream effects on immune function is crucial for unlocking their therapeutic potential and advancing personalized treatment strategies for breast cancer patients.

[0016] Intriguingly, as explained above, combined pairs of the genes revealed that the sum of two of them provided more prognostic information than individual genes in a significant proportion of cases. This innovative approach to combining gene expressions demonstrated the potential for refining prognostic models, paving the way for more nuanced predictions of longevity in breast cancer survivors.

[0017] In conclusion, the present invention underscores the pivotal role of immune gene expression, particularly the 14-gene IGG signature and the identified Core IGG genes, in predicting extended longevity in cancer survivors, preferably in breast cancer survivors. The exploration of novel combinations of these genes opens new avenues for refining prognosis and longevity models, ultimately contributing to the advancement of personalized treatment strategies for patients.

[0018] Moreover, the inventors of the present invention have carried out a validation phase (see the Example 3 and Example 4 below), wherein the relationship between an anti-tumor immune response, measured by a 14-gene B-cell / immunoglobulin (IGG) signature, and mortality risk in 9,638 breast cancer patients across three datasets was assessed. Associations with tumor subtype, stage, and age were examined. IGG was characterized using spatial GeoMx profiling and single-cell RNA seq, and its relationship with tertiary lymphoid structures (TLS) was evaluated. The predictive value of each of the 14 IGG genes for B-cell receptor (BCR) and T- cell receptor (TCR) clonality and longevity was also assessed, along with its association with longevity in other cancer types.

[0019] In this validation phase, the IGG signature was significantly associated with a 41-47% reduction in death risk in breast cancer survivors (p<0.001), regardless of age, tumor stage, or subtype. Similar associations were observed in other cancers, including melanoma. In breast cancer, the IGG signature was significantly linked to OS without relapse in patients aged 41- 70 at diagnosis. Additionally, IGG expression correlated with the presence of TLS and higher B and T-cell poly clonality. A specific subset of 7 IGG genes strongly correlated with BCR and TCR clonality, with predictive power for identifying clonality and improved longevity, especially when combining two of these genes.

[0020] So, this validation phase confirms a significant link between immune gene expression in tumors and extended longevity in breast cancer survivors, even in the absence of recurrence. The IGG signature, particularly its key gene subset, emerges as a powerful marker of sustained antitumor immunity and overall patient fitness. These findings pave the way for personalized treatment strategies that enhance both survival and long-term health outcomes.

[0021] Consequently, according to the discovery phase, the first embodiment of the present invention refers to an in vitro method for identifying biomarker signatures for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, which comprises: a) Measuring the level of expression of at least an immune gene selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17, or any combination thereof comprising between 2 and 14 of said genes, in a biological sample obtained from the patient; b) wherein if a deviation of the expression level is identified, as compared with a pre- established reference value, this is indicative that the biomarker signature may be used for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0022] In a preferred embodiment, the present invention refers to an in vitro method for identifying biomarker signatures for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, which comprises: a) Measuring the level of expression of at least an immune gene selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, or any combination thereof comprising between 2 and 9 of said genes, in a biological sample obtained from the patient; b) wherein if a deviation of the expression level is identified, as compared with a pre- established reference value, this is indicative that the biomarker signature may be used for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0023] In a preferred embodiment, the present invention refers to an in vitro method for identifying biomarker signatures for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, which comprises: a) Measuring the level of expression of at least two immune genes selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC in a biological sample obtained from the patient; b) determining a combination score value by calculating the ratio of the expression of the 2 genes; and c) wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, this is indicative that the biomarker signature may be used for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0024] In a preferred embodiment, the present invention refers to an in vitro method for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, which comprises: a) Measuring the level of expression of at least an immune gene selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, or any combination thereof comprising between 2 and 9 of said genes, in a biological sample obtained from the patient; b) wherein the identification of an increased level of expression, as compared with a pre-established reference value, is indicative that the patient suffering from breast cancer has a longer longevity independent of age and cancer recurrence or relapse.

[0025] In a preferred embodiment, the present invention refers to an in vitro method for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, which comprises: a) Measuring the level of expression of at least two immune genes selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, in a biological sample obtained from the patient; b) determining a combination score value by calculating the ratio of the expression of the 2 genes; and c) wherein if an increase of the combination score value is identified, as compared with a pre-established reference value, this is indicative that the patient suffering from breast cancer has a longer longevity independent of age and cancer recurrence or relapse.

[0026] In a preferred embodiment, the present invention refers to an in vitro method for predicting longevity, in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, which comprises: a) Measuring the level of expression of at least one of the two- immune genes combinations of Table 4 or Table 5 in a biological sample obtained from the patient; b) determining a combination score value by calculating the ratio of the expression of the 2 genes; and c) wherein if an increase of the combination score value is identified, as compared with a pre-established reference value, this is indicative that the patient suffering from breast cancer has a longer longevity independent of age and cancer recurrence or relapse.

[0027] The second embodiment of the present invention refers to the vitro use at least one immune gene selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3- 25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17, or any combination thereof comprising between 2 and 14 of said genes, for identifying biomarker signatures for predicting longevity, in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, or for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0028] In a preferred embodiment the present invention refers to the in vitro at least one immune gene selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, or any combination thereof comprising between 2 and 9 of said genes, for identifying biomarker signatures for predicting longevity, in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, or for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0029] In a preferred embodiment the present invention refers to the in vitro use at least two immune genes selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, for identifying biomarker signatures predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse, or for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0030] In a preferred embodiment the present invention refers to the in vitro use of at least one of the two-immune genes combinations of Table 4 or Table 5 for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0031] The third embodiment of the present invention refers to the in vitro use of a kit comprising reagents for measuring the level of expression of at least one immune gene selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, or any combination thereof comprising between 2 and 9 of said genes, for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0032] In a preferred embodiment the present invention refers to the in vitro use of a kit comprising reagents for measuring the level of expression of at least two immune gene selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0033] In a preferred embodiment the present invention refers to the in vitro use of a kit comprising reagents for measuring the level of expression of at least one of the two-immune genes combinations of Table 4 or Table 5 or for predicting longevity in patients suffering from breast cancer, independent of age and cancer recurrence or relapse.

[0034] In a preferred embodiment, the sample is selected from: tissue, blood, serum or plasma.

[0035] In a preferred embodiment, the patient is suffering from early-stage breast cancer.

[0036] In a preferred embodiment, the patient is suffering from hormone receptor-positive and HER2- negative (HR+ / HER2-) or human epidermal growth factor receptor 2-positive (HER2+) or triple-negative breast cancer. On the other hand, according to the validation phase, the present invention refers to an in vitro method for identifying biomarker signatures for predicting longevity in cancer survivors (those subjects who have suffered from cancer and they have been considered as cured after receiving a cancer treatment), the method comprising measuring the level of expression of at least two immune genes selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF 17, or any combination thereof, in a biological sample obtained from the patient; or to the in vitro use at least two immune genes selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF 17, or any combination thereof, or of a kit comprising reagents for measuring the level of expression of the genes, for identifying biomarker signatures for predicting longevity in cancer survivors.

[0037] The present invention also refers to an in vitro method for predicting longevity, in cancer survivors, the method comprising measuring the level of expression of at least two immune genes selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3- 25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF 17, or any combination thereof, in a biological sample obtained from the patient; or to the in vitro use at least two immune genes selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF 17, or any combination thereof, or of a kit comprising reagents for measuring the level of expression of the genes, for predicting longevity in cancer survivors.

[0038] In a preferred embodiment, the present invention comprises measuring the level of expression of at least two immune genes selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, or any combination thereof, in a biological sample obtained from the patient.

[0039] In a preferred embodiment, the present invention comprises measuring the level of expression of at least two immune genes selected from the group consisting of: TNFRSF 17, IL2RG, POU2AF1, CD27, IGJ, CD79A and / or PIM2, or any combination thereof, in a biological sample obtained from the patient. In a preferred embodiment, the present invention comprises measuring the level of expression of at least one of the two-immune genes combinations of Table 4, Table 5, Table 7, Table 8, Table 9 or Table 10 in a biological sample obtained from the patient.

[0040] In a preferred embodiment, the present invention comprises determining a combination score value by calculating the ratio of the expression of the 2 genes; and wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, this is indicative that the biomarker signature may be used for predicting longevity.

[0041] In a preferred embodiment, the present invention comprises measuring the level of expression of the following gene combination: TNFRSF17, IL2RG, POU2AF1, CD27, IGJ, CD79A and PIM2, in a biological sample obtained from the patient.

[0042] In a preferred embodiment, the cancer survivor is a patient who has successfully overcome cervical cancer, head and neck cancer, lung adenocarcinoma, melanoma or sarcoma.

[0043] In a preferred embodiment, the breast cancer survivor has survived to hormone receptor positive breast cancer and HER2-negative (HR+ / HER2-), human epidermal growth factor receptor 2-positive (HER2+) or triple-negative breast cancer.

[0044] In a preferred embodiment, the identification of an increased level of expression or combination score value, as compared with a pre-established reference value, is indicative that the cancer survivor has a longer longevity; and a reduced level of expression or combination score value, as compared with a pre-established reference value, is indicative that the cancer survivor has a shorter longevity.

[0045] In a preferred embodiment the present invention can be used for identifying those cured patients who have suffered from cancer (preferably breast cancer) with longer longevity and differentiate them from those cured patients who have suffered from cancer (preferably breast cancer) with shorter longevity.

[0046] In a preferred embodiment the present invention can be used for selecting the therapy to be administered to the cured patient who has suffered from cancer (preferably breast cancer), once the longevity of the patient has been predicted by carrying out the present invention. Thus, the strength or intensity of the therapy to be administered to the patient is selected according to the longevity assessed by means of the present invention. If immune genes are overexpressed, this opens the possibility of less intensive treatment even in older patients. If immune genes are downregulated more intensive treatment should be followed even in young patients. The treatments to be administered, according to the assessed longevity, can be summarized as follows:

[0047] • Surgery.

[0048] • Radiotherapy.

[0049] • Systemic therapy: o Chemotherapy. o Anti-HER2 therapy. o Endocrine therapy. o Immunotherapy. o CDK4 / 6 inhibitors.

[0050] • Follow-up care: Care after primary breast cancer treatment, otherwise called 'follow-up care', can be intensive involving regular laboratory tests in asymptomatic people to try to achieve earlier detection of possible metastases. A review has found that follow-up programs involving regular physical examinations and yearly mammography alone are as effective as more intensive programs consisting of laboratory tests in terms of early detection of recurrence or relapse, overall survival and quality of life.

[0051] In a preferred embodiment, the present invention is a computer-implemented invention, wherein a processing unit (hardware) and a software are configured to: a) Receive the expression level values of any of the above cited biomarkers or signatures, b) process the expression level values received for finding substantial variations or deviations, and c) provide an output through a terminal display of the variation or deviation of the expression level.

[0052] In a preferred embodiment, the method of the invention further comprises determining or measuring tumor stage and / or nodal status, using image techniques, for instance by CT scan, ultrasound and / or mammography.

[0053] For the purpose of the present invention the following terms are defined: • The term “pre-established reference value”, when referring to the level of expression of the biomarkers described in the present invention, refers to level measured in control subjects or considering the geometric mean level of house-keeping genes. A “reference” value can be a threshold value or a cut-off value. Typically, a "threshold value" or "cut-off value" can be determined experimentally, empirically, or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and / or clinical conditions, as would be recognized by a person of ordinary skilled in the art. The threshold value must be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit / risk balance (clinical consequences of false positive and false negative). Typically, the optimal sensitivity and specificity (and so the threshold value) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data.

[0054] • The term “variation or deviation” refers to a value which is above or below the pre- established reference value.

[0055] • By the term "comprising" is meant the inclusion, without limitation, of whatever follows the word "comprising". Thus, use of the term "comprising" indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present.

[0056] • By "consisting of’ is meant the inclusion, with limitation to whatever follows the phrase “consisting of’. Thus, the phrase "consisting of’ indicates that the listed elements are required or mandatory, and that no other elements may be present.

[0057] • By “cancer survivors” the present invention refers to those subjects who have suffered from cancer and they have been considered as cured after receiving a cancer treatment.

[0058] Brief description of the figures

[0059] Figure 1. Assessment of the 14-gene IGG signature for predicting longevity in patients with breast cancer without documented recurrence or relapse.

[0060] Figure 2. Association of IGG signature with OS without recurrence or relapse in (a) n=1133 samples of METABRIC and (b) n= 4470 samples of SCAN-B. Figure 3. Correlation between IGG signature and age, and distribution of IGG groups (low, medium, high) across age groups.

[0061] Figure 4. Association of IGG and OS without recurrence across age groups.

[0062] Figure 5. IGG expression in HR+ / HER2-, HER2+ and TNBC, and distribution of IGG groups (low, medium, high) across age groups and breast cancer subtype.

[0063] Figure 6. Genes associated with better OS without recurrence or relapse in univariate Cox regression models in METABRIC and SCAN-B.

[0064] Figure 7. Expression of 7 CIGGs and IGG signatures in the METABRIC dataset.

[0065] Figure 8. Correlation between 7 CIGG genes and age in the METABRIC dataset.

[0066] Figure 9. Amount of variation explained in prognosis defined by chi-square (%2) statistics from likelihood ratio tests.

[0067] Figure 10. Association of the combination of (a) 2 CIGG genes and (b) a single gene with OS without recurrence or relapse in METABRIC and SCAN-B combined.

[0068] Figure 11. Assessment of the 14-gene IGG signature for predicting longevity in breast cancer survivors without a documented recurrence, (a) Schematic representation of the study approach, (b) Cumulative-risk plots with censored recurrences illustrating the association of IGG expression with overall survival (OS) in all patients from METABRIC (n=l,940), SCAN-B (n=6,652), and TCGA datasets (n=l,082). (c) Correlation analysis depicting the relationship between the IGG signature and age in all patients without documented breast cancer recurrence during follow-up in METABRIC (n=l,133), SCAN-B (n=4,470), and TCGA datasets (n=936). (d) Distribution of the IGG signature (low, med, high) across age groups in all patients without documented breast cancer recurrence. Chi-square p-value<0.001 (e) Forest plot presenting the association of the IGG signature with OS in all patients without documented breast cancer recurrence across different age groups, (f) IGG signature expression across breast cancer clinical subtypes in all patients without documented breast cancer recurrence, (g) Distribution of the IGG signature (low, med, high) and age groups, stratified by breast cancer subtype in all patients without documented breast cancer recurrence.

[0069] Figure 12. Characterization of the 14-gene IGG signature in breast cancer, (a) Exemplification of protein-based spatial profiling in an IGG-high tumor versus an IGG-low tumor using the GeoMx digital spatial profiling platform. Green denotes tumor cells, red represents immune cells, and blue signifies cell nuclei (DNA). Selected regions of interest (ROIs) are highlighted in yellow. The heatmap represents the expression on immune proteins in CD45+ ROIs in each sample, (b) Hematoxylin / eosin staining image depicting a tertiary lymphoid structure (TLS) breast tumor (left); IGG signature expression in tumors without TLS (n=69) versus tumors with TLS (n=66) (middle); and distribution of IGG signature high, medium, and low expression groups in TLS-negative and TLS-positive tumors (right), (c) Uniform manifold approximation and projection (UMAP) plots showing cell types identified using single-cell RNA-seq analysis in one IGG-high (BC177) and one IGG-low (BC192) breast cancer sample (left); bar plot distribution of all cell types in each sample (right), (d) H4C staining of CD20 (B-cell marker), CD38 (Plasma cell marker), and CD3 (T-cell marker) in one IGG-high (BC177) and one IGG-low (BC192) breast cancer sample.

[0070] Figure 13. Association between IGG and BCR / TCR clonality and identification of core IGG genes related to longevity in patients with breast cancer without documented recurrence, (a) BCR Shannon entropy across IGG-high (n=10), IGG-medium (n=7), and IGG- low (n=6) tumors (left) and TCR Shannon entropy across IGG-high (n=18), IGG-medium (n=7), and IGG-low (n=8) tumors assessed by immunoSEQ assay, (b) Pearson correlation between BCR and TCR clonality measures and IGG levels in the HER2+ breast cancer CALGB40601 cohort, (c) Venn diagram of the significant individual genes associated with longevity in univariate Cox regression models in METABRIC (n=l , 133), SCAN-B (n=4,470), and TCGA datasets (n=936). (d) Expression of 7 CIGGs and IGG signature in the METABRIC dataset, (e) Amount of variation explained in prognosis of the expression of one single CIGG gene or the expression of the combination of 2 CIGGs (i.e., addition of the log base 2 expression values) as defined by chi-square (%2) statistics obtained from likelihood ratio testing. (1) ROC AUCs of IGG signature and the combination of the 2 CIGGs IL2RG+IGJ to predict BCR Shannon entropy across (left) and TCR Shannon entropy (right), (g) Schematic representation of the characteristics of IGG-low and IGG-high tumors. Figure 14. Association of the 14-gene IGG signature with disease-free survival, OS and longevity across cancer types, (a) Association of the IGG signature with disease-free survival in patients with cancers of the cervix (n=294), head and neck (n=515), lung (n=510), melanoma (n=443), and sarcoma (n=253) in TCGA. (b) Association of the IGG signature with OS in patients with cancers of the cervix (n=294), head and neck (n=515), lung (n=510), melanoma (n=443), and sarcoma (n=253) in TCGA. (c) Association of the IGG signature with OS in all patients with censored recurrences with cancers of the cervix (n=294), head and neck (n=515), lung (n=510), melanoma (n=443), and sarcoma (n=253) in TCGA. (d) Association of the IGG signature with OS in all patients excluding recurrences with cancers of the cervix (n=226), head and neck (n=321), lung (n=304), melanoma (n=138), and sarcoma (n=l 16) in TCGA.

[0071] Detailed description of the invention

[0072] The present invention is illustrated by means of the examples set below, without the intention of limiting its scope of protection.

[0073] DISCOVERY PHASE

[0074] Example 1. MATERIAL AND METHODS

[0075] Example 1.1. Patient Datasets

[0076] The METABRIC dataset was sourced from the eBio Cancer Genomics Portal (htp: / / cbioportal.org), while data from the most recent version of the SCAN-B dataset were obtained from Mendeley Data (htt s : / / data. m en del ey . com / datasets / y zxtxn4nm d) . Patients who experienced breast cancer recurrence or relapse were systematically excluded from both datasets. The final analysis comprised 4,470 patients from SCAN-B and 1,133 patients from METABRIC, ensuring a cohort that did not encounter recurrence or relapse during the followup period. The median follow-up in these patients was 96.9 months in SCAN-B, and 185.0 months in METABRIC.

[0077] Example 1.2. Gene Expression

[0078] Z-scores or log2 -transformed normalized expression values were employed for single genes and for deriving the 14-gene IGG signature, depending on the available gene expression data for each dataset. The IGG signature, previously identified through unsupervised clustering of 550 node-negative breast tumors, was externally validated across diverse clinical cohorts involving over 1,000 patients. Comprising 14 genes associated with various aspects of lymphocyte progenitor maturation, activation, differentiation, immunoglobulin production, chemotaxis, and lymphocyte activity regulation, the IGG score was calculated as the mean expression of the available genes in datasets where the expression of some IGG genes was missing.

[0079] Example 1.3. Core IGG Genes (CIGGs)

[0080] Genes consistently associated with longevity across both datasets were identified as Core IGG genes (CIGGs). We further investigated whether the combined assessment of pairs of CIGGs enhanced longevity prediction compared to individual genes. Various mathematical operations, including addition, subtraction, multiplication, and ratio, were employed to combine the expressions of two CIGGs.

[0081] Example 1.4. Statistical Analysis

[0082] Univariate and multivariable Cox regression models were employed, with the goodness-of-fit of each model assessed through the chi-square (%2) statistic obtained from a likelihood ratio test. In addition, correlations between two variables were evaluated using the Pearson method. The comparison between models combining two CIGGs and models utilizing individual genes aimed to ascertain the optimal approach for enhancing longevity prediction. A 2-sided alpha of 0.05 was set as the significance level for all statistical analyses.

[0083] Example 2. RESULTS

[0084] Example 2.1. IGG association with longevity

[0085] Our study addresses this gap by exploring the association between the 14-gene IGG signature and OS without recurrence or relapse, defined as longevity. Leveraging comprehensive gene expression and clinical data from two distinct cohorts of early-stage breast cancer patients, namely SCAN-B and METABRIC (Figure 1). The continuous expression of the 14-gene IGG signature was significantly associated with OS without recurrence or relapse in both cohorts (p<0.0001), independent of hormone receptor expression, HER2 status, tumor size, and nodal status (Table 1 and Figure 2). A moderate correlation between the IGG signature and age was observed (Figure 3). In patients aged below <70 years old, IGG signature was significantly associated with OS without relapse recurrence or relapse independently of age (METABRIC: HR [95%CI]=0.82 [0.66-1.00], p=0.050; SCAN-B: HR [95%CI]=0.82 [0.71-0.94], p=0.005). Across age groups, a statistically significant association of the IGG signature with OS was observed in patients aged >40 and <70 years old (Figure 4). IGG signature was higher in HER2+ and TNBC tumors. Additionally, IGG-low disease increased with age, especially in patients aged >70 years old and with HR+ / HER2- breast cancer (Figure 5). Although IGG expression is independent of age and tumor subtype, the three variables are related, with older ages associated with lower IGG tumor infiltration.

[0086] Table 1. Association of IGG signature with OS without recurrence or relapse in Cox regression models.

[0087] Study n OS events HR (95%CI) p-value univariate 1133 416 0.67 (0.58-0.77) <0.0001

[0088] METABRIC bivariate* 1133 416 0.72 (0.62-0.84) <0.0001 multivariable** 823 287 0.73 (0.60-0.88) 0.0012 univariate 4470 508 0.78 (0.73-0.85) <0.001

[0089] SCAN-B bivariate* 4361 499 0.77 (0.72-0.84) <0.001 multivariable** 4211 477 0.79 (0.73-0.86) <0.001

[0090] * adjusted by IHC subtype; ** adjusted by IHC subtype, T stage and N status

[0091] Example 2.2. Identification of the CIGGs

[0092] To identify the key genes influencing longevity within the IGG signature, we assessed the association of the genes composing the signature with OS without recurrence or relapse in the METABRIC and SCAN-B datasets. Seven genes (TNFRSF17 [BCMA], IL2RG, POU2AF1, CD27, IGJ [JCHAIN], CD79A, and PIM2) across both datasets exhibited a statistically significant association with longevity (p<0.0001) defining the core IGG genes (CIGG) (Figure 6 and Table 2). A moderate correlation coefficient of 0.63 was observed among CIGGs (Figure 7). Additionally, IGKC and LAX1 were also significantly associated with longevity (p<0.0001) in METABRIC and SCAN-B datasets, respectively (Figure 6 and Table 2).

[0093] No substantial association was observed between the expression of each CIGG and age (Figure 8). In patients with less than 70 years of age, the continuous expression of the 7 CIGGs were significantly associated with longevity in both cohorts (p<0.0001), independent of age (as a continuous variable), hormone receptor expression, HER2 status, tumor size, and nodal status

[0094] (Table 3)

[0095] Table 2. Association of individual genes with OS without recurrence or relapse in univariate Cox regression models.

[0096] Study gene n OS events HR (95%CI) pvalue

[0097] CD27 1133 416 0.76 (0.68-0.84) <0.0001

[0098] TNFRSF17 1133 416 0.70 (0.62-0.78) <0.0001

[0099] IGJ 1133 416 0.69 (0.62-0.76) <0.0001

[0100] POU2AF1 1133 416 0.78 (0.7-0.88) <0.0001

[0101] METABRIC CD79A 1133 416 0.75 (0.67-0.84) <0.0001

[0102] PIM2 1133 416 0.80 (0.72-0.89) <0.0001

[0103] IL2RG 1133 416 0.83 (0.75-0.92) 0.0005

[0104] IGKC 1133 416 0.78 (0.71-0.85) <0.0001

[0105] CD27 4470 508 0.73 (0.66-0.80) <0.0001

[0106] TNFRSF17 4470 508 0.73 (0.66-0.80) <0.0001

[0107] IGJ 4470 508 0.68 (0.63-0.74) <0.0001

[0108] POU2AF1 4470 508 0.74 (0.67-0.82) <0.0001

[0109] SCAN-Bv'

[0110] CD79A 4470 508 0.75 (0.68-0.82) <0.0001

[0111] PIM2 4470 508 0.84 (0.76-0.93) 0.0004

[0112] IL2RG 4470 508 0.80 (0.73-0.87) <0.0001

[0113] LAX1 4470 508 0.82 (0.75-0.91) 0.0001

[0114] Table 3. Association of each CIGG gene with OS without recurrence or relapse in patients with less than 70 years of age in a multivariable* Cox regression model.

[0115] Study gene n OS events HR (95%CI) pvalue

[0116] CD27 3686 290 0.82 (0.73-0.93) 0.0013

[0117] TNFRSF17 3686 290 0.85 (0.74-0.96) 0.0111

[0118] IGJ 3686 290 0.90 (0.84-0.97) 0.0046

[0119] METABRIC

[0120] + SCAN-B POU2AFI 3686 290 0.83 (0.73-0.95) 0.0064

[0121] CD79A 3686 290 0.84 (0.76-0.94) 0.0013

[0122] PIM2 3686 290 0.86 (0.75-0.99) 0.0315

[0123] IL2RG 3686 290 0.86 (0.76-0.96) 0.0096

[0124] *adjusted by study, age (as a continuous variable), IHC subtype, T stage and N status

[0125] Example 2.3. Combination of CIGGs and longevity Combining the expression of two CIGGs by adding their gene expression provided more prognostic information than a single gene (p<0.001) in 16 combinations (Table 4 and Figures 9-10) in patients <70 years old. Two-gene subtraction, multiplication, or ratio decreased the prognostic information (Table 4 and Figure 9). All the combinations of 2 CIGGs in METABRIC and SCAN-B combined were significantly associated with low risk of death, independently of study and treatment arm (Table 5).

[0126] Table 4. Amount of variation explained in prognosis defined by chi-square (%2) statistics from likelihood ratio tests.

[0127] Study Gene combination Genel Gene2 Sum Substract Multiply Ratio

[0128] TNFRSF17 IL2RG 25.142 15.355 25.446 3.056 3.136 2.794

[0129] TNFRSF17 CD27 25.142 24.177 27.851 0.03 0.805 0.072

[0130] TNFRSF17 IGJ 25.142 28.325 30.338 3.609 0.317 0.585

[0131] TNFRSF17 CD79A 25.142 23.424 26.32 0.101 0.921 0.001

[0132] METAB IL2RG IGJ 15.355 28.325 32.906 5.808 14.279 0.022

[0133] RIC + POU2AF1 CD27 21.892 24.177 25.157 0.273 1.322 4.374

[0134] SCANB POU2AF1 IGJ 21.892 28.325 31.327 2.641 0.661 0.05

[0135] POU2AF1 CD79A 21.892 23.424 23.701 0.779 1.491 1.069

[0136] CD27 IGJ 2AAT1 28.325 33.231 1.676 2.592 0.019

[0137] CD27 CD79A 2AAT1 23.424 25.342 0.016 1.171 0.048

[0138] IGJ CD79A 28.325 23.424 31.566 1.727 3.113 0.01

[0139] IL2RG IGKC 4.830 8.528 9.524 0.435 2.091 0.965

[0140] METAB POU2AF1 IGKC 10.166 8.528 10.942 0.000 0.009 4.560

[0141] RIC CD27 IGKC 10.405 8.528 11.254 0.032 1.545 0.073

[0142] PIM2 IGKC 5.923 8.528 8.731 0.490 0.104 1.505

[0143] SCANB PIM2 LAX1 4.368 5.081 5.108 0.054 0.138 1.035 Table 5. Association of 2 CIGGs (in combination into a single score) with OS without recurrence or relapse in patients <70 years old in METABRIC and SCAN-B datasets.

[0144] , Low High .

[0145] Study Gene combinationn n-event HR95%CI 95%CI Pvalue

[0146] TNFRSF17 IL2RG 4088 350 0.839 0.781 0.901 1.36E-06

[0147] TNFRSF17 CD27 4088 350 0.853 0.802 0.907 3.67E-07

[0148] TNFRSF17 IGJ 4088 350 0.862 0.817 0.909 5.50E-08

[0149] TNFRSF17 CD79A 4088 350 0.861 0.811 0.914 8.40E-07

[0150] IL2RG IGJ 4088 350 0.832 0.781 0.886 9.75E-09 POU2AF1 CD27 4088 350 0.862 0.811 0.916 1.69E-06

[0151] POU2AF1 IGJ 4088 350 0.855 0.809 0.904 3.43E-08

[0152] POU2AF1 CD79A 4088 350 0.870 0.820 0.923 3.59E-06

[0153] CD27 IGJ 4088 350 0.853 0.808 0.900 8.61E-09

[0154] CD27 CD79A 4088 350 0.868 0.819 0.919 1.15E-06

[0155] IGJ CD79A 4088 350 0.860 0.816 0.907 2.35E-08

[0156] METABRIC IL2RG IGKC 808 194 0.877 0.805 0.955 2.54E-03 POU2AF1 IGKC 808 194 0.875 0.807 0.948 1.11E-03

[0157] CD27 IGKC 808 194 0.875 0.809 0.946 7.70E-04

[0158] PIM2 IGKC 808 194 0.889 0.821 0.962 3.49E-03

[0159] SCANB PIM2 LAX1 3280 156 0.916 0.855 0.982 1.29E-02

[0160] VALIDATION PHASE

[0161] Example 3. MATERIAL AND METHODS

[0162] Example 3.1. Patient Datasets

[0163] The METABRIC (n=l,904) and TCGA (n= 1,082) breast cancer datasets and pancancer TCGA datasets (i.e.: cervix [n=294], head and neck [n=515], lung [n=510], melanoma [n=443], and sarcoma [n=253]) were sourced from the cBioPortal for Cancer Genomics (http: / / cbioportal.org), while data from the most recent version of the SCAN-B (n=6,652) dataset were obtained from Mendeley Data. Except for the competing risk analyses, patients who experienced breast cancer recurrence were systematically excluded from both datasets. The final analysis comprised 1,133 patients from METABRIC, 4,470 patients from SCAN-B and 936 patients with breast cancer from TCGA, ensuring a cohort that did not encounter recurrence during the follow-up period. The median follow-up in these patients was 96.9 months in SCAN-B, 185.0 months in METABRIC, and 130.0 months in TCGA. Normal breast tissue gene expression data from 168 women was downloaded from GTEx (https: / / gtexportal.org / home / ). RNA sequencing and BCR and TCR repertoire metrics from the CALGB40601 neoadjuvant HER2+ study was obtained from a previously published study.

[0164] Example 3.2. In silica gene expression analysis

[0165] Z-scores (METABRIC, TCGA) or log2 -transformed (SCAN-B) normalized gene expression values were employed for single genes and for deriving the 14-gene IGG signature, depending on the available gene expression data for each dataset.

[0166] Example 3.3. Core IGG Genes (CIGGs)

[0167] Genes consistently associated with longevity across METABRIC SCAN-B and TCGA datasets were identified as Core IGG genes (CIGGs). We further investigated whether the combined assessment of pairs of CIGGs enhanced longevity prediction compared to individual genes. Various mathematical operations, including addition, subtraction, multiplication, and ratio, were employed to combine the expressions of two CIGGs.

[0168] Example 3.4. Clinical samples

[0169] To study the immune component of breast cancers with different levels of IGG signature, 132 formalin-fixed paraffin-embedded (FFPE) samples from patients with early-stage breast cancer treated at Hospital Clinic of Barcelona (n=122) and Institut Catala d’Oncologia (n=10) were selected, including 35 HR+ / HER2-, 84 HER2+ and 13 TNBC samples. The proportion of tumor infiltrating lymphocytes (%TILs) was determined on hematoxilin and eosin (H / E) slides according to International TILs Working Group Guidelines. The presence of TLS was defined as spatially organized, non-encapsulated areas of immune cell aggregates on H&E slides. Additionally, 6 biopsies from patients with untreated HER2+ breast cancer were obtained to conduct single cell RNA sequencing.

[0170] Example 3.5. Gene expression analysis of clinical samples

[0171] RNA was extracted from FFPE tumor diagnostic samples using the High Pure FFPET RNA isolation kit (Roche, Indianapolis, IN, USA). One to five 10-pm FFPE slides depending on tumor cellularity were used for each tumor sample, and macrodissection was performed, when needed, to avoid normal tissue contamination. A minimum of 100 ng of total RNA was analyzed on the nCounter platform (Nanostring Technologies, Seattle, USA) using a 192gene custom panel, including 14 genes of the IGG signature, which was determined using R software v4.0.3.

[0172] Example 3.6. GeoMx DSP data acquisition and analysis

[0173] FFPE tumor biopsies from 135 patients were stained with fluorescently labeled with CD45 and PanCK antibodies, DAPI and a multiplexed panel of protein antibodies that contained a photocleavable indexing oligonucleotide, enabling subsequent readouts. ROIs were selected on the GeoMx DSP platform (Nanostring Technologies), segmented according to CD45 and PanCK staining and illuminated using UV light. Released indexing oligonucleotides from each AOI were collected and deposited into designated wells on a microtiter plate, allowing for well indexing of each AOI during nCounter (Nanostring Technologies) readout. For each tissue sample, counts for each marker was obtained from an average of 4.44 (range 1-12) CD45+ AOIs. Raw protein counts for each marker in each AOI were generated using nCounter. The raw counts were normalized with the AOI surface area. All normalized counts were log2 transformed.

[0174] Example 3.7. TCR and BCR sequencing

[0175] DNA from FFPE tumor samples with different IGG signature levels was purified with QIAamp DNA FFPE Tissue Kit (QIAGEN) according to manufacturer’s instructions and BCR and TCR were sequenced from 23 and 33 DNA samples, respectively, using the immunoSEQ Assay (Adaptive Biotechnologies). The somatically rearranged Homo Sapiens TCR locus complementarity-determining region 3 (CDR3) and BCR IgH locus CDR3 was amplified from DNA samples using a two-step, amplification bias-controlled multiplex PCR approach. CDR3 and reference gene libraries were sequenced on an Illumina instrument according to the manufacturer’s instructions. Raw sequence reads were demultiplexed according to Adaptive’s proprietary barcode sequences. Demultiplexed reads were further processed to remove adapter and primer sequences; identify and remove primer dimer, germline and other contaminant sequences. The filtered data were clustered using both the relative frequency ratio between similar clones and a modified nearest-neighbor algorithm, to merge closely related sequences to correct for technical errors introduced through PCR and sequencing. The resulting sequences allowed annotation of the V, D, and J genes and the Nl, N2 regions constituting each unique CDR3 and the translation of the encoded CDR3 amino acid sequence. Gene definitions were based on annotation in accordance with the IMGT database (www.imgt.org). The set of observed biological TCR and BCR IgH CDR3 sequences was normalized to correct for residual multiplex PCR amplification bias and quantified against a set of synthetic CDR3 sequence analogues. Data was analyzed using the immunoSEQ Analyzer toolset.

[0176] Example 3.8. Single-cell RNA sequencing

[0177] Fresh core biopsies obtained from 6 patient with HER2+ breast cancer were collected by ultrasound-guided breast biopsy and the tissue (~3-5 mm3) was supplied in 5 mL of RPMI medium. Tumor fragments were dissociated using enzymatic digestion (Human Tumor Dissociation Kit, Miltenyi). Then, CD45+ cells were isolated using Human CD45 TIL MicroBeads (Miltenyi) and stored in PBS containing 0.005% BSA. CD45+ cells were processed with the 10x Genomics Chromium Controller (10X Genomics Inc.) and single-cell gene expression and TCR / BCR libraries were produced with the Chromium Single Cell 5' Library assay (10X Genomics Inc.), sequenced on an Illumina NovaSeq6000 (100 cycle kit). Sequencing reads were aligned with CellRanger Single Cell Software (version 6.1.1, 10X Genomics Inc.) and mapped against the human GRCh38 reference genome (GENCODE v32 / Ensembl 98). Quality control, cell annotation and cluster identification were performed based on (https: / / www.ncbi.nlm.nih.gov / pmc / articles / PMC7940606 / ) rationale.

[0178] Example 3.9. Statistical Analysis

[0179] Cumulative incidence functions were used to estimate death without recurrence in a competing risk setting using recurrence as a competing event. Fine & Gray competing risks regression were used to obtain a sub -distribution hazard ratio with 95% CI. In the sensitivity analysis of the subset of patients without recurrence, Kaplan-Meier curves were used to estimate survival outcomes and the log-rank test was used for statistical comparisons. Univariate and multivariable Cox regression and logistic regression models were employed, with the goodness-of-fit of each model assessed through the chi-square (%2) statistic obtained from a likelihood ratio test. The differences in variable distribution were explored by Chisquared test. Correlations between two variables were evaluated using the Pearson method. Unpaired t-tests and ANOVAs were employed to ascertain the comparative analysis of numerical variables among different groups. A two-sided alpha of 0.05 was set as the significance level for all statistical analyses. No data imputation was performed. All statistical analysis were performed using R software.

[0180] Example 4. RESULTS

[0181] Example 4.1. IGG, age and longevity

[0182] We investigated the association between the IGG signature and OS in patients with early stage breast cancer who have not experienced a relapse, a condition we defined as 'longevity' (Figure Ila). Leveraging comprehensive gene expression and clinical data from 3 distinct cohorts of 9,638 patients with early-stage breast cancer and long-term follow-up (i.e., METABRIC, SCAN-B and TCGA), we observed a significant association between IGG expression and OS without recurrence both treating recurrences as a competing event (Figure 11b) and excluding recurrences, independently from breast cancer subtype, tumor stage or nodal status. This association persisted in patients with a very low risk of relapse, and patients with >5 years of follow-up without a documented recurrence. A weak negative correlation between the IGG signature and age was observed (Figure llc-d). However, the IGG signature was significantly associated with OS without a relapse in patients aged 41-70 years old at diagnosis, which represented 66.7% to 72.9% of all patients (Figure He). Across breast cancer subtypes, the expression of the IGG signature was higher in HER2+ and TNBC than in hormone receptor- positive / HER2- (HR+ / HER2-) (Figure Ilf); however, 26.2% of HR+ / HER2- tumors were IGG-high. Finally, IGG decreased with age across breast cancer subtypes, especially in patients >70 years old with HR+ / HER2- disease (Figure 11g). Of note, the IGG signature assessed in normal breast tissue was not associated with age.

[0183] Example 4.2. IGG, spatial profiling, and tertiary lymphoid structures (TLS)

[0184] The IGG signature is measured from bulk RNA obtained from a tumor sample. To gain deeper insights of the tumor microenvironment and its relationship with the IGG signature, we performed GeoMx spatial profiling of stroma from 132 patients with breast cancer, which had been previously categorized as IGG-high, IGG-med, and IGG-low, according to preestablished cutoffs of the HER2DX genomic test (Figure 12a). We selected 458 CD45-positive (CD45+) tumor microenvironment (TME) areas of interest (AOIs), and we measured 42 immune-related proteins. Compared to IGG-med and IGG-low tumors, IGG-high tumors showed increased expression of plasma cells (CD27), activated T cells or antigen presenting cells (PD-L1 and CD40) and decreased expression of M2 -like macrophages (CD 163) and stromal fibronectin. Additionally, we measured the 42 immune-related proteins in 134 TLS selected AOIs, and compared their expression to the TME AOIs. TLS were defined as spatially organized, nonencapsulated areas of immune cell aggregates. We observed an association between the IGG signature expression and the presence of TLS in the tumor stroma (Figure 12b). Specifically, 55.2% of tumors with TLS were IGG-high compared to 25% of tumors without TLS (p<0.001) (Figure 12b). To better understand the biology associated with TLS, we focused our attention on the expression of immune proteins in the 134 TLS regions using spatial profiling. Twenty one proteins (51.2%, 21 / 41) were found differentially expressed between TLS and non-TLS TME regions (False Discovery Rate <5%); among them, CD20 (B-cell), CD27 (plasma cell), CD11c (dendritic cells), and CD3 (T-cell-related) were found more expressed in TLS compared to non-TLS regions, while SMA, fibronectin, and CD 163 (M2 -like macrophages) were found downregulated in TLS compared to non-TLS regions. Using the list of the significant proteins, we established a TLS related protein signature. We applied this signature to tumor samples that did not visually display TLS. Notably, we observed a significant increase expression of this signature in IGG-high tumors compared to those with IGG-med / low levels. This result underscores the IGG signature’s capacity to reflect TLS-like biological processes in the tumor stroma, even in the absence of microscopically observable TLS.

[0185] Example 4.3. IGG and CD45+ single cell RNA sequencing

[0186] To explore the intricacies of the IGG signature at the cell level, single-cell RNA sequencing of fresh CD45+ cells was performed from 6 independent breast cancer biopsies with different IGG signature expression levels. In the tumor with the highest IGG signature score (BC177), 24.0% of CD45+ cells were identified as activated B-cells and plasma cells, while these represented only 1.4% of all CD45+ cells in the sample with the lowest IGG signature score (BC192) (Figure 12c). The RNA sequencing results observed in these 2 cases were confirmed by immunohistochemistry staining of CD20 (B-cell), CD38 (Plasma cell), and CD3 (T-cell) (Figure 12d).

[0187] Example 4.4. Interplay of IGG and B / T-Cell clonality

[0188] Next, we analyzed BCR and TCR clonality in 23 and 33 breast tumors with known IGG expression values, respectively, using the immunoSEQ DNA-based assay. Overall, a higher polyclonality of B-cells and T-cells (i.e., lower Simpson clonality index and higher Shannon entropy) was observed in IGG-high tumors compared to IGG med / low tumors (Figure 13a). Similar results were obtained from an external in-silico validation breast cancer dataset (n=264, CALGB-40601) based on bulk RNA-seq data (Figure 13b).

[0189] Example 4.5. Core IGG genes, longevity, and B / T-Cell clonality

[0190] We then assessed the association of each IGG gene with longevity in the METABRIC, SCAN- B and TCGA datasets. Seven genes (i.e., TNFRSF17 [BCMA], IL2RG, POU2AF1, CD27, IGJ [JCHAIN], CD79A, and PIM2 defining the Core IGG genes (CIGG), exhibited a statistically significant association with longevity across the 3 datasets (Figure 13c and Table 6). Table 6. Significant association of the expression of 7 individual genes of the 14-gene IGG signature with OS in patients without a documented breast cancer recurrence.

[0191] Univariate Cox regression models were performed for each gene. os Study gene n HR (95%CI) pvalue

[0192] C V Cll t

[0193] CD27 1133 416 0.76 (0.68-0.84) <0.0001

[0194] TNFRSF17 1133 416 0.70 (0.62-0.78) <0.0001

[0195] IGJ 1133 416 0.69 (0.62-0.76) <0.0001

[0196] METABRIC POU2AF1 1133 416 0.78 (0.7-0.88) <0.0001

[0197] CD79A 1133 416 0.75 (0.67-0.84) <0.0001

[0198] PIM2 1133 416 0.80 (0.72-0.89) <0.0001

[0199] IL2RG 1133 416 0.83 (0.75-0.92) 0.0005

[0200] CD27 4470 508 0.73 (0.66-0.8) <0.0001

[0201] TNFRSF17 4470 508 0.73 (0.66-0.8) <0.0001

[0202] IGJ 4470 508 0.68 (0.63-0.74) <0.0001

[0203] SCAN-B POU2AF1 4470 508 0.74 (0.67-0.82) <0.0001

[0204] CD79A 4470 508 0.75 (0.68-0.82) <0.0001

[0205] PIM2 4470 508 0.84 (0.76-0.93) 0.0004

[0206] IL2RG 4470 508 0.80 (0.73-0.87) <0.0001

[0207] A moderate correlation coefficient (Cor=0.63) among the CIGGs suggested that they may track slightly different immune components (Figure 13d). To evaluate if the combination of 2 CIGGs increases the ability to predict longevity over a single CIGG, we added the log base 2 expression values of 2 CIGGs. The combination of the 2 CIGGs into a single score provided a better association with longevity, when compared to single CIGG expression (p<0.001) (Figure 3e and Table 7).

[0208] Table 7 Table 7. Amount of variation explained in prognosis defined by chi-square (%2) statistics from likelihood ratio tests.

[0209] Gene combination Gene 1 Gene 2 Sum Substract Multiply Ratio

[0210] TNFRSF17+IL2RG 27.654 18.675 28.800 2.533 4.241 1.542

[0211] TNFRSF17+POU2AF1 27.654 23.205 27.242 4.947 0.574 1.490

[0212] TNFRSF17+CD27 27.654 28.389 31.514 0.023 0.118 6.704

[0213] TNFRSF17+IGJ 27.654 29.974 32.541 3.581 1.631 2.445

[0214] TNFRSF17+CD79A 27.654 31.075 31.800 0.394 0.134 0.834

[0215] TNFRSF17+PIM2 27.654 12.534 21.410 4.058 2.593 0.298

[0216] IL2RG+POU2AF1 18.675 23.205 24.188 0.191 0.305 0.295

[0217] IL2RG+CD27 18.675 28.389 26.050 6.137 0.442 6.662

[0218] IL2RG+IGJ 18.675 29.974 35.823 5.263 15.253 0.387

[0219] IL2RG+CD79A 18.675 31.075 28.887 5.712 0.868 0.294

[0220] IL2RG+PIM2 18.675 12.534 18.196 0.000 0.129 0.328

[0221] POU2AF1+CD27 23.205 28.389 28.206 4.695 0.448 5.146

[0222] POU2AF1+IGJ 23.205 29.974 32.966 6.856 3.688 1.068

[0223] POU2AF1+CD79A 23.205 31.075 28.688 12.101 0.715 0.098

[0224] POU2AF1+PIM2 23.205 12.534 18.569 0.226 2.701 0.323

[0225] CD27+IGJ 28.389 29.974 36.427 1.779 4.654 0.274

[0226] CD27+CD79A 28.389 31.075 31.665 0.265 0.374 3.315

[0227] CD27+PIM2 28.389 12.534 22.218 3.938 1.357 5.244

[0228] IGJ+CD79A 29.974 31.075 36.669 1.368 6.348 0.014

[0229] IGJ+PIM2 29.974 12.534 27.374 6.496 0.046 0.604

[0230] CD79A+PIM2 31.075 12.534 22.701 7.104 1.823 1.433

[0231] Overall, 21 CIGG combinations increased the ability to predict longevity (Table 8), and the Immunoglobulin J Chain gene (i.e., / GJ, also known as JCHAIN) and the Interleukin 2 Receptor Subunit Gamma gene (i.e., ILRG2) emerged as one of the top gene combinations. Table 8. Association of the 21 2-CIGGs (combination into a single score) with OS without recurrence in patients <70 years old in METABRIC and SCAN-B datasets.

[0232] Gene combination n n events HR Low 95%CI High 95%CI p-value

[0233] TNFRSF17+IL2RG 4844 381 0.835 0.780 0.894 2.42E-07

[0234] TNFRSF17+POU2AF1 4844 381 0.847 0.794 0.904 5.87E-07

[0235] TNFRSF17+CD27 4844 381 0.845 0.795 0.898 5.55E-08

[0236] TNFRSF17+IGJ 4844 381 0.861 0.817 0.907 1.65E-08

[0237] TNFRSF17+CD79A 4844 381 0.846 0.797 0.899 5.90E-08

[0238] TNFRSF17+PIM2 4844 381 0.875 0.824 0.928 8.83E-06

[0239] IL2RG+POU2AF1 4844 381 0.841 0.783 0.904 2.41E-06

[0240] IL2RG+CD27 4844 381 0.852 0.800 0.908 7.53E-07

[0241] IL2RG+IGJ 4844 381 0.832 0.783 0.883 1.93E-09

[0242] IL2RG+CD79A 4844 381 0.840 0.787 0.898 2.28E-07

[0243] IL2RG+PIM2 4844 381 0.875 0.821 0.932 3.85E-05

[0244] POU2AF1+CD27 4844 381 0.845 0.792 0.901 2.98E-07

[0245] POU2AF1+IGJ 4844 381 0.847 0.800 0.897 1.09E-08

[0246] POU2AF1+CD79A 4844 381 0.845 0.792 0.901 2.99E-07

[0247] POU2AF1+PIM2 4844 381 0.876 0.823 0.933 3.68E-05

[0248] CD27+IGJ 4844 381 0.849 0.805 0.895 1.46E-09

[0249] CD27+CD79A 4844 381 0.849 0.800 0.901 5.21E-08

[0250] CD27+PIM2 4844 381 0.872 0.822 0.925 5.25E-06

[0251] IGJ+CD79A 4844 381 0.850 0.806 0.896 1.61E-09

[0252] IGJ+PIM2 4844 381 0.867 0.821 0.915 2.34E-07

[0253] CD79A+PIM2 4844 381 0.872 0.823 0.925 4.60E-06 Next, we explored the association of each individual CIGG, or any combinations of 2 CIGGs, with BCR / TCR entropy. The combination of 2 CIGGs had similar prediction of BCR (Table 9) and TCR (Table 10) Shannon entropy as the IGG signature (Figure 3f).

[0254] Table 9. Association of gene combinations and BCR Shannon Entropy

[0255] Table 10. Association of gene combinations and TCR Shannon Entropy Overall, these results underscore the value of the IGG signature and the combination of CIGGs from bulk RNA to capture an effective adaptive B-cell and T-cell immune response.

[0256] Example 4.6. IGG and longevity in other cancer-types In the pan-cancer TCGA dataset, we previously reported an association of the IGG signature with OS in cervical cancer (n=294), head and neck cancer (n=515), lung adenocarcinoma (n=510), melanoma (n=443), and sarcoma (n=253). Here we focused on the association of the IGG signature with longevity. Like breast cancer, the association of the IGG signature with OS was found to be independent of cancer relapse in a combined patient-level metanalysis (HR=0.68, 95% CI 0.61-0.75, p<0.001) and was consistent across the studied cancer-types, except for cervical cancer (Figure 14).

Claims

CLAIMS1. In vitro method for identifying biomarker signatures for predicting longevity in cancer survivors, the method comprising measuring the level of expression of at least two immune genes selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17m a biological sample obtained from the patient.

2. In vitro use at least two immune genes selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17, or of a kit comprising reagents for measuring the level of expression of the genes, for identifying biomarker signatures for predicting longevity in cancer survivors.

3. In vitro method, or in vitro use, according to any of the previous claims, which comprises measuring the level of expression of at least two immune gene selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AF1, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, in a biological sample obtained from the patient.

4. In vitro method, or in vitro use, according to any of the previous claims, which comprises measuring the level of expression of at least two immune gene selected from the group consisting of: TNFRSF17, IL2RG, POU2AF1, CD27, IGJ, CD79A and / or PIM2.

5. In vitro method, or in vitro use, according to any of the previous claims, which comprises: a. Determining a combination score value by calculating the ratio of the expression of the 2 genes; and b. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, this is indicative that the biomarker signature may be used for predicting longevity in cancer survivors.

6. In vitro method for predicting longevity, in cancer survivors, the method comprising measuring the level of expression of at least two immune genes selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17, in a biological sample obtained from the patient.

7. In vitro use at least two immune genes selected from the group consisting of: CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2,POU2AF1 and / or TNFRSF17, or of a kit comprising reagents for measuring the level of expression of the genes, for predicting longevity in cancer survivors.

8. In vitro method, or in vitro use, according to any of the claims 6 or 7, which comprises: measuring the level of expression of at least two immune genes selected from the group consisting of: CD27, TNFRSF17, IGJ, POU2AFI, CD79A, PIM2, IL2RG, LAX1 and / or IGKC, in a biological sample obtained from the patient.

9. In vitro method, or in vitro use, according to any of the claims 6 to 8, which comprises measuring the level of expression of at least two immune genes selected from the group consisting of: TNFRSF17, IL2RG, POU2AF1, CD27, IGJ, CD79A and / or PIM2, in a biological sample obtained from the patient.

10. In vitro method, or in vitro use, according to any of the claims 6 to 9, which comprises measuring the level of expression of at least one of the two-immune genes combinations of Table 4, Table 5, Table 7, Table 8, Table 9 or Table 10 in a biological sample obtained from the patient.

11. In vitro method, or in vitro use, according to any of the claims 6 to 10, which comprises: a. Determining a combination score value by calculating the ratio of the expression of the 2 genes; and b. Wherein if an increase of the combination score value is identified, as compared with a pre-established reference value, this is indicative that the cancer survivor has a longer longevity.

12. In vitro method, or in vitro use, according to any of the claims 6 to 11, the method comprising measuring the level of expression of the following gene combination: TNFRSF17, IL2RG, POU2AF1, CD27, IGJ, CD79A and PIM2.

13. In vitro method, or in vitro use, according to any of the previous claims, wherein the sample is selected form: tissue, blood, serum or plasma.

14. In vitro method, or in vitro use, according to any of the previous claims, wherein the cancer survivor is a patient who has successfully overcome cervical cancer, head and neck cancer, lung adenocarcinoma, melanoma, or sarcoma .

15. In vitro method, or in vitro use, according to any of the previous claims, wherein the breast cancer survivor has survived to hormone receptor positive breast cancer and HER2 -negative (HR+ / HER2-), human epidermal growth factor receptor 2-positive (HER2+) or triple-negative breast cancer.

16. In vitro method, or in vitro use, according to any of the previous claims, wherein the identification of an increased level of expression or combination score value, ascompared with a pre-established reference value, is indicative that the cancer survivor has a longer longevity.

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