Development and validation of an in vitro method for the prognosis of patients suffering from her2-positive breast cancer

Abstract

The present invention refers to an in vitro method for the prognosis of patients suffering from HER2+ breast cancer, for the prediction of response to anti-HER2 therapies and / or for predicting survival benefit from anti-HER2 therapies.

Description

Strategies to either escalate or de-escalate systemic therapy in early-stage HER2-positive breast cancer to improve survival outcomes and quality of life have been explored, such as decreasing the number of cycles of chemotherapy and the duration of trastuzumab, increasing HER2 blockade with pertuzumab or neratinib, or switching anti-HER2 therapy to trastuzumab emtansine in patients who do not achieve a pathological complete response (pCR) following neoadjuvant therapy. Despite these advances, most patients with early-stage, HER2-positive breast cancer are cured with chemotherapy and trastuzumab alone. Several variables beyond tumor burden have been associated with patients' prognosis and / or treatment response in early-stage, HER2-positive breast cancer. For example, percentage of stromal tumor-infiltrating lymphocytes (TILs), hormone receptor status, and the intrinsic molecular subtypes of breast cancer are all linked to response and / or survival. However, decisions today about escalation or de-escalation of systemic therapies are based on tumor size, nodal status, expression of the hormone receptors, and response to neoadjuvant therapy (i.e., pCR or not). Therefore, a tool that integrates these multiple variables together to help guide therapy in early-stage, HER2-positive breast cancer is needed and would perform better than any single feature. Although in 2020 we reported HER2DX to build a multivariable prognostic score in early-stage HER2-positive breast cancer, which integrates information including tumor size and nodal staging, TILs, intrinsic molecular subtype, and the expression of 13 individual genes, the present invention aims to validate new signatures which can be used to improve the prognosis of patients suffering from HER2+ breast cancer, the prediction of response to anti-HER2 therapies and / or the prediction survival benefit from anti-HER2 therapies. DESCRIPTION OF THE INVENTION Brief description of the invention As explained above, the present invention refers to an in vitro method for the prognosis of patients suffering from HER2+ breast cancer, for the prediction of response to anti-HER2 therapies and / or for predicting survival benefit from anti-HER2 therapies. Particularly, the inventors of the present invention have developed an improved assay, called HER2DX assay, wherein the gene expression of up to 27 genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 and / or TCAP], optionally in combination with clinical features, is used for the prognosis of patients suffering from HER2+ breast cancer or for the prediction of response to anti-HER2 therapies. This means that any of the above identified 27 genes can be used in the context of the present invention, preferably any combination thereof comprising between 2 and 27 genes, for the prognosis of patients suffering from HER2+ breast cancer, for the prediction of response to anti-HER2 therapies and / or for predicting survival benefit from anti-HER2 therapies. On the other hand, the gene expression of up to 4 genes [CD86, FGFR2, ERBB3 and / or FA2H] is used for predicting survival benefit from anti-HER2 therapies. This means that any of the above identified 4 genes can be used in the context of the present invention, preferably any combination thereof comprising between 2 and 4 genes, for the prediction of response to anti-HER2 therapies and / or for predicting survival benefit from anti-HER2 therapies. In a preferred embodiment, the 27 gene variables included in HER2DX supervised learning algorithm are split into 4 gene expression signatures tracking immune infiltration, tumor cell proliferation, luminal differentiation, and the expression of the HER2 amplicon, giving rise to a single score. The 4 gene expression signatures are as follows: HER2DX risk score (for the prognosis of patients suffering from HER2+ breast cancer): • Immune signature (IGG) (14 genes): [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17], • Tumor cell proliferation signature (PROLIF) (4 genes): [EXO1, ASPM, NEK2 and / or KIF23], • Luminal differentiation signature (LUM) (5 genes): [BCL2, DNAJC12, AGR3, AFF3 and / or ESR1], • HER2 amplicon signature (HER2) (4 genes): [ERBB2, GRB7, STARD3 and / or TCAP], The coefficients of the HER2DX prognostic risk score full model are as follows: LUM: -0.087, PROLIF: 0.129, HER2: 0.00, IGG: -0.328, T_Stage (T1 vs T2-4): 0 vs. 0.431, N_Stage (NO vs Nl): 0 vs. 1.151, N_Stage (NO vs. N2-3): 0 vs. 1.58. HER2DX pCR probability score (for the prediction of response to anti-HER2 therapies): • Immune signature (IGG) (14 genes): [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17], • Tumor cell proliferation signature (PROLIF) (4 genes): [EXO1, ASPM, NEK2 and / or KIF23], • Luminal differentiation signature (LUM) (5 genes): [BCL2, DNAJC12, AGR3, AFF3 and / or ESR1], • HER2 amplicon signature (HER2) (4 genes): [ERBB2, GRB7, STARD3 and / or TCAP] The coefficients of the HER2DX pCR probability score model are as follows: LUM: -0.365. PROLIF: 0.374. HER2: 0.215. IGG: 0.184. T_Stage (T1 vs. T2-4): 0 vs. -0.630. N_Stage (NO vsNl-3): Ovs. -0.251. In order to validate these signatures, 434 HER2+ tumors from the Short-HER trial were used to train a prognostic risk model; 268 cases from an independent cohort were used to verify the accuracy of the HER2DX risk score. In addition, 116 cases treated with neoadjuvant anti-HER2-based chemotherapy were used to train a predictive model of pathological complete response (pCR); two independent cohorts of 91 and 67 cases were used to verify the accuracy of the HER2DX pCR probability score. HER2DX variables were associated with good outcome (i.e., immune, and luminal) and poor outcome (i.e., proliferation, and tumor and nodal staging). In an independent cohort, continuous HER2DX risk score was significantly associated with disease-free survival (DFS) (p=0.002); the 5-year DFS in the low-risk group was 95.3% (92.4-98.2%). For the neoadjuvant pCR predictor training cohort, HER2DX variables were associated with pCR (i.e., immune, proliferation and HER2 amplicon) and non-pCR (i.e., luminal, and tumor and nodal staging). In both independent test set cohorts, continuous HER2DX pCR probability score was significantly associated with pCR (p<0.0001). A weak negative correlation was found between the two HER2DX scores (correlation coefficient -0.19). The two HER2DX tests provide accurate estimates of the risk of recurrence, and the probability to achieve a pCR, in early-stage HER2-positive breast cancer. Thus, in conclusion, HER2DX is a novel 27-gene expression and clinical feature-based classifier intended for clinical use for patients with early-stage HER2-positive breast cancer. The assay optionally integrates clinical data with genomic data capturing tumor- and immune-related biology and predicts two different clinical endpoints, namely, long-term survival and probability of achieving a pCR. We validate these two novel assays, one for survival and one for predicting pCR, using multiple datasets, thus providing a high level of technical and clinical validation. Interestingly, the HER2DX risk score and HER2DX pCR probability score provide complementary information, opening an opportunity to better guide therapy through use of predictions of both response and survival. 2022419002   26 Jun 2026 In a preferred embodiment 23 out of the 27 genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAXI, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and / or ESRI] were used for the prognosis of patients suffering from HER2+ breast cancer, and 27 genes [CD27, CD79A, HLA-C, IGJ, 5 IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAXI, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 and / or TCAP] were used for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or nonresponder patients to anti-HER2 therapies. 10 Any discussion of the prior art throughout the specification should in no way be considered as an admission that such prior art is widely known or forms part of common general knowledge in the field. 15 Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise”, “comprising”, and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to”. 20 It is an object of the present invention to overcome or ameliorate at least one of the disadvantages of the prior art, or to provide a useful alternative. In one aspect, the present invention relates to an in vitro method for identifying biomarker signatures for the prognosis of patients suffering from HER2+ breast cancer, which comprises: 25                  a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESR1], in a biological sample obtained from the patient; 30                 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 2022419002   26 Jun 2026 biomarker signature may be used for the prognosis of patients suffering from HER2+ breast cancer. In another aspect, the present invention relates to an in vitro method for identifying biomarker 5 signatures for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies, which comprises: 10 15 a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESR1, ERBB2, GRB7, STARD3 or TCAP], 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 the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. 20 In one aspect, the present invention relates to an in vitro use of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and ESR1] for the prognosis of patients suffering from HER2+ breast cancer. 25 In another aspect, the present invention relates to an in vitro use of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESR1, ERBB2, GRB7, STARD3 and TCAP] for the prediction of response to anti-30 HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. The first embodiment of the present invention refers to an in vitro method for the prognosis of patients suffering from HER2+ breast cancer, which comprises measuring the level of 2022419002   26 Jun 2026 expression of at least a gene selected from the group comprising: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAXI, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 23 of said genes, in a biological sample 5 obtained from the patient, wherein: a. A statistically significant overexpression of at least one gene selected from the group comprising: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAXI, NTN3, PIM2, POU2AF1 and / or TNFRSF17], or any combination thereof comprising between 2 and 14 of said genes, with respect to a pre-established reference 10           level of expression, is indicative of good prognosis, and / or b. A statistically significant overexpression of at least one gene selected from the group comprising: [EX01, ASPM, NEK2 and / or KIF23], or any combination thereof comprising between 2 and 4 of said genes, with respect to a pre established reference level of expression, is indicative of poor prognosis, and / or 15       c. A statistically significant overexpression of at least one gene selected from the group comprising: [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 5 of said genes, with respect to a pre-established reference level of expression, is indicative of good prognosis. The second embodiment of the present invention refers to an in vitro method for the prognosis of patients suffering from HER2+ breast cancer, which comprises measuring the level of expression of at least a gene selected from the group comprising: [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, wherein a statistically significant overexpression of at least one gene selected from the group comprising: [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, with respect to a pre-established reference level of expression, is indicative of good prognosis. The third embodiment of the present invention refers to an in vitro method for the prognosis of patients suffering from HER2+ breast cancer, which comprises measuring the level of expression of at least a gene selected from the group comprising: [EX01, ASPM, NEK2 and / or KIF23], or any combination thereof comprising between 2 and 4 of said genes, wherein a statistically significant overexpression of at least one gene selected from the group comprising: [EXO1, ASPM, NEK2 and / or KIF23], or any combination thereof comprising between 2 and 4 of said genes, with respect to a pre-established reference level of expression, is indicative of poor prognosis. The fourth embodiment of the present invention refers to an in vitro method for the prognosis of patients suffering from HER2+ breast cancer, which comprises measuring the level of expression of at least a gene selected from the group comprising: [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 5 of said genes, wherein a statistically significant overexpression of at least one gene selected from the group comprising: [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 5 of said genes, with respect to a pre-established reference level of expression, is indicative of good prognosis. The fourth embodiment of the present invention refers to an in vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies, which comprises measuring the level of expression of at least a gene selected from the group comprising: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 and / or TCAP], or any combination thereof comprising between 2 and 27 of said genes, in a biological sample obtained from the patient, wherein: a. A statistically significant overexpression of at least one gene selected from the group comprising: [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 genes, with respect to a preestablished reference level of expression, is indicative that the patient is a responder patient to anti-HER2 therapies, and / or b. A statistically significant overexpression of at least one gene selected from the group comprising: [EXO1, ASPM, NEK2 and / or KIF23], or any combination thereof comprising between 2 and 4 genes, with respect to a pre-established reference level of expression, is indicative that the patient is a responder patient to anti-HER2 therapies, and / or c. A statistically significant overexpression of at least one gene selected from the group comprising: [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 5 genes, with respect to a preestablished reference level of expression, is indicative that the patient is a nonresponder patient to anti-HER2 therapies, and / or d. A statistically significant overexpression of at least one gene selected from the group comprising: [ERBB2, GRB7, STARD3 and / or TCAP], or any combination thereof comprising between 2 and 4 genes, with respect to a preestablished reference level of expression, is indicative that the patient is a responder patient to anti-HER2 therapies. The fifth embodiment of the present invention refers to an in vitro method for the prediction of response to anti-HER2 therapies in patients suffering from 1TER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies, which comprises measuring the level of expression of at least a gene selected from the group comprising: [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, with respect to a pre-established reference level of expression, in a biological sample obtained from the patient, wherein a statistically significant overexpression of at least one gene selected from the group comprising: [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, with respect to a pre-established reference level of expression, is indicative that the patient is a responder patient to anti-HER2 therapies. The sixth embodiment of the present invention refers to an in vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies, which comprises measuring the level of expression of at least a gene selected from the group comprising: [EXO1, ASPM, NEK2 and / or KIF23], or any combination thereof comprising between 2 and 4 of said genes, with respect to a pre-established reference level of expression, wherein a statistically significant overexpression of at least one gene selected from the group comprising: [EXO1, ASPM, NEK2 and / or KIF23], or any combination thereof comprising between 2 and 4 of said genes, with respect to a pre-established reference level of expression, is indicative that the patient is a responder patient to anti-HER2 therapies. The seventh embodiment of the present invention refers to an in vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies, which comprises measuring the level of expression of at least a gene selected from the group comprising: [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 5 of said genes, with respect to a pre-established reference level of expression, wherein a statistically significant overexpression of at least one gene selected from the group comprising: [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 5 of said genes, with respect to a preestablished reference level of expression, is indicative that the patient is a non-responder patient to anti-HER2 therapies. The eight embodiment of the present invention refers to an in vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies, which comprises measuring the level of expression of at least a gene selected from the group comprising: [ERBB2, GRB7, STARD3 and / or TCAP], or any combination thereof comprising between 2 and 4 of said genes, with respect to a pre-established reference level of expression, wherein a statistically significant overexpression of at least one gene selected from the group comprising: [ERBB2, GRB7, STARD3 and / or TCAP], or any combination thereof comprising between 2 and 4 of said genes, with respect to a pre-established reference level of expression, is indicative that the patient is a responder patient to anti-HER2 therapies. The ninth embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 23 genes, for the prognosis of patients suffering from HER2+ breast cancer. The tenth embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [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 genes, for the prognosis of patients suffering from HER2+ breast cancer. The eleventh embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [EXO1, ASPM, NEK2 and / or KIF23], or any combination thereof comprising between 2 and 4 genes, for the prognosis of patients suffering from HER2+ breast cancer. The twelfth embodiment of the present invention refers to the in vitro use of at least one gene selected from the group comprising: [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 5 genes, for the prognosis of patients suffering from HER2+ breast cancer. The thirteenth embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and / or ESRI, ERBB2, GRB7, STARD3 and / or TCAP], or any combination thereof comprising between 2 and 27 genes, for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. The fourteenth embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [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 genes, for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. The fifteenth embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [EX01, ASPM, NEK2 and / or KIF23], or any combination thereof comprising between 2 and 4 genes, for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. The sixteenth embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 5 genes, for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. The seventeenth embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [ERBB2, GRB7, STARD3 and / or TCAP], or any combination thereof comprising between 2 and 4 genes, for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. In a preferred embodiment, the present invention further comprises identifying the nodal status (pNl) and / or tumor staging (pT2-4) wherein the identification of nodal status Nl-3 and / or tumor status T2-4 is indicative of bad prognosis or that the patient is a non-responder patient to anti-HER2 therapies. In a preferred embodiment, the patient is suffering from HER2+ breast cancer. In a preferred embodiment, the sample is selected form: tissue, blood, serum or plasma. In a preferred embodiment, the anti-HER2 therapy is a drug selected from: trastuzumab, pertuzumab, lapatinib, pyrotinib, poziotinib, tucatinib, neratinib, trastuzumab deruxtecan, SYD985 or ado-trastuzumab emtansine. The eighteenth embodiment of the present invention refers to a kit comprising reagents for measuring the level of expression of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or any combination thereof comprising between 2 and 23 genes, preferably consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17], [EXO1, ASPM, NEK2 and / or KIF23], or [BCL2, DNAJC12, AGR3, AFF3 and / or ESR1], The nineteenth embodiment of the present invention refers to a kit comprising reagents for measuring the level of expression of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and / or ESRI, ERBB2, GRB7, STARD3 and / or TCAP], or any combination thereof comprising between 2 and 27 genes, preferably consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17], or [EXO1, ASPM, NEK2 and / or KIF23], or [BCL2, DNAJC12, AGR3, AFF3 and / or ESRI], or [ERBB2, GRB7, STARD3 and / or TCAP], The twentieth embodiment of the present invention refers to anti-HER2 therapy, or any pharmaceutical composition comprising thereof, optionally including pharmaceutically acceptable excipients or carriers, for use in the treatment of patients suffering from HER2+ breast cancer wherein the patient has been classified as responder patient because it is characterized by showing a statistically higher expression level, as compared with a preestablished threshold value, of at least a gene selected from the group comprising: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and / or TNFRSF17], or [EXO1, ASPM, NEK2 and / or KIF23] or [ERBB2, GRB7, STARD3 and / or TCAP], wherein the anti-HER2 therapy is optionally selected from: trastuzumab, pertuzumab, lapatinib, pyrotinib, poziotinib, tucatinib, neratinib, trastuzumab deruxtecan, SYD985 or ado-trastuzumab emtansine. In this sense, the present invention also refers to a method for treating a patient suffering from HER2+ breast cancer which comprised the administration of a therapeutically effective dose or amount of anti-HER2 compound, once the patient has been previously classified as responder patient following any of the above-cited methods. The twenty-first embodiment of the present invention refers to an in vitro method for predicting survival benefit from anti-HER2 therapy of patients suffering from HER2+ breast cancer treated with anti-HER2 therapies which comprises measuring the level of expression of at least a gene selected from the group comprising: [CD86, FGFR2, ERBB3 and / or FA2H] in a biological sample obtained from the patient, wherein a statistically significant overexpression of at least one gene selected from the group comprising: [CD86, FGFR2, ERBB3 and / or FA2H], or any combination thereof comprising between 2 and 4 genes, with respect to a pre-established reference level of expression, is indicative of survival benefit of patients suffering from HER2+ breast cancer treated with anti-HER2 therapies. The twenty-second embodiment of the present invention refers to the in vitro use of at least a gene selected from the group comprising: [CD86, FGFR2, ERBB3 and / or FA2H] for predicting survival benefit of patients suffering from HER2+ breast cancer treated with anti-HER2 therapies. The twenty-third embodiment of the present invention refers to a kit comprising reagents for measuring the level of expression of a group of genes consisting of [CD86, FGFR2, ERBB3 and / or FA2H], Particularly, although the method of the invention involves up to 23 or 27 genes, it is important to consider that the present invention offers strong data showing that the combination of at least 2 genes, tracking the luminal, proliferation and immune pathways is prognostic in early-stage HER2+ breast cancer (Example 2.6) and that the combination of at least 2 genes tracking the luminal, HER2 amplicon, proliferation and immune signatures is predictive of pathological complete response (pCR) (Example 2.7). So, in a preferred embodiment, the present invention also refers to: In vitro method for identifying biomarker signatures for the prognosis of patients suffering from HER2+ breast cancer, which comprises: a) Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESRI], 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 the prognosis of patients suffering from HER2+ breast cancer. In vitro method for the prognosis of patients suffering from HER2+ breast cancer which comprises: a) Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESRI], 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 of the prognosis of patients suffering from HER2+ breast cancer. In vitro method for the prognosis of patients suffering from HER2+ breast cancer which comprises: a) Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESRI], 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, wherein the ratio is calculated by: i. Combining a first gene comprised in the immune signature with a second gene comprised in the tumor cell proliferation signature; or ii. Combining a first gene comprised in the immune signature with a second gene comprised in the luminal differentiation signature; or iii. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the tumor cell proliferation signature; or iv. Combining a first gene comprised in the immune signature selected from the group consisting of CD79A, CD27, IGJ, POU2AF1, TNFRSF17, IL2RG, PIM2 or IGL with a second gene comprised in the immune signature selected from the group consisting of: CD27, CXCL8, HLA-C, IGLV3-25, IL2RG, LAX1, NTN3, PIM2 or POU2AF1; c) wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23] and the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESRI]; and d) wherein if a deviation of the combination score value is identified, as compared with a preestablished reference value, is indicative of good prognosis. In vitro method for the prognosis of patients suffering from HER2+ breast cancer which comprises: a) Measuring the level of expression of at least two genes selected from the gene combinations of Table 7A, 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) herein if a deviation of the combination score value is identified, as compared with a preestablished reference value, is indicative of good prognosis. In vitro method for the prognosis of patients suffering from HER2+ breast cancer which comprises: a) Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESRI], 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, wherein the ratio is calculated by: i. Combining a first gene comprised in the tumor cell proliferation signature with a second gene comprised in the immune signature; or ii. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the immune signature; or iii. Combining a first gene comprised in the tumor cell proliferation signature with a second gene comprised in the luminal differentiation signature; or iv. Combining a first gene comprised in the immune signature selected from the group consisting of: CD27, CXCL8, HLA-C, IGLV3-25, IL2RG, LAX1, NTN3, PIM2 or POU2AF1 with a second gene comprised in the immune signature selected from the group consisting of: CD79A, CD27, IGJ, POU2AF1, TNFRSF17, IL2RG, PIM2 or IGL; c) wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23] and the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESRI]; and d) wherein if a deviation of the combination score value is identified, as compared with a preestablished reference value, is indicative of poor prognosis. In vitro method for the prognosis of patients suffering from HER2+ breast cancer which comprises: a) Measuring the level of expression of at least two genes selected from the gene combinations of Table 7B, 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 preestablished reference value, is indicative of poor prognosis. In vitro method for the prognosis of patients suffering from HER2+ breast cancer which comprises measuring the level of expression of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and ESR1], In vitro method for identifying biomarker signatures for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies, which comprises: a) Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, P0U2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 or TCAP], 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 the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or nonresponder patients to anti-HER2 therapies. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies which comprises: a) Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 or TCAP], 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 of the response to anti-HER2 therapies in patients suffering from HER2+ breast cancer. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies which comprises: a) Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 or TCAP], in a biological sample obtained from the patient; d) determining a combination score value by calculating the ratio of the expression of the 2 genes, wherein the ratio is calculated by: i. Combining a first gene comprised in the immune signature with a second gene comprised in the luminal differentiation signature; or ii. Combining a first gene comprised in the tumor cell proliferation signature with a second gene comprised in the luminal differentiation signature; or iii. Combining a first gene comprised in the HER2 amplicon signature with a second gene comprised in the immune signature; or iv. Combining a first gene comprised in the HER2 amplicon signature with a second gene comprised in the tumor cell proliferation signature; or v. Combining a first gene comprised in the HER2 amplicon signature with a second gene comprised in the luminal differentiation signature; or vi. Combining a first gene comprised in the immune signature selected from the group consisting of: IGKC, IGL or LAX1 with a second gene comprised in the immune signature selected from the group consisting of: HLA-C, CD27, IGJ, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17; or vii. Combining a first gene comprised in the luminal differentiation signature selected from the group consisting of: AFF3, BCL2 or DNAJC12, with a second gene comprised in the luminal differentiation signature selected from the group consisting of: ESRI or AGR3; or viii. Combining the first gene ASPM comprised in the tumor cell proliferation signature with the second gene NEK2 comprised in the tumor cell proliferation signature; and c) wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23], the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESRI] and the HERZ amplicon signature comprises the genes: [ERBB2, GRB7, STARD3 aor TCAP], and d) wherein if a deviation of the combination score value is identified, as compared with a preestablished reference value, is an indication that the patients suffering from HER2+ breast cancer may respond to anti-HER2 therapies. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies which comprises: a) Measuring the level of expression of at least two genes selected from the gene combinations of Table 9A, 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, is an indication that the patients suffering from HER2+ breast cancer may respond to anti-HER2 therapies. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies which comprises: a) Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 or TCAP], 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, wherein the ratio is calculated by: i. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the immune signature; or ii. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the tumor cell proliferation signature; or iii. Combining a first gene comprised in the immune differentiation signature with a second gene comprised in the HER2 amplicon signature; or iv. Combining a first gene comprised in the tumor cell proliferation signature with a second gene comprised in the HER2 amplicon signature; or v. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the HERZ amplicon signature; or vi. Combining a first gene comprised in the immune signature selected from the group consisting of: HLA-C, CD27, IGJ, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17 with a second gene comprised in the immune signature selected from the group consisting of: IGKC, IGL or LAX1; or vii. Combining a first gene comprised in the luminal differentiation signature selected from the group consisting of: ESRI or AGR3 with a second gene comprised in the luminal differentiation signature selected from the group consisting of: AFF3, BCL2, orDNAJC12; or viii. Combining the first gene NEK2 comprised in the tumor cell proliferation signature with the second gene ASPM comprised in the tumor cell proliferation signature; and c) wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23], the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESRI] and the HER2 amplicon signature comprises the genes: [ERBB2, GRB7, STARD3 or TCAP], and d) wherein if a deviation of the combination score value is identified, as compared with a preestablished reference value, is an indication that the patients suffering from HER2+ breast cancer may not respond to anti-HER2 therapies. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies which comprises: a) Measuring the level of expression of at least two genes selected from the gene combinations of Table 9B, 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, is an indication that the patients suffering from HER2+ breast cancer may not respond to anti-HER2 therapies. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies which comprises measuring the level of expression of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 and TCAP], In a preferred embodiment the method further comprises identifying the nodal status (pNl) and / or tumor staging (pT2-4) wherein the identification of nodal status Nl-3 and / or tumor status T2-4 is indicative of bad prognosis or that the patient is a non-responder patient to anti-HER2 therapies. In a preferred embodiment the patient is suffering from HER2+ breast cancer. In a preferred embodiment the sample is selected form: tissue, blood, serum or plasma. In a preferred embodiment the anti-HER2 therapy is a drug selected from: trastuzumab, pertuzumab, lapatinib, pyrotinib, poziotinib, tucatinib, neratinib, trastuzumab deruxtecan, SYD985 or ado-trastuzumab emtansine. In vitro use at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESRI] for identifying biomarker signatures for the prognosis of patients suffering from HER2+ breast cancer. In vitro use of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESRI] for the prognosis of patients suffering from HER2+ breast cancer. In vitro use of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESRI] wherein the first gene is comprised in the immune signature and the second gene is comprised in the tumor cell proliferation signature, or wherein the first gene is comprised in the immune signature and the second gene is comprised in the luminal differentiation signature, or wherein the first gene is comprised in the luminal differentiation signature and the second gene is comprised in the tumor cell proliferation signature, or wherein the first gene is comprised in the immune signature and is selected from the group consisting of: CD79A, CD27, IGJ, POU2AF1, TNFRSF17, IL2RG, PIM2 or IGL and the second gene is comprised in the immune signature and is selected from the group consisting of: CD27, CXCL8, HLA-C, IGLV3-25, IL2RG, LAX1, NTN3, PIM2 or POU2AF1; and wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23], and the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESRI]; for the prognosis of patients suffering from HER2+ breast cancer. In vitro use of at least two genes selected from the gene combinations of Table 7A for the prognosis of patients suffering from HER2+ breast cancer. In vitro use of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESRI] wherein the first gene is comprised in the tumor cell proliferation signature and the second gene is comprised in the immune signature, or wherein the first gene is comprised in the luminal differentiation signature and the second gene is comprised in the immune signature, or wherein the first gene is comprised in the tumor cell proliferation signature and the second gene is comprised in the luminal differentiation signature, or wherein the first gene is comprised in the immune signature and it is selected from the group consisting of: CD27, CXCL8, HLA-C, IGLV3-25, IL2RG, LAX1, NTN3, PIM2 or POU2AF1 and the second gene is comprised in the immune signature and it is selected from the group consisting of: CD79A, CD27, IGJ, POU2AF1, TNFRSF17, IL2RG, PIM2 or IGL; and wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23], and the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESRI]; for the prognosis of patients suffering from HER2+ breast cancer. In vitro use of at least two genes selected from the gene combinations of Table 7B for the prognosis of patients suffering from HER2+ breast cancer. In vitro use of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and ESRI] for the prognosis of patients suffering from HER2+ breast cancer. In vitro use of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 or TCAP] for identifying biomarker signatures for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. In vitro use of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 or TCAP] for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or nonresponder patients to anti-HER2 therapies. In vitro use of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 or TCAP] wherein the first gene is comprised in the immune signature and the second gene is comprised in the luminal differentiation signature; or wherein the first gene is comprised in the tumor cell proliferation signature and the second gene is comprised in the luminal differentiation signature; or wherein the first gene is comprised in the HER2 amplicon signature and the second gene is comprised in the immune signature; or wherein the first gene is comprised in the HER2 amplicon signature and the second gene is comprised in the tumor cell proliferation signature; or wherein the first gene is comprised in the HER2 amplicon signature and the second gene is comprised in the luminal differentiation signature; or wherein the first gene is comprised in the immune signature and it is selected from the group consisting of: IGKC, IGL or LAX1 and the second gene is comprised in the immune signature and it is selected from the group consisting of: HLA-C, CD27, IGJ, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17; or wherein the first gene is comprised in the luminal differentiation signature and it is selected from the group consisting of: AFF3, BCL2 or DNAJC12 and the second gene is comprised in the luminal differentiation signature and it is selected from the group consisting of: ESRI or AGR3; or wherein the first gene is ASPM comprised in the tumor cell proliferation and the second gene is NEK2 comprised in the tumor cell proliferation signature; and wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23], the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESRI] and the HER2 amplicon signature comprises the genes: [ERBB2, GRB7, STARD3 or TCAP], for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. In vitro use of at least two genes selected from the gene combinations of Table 9A for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. In vitro use of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 or TCAP] wherein the first gene is comprised in the luminal differentiation signature and the second gene is comprised in immune signature, or wherein the first gene is comprised in the luminal differentiation signature and the second gene is comprised in the tumor cell proliferation signature, or wherein the first gene is comprised in the immune signature and the second gene is comprised in the HER2 amplicon signature, or wherein the first gene is comprised in the tumor cell proliferation signature and the second gene is comprised in the HER2 amplicon signature, or wherein the first gene is comprised in the luminal differentiation signature and the second gene is comprised in the HER2 amplicon signature; or wherein the first gene is comprised in the immune signature and it is selected from the group consisting of: HLA-C, CD27, IGJ, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17 and the second gene is comprised in the immune signature and it is selected from the group consisting of: IGKC, IGL or LAX1; or wherein the first gene is comprised in the luminal differentiation signature and it is selected from the group consisting of: ESRI or AGR3 and the second gene is comprised in the luminal differentiation signature and it is selected from the group consisting of: AFF3, BCL2, or DNAJC12; or wherein the first gene is NEK2 comprised in the tumor cell proliferation and the second gene is ASPM comprised in the tumor cell proliferation signature; and wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23], the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESRI] and the HER2 amplicon signature comprises the genes: [ERBB2, GRB7, STARD3 or TCAP], for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. In vitro use of at least two genes selected from the gene combinations of Table 9B for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. In vitro use of a group of genes consisting of [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 and TCAP] for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies. Anti-HER2 therapy, or any pharmaceutical composition comprising thereof, optionally including pharmaceutically acceptable excipients or carriers, for use in the treatment of patients suffering from HER2+ breast cancer, wherein the method comprises predicting the response to anti-HER2 therapies in the patients suffering from HER2+ breast cancer or classifying patients into responder or non-responder patients to anti-HER2 therapies, by following the method of the invention. Anti-HER2 therapy, or any pharmaceutical composition comprising thereof, optionally including pharmaceutically acceptable excipients or carriers, for use in the treatment of patients suffering from HER2+ breast cancer wherein the anti-HER2 therapy is optionally selected from: trastuzumab, pertuzumab, lapatinib, pyrotinib, poziotinib, tucatinib, neratinib, trastuzumab deruxtecan, SYD985 or ado-trastuzumab emtansine. The present invention also refers to a method for detecting a biomarker signature in a test sample from patients suffering from HER2+ breast cancer the method comprising: a) Contacting the test sample with a reagent specific to the biomarker, b) amplifying the biomarker to produce an amplification product in the test sample; and c) measuring the level by determining the level of the amplification product in the test sample. 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. In a preferred embodiment, the method of the invention further comprises determining or measuring tumor stage and / or nodal status, for instance by CT scan, ultrasound and / or mammography. For the purpose of the present invention the following terms are defined: • The term “pre-established reference value”, when referring to the level of the biomarkers described in the present invention, refers to the geometric mean level of the 5 house-keeping genes observed in the patients, namely: GAPD, PUM1, ACTB, RPLP0 and PSMC4. 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. • The term “variation or deviation” refers to a value which is above or below the preestablished reference value. • 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. • 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. • “Pharmaceutically acceptable excipient or carrier” refers to an excipient that may optionally be included in the compositions of the invention and that causes no significant adverse toxicological effects to the patient. • By “therapeutically effective dose or amount” of a composition is intended an amount that, when administered as described herein, brings about a positive therapeutic response in a subject having HER2+ breast cancer. The exact amount required will vary from subject to subject, depending on the age, and general condition of the subject, the severity of the condition being treated, mode of administration, and the like. An appropriate “effective” amount in any individual case may be determined by one of ordinary skill in the art using routine experimentation, based upon the information provided herein. Brief description of the figures Figure 1. Summary of the different cohorts of patients evaluated during HER2DX development and validation. Figure 2. Survival outcomes of HER2DX low- and high-risk groups in early-stage HER2-positive breast cancer. (A) DRFS in Short-HER dataset; (B) DFS in Short-HER dataset; (C) OS in Short-HER dataset; (D) DFS in an independent combined validation dataset. Figure 3. Summary of the variables included in the HER2DX assay and their association with each clinical endpoint. Figure 4. Survival curves based on CD86 expression and treatment arm. Low and high CD86 expression is defined by the median. Time is defined by months. DMFS96, distant metastasis-free survival at 96 months. Figure 5. Venn diagram representing the number of combination scores (2-gene combination scores) significantly associated with survival outcome across the 5 datasets. Figure 6. Venn diagram representing the number of combination scores (2-gene combination scores) significantly associated with pCR in the 3 datasets. Detailed description of the invention The present invention is illustrated by means of the examples set below, without the intention of limiting its scope of protection. Example 1. MATERIAL AND METHODS Example 1.1. Study design and participants A summary of all the cohorts evaluated is available in Figure 1. Short-HER was a randomized, multicentric, investigator-driven phase 3 study, aimed to assess the noninferiority of 9 weeks versus 1 year of adjuvant trastuzumab combined with chemotherapy. Briefly, women aged 18-75 with surgically resected, HER2+ breast cancer, suitable for adjuvant chemotherapy were eligible. Women had to have node positivity, or in case of nodenegativity, at least one of the following features: tumor size >2 cm, grade 3, presence of lympho-vascular invasion, Ki67>20%, age <35 years or hormone receptor negativity. Patients with stage IIIB / IV disease were not eligible. A total of 1,254 patients with a performance status of 0-1 were randomized from 17th December 2007 to 6th October 2013 to arm A or arm B Chemotherapy in arm A (long) consisted of adriamycin 60mg / m2 plus cyclophosphamide 600 mg / m2 or epirubicin 90 mg / m2 plus cyclophosphamide 600 mg / m2 every 3 weeks for 4 courses followed by paclitaxel 175 mg / m2 or docetaxel 100 mg / m2 every 3 weeks for 4 courses. Trastuzumab was administered every 3 weeks for 18 doses, starting with the first taxane dose. Chemotherapy in arm B (short) consisted of docetaxel 100 mg / m2 every 3 weeks for 3 courses followed by 5-fluorouracil 600 mg / m2, epirubicin 60 mg / m2, cyclophosphamide 600 mg / m2 every 3 weeks for 3 courses. Trastuzumab was administered weekly for 9 weeks, starting concomitantly with docetaxel. When indicated, radiation and hormonal therapy were carried out according to local standard. Median follow-up was 98.4 months. PAMELA was an open-label, single-group, phase 2 trial from 22nd October 2013 to 30th November 2015 aimed to the ability of the PAM50 HER2-enriched subtype to predict pCR at the time of surgery. Patients with HER2+ disease, stage I-IIIA and a performance status of 01 were given lapatinib (1,000 mg per day) and trastuzumab for 18 weeks; hormone receptorpositive patients were additionally given letrozole (2.5 mg per day) or tamoxifen (20 mg per day) according to menopausal status. Treatment after surgery was left to treating physician discretion. Median follow-up was 68.1 months. The Hospital Clinic and Padova University HER2-positive cohorts are consecutive series of patients with early-stage HER2+ breast cancer and a performance status of 0-1 treated, as per standard practice, from 28th June 2005 to 26th September 2020 (Hospital Clinic) and 23rd February 2009 to 26th May 2016 (Padova University cohort), with neoadjuvant trastuzumabbased multi-agent chemotherapy for 3-6 months, followed by surgery. Adjuvant treatment was completed with trastuzumab for up to 1 year, and a minimum of 5 years of hormonal therapy for patients with hormone receptor-positive tumors. Radiation therapy was administered according to local guidelines. Median follow-up of Hospital Clinic and Padova University cohorts were 43.1 and 49.9 months, respectively. Three publicly available gene expression-based datasets that included clinical data and survival outcome from patients with HER2-positive early-stage breast cancer were explored. All the data from The Cancer Genome Atlas (TCGA) and METABRIC datasets were obtained from the cbioportal webpage. The data from the SCAN-B dataset was obtained from GEO, under accession number GSE81540. The gene expression data from TCGA and SCAN-B is RNA-sequencing-based, whereas the gene expression data from METABRIC is microarray-based. No clear information regarding the type of locoregional and systemic therapy is available from these datasets, although patients in METABRIC did not receive anti-HER2 therapy. Finally, we included two cohorts of consecutive patients with newly diagnosed HER2-negative breast cancer from Hospital Clinic and from the SOLTI-1805 TOT-HER3 trial, a window-of-opportunity trial. Only baseline pre-treated tumors were analyzed. No follow-up was available. The study was performed in accordance with Good Clinical Practice guidelines and the World Medical Association Declaration of Helsinki. Approvals for the study were obtained from independent ethics committees. Example 1.2. Tumor sample procedures Gene expression assays were performed on tumor samples from Short-HER, PAMELA, Padova University cohort and Hospital Clinic of Barcelona cohort at the Translational Genomics and Targeted Therapies in Solid Tumors at IDIBAPS. A minimum of ~ 125 ng of total RNA was used to measure the expression of 185 breast cancer-related genes and 5 housekeeping genes (GAPD, PUM1, ACTB, RPLP0 and PSMC4) using the nCounter platform (Nanostring Technologies, Seattle, USA). Finally, TILs in Short-HER were assessed on a single hematoxylin-eosin-stained slide and stromal TILs were scored according to predefined criteria. Example 1.3. HER2DX gene signatures HER2DX is based on 4 different gene signatures comprising 27 genes, which capture various biological processes, including immune infiltration, tumor cell proliferation, luminal differentiation, and expression of the HER2 amplicon. The immune signature selected for HER2DX was the 14-gene immunoglobulin (IGG) module (i.e., CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 and TNFRSF17), previously identified by unsupervised clustering of human breast tumors. The IGG signature has previously shown strong independent prognostic value in a large breast cancer dataset, where patients did not receive adjuvant systemic therapy. The other three gene signatures were identified from unsupervised clustering of the Short-HER HER2-positive dataset using data from 185-breast cancer-related genes. The genes selected were obtained from highly correlated gene clusters (correlation coefficient > 0.80); the tumor cell proliferation signature includes 4 genes (i.e., EXO1, ASPM, NEK2 and KIF23), the luminal differentiation signature includes 5 genes (i.e., BCL2, DNAJC12, AGR3, AFF3 and ESRI), and the HER2 amplicon signature includes 4 genes located in the 17qll-12 chromosome (i.e., ERBB2, GRB7, STARCH and TCAP). For each signature, the mean gene expression was calculated for each patient. Example 1.4. Outcomes The co-primary objectives of this study were to derive and validate two independently trained HER2DX scores: a prognostic risk score, and a pCR probability score. In the prognostic training dataset (i.e., Short-HER), the survival endpoint was DRFS, calculated as the time between randomization and distant recurrence or death before recurrence. In the validation prognostic dataset, the survival endpoint was DFS due to the availability of the data, which was calculated as the time between randomization and any of the following events, whichever first: local, regional, and distant recurrence; contralateral breast cancer, excluding in situ carcinoma; other second invasive primary cancer; death before recurrence or second primary cancer. In all neoadjuvant datasets, pCR at surgery was defined as no invasive tumor cells in the breast and axilla. The secondary objectives were: 1) to describe the clinical-pathological features of the HER2DX risk groups; 2) to explore in-silico the association of HER2DX risk score with overall survival (OS) in publicly available datasets of HER2-positive early-stage breast cancer; 3) to evaluate the value of ERBB2 mRNA to predict HER2 status according to the ASCO / CAP guidelines. Example 1.5. HER2DX risk score development and validation The 434 patients enrolled in the Short-HER trial were used as the training dataset. Patient samples in the training dataset were split into a training set (67% of samples) and a testing set (remaining 33% of samples), balancing for distant relapse-free survival (DRFS) event and treatment arm. Prognostic models of different feature sets were compared by C-index, the index of rank concordance for survival data. These feature sets were evaluated by MonteCarlo cross validation (MCCV) with 100 iterations. Cox proportional hazard models were fit with ridge regression or elastic net in each iteration of training and evaluated in the MCCV testing sets. A single cut-off from the final HER2DX risk score was selected to split patients into low- and high-risk groups. The criteria to select this cut-off was that the low-risk group must have a lower boundary of the 95% confidence interval of the DRFS estimate above 90% at 3, 5 and 7 years. The final HER2DX risk score was tested, as a continuous variable and using the prespecified cut-off, in 268 patients from the validation dataset. The validation dataset was composed of patients from Hospital Clinic of Barcelona HER2-positive cohort (n=147), PAMELA (n=84) and the Padova University cohort (n=37). The median follow-up of the validation dataset was 51.0 months. To further evaluate the prognostic value of the HER2DX risk score, the HER2DX algorithm was evaluated in-silico across three publicly available datasets of patients with early-stage HER2-positive breast cancer (i.e., TCGA, METABRIC and SCAN-B). HER2DX risk models with and without clinical variables (i.e., tumor and nodal staging) were explored as continuous variables due to the known technical biases between different genomic platforms. Example 1.6. HER2DX pCR probability score development and validation One-hundred and sixteen patients with early-stage HER2-positive breast cancer treated with neoadjuvant trastuzumab-based chemotherapy at Hospital Clinic of Barcelona were used as the training dataset for the HER2DX pCR probability score. Patient samples in the training dataset were split into a training set (67% of samples) and a testing set (remaining 33% of samples), balancing for pCR status. Logistic regression models were fit with ridge regression in each iteration of training and evaluated in the MCCV testing sets. Two cut-offs based on tertiles in the training dataset was defined to split patients into three groups: low pCR probability, medium pCR probability and high pCR probability. The final HER2DX pCR probability score was tested, as a continuous variable and using the pre-specified cut-offs, in 158 patients from two validation datasets. The first validation dataset was composed of 67 patients treated with trastuzumab-based chemotherapy from Padova University cohort (n=37) and Hospital Clinic of Barcelona cohort (n=30). The second validation dataset was composed of 91 patients treated with neoadjuvant lapatinib and trastuzumab without chemotherapy from the PAMELA study. Example 1.7. HER2DX ERBB2 mRNA expression assay A cohort of 637 patients with primary invasive breast cancer and known HER2 status according to the ASCO / CAP guidelines was evaluated using the HER2DX assay and used as the training dataset to predict clinical HERZ status. This dataset was composed of 203 patients with newly diagnosed early-stage HER2-negative at Hospital Clinic breast cancer and the Short-HER HERZ-positive cohort of 434 patients. The optimal cutoff of ERBB2 expression to predict HER2 clinical status (positive versus negative) was obtained from a receiver operation curve and Youden index analysis. The optimal ERBB2 cutoff was validated in an independent cohort of 353 HER2-negative and HER2+ cases from the SOLTI-1805 TOT-HER3 HER2-negative trial (n=85), Hospital Clinic of Barcelona HER2-positive cohort (n=147), PAMELA (n=84) and Padova University cohort (n=37). Example 1.8. General statistical procedures For description purposes, 3-, 5- and 7-year estimates of DRFS or DFS were calculated by Kaplan-Meier. Univariate and multivariable Cox proportional hazard regression analyses were used to investigate the association of each variable with survival outcome. To evaluate the prognostic contribution of each variable, likelihood ratio values (^2) were used to measure and compare the relative amount of prognostic information. Categorical variables were expressed as number (%) and compared by %2 test or Fisher's exact test. Logistic regression analyses were performed to investigate the association of each variable with pCR. C-index and receiver operating characteristic (ROC) curves were used as a performance measure. The significance level was set to a 2-sided alpha of 0.05. We used R version 4.0.5. for all the statistical analyses. 5 Example 1.9. Role of the funding source The study was designed and performed by investigators from Padova University, Hospital Clinic and Reveal Genomics. All authors had full access to all data in the study and had final responsibility for the decision to submit for publication. Example 2. RESULTS .0 Example 2.1. HER2DX risk score development and validation To build a prognostic model, clinical-pathological and gene expression data were available from 434 (35%) of 1,254 patients in the Short-HER trial (Table 1). Table 1 AH patients HERlDXLo'w.Risk HER2DX Hiuh-Risk N % x ..........................%................................................ X ...................%.............................................................................................. p-value* 4 34      - 49 3% BIBB1 50 ;% ■■■■■■■■» ■illBiii 554 55.6 ...............55.1....... ; 0.580............. IIBliHlIBIIIIII 378 llllilll 11811111 ||||HU||| 200 llllilll 1.---. Biilli 17 6% lllBill pT T1 234 53 J% 152 70.4% 82 37.6% <0.001 12 187 43.1% 63 29.2% 124 565% ! - 1 11 3 Xo : r . 1 ■ 5 X illlllllOll 255 Lil Bill! 111111181111 llllilll liillll jjilil lllllllll ■■1 ■ S% llllilll jMBiilill Estrogen receptor Matus .rositee 321 74.0% 155 718% 166 76.1% 0.326 • -.... .. Hi 2.6 0% -1 ................... ....._J2_ _________ 1 • Uln, 11! JI 111 Arm A (long; .1 |||||B||| llllilll IllliiiSIIII liillll ■ISMIiBB 9 702 :              : i 213 1114 48 2% Hill 50 0%. Grade Gradel 6 1.4% 0 0.0% 6 2.8% 0.334 Grade 2 115 26.8% 65 30.5% 50 23.1% .....................Grades......................................................... 308 1 148 69.5% 1 . I ! Ill' 1 ill.....bl- ,'<■ ■■■■III Luuiu.nl A 128 29 5«!?. iiiiiMiiii ■ ir. ' J. 36 8 X" 4 6% 26 llllllllllll 9 (IOS 213 iliKBSSl / lt liillll 48 2% iilill Bn sal iik? Hl ’UtSasgl / fl liillll 6.3»,;, 1 i 111¾¾¾¾¾¾ Hi !■■■■ 10 6% ■■431 Patient baseline characteristics of the Short-HER dataset. TILs: tumour-infiltrating lymphocytes; *• p-values represent comparison between HERDX low-risk and high-risk groups. Mean age was 55.4 and most tumors were 2 cm or less (T1 stage), node-negative (NO stage), hormone receptor-positive and histological grade 3. In this cohort, our previous study showed that the best prognostic models integrated tumor size, nodal status, TILs, and the main biology associated with the 4 intrinsic subtypes Based on these previous findings, we re-10 develop HER2DX risk score based on 4 gene expression-based signatures tracking immune infiltration, tumor cell proliferation, HER2 amplicon expression and tumor cell luminal differentiation, together with tumor stage (T1 vs T2 vs. T3-4) and nodal stage (NO vs. N1 vs. N2-3). To capture immune infiltration, we selected our previously described IGG signature, which has shown a strong prognostic value in early-stage breast cancer. HER2DX variables 15 were associated with good outcome (i.e., immune / IGG, and luminal) and poor outcome (i.e., proliferation, and tumor and nodal staging) when tested in univariate analyses. Overall, the predictive performance (C-index) of the HER2DX risk score in Short-HER was 0.74, which was very similar (0.72) to the C-index of our previously reported HER2DX risk model based on 17 different variables. Of note, when we tried to add more variables into the current HER2DX risk model, including TILs, intrinsic subtypes, and individual genes, the predictive performance of HER2DX did not improve. HER2DX measured as a continuous variable was significantly associated with distant relapse-free survival (DRFS) in the Short-HER 434 patient-dataset (p<0.001). To select a clinically relevant cutoff, we defined low-risk as a group of patients with a 3-, 5- and 7-year DRFS with a lower boundary of the 95% confidence interval (CI) >90%. This selected cutoff identified 49.8% of patients (n=216) as low risk. The 3-, 5- and 7-year DRFS of the low-risk population was 97.7% (95% CI 95.7-99.7), 95.3% (95% CI 92.5-98.2) and 94.0% (95% CI 90.6-97.4), respectively (Figure 2A). The 3-, 5- and 7-year DRFS of the high-risk population was 90.4% (95% CI 86.5-94.4), 84.3% (95% CI 79.6-89.3) and 78.6% (95% CI 73.2-84.5), respectively. The DRFS, DFS and OS hazard ratios (HRs) between the low- and high-risk groups were 0.26 (95% CI 0.1-0.5), 0.51 (95% CI 0.3-0.8) and 0.45 (95% CI 0.2-0.9), respectively (Figure 2A-C). In terms of clinical-pathological characteristics, the two risk-groups showed statistically significant differences in terms of TILs, nodal status, tumor size, and intrinsic subtype (Table 1). A dataset of 268 patients with early-stage HER2-positive disease obtained from a combined cohort of three neoadjuvant studies was used for an independent evaluation of the HER2DX score (the score was determined on pretreatment specimens before starting neoadjuvant therapy; Table 2). Table 2 All patients HER2DX low Risk BBER2BX Higk Risk x % X ..................%....................................................... X 13? ........%....................................................... IliiillH E> value* A?e (tuean) ............56 J..... .....5&2....... .........................»3 ...........................0.980............. ||||||^^ ■BUI mini 108 liSHIIII IS ■■■ IIBBSililll is Cinical taiBor stag* T1 84 21.3% 61 45.0% 23 17.4% 0.001 T.M I SI n.................... ...........5¾¾.............................. HW ......82J%...................... t hmi.il in. 1 > -■ i^i- NO 16?. 111m ■... IIBSSlillll Mig ■■■ 11¾¾¾¾ 106 ■n 6% 106 lioSBIIl Pat huk aic a! respoUse pCR lit 44.0% 58 42.6% 60 45J% 0.734 Residual disease 150 56.0% 78 ■ri 4% .......72....... v| Y’;, U"!S>..... -i.pl 'i a .1 ' l';i IHlII 96 |||||J| 11111111 lllllilU ■HI llllilllB ■■■■ •r? ?% lairinMc subtype Lnmittal A 43 19.1% 30 22.1% 13 9.8% Luminal B 30 12.4% 15 11 0% 15 11,4% 0.003 HEILLenr idled 15# 51.7% 67 49.2% 91 69.0% Basal-like 16 7.9% 8 5.9% 8 6.0% Nonnal-like 21 9.0% 16 11.8% 5 3.8% Study MMsU. 84 313% 46 33.8% 38 28.8% 0.673 HOSPITAL CLINIC 147 549% 72 53.0% 75 56,8% PADOW 37 13.8% 18 13.2% 19 14.4% Patient baseline characteristics of the combined prognostic validation dataset. TILs: tumour-infiltrating lymphocytes; pCR: pathological complete response; *, p-values represent comparison between HERDX low-risk 5                                              and high-risk groups. The evaluation dataset was composed of 147 patients from Hospital Clinic, 84 (56%) of 151 from PAMELA and 37 from the Padova University cohort. All patients received chemotherapy and 1 year of trastuzumab; 84 (31%) of 268 patients received dual HER2 10 blockade with lapatinib and trastuzumab for 4.5 to 6.0 months, and 66 (25%) of 268 received four to six cycles of neoadjuvant pertuzumab. Despite heterogeneity in systemic therapies, there were no significant differences in DFS across the four cohorts, or between patients treated with trastuzumab-only versus dual HER2 blockade. 15 In the independent prognostic dataset, HER2DX score as a continuous variable was significantly associated with DFS (HR 1.03, 95% CI 1.0-1.1, p=0.002) In this dataset, for every 10-unit increase (from 0 to 100) in HER2DX risk score, there was a 30% increase in the hazard for the event. According to the prespecified cutoffs, the HER2DX low-risk group had longer DFS than the high-risk (HR 0.21, 95% CI 0.1-0.6, p-value=0.005) (Figure 2B). 5-year DFS in the HER2DX low-risk and high-risk groups was 95.3% (95% CI 92.4-98.2) and 84.0% (79.6-89.3), respectively. 7-year DFS in the HER2DX low-risk and high-risk groups was 93.9% (95% CI 90.6-97.4) and 78.6% (73.2-84.5), respectively. The C-index of the HER2DX risk score was 0.73 for all patients. To further explore the prognostic value of the HER2DX risk score, we interrogated three publicly available breast cancer datasets (i.e., TCGA, METABRIC and SCAN-B), which include clinical data, overall survival (OS) outcome and gene expression data for a total of 810 patients with early-stage HER2-positive breast cancer. The HER2DX algorithm was applied in each dataset with and without clinical features (i.e., tumor and nodal staging) (Table 3). Table 3 HR 95^(1 |i \ .line X.* N( \\ B(n 3“8i J: P D.\ H.k - ..: -.(4 P’= ■Swlill lliiOlliBiBlIll IO i! f I) : ■ ■ k • • •:. ■ ■1 ’ ■ 1' ■; i. : ■ i TCGA (n-196) HER2DX risk score (GEP) 5.8 2 4 13 £ <0.001 H 6 HER2DX nsk score (GEP+Cltmcal) i ■ 1.8-8.6 0.001 15.4 MFiABRK iu Z3ni 11’ 'DX i:-k           P- • Ji :. D:^ i:-k -...-.--01 P :       :1 i lilllMiiMji Table 3. Association of the HER2DX risk score* with overall survival across three publicly available datasets. * HER2DX risk score was evaluated using the 4 gene expression-based variables (GEP), and the full HER2DX risk core which includes tumor and nodal staging (GEP+Clinical). To evaluate the prognostic contribution of each score, likelihood ratio values (y2) were used to measure and compare the relative amount of prognostic information. HR, hazard ratio; CI, confidence interval. SCAN-B dataset (source: GSE81540); The Cancer Genome Atlas (TCGA) dataset (source: https: / / www.cbioportal.org / ); METABRIC dataset (source: https: / / ww w.cbioportal. org / f A statistically significant association between HER2DX risk score as a continuous variable and OS was observed across the tested public datasets. Overall, these in-silico results support the strong prognostic value of HER2DX. Example 2.2. HER2DX pCR probability score development and validation To build a predictive model, we evaluated the HER2DX assay in pre-treated tumors from 120 patients with early-stage HER2-positive breast cancer treated with neoadjuvant trastuzumab- 5 based chemotherapy (Table 4). Table 4 Validation cohorts Training cohort P.VMFLA             Clinic / Padoi a N % N %       N % tii<>th-        1..1(1:511111- Ami HF l<2 . 1.. .’ 1.-                : i 1 -i. 1 :■ -|l- rra>tuzninab and pawotnab Vj............. Tn,s llllllllllll d- 1 ■■■■■■■I 6V v: 5% C 0 0% liMlMMgaM % 3 5S 36 0% :■      1 :i:% 0      (SC,                 6?    1007-( 0     09%            -IS   71.6,% 0    0 0%           19   ;%.4% 56 ? 75   82.4% :       52   88.« Id        1 .      .                      ■          11-( = Clinical tumen st:w T1 32 27.0 36 39.6%          17 25.4% 1.: 1 84   72.4% 55 60.4%         50 74.6% < hull.11 ii.tc1.iI 'laii- ■itllOsilW „ .... .)   C.0% ■ % ■                                   7                - (%%        %  67.2% 40 7%         % Paihokeiral response pCR 60 51.7% 32 35.2%         30 4<S% Sesidual disease 56 483% 59 64.8%          37 55.2% Ibn num.- r.-. i-pt-.i -.T.mi. Nepicr.r 79   68.1%      19 XI   51 9%      42 48 46.20^            19 71.6% ■■■■■I Intriusic subtype Luminal A 24 20.7% » 11.0%         5 13.4% Tawimal B 10 8.6% 8 8.8%           13 19.4% HERXenridrf 66  50% 62 68.1%         35 522% Basal-like 8   <9% 6 6.6%          2 3:0% Nbnnal-like 8   6.9% 5 5.5%          8 12.0% 10 Patient characteristics of the training and validation neoadjuvant datasets. TILs: tumour-infiltrating lymphocytes; pCR: pathological complete response. Mean age was 55.4 (SD 10.2) and most tumors were 2 cm or less (T1 stage), node-negative (NO stage), hormone receptor-positive and histological grade 3. The 4 gene signatures (i.e., HER2 amplicon, immune / IGG, luminal and proliferation) and the 2 clinical variables (i.e., tumor and nodal staging) were used to train a HER2DX pCR probability score. HER2DX 5 variables were associated with pCR (i.e., immune / IGG, and proliferation) and non-pCR (i.e., luminal, and tumor and nodal staging). Overall, the predictive performance (AUC) of the HER2DX pCR probability score in the training dataset was 0.81. Two cohorts of 97 and 67 patients with early-stage HER2-positive disease treated with 10 neoadjuvant anti-HER2-based therapy was used for an independent validation of the HER2DX pCR probability score (the score was determined at baseline before starting neoadjuvant therapy; Table 5). Table 5 FIER2DX ;>("R probahilitv score* Low          lledhnn            High N %       N %          N %            P-value N                                  88 -           S3 -                 103 - Chemotlierap) backbone           f:4               -S 69 9%           61 X X-.              0 i In Vsr: Hl Ri iFl.-l-npv Trastuzumab-only             38   43.2%     39   47.0%          40   38.8% Trastuzumab and lapatimb      24   27.3%     25   30.1%          42   40.8%             0.249 Trastuzumab and pertuzumab 26   29.5%      19   22.9%          21   20.4% iiiSilllBIIIIIIlilM ■HLsO-29                    77   92.8%     73   90.1%          75   75.0%             ftnn. 0.001 TILs>30                   6    7.2%      8    9 9%          25   25.9% Clinical tumor stage Tl.                       21  23.:9%    23   2Z7%         « 39.8%           0M4 T24                        67   76.1%     60   72.3%          62   60.244 i           il 11-nki 1 ■ 1 NO                        57   64.8%     46   55.4%         61   59.2%            Q453 Nl-3                      31   35.2%     37   44.6%         42   40.8% HorniBn® receptor status Positive                    82 93.2»     58   69 9%         36  35.0%               W1 Negative                    6    6.8%      25   30.1%          67   65.0% '’IOS..:..;.:;:;.;.:::;::: fin Til. <i. 'iibf.tM- Luminal A                   37   42.1%     5    6.0%           1    1.0% LuminalB                   18   20.5%     10    12.1%          3    2..9% HER2-enriched               28 31.8»     56   67.5%          79   76.7%             ^0.001 Ba^l-hke                   1    11%      I     I ?.%           H B6L 15          Normal-like                   4    4.5%       11    13.2%           6    5.8% Patient characteristics of the training and validation neoadjuvant datasets combined according to HER2DX pCR probability score. *Groups using the pre-specified cutoffs are shown. TILs: tumour-infiltrating lymphocytes. In both cohorts, HER2DX pCR probability score as a continuous variable was found statistically significantly associated with pCR (p<0.001). Overall, the predictive performances (AUC) of the HER2DX pCR probability score in the PAMELA study and the trastuzumab-based chemotherapy cohort were 0.80 and 0.77, respectively. As expected, statistically significant differences in pCR rates across the three response groups (i.e., defined by tertiles, which were determined in the training dataset), were observed (Table 6). Table 6 , r l •...... k  .., ) Low Met! in tn High P value s % N % N % pCR. rates fct cofcort 2* 2 / 24 8.3% 4 / 25 16.0% 26 / 42 61.9% <0.001 pCR rates across the two validation neoadjuvant datasets according to HER2DXpCR probability score. ^Validation cohort 1 includes 67patients treated with trastuzumab-based chemotherapy. Validation cohort 2 includes 91 patients who participated in the PAMELA trial. Groups using the pre-specified cut-offs are shown. Example 2.3. Relationships between both HER2DX scores To determine the similarity (or lack thereof) between both HER2DX scores, we evaluated a combined HER2-positive dataset that included Short-HER (n=434) and the validation prognostic dataset (n=268). Overall, the correlation coefficient of both HER2DX scores was weak (i.e., -0.19). In patients with HER2DX low-risk, 46.3% (163 / 352) were identified as HER2DX high probability of pCR and 53.7% (189 / 352) as HER2DX low / med probability of pCR. In patients with HER2DX high-risk, 33.1% (116 / 350) were identified as having a HER2DX high probability of pCR and 66.9% (234 / 350) as having a HER2DX low / med probability of pCR. Example 2.4. HER2DX ERBB2 mRNA expression assay ERBB2 mRNA expression within HER2-positive breast cancer can help identify patients with a high response to anti-HER2 therapies, including T-DM1. In addition, ERBB2 mRNA expression can help identify HER2 status according to the ASCO / CAP guidelines. To build an ERBB2 mRNA expression assay that tracks with clinical HER2 status, we combined the Short-HER HER2-positive cohort (n=434) with a HER2-negative cohort of patients newly diagnosed of early-stage breast cancer at Hospital Clinic (n=203). Overall, the mean ERBB2 expression (in log base 2) in HER2-negative and HER2-positive disease was -2.01 and 1.24, respectively (a 6.5-fold difference). The ROC AUC of ERBB2 expression to predict clinical HER2 status was 0.97 with a 90% sensitivity and 98% specificity. Using Youden's analysis, an optimal cutoff of -0.98 was identified. 3.4% of clinically defined HER2-negative cases were identified as ERBB2-positive by mRNA, and 9.7% of clinically defined HER2-positive cases were identified as ERBB2-negative / low. The optimal cutoff to predict HER2 status was tested in an independent dataset of 85 HER2-negative and 268 HER2-positive cases (Figure 1). Overall, the mean ERBB2 expression (in log base 2) in HER2-negative and HER2-positive disease was -2.17 and 0.96, respectively (a 6.3-fold difference). The ROC AUC of ERBB2 expression to predict clinical HER2 status was 0.96 with an 84% sensitivity and 100% specificity. No HER2-negative cases were identified as ERBB2-positive, and 16.4% of HER2-positive cases were identified as ERBB2-negative / low. Example 2.5. Interaction between 4 individual genes (as a continuous variable) and treatment arm (9 weeks vs 1-year) in terms of DMFS at 96 months A total of 4 genes (i.e., CD86, FA2H, FGFR2 and ERBB3) were found associated with trastuzumab benefit in terms of DMFS according to treatment duration (i.e., 1-year versus 9-weeks). Low CD86 expression (as a continuous variable) was found associated with more benefit if patients are treated for 1-year compared to 9-weeks (CD86*Arm, 9 weeks trastuzumab treatment versus 1-year, Hazard Ratio=0.350, interaction p-value=0.0017). Low FA2H expression (as a continuous variable) was found associated with more benefit if patients are treated for 1-year compared to 9-weeks (FA2H*Arm, 9 weeks trastuzumab treatment versus 1-year, Hazard Rati o=0.65, interaction p-value=0.046). High FGFR2 expression (as a continuous variable) was found associated with more benefit if patients are treated for 1-year compared to 9-weeks (FGFR2*Arm, 9 weeks trastuzumab treatment versus 1-year, Hazard Rati0=1.68, interaction p-value=0.027). Finally, high ERBB3 expression (as a continuous variable) was found associated with more benefit if patients are treated for 1-year compared to 9-weeks (ERBB3*Arm, 9 weeks trastuzumab treatment versus 1-year, DMFS96 Hazard Ratio=1.99, interaction p-value=0.035). Example 2.6. Combinations of at least 2 genes tracking the luminal, proliferation and immune pathways is prognostic in early-stage HER2+ breast cancer The HER2DX risk score of the HER2DX assay consists of 23 genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EX01, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and ESRI] and it is used to predict prognosis in patients with HER2-positive (HER2+) breast cancer. The 23 genes are part of one of the following 3 gene expression signatures: luminal differentiation signature (n=5 genes), tumor cell proliferation (n=4) and immune signature (n=14). We evaluated the prognostic value of gene pairs (i.e., combination of 2 genes) included in the 3 signatures across 5 different datasets of patients with early-stage HER2+ breast cancer, including: 1) Short-HER dataset using distant-metastasis free survival (DMFS) as the survival endpoint: 434 patients with HER2+ breast cancer treated with adjuvant anti-HER2 therapy in the context of the Short-HER phase III clinical trial. 2) Short-HER dataset using overall survival (OS) as the endpoint: 434 patients with HER2+ breast cancer treated with adjuvant anti-HER2 therapy in the context of the Short-HER phase III clinical trial. 3) TCGA dataset using OS as the endpoint: 164 patients with HER2+ breast cancer. 4) METABRIC dataset using the OS as the endpoint: 236 patients with HER2+ breast cancer. 5) SCAN-B dataset using the OS as the endpoint: 378 patients with HER2+ breast cancer. For each pair of genes, a combination score was determined by calculating the ratio of the expression of the 2 genes, as follows: Combination score = gene 1 mRNA level (log2 value) - gene 2 mRNA level (log2 value) Univariate Cox models for DMFS and OS were used to test the prognostic significance of each combination score. As proof of concept, we identified several pairs significantly associated with prognosis in 2 or more datasets (Figure 5 and Table 7). Table 7A. List of 78 combination scores significantly associated with good survival outcome in 2 or more datasets Signatures Gene combination Dataset n n events Hazard Ratio 95% CI lower limit 95% CI higher limit p-value IGG_IGG CD79A_CD27 SCANB 378 46 0.721 0.553 0.94 0.01548 SHORTHER_DMFS 434 63 0.712 0.558 0.909 0.00635 SHORTHER_OS 434 87 0.776 0.625 0.963 0.02148 TCGA 164 23 0.594 0.421 0.837 0.0029 IGG_IGG CD27_CXCL8 SCANB 378 46 0.705 0.533 0.932 0.01399 TCGA 164 23 0.63 0.423 0.938 0.0229 IGG_IGG CD79A_CXCL8 SCANB 378 46 0.656 0.496 0.868 0.00318 SHORTHER-DMFS 434 63 0.752 0.584 0.968 0.02688 SHORTHER_OS 434 87 0.798 0.641 0.993 0.04298 TCGA 164 23 0.574 0.397 0.829 0.00309 IGG_IGG IGJ_CXCL8 SCANB 378 46 0.664 0.51 0.865 0.00238 TCGA 164 23 0.588 0.395 0.873 0.00852 IGG_IGG POU2AF1_CXCL8 SCANB 378 46 0.643 0.487 0.848 0.00179 TCGA 164 23 0.667 0.46 0.965 0.03173 IGG_IGG TNFRSF17_CXCL8 SCANB 378 46 0.623 0.47 0.825 0.00096 TCGA 164 23 0.618 0.423 0.903 0.01287 IGG_IGG CD27_HLA.C SCANB 378 46 0.62 0.475 0.807 0.00039 TCGA 164 23 0.41 0.254 0.663 0.00027 IGG_IGG CD79A_HLA.C SCANB 378 46 0.62 0.474 0.811 0.00049 SHORTHER_DMFS 434 63 0.735 0.581 0.931 0.01059 TCGA 164 23 0.425 0.278 0.649 0.00008 IGG_IGG IGJ-HLA.C SCANB 378 46 0.647 0.501 0.835 0.00084 TCGA 164 23 0.481 0.31 0.745 0.00106 IGG_IGG IL2RG_HLA.C SCANB 378 46 0.646 0.496 0.841 0.00115 TCGA 164 23 0.49 0.301 0.798 0.00417 IGG_IGG PIM2_HLA.C SCANB 378 46 0.597 0.438 0.813 0.00105 TCGA 164 23 0.605 0.385 0.949 0.02882 IGG_IGG POU2AF1_HLA.C SCANB 378 46 0.59 0.448 0.778 0.00019 TCGA 164 23 0.561 0.367 0.858 0.00767 IGG_IGG TNFRSF17_HLA.C SCANB 378 46 0.58 0.44 0.763 0.0001 TCGA 164 23 0.504 0.327 0.777 0.00192 IGG_IGG IGL_IGLV3.25 SHORTHER_DMFS 434 63 0.715 0.572 0.894 0.00325 SHORTHER_OS 434 87 0.803 0.659 0.979 0.02967 IGG_IGG CD79A_IL2RG SCANB 378 46 0.695 0.53 0.91 0.00823 SHORTHER_DMFS 434 63 0.694 0.535 0.9 0.00588 SHORTHER_OS 434 87 0.754 0.601 0.947 0.01508 TCGA 164 23 0.525 0.366 0.753 0.00047 IGG_IGG IGJJL2RG SCANB 378 46 0.747 0.565 0.989 0.0414 TCGA 164 23 0.591 0.385 0.908 0.01638 IGG_IGG TNFRSF17_IL2RG SCANB 378 46 0.672 0.508 0.889 0.0053 TCGA 164 23 0.631 0.416 0.957 0.03039 IGGJGG CD79A_LAX1 SHORTHER_DMFS 434 63 0.733 0.583 0.922 0.00786 SHORTHER_OS 434 87 0.768 0.631 0.935 0.00853 TCGA 164 23 0.538 0.383 0.756 0.00036 IGGJGG POU2AF1_LAX1 SHORTHER_DMFS 434 63 0.772 0.607 0.98 0.03362 SHORTHER_OS 434 87 0.758 0.617 0.931 0.00817 IGGJGG TNFRSF17_LAX1 SCANB 378 46 0.763 0.583 0.997 0.04779 TCGA 164 23 0.565 0.365 0.874 0.01038 IGGJGG CD27_NTN3 SCANB 378 46 0.717 0.541 0.949 0.01992 TCGA 164 23 0.51 0.316 0.822 0.00571 IGGJGG CD79A_NTN3 METABRIC 236 147 0.819 0.692 0.969 0.02015 SCANB 378 46 0.679 0.515 0.893 0.00574 SHORTHER_DMFS 434 63 0.746 0.577 0.965 0.02556 SHORTHER_OS 434 87 0.782 0.628 0.975 0.02862 TCGA 164 23 0.476 0.311 0.728 0.00061 IGGJGG IGJ_NTN3 SCANB 378 46 0.685 0.524 0.897 0.00587 TCGA 164 23 0.531 0.358 0.788 0.00167 IGGJGG IL2RG_NTN3 SCANB 378 46 0.744 0.559 0.99 0.04235 TCGA 164 23 0.6 0.376 0.959 0.03276 IGGJGG PIM2_NTN3 METABRIC 236 147 0.83 0.699 0.985 0.03343 SCANB 378 46 0.742 0.558 0.987 0.04032 IGGJGG POU2AF1_NTN3 METABRIC 236 147 0.823 0.695 0.975 0.02429 SCANB 378 46 0.665 0.504 0.877 0.00384 SHORTHER_OS 434 87 0.8 0.64 0.999 0.04933 TCGA 164 23 0.605 0.405 0.904 0.0141 IGGJGG TNFRSF17_NTN3 METABRIC 236 147 0.842 0.71 0.998 0.0479 SCANB 378 46 0.648 0.491 0.853 0.00203 TCGA 164 23 0.532 0.342 0.828 0.00515 IGGJGG CD79A_PIM2 SCANB 378 46 0.716 0.556 0.921 0.00938 SHORTHER_DMFS 434 63 0.72 0.573 0.905 0.00493 SHORTHER_OS 434 87 0.822 0.676 0.998 0.04802 TCGA 164 23 0.504 0.353 0.719 0.00016 IGGJGG IGJPIM2 SCANB 378 46 0.734 0.563 0.957 0.02213 TCGA 164 23 0.58 0.397 0.848 0.00494 IGGJGG POU2AF1_PIM2 SCANB 378 46 0.684 0.534 0.875 0.00256 TCGA 164 23 0.675 0.469 0.971 0.03418 IGGJGG TNFRSF17_PIM2 SCANB 378 46 0.659 0.512 0.847 0.00112 TCGA 164 23 0.597 0.409 0.872 0.00762 IGGJGG CD79A_POU2AF1 SHORTHER_DMFS 434 63 0.787 0.626 0.989 0.03955 TCGA 164 23 0.602 0.458 0.791 0.00027 IGG_LUM HLA.C_AGR3 SCANB 378 46 0.746 0.569 0.977 0.03305 METABRIC 236 147 0.842 0.711 0.998 0.04755 IGG_LUM CD79A_AGR3 METABRIC 236 147 0.817 0.697 0.958 0.01307 SHORTHER_DMFS 434 63 0.772 0.598 0.996 0.04676 IGG_LUM CD79A_BCL2 SHORTHER_DMFS 434 63 0.785 0.617 0.998 0.04796 TCGA 164 23 0.558 0.351 0.887 0.01372 IGG_LUM CD79A_DNAJC12 METABRIC 236 147 0.847 0.724 0.992 0.03926 SCANB 378 46 0.751 0.569 0.993 0.04412 TCGA 164 23 0.573 0.372 0.883 0.01153 IGG_LUM IGJ_DNAJC12 SCANB 378 46 0.74 0.564 0.971 0.0297 TCGA 164 23 0.61 0.401 0.926 0.02045 IGG_LUM POU2AF1_DNAJC12 METABRIC 236 147 0.853 0.728 1 0.04981 SCANB 378 46 0.741 0.56 0.98 0.03588 IGG_LUM TNFRSF17_DNAJC12 SCANB 378 46 0.717 0.541 0.949 0.0199 TCGA 164 23 0.628 0.4 0.986 0.04318 IGG_LUM CD79A_ESR1 METABRIC 236 147 0.843 0.722 0.985 0.03135 TCGA 164 23 0.534 0.317 0.9 0.01848 IGG_LUM CD27_ASPM SCANB 378 46 0.593 0.449 0.785 0.00025 SHORTHER_DMFS 434 63 0.78 0.612 0.995 0.0458 TCGA 164 23 0.4 0.24 0.668 0.00046 IGG_PROLIF CD79A_ASPM METABRIC 236 147 0.833 0.715 0.97 0.01903 SCANB 378 46 0.598 0.456 0.785 0.00021 SHORTHER-DMFS 434 63 0.702 0.546 0.903 0.00583 SHORTHER-OS 434 87 0.789 0.638 0.975 0.0283 TCGA 164 23 0.41 0.266 0.633 0.00006 IGG_PROLIF IGJ_ASPM SCANB 378 46 0.61 0.469 0.793 0.00022 SHORTHER_DMFS 434 63 0.784 0.618 0.995 0.04582 TCGA 164 23 0.46 0.302 0.701 0.0003 IGG_PROLIF IL2RG_ASPM SCANB 378 46 0.627 0.479 0.82 0.00065 TCGA 164 23 0.453 0.278 0.738 0.0015 IGG_PROLIF LAX1_ASPM SCANB 378 46 0.535 0.397 0.721 0.00004 TCGA 164 23 0.495 0.311 0.787 0.003 IGG_PROLIF PIM2_ASPM METABRIC 236 147 0.83 0.709 0.972 0.02048 SCANB 378 46 0.554 0.408 0.752 0.00015 SHORTHER_DMFS 434 63 0.779 0.606 1 0.04998 TCGA 164 23 0.448 0.275 0.73 0.00127 IGG_PROLIF POU2AF1_ASPM METABRIC 236 147 0.834 0.712 0.976 0.02366 SCANB 378 46 0.567 0.429 0.751 0.00007 SHORTHER_DMFS 434 63 0.762 0.6 0.967 0.0257 TCGA 164 23 0.51 0.337 0.772 0.00143 IGG_PROLIF TNFRSF17_ASPM METABRIC 236 147 0.855 0.732 0.999 0.04882 SCANB 378 46 0.555 0.418 0.737 0.00005 SHORTHER_DMFS 434 63 0.775 0.609 0.986 0.03818 TCGA 164 23 0.438 0.274 0.698 0.00053 IGG_PROLIF CD27_EXO1 METABRIC 236 147 0.856 0.733 0.998 0.04729 SCANB 378 46 0.564 0.423 0.753 0.0001 SHORTHER_DMFS 434 63 0.695 0.54 0.895 0 0048 SHORTHER_OS 434 87 0.79 0.64 0.976 0.02872 TCGA 164 23 0.315 0.184 0.539 0.00003 IGG_PROLIF CD79A_EXO1 METABRIC 236 147 0.815 0.697 0.952 0.00994 SCANB 378 46 0.573 0.435 0.756 0.00008 SHORTHER_DMFS 434 63 0.666 0.518 0.856 0.00149 SHORTHER_OS 434 87 0.759 0.614 0.939 0.01108 TOGA 164 23 0.423 0.291 0.613 0.00001 IGG_PROLIF HLA.C_EXO1 SCANB 378 46 0.734 0.557 0.967 0.02815 TCGA 164 23 0.541 0.339 0.865 0.01021 IGG_PROLIF IGJ_EXO1 SCANB 378 46 0.58 0.442 0.761 0.00008 SHORTHER_DMFS 434 63 0.753 0.594 0.955 0.01933 SHORTHER_OS 434 87 0.819 0.67 1 0.0496 TCGA 164 23 0.423 0.279 0.641 0.00005 SHORTHER_DMFS 434 63 0.72 0.557 0.931 0.01232 SHORTHER-OS 434 87 0.798 0.645 0.988 0.03819 IGG_PROLIF IL2RG_EXO1 METABRIC 236 147 0.841 0.716 0.988 0.03497 SCANB 378 46 0.597 0.453 0.788 0.00027 SHORTHER_DMFS 434 63 0.745 0.581 0.955 0.0203 TCGA 164 23 0.352 0.211 0.587 0.00006 IGG_PROLIF LAX1-EXO1 SCANB 378 46 0.519 0.387 0.698 0.00001 SHORTHER_DMFS 434 63 0.722 0.567 0.92 0.00851 TCGA 164 23 0.42 0.26 0.678 0.00039 IGG_PROLIF PIM2_EXO1 METABRIC 236 147 0.811 0.692 0.95 0.00931 SCANB 378 46 0.52 0.38 0.712 0.00004 SHORTHER_DMFS 434 63 0.729 0.571 0.93 0.01109 SHORTHER_OS 434 87 0.788 0.637 0.976 0.02904 TCGA 164 23 0.327 0.188 0.567 0.00007 IGG_PROLIF POU2AF1—EXO1 METABRIC 236 147 0.813 0.692 0.955 0.01155 SCANB 378 46 0.542 0.408 0.721 0.00003 SHORTHER-DMFS 434 63 0.691 0.54 0.884 0.00322 SHORTHER_OS 434 87 0.778 0.631 0.958 0.01838 TCGA 164 23 0.485 0.331 0.711 0.00021 IGG_PROLIF TNFRSF17_EXO1 METABRIC 236 147 0.835 0.715 0.975 0.02271 SCANB 378 46 0.521 0.389 0.7 0.00001 SHORTHER_DMFS 434 63 0.691 0.54 0.884 0.00333 SHORTHER_OS 434 87 0.801 0.649 0.987 0.0377 TCGA 164 23 0.388 0.242 0.622 0.00008 IGG_PROLIF CD27_KIF23 METABRIC 236 147 0.839 0.715 0.983 0.02967 SCANB 378 46 0.607 0.458 0.804 0.0005 SHORTHER_DMFS 434 63 0.706 0.548 0.91 0.00729 SHORTHER_OS 434 87 0.792 0.638 0.983 0.03412 TCGA 164 23 0.359 0.21 0.612 0.00017 IGG_PROLIF CD79A_KIF23 METABRIC 236 147 0.798 0.679 0.939 0.00634 SCANB 378 46 0.604 0.458 0.797 0.00036 SHORTHER_DMFS 434 63 0.659 0.507 0.856 0.00178 SHORTHER_OS 434 87 0.752 0.603 0.937 0.01124 TCGA 164 23 0.402 0.264 0.613 0.00002 IGG_PROLIF IGJ_KIF23 SCANB 378 46 0.608 0.465 0.796 0.00029 SHORTHER_DMFS 434 63 0.754 0.593 0.958 0.02083 SHORTHER_OS 434 87 0.818 0.669 1 0.04969 TCGA 164 23 0.452 0.296 0.689 0.00023 SHORTHER_DMFS 434 63 0.721 0.554 0.939 0.01511 SHORTHER_OS 434 87 0.797 0.641 0.992 0.04216 IGG_PROLIF IL2RG_KIF23 METABRIC 236 147 0.828 0.699 0.979 0.02744 SCANB 378 46 0.644 0.492 0.841 0.00126 SHORTHER-DMFS 434 63 0.744 0.576 0.959 0.02258 TCGA 164 23 0.402 0.238 0.678 0.00063 IGG_PROLIF LAX1_KIF23 SCANB 378 46 0.555 0.414 0.745 0.00009 SHORTHER_DMFS 434 63 0.741 0.577 0.951 0.01846 TCGA 164 23 0.489 0.308 0.775 0.00231 IGG_PROLIF PIM2_KIF23 METABRIC 236 147 0.786 0.668 0.925 0.00367 SCANB 378 46 0.57 0.42 0.773 0.00031 SHORTHER_DMFS 434 63 0.719 0.553 0.936 0.01402 SHORTHER_OS 434 87 0.784 0.627 0.98 0.03237 TCGA 164 23 0.335 0.183 0.614 0 0004 IGG_PROLIF POU2AF1—KIF23 METABRIC 236 147 0.795 0.674 0.939 0.00674 SCANB 378 46 0.573 0.432 0.762 0.00013 SHORTHER_DMFS 434 63 0.705 0.55 0.903 0.00569 SHORTHER_OS 434 87 0.783 0.633 0.969 0.02473 TCGA 164 23 0.495 0.326 0.751 0.00094 IGG_PROLIF TNFRSF17_KIF23 METABRIC 236 147 0.809 0.69 0.949 0.00935 SCANB 378 46 0.553 0.413 0.739 0.00006 SHORTHER_DMFS 434 63 0.703 0.544 0.908 0.00687 TCGA 164 23 0.452 0.293 0.698 0.00034 IGG_PROLIF CD27_NEK2 SCANB 378 46 0.625 0.473 0.826 0.00095 TCGA 164 23 0.386 0.233 0.637 0.0002 IGG_PROLIF CD79A_NEK2 SCANB 378 46 0.619 0.471 0.814 0.00059 SHORTHER_DMFS 434 63 0.717 0.557 0.924 0.01004 TCGA 164 23 0.42 0.279 0.634 0.00004 IGG_PROLIF IGJ_NEK2 SCANB 378 46 0.621 0.474 0.814 0.00055 TCGA 164 23 0.457 0.301 0.693 0.00023 IGG_PROLIF IL2RG_NEK2 SCANB 378 46 0.66 0.504 0.865 0.00258 TCGA 164 23 0.437 0.271 0.704 0.00068 IGG_PROLIF LAX1_NEK2 SCANB 378 46 0.572 0.427 0.768 0.0002 TCGA 164 23 0.493 0.309 0.787 0.00305 IGG_PROLIF PIM2_NEK2 SCANB 378 46 0.585 0.43 0.798 0.0007 TCGA 164 23 0.41 0.248 0.679 0.00053 IGG_PROLIF POU2AF1_NEK2 SCANB 378 46 0.591 0.446 0.784 0.00026 SHORTHER_DMFS 434 63 0.771 0.606 0.981 0.03424 TCGA 164 23 0.512 0.34 0.77 0.00132 IGG_PROLIF TNFRSF17_NEK2 SCANB 378 46 0.573 0.43 0.762 0.00013 SHORTHER_DMFS 434 63 0.779 0.612 0.992 0.04325 TCGA 164 23 0.439 0.278 0.693 0.00042 LUMPROLIF BCL2_EXO1 SCANB 378 46 0.675 0.499 0.911 0.01034 TCGA 164 23 0.599 0.381 0.943 0.02672 LUMPROLIF BCL2_KIF23 METABRIC 236 147 0.848 0.724 0.992 0.03902 SCANB 378 46 0.692 0.519 0.923 0.01218 LUMPROLIF BCL2_NEK2 SCANB 378 46 0.698 0.518 0.94 0.01776 TCGA 164 23 0.633 0.402 0.998 0.04918 Table 7B. List of 78 combination scores significantly associated with poor survival outcome in 2 or more datasets Gene combination Signatures Dataset n n events Hazard Ratio 95% CI lower limit 95% CI higher limit p-value CD27-CD79A IGG_IGG SHORTHER_OS 434 87 1.289 1.038 1.601 0.02148 SCANB 378 46 1.388 1.064 1.809 0.01548 SHORTHER_DMFS 434 63 1.404 1.1 1.792 0.00635 TCGA 164 23 1.684 1.195 2.373 0.0029 CXCL8_CD27 IGG_IGG SCANB 378 46 1.419 1.073 1.877 0.01399 TCGA 164 23 1.587 1.066 2.363 0.0229 CXCL8_CD79A IGG_IGG SHORTHER_OS 434 87 1.253 1.007 1.559 0.04298 SHORTHER_DMFS 434 63 1.33 1.033 1.711 0.02688 SCANB 378 46 1.525 1.152 2.018 0.00318 TCGA 164 23 1.744 1.206 2.52 0.00309 CXCL8_IGJ IGG_IGG SCANB 378 46 1.506 1.156 1.962 0.00238 TCGA 164 23 1.702 1.145 2.529 0.00852 CXCL8_POU2AF1 IGG_IGG TCGA 164 23 1.5 1.036 2.172 0.03173 SCANB 378 46 1.555 1.179 2.052 0.00179 CXCL8_TNFRSF17 IGG_IGG SCANB 378 46 1.606 1.212 2.128 0.00096 TCGA 164 23 1.618 1.107 2.365 0.01287 HLA.C_CD27 IGG_IGG SCANB 378 46 1.614 1.239 2.103 0.00039 TCGA 164 23 2.439 1.509 3.942 0.00027 HLA.C_CD79A IGG_IGG SHORTHER_DMFS 434 63 1.36 1.074 1.721 0.01059 SCANB 378 46 1.612 1.233 2.109 0.00049 TCGA 164 23 2.354 1.54 3.599 0.00008 HLA.C_IGJ IGG_IGG SCANB 378 46 1.547 1.198 1.998 0.00084 TCGA 164 23 2.08 1.342 3.224 0.00106 HLA.C_IL2RG IGG_IGG SCANB 378 46 1.548 1.19 2.015 0.00115 TCGA 164 23 2.04 1.253 3.323 0.00417 HLA.C_PIM2 IGG_IGG TCGA 164 23 1.654 1.053 2.597 0.02882 SCANB 378 46 1.676 1.23 2.282 0.00105 HLA.C_POU2AF1 IGG_IGG SCANB 378 46 1.694 1.285 2.233 0.00019 TCGA 164 23 1.781 1.165 2.723 0.00767 HLA.C_TNFRSF17 IGG_IGG SCANB 378 46 1.726 1.31 2.273 0.0001 TOGA 164 23 1.983 1.287 3.056 0.00192 IGLV3.25JGL IGGJGG SHORTHER_OS 434 87 1.245 1.022 1.517 0.02967 SHORTHER_DMFS 434 63 1.398 1.118 1.747 0.00325 IL2RG_CD79A IGGJGG SHORTHER_OS 434 87 1.325 1.056 1.664 0.01508 SCANB 378 46 1.439 1.099 1.885 0.00823 SHORTHER_DMFS 434 63 1.442 1.111 1.87 0.00588 TOGA 164 23 1.906 1.328 2.735 0.00047 IL2RGJGJ IGGJGG SCANB 378 46 1.338 1.011 1.77 0.0414 TOGA 164 23 1.691 1.101 2.598 0.01638 IL2RGJTNFRSF17 IGGJGG SCANB 378 46 1.488 1.125 1.967 0.0053 TCGA 164 23 1.585 1.045 2.404 0.03039 LAX1_CD79A IGGJGG SHORTHER_OS 434 87 1.302 1.07 1.586 0.00853 SHORTHER_DMFS 434 63 1.364 1.085 1.716 0.00786 TCGA 164 23 1.858 1.323 2.611 0.00036 LAX1_POU2AF1 IGGJGG SHORTHER_DMFS 434 63 1.296 1.02 1.647 0.03362 SHORTHER_OS 434 87 1.319 1.074 1.62 0.00817 LAX1JNFRSF17 IGGJGG SCANB 378 46 1.311 1.003 1.715 0.04779 TCGA 164 23 1.769 1.144 2.737 0.01038 NTN3_CD27 IGGJGG SCANB 378 46 1.395 1.054 1.847 0.01992 TCGA 164 23 1.961 1.216 3.16 0.00571 NTN3_CD79A IGGJGG METABRIC 236 147 1.221 1.032 1.445 0.02015 SHORTHER_OS 434 87 1.278 1.026 1.592 0.02862 SHORTHER_DMFS 434 63 1.341 1.036 1.734 0.02556 SCANB 378 46 1.474 1.119 1.941 0.00574 TCGA 164 23 2.103 1.374 3.217 0.00061 NTN3JGJ IGGJGG SCANB 378 46 1.459 1.115 1.91 0.00587 TCGA 164 23 1.883 1.269 2.794 0.00167 NTN3JL2RG IGGJGG SCANB 378 46 1.344 1.01 1.788 0.04235 TCGA 164 23 1.666 1.043 2.661 0.03276 NTN3_PIM2 IGGJGG METABRIC 236 147 1.205 1.015 1.43 0.03343 SCANB 378 46 1.347 1.013 1.791 0.04032 NTN3_POU2AF1 IGGJGG METABRIC 236 147 1.215 1.026 1.439 0.02429 SHORTHER_OS 434 87 1.25 1.001 1.561 0.04933 SCANB 378 46 1.503 1.14 1.982 0.00384 TCGA 164 23 1.653 1.107 2.47 0.0141 NTN3_TNFRSF17 IGGJGG METABRIC 236 147 1.187 1.002 1.408 0.0479 SCANB 378 46 1.544 1.172 2.035 0.00203 TCGA 164 23 1.88 1.208 2.927 0.00515 PIM2_CD79A IGGJGG SHORTHER_OS 434 87 1.217 1.002 1.478 0.04802 SHORTHER_DMFS 434 63 1.389 1.105 1.746 0.00493 SCANB 378 46 1.397 1.086 1.798 0.00938 TCGA 164 23 1.986 1.392 2.834 0.00016 PIM2JGJ IGGJGG SOANE 378 46 1.362 1.045 1.775 0.02213 TOGA 164 23 1.724 1.179 2.521 0.00494 PIM2_POU2AF1 IGGJGG SOANE 378 46 1.463 1.143 1.873 0.00256 TOGA 164 23 1.481 1.03 2.131 0.03418 PIM2JTNFRSF17 IGGJGG SOANE 378 46 1.518 1.181 1.951 0.00112 TOGA 164 23 1.675 1.147 2.448 0.00762 POU2AF JCD79A IGGJGG SHORTHER_DMFS 434 63 1.271 1.012 1.597 0.03955 TOGA 164 23 1.661 1.264 2.182 0.00027 AGR3_CD79A LUMJGG METABRIC 236 147 1.223 1.043 1.435 0.01307 SHORTHER_DMFS 434 63 1.296 1.004 1.673 0.04676 BCL2-CD79A LUMJGG SHORTHER_DMFS 434 63 1.274 1.002 1.62 0.04796 TOGA 164 23 1.793 1.127 2.852 0.01372 DNAJC12_CD79A LUMJGG METABRIC 236 147 1.18 1.008 1.382 0.03926 SOANE 378 46 1.331 1.008 1.758 0.04412 TOGA 164 23 1.746 1.133 2.691 0.01153 DNAJC12JGI LUMJGG SOANE 378 46 1.352 1.03 1.775 0.0297 TOGA 164 23 1.64 1.079 2.492 0.02045 DNAJC12_POU2AF1 LUMJGG METABRIC 236 147 1.172 1 1.374 0.04981 SOANE 378 46 1.349 1.02 1.785 0.03588 DNAJC12_TNFRSF17 LUMJGG SOANE 378 46 1.395 1.054 1.847 0.0199 TOGA 164 23 1.592 1.014 2.498 0.04318 ESRJCD79A LUMJGG METABRIC 236 147 1.186 1.015 1.386 0.03135 TOGA 164 23 1.872 1.111 3.155 0.01848 AGR3_HLA.C LUMJGG METABRIC 236 147 1.187 1.002 1.407 0.04755 SOANE 378 46 1.341 1.024 1.756 0.03305 ASPM_CD27 PROLIFJGG SHORTHER_DMFS 434 63 1.281 1.005 1.634 0.0458 SOANE 378 46 1.685 1.274 2.229 0.00025 TOGA 164 23 2.499 1.497 4.172 0.00046 ASPM_CD79A PROLIFJGG METABRIC 236 147 1.201 1.03 1.399 0.01903 SHORTHER_OS 434 87 1.268 1.026 1.567 0.0283 SHORTHER_DMFS 434 63 1.424 1.108 1.831 0.00583 SOANE 378 46 1.672 1.274 2.194 0.00021 TOGA 164 23 2.439 1.581 3.762 0.00006 ASPMJGJ PROLIFJGG SHORTHER_DMFS 434 63 1.275 1.005 1.619 0.04582 SOANE 378 46 1.641 1.262 2.133 0.00022 TOGA 164 23 2.172 1.427 3.306 0.0003 ASPMJL2RG PROLIFJGG SOANE 378 46 1.596 1.22 2.088 0.00065 TOGA 164 23 2.208 1.354 3.6 0.0015 ASPM_LAX1 PROLIFJGG SOANE 378 46 1.869 1.388 2.517 0.00004 TOGA 164 23 2.021 1.27 3.216 0.003 ASPMJTM2 PROLIFJGG METABRIC 236 147 1.205 1.029 1.41 0.02048 SHORTHER_DMFS 434 63 1.284 1 1.649 0.04998 SOANE 378 46 1.806 1.33 2.451 0.00015 TOGA 164 23 2.233 1.37 3.639 0.00127 ASPM_POU2AF1 PROLIF_IGG METABRIC 236 147 1.199 1.025 1.404 0.02366 SHORTHER_DMFS 434 63 1.313 1.034 1.668 0.0257 SOANE 378 46 1.763 1.332 2.334 0.00007 TOGA 164 23 1.96 1.296 2.963 0.00143 ASPM_TNFRSF17 PROLIF_IGG METABRIC 236 147 1.169 1.001 1.366 0.04882 SHORTHER_DMFS 434 63 1.291 1.014 1.643 0.03818 SOANE 378 46 1.802 1.357 2.393 0.00005 TOGA 164 23 2.285 1.432 3.645 0.00053 EXO1_CD27 PROLIF_IGG METABRIC 236 147 1.169 1.002 1.364 0.04729 SHORTHER_OS 434 87 1.265 1.025 1.562 0.02872 SHORTHER_DMFS 434 63 1.438 1.117 1.851 0 0048 SOANE 378 46 1.773 1.328 2.366 0.0001 TOGA 164 23 3.176 1.855 5.437 0.00003 EXO1_CD79A PROLIF_IGG METABRIC 236 147 1.228 1.05 1.435 0.00994 SHORTHER_OS 434 87 1.317 1.065 1.629 0.01108 SHORTHER_DMFS 434 63 1.502 1.168 1.93 0.00149 SOANE 378 46 1.745 1.324 2.3 0.00008 TOGA 164 23 2.366 1.63 3.434 0.00001 EXOl-HLA.C PROLIF_IGG SOANE 378 46 1.363 1.034 1.797 0.02815 TOGA 164 23 1.848 1.157 2.953 0.01021 EXO1_IGJ PROLIF_IGG SHORTHER_OS 434 87 1.221 1 1.492 0.0496 SHORTHER_DMFS 434 63 1.327 1.047 1.683 0.01933 SOANE 378 46 1.724 1.314 2.262 0.00008 TOGA 164 23 2.366 1.561 3.586 0.00005 EXO1_IGL PROLIF_IGG SHORTHER_OS 434 87 1.253 1.012 1.551 0.03819 SHORTHER_DMFS 434 63 1.389 1.074 1.796 0.01232 EXO1_IL2RG PROLIF_IGG METABRIC 236 147 1.189 1.012 1.397 0.03497 SHORTHER_DMFS 434 63 1.343 1.047 1.722 0.0203 SOANE 378 46 1.674 1.268 2.209 0.00027 TOGA 164 23 2.84 1.704 4.735 0.00006 EXO1_LAX1 PROLIF_IGG SHORTHER_DMFS 434 63 1.385 1.087 1.765 0.00851 SOANE 378 46 1.925 1.433 2.587 0.00001 TOGA 164 23 2.384 1.476 3.851 0.00039 EXO1_PIM2 PROLIF_IGG METABRIC 236 147 1.234 1.053 1.445 0.00931 SHORTHER_OS 434 87 1.269 1.025 1.571 0.02904 SHORTHER_DMFS 434 63 1.372 1.075 1.75 0.01109 SOANE 378 46 1.922 1.405 2.629 0.00004 TOGA 164 23 3.062 1.764 5.316 0.00007 EXO1_POU2AF1 PROLIF_IGG METABRIC 236 147 1.231 1.048 1.446 0.01155 SHORTHER_OS 434 87 1.286 1.043 1.585 0.01838 SHORTHER_DMFS 434 63 1.447 1.132 1.851 0.00322 SCANB 378 46 1.845 1.387 2.453 0.00003 TCGA 164 23 2.063 1.407 3.024 0.00021 EXO1_TNFRSF17 PROLIF_IGG METABRIC 236 147 1.198 1.026 1.399 0.02271 SHORTHER-OS 434 87 1.249 1.013 1.541 0.0377 SHORTHER-DMFS 434 63 1.447 1.131 1.851 0.00333 SCANB 378 46 1.918 1.429 2.574 0.00001 TCGA 164 23 2.575 1.608 4.124 0.00008 KIF23_CD27 PROLIF_IGG METABRIC 236 147 1.193 1.018 1.398 0.02967 SHORTHER_OS 434 87 1.263 1.018 1.568 0.03412 SHORTHER_DMFS 434 63 1.416 1.098 1.826 0.00729 SCANB 378 46 1.647 1.243 2.182 0.0005 TCGA 164 23 2.786 1.634 4.752 0.00017 KIF23_CD79A PROLIF_IGG METABRIC 236 147 1.252 1.066 1.472 0.00634 SHORTHER_OS 434 87 1.33 1.067 1.657 0.01124 SHORTHER_DMFS 434 63 1.519 1.169 1.974 0.00178 SCANB 378 46 1.655 1.255 2.183 0.00036 TCGA 164 23 2.486 1.632 3.787 0.00002 KIF23_IGJ PROLIF_IGG SHORTHER_OS 434 87 1.223 1 1.495 0.04969 SHORTHER_DMFS 434 63 1.326 1.044 1.686 0.02083 SCANB 378 46 1.644 1.257 2.152 0.00029 TCGA 164 23 2.213 1.451 3.376 0.00023 KIF23_IGL PROLIF_IGG SHORTHER_OS 434 87 1.254 1.008 1.561 0.04216 SHORTHER_DMFS 434 63 1.387 1.065 1.805 0.01511 KIF23_IL2RG PROLIF_IGG METABRIC 236 147 1.208 1.021 1.43 0.02744 SHORTHER_DMFS 434 63 1.345 1.043 1.735 0.02258 SCANB 378 46 1.554 1.189 2.031 0.00126 TCGA 164 23 2.487 1.475 4.194 0.00063 KIF23_LAX1 PROLIF_IGG SHORTHER_DMFS 434 63 1.35 1.052 1.732 0.01846 SCANB 378 46 1.802 1.343 2.417 0.00009 TCGA 164 23 2.046 1.291 3.244 0.00231 KIF23_PIM2 PROLIF_IGG METABRIC 236 147 1.273 1.082 1.497 0.00367 SHORTHER_OS 434 87 1.276 1.021 1.596 0.03237 SHORTHER_DMFS 434 63 1.39 1.069 1.808 0.01402 SCANB 378 46 1.755 1.293 2.381 0.00031 TCGA 164 23 2.984 1.63 5.462 0.0004 KIF23_POU2AF1 PROLIF_IGG METABRIC 236 147 1.257 1.065 1.484 0.00674 SHORTHER_OS 434 87 1.276 1.032 1.58 0.02473 SHORTHER_DMFS 434 63 1.418 1.107 1.817 0.00569 SCANB 378 46 1.744 1.312 2.317 0.00013 TCGA 164 23 2.021 1.332 3.066 0.00094 KIF23_TNFRSF17 PROLIF_IGG METABRIC 236 147 1.235 1.053 1.449 0.00935 SHORTHER_DMFS 434 63 1.423 1.102 1.838 0.00687 SCANB 378 46 1.809 1.353 2.419 0.00006 TCGA 164 23 2.213 1.433 3.417 0.00034 NEK2_CD27 PROLIF_IGG SCANB 378 46 1.599 1.21 2.112 0.00095 TCGA 164 23 2.593 1.57 4.284 0.0002 NEK2_CD79A PROLIF_IGG SHORTHER_DMFS 434 63 1.394 1.083 1.796 0.01004 SCANB 378 46 1.616 1.229 2.124 0.00059 TCGA 164 23 2.378 1.578 3.585 0.00004 NEK2_IGJ PROLIF_IGG SCANB 378 46 1.61 1.229 2.11 0.00055 TCGA 164 23 2.19 1.442 3.325 0.00023 NEK2_IL2RG PROLIF_IGG SCANB 378 46 1.514 1.156 1.983 0.00258 TCGA 164 23 2.288 1.42 3.688 0.00068 NEK2_LAX1 PROLIF_IGG SCANB 378 46 1.747 1.302 2.344 0 0002 TCGA 164 23 2.026 1.27 3.234 0.00305 NEK2_PIM2 PROLIF_IGG SCANB 378 46 1.708 1.253 2.328 0.0007 TCGA 164 23 2.437 1.473 4.033 0.00053 NEK2_POU2AF1 PROLIF_IGG SHORTHER-DMFS 434 63 1.298 1.02 1.651 0.03424 SCANB 378 46 1.691 1.276 2.241 0.00026 TCGA 164 23 1.954 1.298 2.941 0.00132 NEK2_TNFRSF17 PROLIF_IGG SHORTHER_DMFS 434 63 1.283 1.008 1.634 0.04325 SCANB 378 46 1.746 1.312 2.325 0.00013 TCGA 164 23 2.28 1.442 3.604 0.00042 EXO1_BCL2 PROLIF_LUM SCANB 378 46 1.482 1.097 2.002 0.01034 TCGA 164 23 1.669 1.061 2.624 0.02672 KIF23_BCL2 PROLIF_LUM METABRIC 236 147 1.18 1.008 1.38 0.03902 SCANB 378 46 1.446 1.084 1.928 0.01218 NEK2_BCL2 PROLIF_LUM SCANB 378 46 1.433 1.064 1.929 0.01776 TCGA 164 23 1.579 1.002 2.489 0.04918 5 The combination scores indicative of good prognosis represent different combinations of the 3 signatures (i.e., immune-proliferation, immune-luminal, luminal-proliferation and immune-immune). Specifically, 45% (n=35) of the combination scores are pairs composed of genes from the immune-proliferation signatures, 10% (n=8) are pairs composed of genes coming from the immune-luminal signatures, and 4% (n=3) are pairs composed of genes from the 10 luminal-proliferation signatures (Table 8). The combination scores indicative of poor prognosis represent different combinations of the 3 signatures (i.e., proliferation-immune, luminal-immune, proliferation-luminal and immune-immune). Specifically, 45% (n=35) of the combination scores are pairs composed of genes from the proliferation-immune signatures, 10% (n=8) are pairs composed of genes coming from the luminal-immune signatures, and 4% (n=3) are pairs composed of genes from the proliferation-luminal signatures (Table 8). Table 8 Gene 2 IGG LUM PROLIF w IGG 32 8 35 c <u LUM 8 0 3 0 PROLIF 35 3 0 * IGG: Immune signature, LUM: luminal signature, PROLIF: proliferation signature Example 2.7. Combination of 2 genes tracking the luminal, HER2 amplicon, proliferation and immune signatures is predictive of pathological complete response (pCR) The HER2DX pCR score of the HER2DX assay consists of 27 genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESRI, ERBB2, GRB7, STARD3 and TCAP] and predicts pCR in patients with HER2-positive (HER2+) breast cancer following neoadjuvant systemic anti-HER2-based therapy. The 27 genes are part of one of the following 4 gene expression signatures: Luminal differentiation signature (n=5 genes), HER2 amplicon signature (n=4), tumor cell proliferation signature (n=4) and immune signature (n=14). We evaluated the association of gene pairs (i.e., combination of 2 genes) included in the 4 gene expression signatures across 3 different datasets of patients with early-stage HER2+ breast cancer treated with neoadjuvant systemic anti-HER2-based therapy, including: 1) Cohort 1, 117 patients with HER2+ breast cancer treated with neoadjuvant anti-HER2-based chemotherapy at Hospital Clinic Barcelona. 2) Cohort 2, 88 patients with neoadjuvant trastuzumab and lapatinib without chemotherapy in the context of the PAMELA phase II clinical trial. 3) Cohort 3, 67 patients with HER2+ breast cancer treated with neoadjuvant anti-HER2-based chemotherapy at Hospital Clinic Barcelona (n=30) and Padova University (n=37). 5 For each pair of genes, a combination score was determined by calculating the ratio of the expression of the 2 genes, as follows: Combination score = gene 1 mRNA level (log2 value) - gene 2 mRNA level (log2 value) 10 Univariate logistic regression models for pCR were used to test the ability of each combination score to predict pCR. As proof of concept, we identified several pairs significantly associated with prediction of response (pCR) in 2 or more datasets (Figure 6 and Table 9). 15 Table 9A. List of 146 combination scores significantly associated with pCR across the 3 datasets. Gene combination signatures Dataset Odds ratio 95% CI lower limit 95% CI higher limit p-value ERBB2_HLA.C HER2_IGG Cohort 2 1.740 1.052 3.063 0.04058 Cohort 1 1.860 1.265 2.827 0.00234 Cohort 3 1.911 1.146 3.364 0.01730 ERBB2_NTN3 HER2_IGG Cohort 1 2.164 1.450 3.363 0.00030 Cohort 3 2.369 1.352 4.566 0.00504 GRB7_CXCL8 HER2_IGG Cohort 1 1.500 1.034 2.239 0.03802 Cohort 3 1.698 1.027 2.954 0.04697 GRB7_HLA.C HER2_IGG Cohort 3 1.839 1.106 3.224 0.02412 Cohort 1 1.991 1.348 3.046 0.00087 GRB7_NTN3 HER2_IGG Cohort 3 2.144 1.250 3.991 0.00932 Cohort 1 2.310 1.538 3.625 0.00012 STARD3-NTN3 HER2_IGG Cohort 3 1.878 1.108 3.458 0.02818 Cohort 1 2.092 1.401 3.267 0.00058 TCAP_HLA.C HER2_IGG Cohort 2 1.661 1.033 2.840 0.04539 Cohort 3 1.861 1 121 3.238 0.02046 TCAP_NTN3 HER2_IGG Cohort 1 1.500 1.037 2.219 0.03543 Cohort 2 1.780 1.098 3.071 0.02597 Cohort 3 2.247 1.309 4.216 0.00595 ERBB2_AFF3 HER2_LUM Cohort 1 2.243 1 498 3.508 0.00018 Cohort 3 2.791 1.574 5.503 0.00112 Cohort 2 3.861 2.044 8.395 0.00015 ERBB2_AGR3 HER2_LUM Cohort 1 2.274 1.518 3.554 0.00014 Cohort 2 3.037 1.765 5.666 0.00016 Cohort 3 3.440 1.867 7.184 0.00027 ERBB2_BCL2 HER2_LUM Cohort 1 2.517 1.661 4.000 0.00003 Cohort 3 2.913 1.631 5.770 0.00080 Cohort 2 3.450 1.736 8.188 0.00162 ERBB2_DNAJC12 HER2_LUM Cohort 3 2.294 1.345 4.204 0.00390 Cohort 2 2.883 1.542 6.225 0.00266 Cohort 1 2.991 1.907 5.016 0.00001 ERBB2_ESR1 HER2_LUM Cohort 1 2.464 1.639 3.862 0.00003 Cohort 3 3.071 1.726 6.026 0.00037 Cohort 2 4.453 2.273 10.439 0.00010 GRB7-AFF3 HER2_LUM Cohort 1 2.357 1.565 3.718 0.00009 Cohort 3 2.577 1.468 5.001 0.00217 Cohort 2 3.003 1.698 5.893 0.00046 GRB7_AGR3 HER2_LUM Cohort 1 2.383 1.582 3.753 0.00007 Cohort 2 2.755 1 621 5.046 0.00041 Cohort 3 3.238 1.779 6.633 0.00039 GRE7_BCL2 HER2_LUM Cohort 2 2.467 1.383 4.921 0.00492 Cohort 1 2.662 1.748 4.255 0.00001 Cohort 3 2.678 1 518 5.220 0.00156 GRB7_DNAJC12 HER2_LUM Cohort 2 2.165 1.261 4.078 0.00913 Cohort 3 2.201 1.296 4.002 0.00558 Cohort 1 3.136 1.990 5.280 0.00000 GRE7_ESR1 HER2_LUM Cohort 1 2.553 1.691 4.025 0.00002 Cohort 3 2.902 1.641 5.642 0.00062 Cohort 2 3.452 1.894 7.148 0.00021 STARD3_AFF3 HER2_LUM Cohort 1 2.305 1.530 3.643 0.00014 Cohort 3 2.635 1.491 5.177 0.00199 Cohort 2 3.248 1.836 6.356 0.00017 STARD3-AGR3 HER2_LUM Cohort 1 2.218 1.486 3.445 0.00019 Cohort 2 2.697 1.596 4.905 0.00047 Cohort 3 3.424 1.854 7.189 0.00031 STARD3_BCL2 HER2_LUM Cohort 2 2.536 1.448 4.896 0.00250 Cohort 3 2.715 1.542 5.254 0.00124 Cohort 1 2.767 1.786 4.550 0.00002 STARD3_DNAJC12 HER2_LUM Cohort 3 2.115 1.250 3.829 0.00798 Cohort 2 2.230 1.292 4.198 0.00715 Cohort 1 3.150 1.979 5.402 0.00001 STARD3-ESR1 HER2_LUM Cohort 1 2.512 1.666 3.952 0.00003 Cohort 3 2.998 1.690 5.859 0.00046 Cohort 2 3.960 2 120 8.458 0.00008 TCAP_AFF3 HER2_LUM Cohort 1 1.897 1.287 2.893 0.00182 Cohort 3 2.639 1.500 5.125 0.00173 Cohort 2 4.568 2.379 10.189 0.00003 TCAP_AGR3 HER2_LUM Cohort 1 1.920 1.305 2.916 0.00137 Cohort 2 3.129 1.805 5.942 0.00015 Cohort 3 3.245 1.781 6.661 0.00040 TCAP_BCL2 HER2_LUM Cohort 1 2.111 1.411 3.303 0.00053 Cohort 3 2.939 1.645 5.824 0.00073 Cohort 2 3.556 1.896 7.675 0.00033 TCAP_DNAJC12 HER2_LUM Cohort 3 2.271 1.325 4.207 0.00488 Cohort 1 2.395 1.574 3.850 0.00012 Cohort 2 3.017 1.647 6.244 0.00105 TCAP-ESRl HER2_LUM Cohort 1 2.145 1.446 3.294 0.00026 Cohort 3 2.990 1.687 5.839 0.00047 Cohort 2 5.296 2.573 13.484 0.00006 ERBB2_ASPM HER2_PROLIF Cohort 1 1.507 1.040 2.245 0.03519 Cohort 3 1.792 1.078 3.147 0.03096 ERBB2_EXO1 HER2_PROLIF Cohort 1 1.598 1.100 2.385 0.01676 Cohort 3 1.680 1.019 2.892 0.04878 Cohort 2 1.943 1 142 3.627 0.02271 ERBB2_KIF23 HER2_PROLIF Cohort 1 1.627 1.117 2.442 0.01399 Cohort 2 1.871 1.097 3.524 0.03332 Cohort 3 2.080 1.235 3.727 0.00867 ERBB2_NEK2 HER2_PROLIF Cohort 1 1.632 1 124 2.434 0.01235 Cohort 2 1.973 1.143 3.788 0.02449 Cohort 3 2.179 1.282 3.976 0.00638 GRB7_ASPM HER2_PROLIF Cohort 1 1.647 1.129 2.484 0.01241 Cohort 3 1.712 1.035 2.974 0.04338 GRB7_KIF23 HER2_PROLIF Cohort 1 1.781 1.215 2.698 0.00428 Cohort 3 1.954 1.165 3.476 0.01525 GRB7_NEK2 HER2_PROLIF Cohort 1 1.770 1.211 2.668 0.00434 Cohort 3 2.050 1.217 3.676 0.01010 STARD3_KIF23 HER2_PROLIF Cohort 1 1.474 1.021 2.171 0.04271 Cohort 3 1.779 1.071 3.121 0.03285 STARD3_NEK2 HER2_PROLIF Cohort 1 1.494 1.034 2.202 0.03629 Cohort 3 1.898 1.130 3.409 0.02135 TCAP_EXO1 HER2_PROLIF Cohort 3 1.669 1.015 2.848 0.04950 Cohort 2 1.952 1.185 3.459 0.01328 TCAP_KIF23 HER2_PROLIF Cohort 2 1.815 1.117 3.160 0.02283 Cohort 3 1.996 1.190 3.557 0.01242 TCAP_NEK2 HER2_PROLIF Cohort 2 1.915 1.160 3.440 0.01771 Cohort 3 2.071 1.230 3.709 0.00896 IGKC_HLA.C IGG_IGG Cohort 1 1.519 1.048 2.262 0.03179 Cohort 2 1.676 1.029 2.865 0.04592 IGL_CD27 IGG_IGG Cohort 1 1.510 1 040 2.253 0.03545 Cohort 2 2.709 1.569 5.197 0.00099 IGL_HLA.C IGG_IGG Cohort 1 1.631 1.117 2.458 0.01426 Cohort 2 2.649 1.534 5.009 0.00112 IGLJGJ IGG_IGG Cohort 1 1.544 1.062 2.311 0.02744 Cohort 2 3.013 1.695 6.058 0.00062 IGL_LAX1 IGG_IGG Cohort 1 1.479 1.021 2.196 0.04370 Cohort 2 2.249 1.334 4.123 0.00441 IGL_NTN3 IGG_IGG Cohort 1 1.458 1.010 2.149 0.04886 Cohort 2 2.185 1.313 3.885 0.00433 IGL_PIM2 IGG_IGG Cohort 1 1.600 1.095 2.415 0.01903 Cohort 2 2.746 1.561 5.440 0.00134 IGL_POU2AF1 IGG_IGG Cohort 1 1.585 1.086 2.391 0.02127 Cohort 2 2.072 1.242 3.749 0.00889 IGL_TNFRSF17 IGG_IGG Cohort 1 1.519 1.046 2.266 0.03307 Cohort 2 2.082 1.243 3.780 0.00893 LAX1_HLA.C IGG_IGG Cohort 1 1.487 1.026 2.211 0.04140 Cohort 2 1.680 1.048 2.833 0.03831 Cohort 3 1.721 1.031 3.084 0.04877 CD27_AFF3 IGG_LUM Cohort 1 1.982 1.338 3.050 0.00105 Cohort 3 2.389 1 378 4.501 0.00358 Cohort 2 3.918 2.009 9.040 0.00031 CD27_AGR3 IGG_LUM Cohort 1 1.874 1.276 2.836 0.00192 Cohort 2 2.461 1.472 4.387 0.00110 Cohort 3 3.046 1 712 5.965 0.00041 CD27_BCL2 IGG_LUM Cohort 1 2.768 1.761 4.630 0.00003 Cohort 3 3.303 1.765 7.165 0.00070 Cohort 2 3.563 1.800 8.080 0.00089 CD27_DNAJC12 IGG_LUM Cohort 2 1.959 1.155 3.616 0.01974 Cohort 3 2.010 1.188 3.655 0.01386 Cohort 1 2.609 1.684 4.304 0.00006 CD27_ESR1 IGG_LUM Cohort 1 2.380 1.582 3.735 0.00007 Cohort 3 2.895 1.645 5.565 0.00055 Cohort 2 4.108 2.110 9.599 0.00020 CD79A_AFF3 IGG_LUM Cohort 1 1.929 1.304 2.969 0.00162 Cohort 3 2.140 1.254 3.937 0.00848 Cohort 2 3.394 1.835 7.157 0.00037 CD79A_AGR3 IGG_LUM Cohort 1 1.929 1.306 2.955 0.00150 Cohort 2 2.504 1.487 4.528 0.00110 Cohort 3 2.701 1.546 5.126 0.00104 CD79A_BCL2 IGG_LUM Cohort 1 2.211 1.471 3.469 0.00027 Cohort 3 2.398 1.380 4.567 0.00375 Cohort 2 2.502 1.461 4.649 0.00171 CD79A_DNAJC12 IGG_LUM Cohort 3 1.824 1.093 3.235 0.02819 Cohort 2 1.989 1.195 3.548 0.01231 Cohort 1 2.287 1 513 3.627 0.00019 CD79A_ESR1 IGG_LUM Cohort 1 2.365 1.562 3.758 0.00011 Cohort 3 2.596 1.497 4.878 0.00139 Cohort 2 3.509 1.922 7.250 0.00017 CXCL8_AFF3 IGG_LUM Cohort 1 1.716 1.174 2.587 0.00697 Cohort 3 2.027 1.198 3.689 0.01271 Cohort 2 3.687 1.991 7.794 0.00015 CXCL8_AGR3 IGG_LUM Cohort 1 1.732 1.187 2.593 0.00561 Cohort 3 2.620 1.509 4.936 0.00129 Cohort 2 2.813 1.661 5.155 0.00030 CXCL8_BCL2 IGG_LUM Cohort 1 1.878 1.269 2.895 0.00256 Cohort 3 2.088 1.215 3.938 0.01293 Cohort 2 3.122 1.751 6.217 0.00037 CXCL8_DNAJC12 IGG_LUM Cohort 3 1.724 1.041 3.006 0.04178 Cohort 1 2.149 1.427 3.401 0.00051 Cohort 2 3.273 1.705 7.230 0.00120 CXCL8_ESR1 IGG_LUM Cohort 1 1.986 1.347 3.023 0.00082 Cohort 3 2.420 1.414 4.440 0.00224 Cohort 2 4.156 2.161 9.413 0.00012 HLA.C_AFF3 IGG_LUM Cohort 1 1.707 1.171 2.554 0.00688 Cohort 3 1.984 1 175 3.596 0.01516 Cohort 2 3.324 1.817 6.937 0.00038 HLA.C_AGR3 IGG_LUM Cohort 1 1.739 1.194 2.597 0.00499 Cohort 2 2.457 1.467 4.411 0.00122 Cohort 3 2.755 1 574 5.244 0.00086 HLA.C_BCL2 IGG_LUM Cohort 1 2.014 1.361 3.087 0.00075 Cohort 3 2.151 1.246 4.032 0.00990 Cohort 2 2.951 1.560 6.587 0.00290 HLA.C_DNAJC12 IGG_LUM Cohort 2 1.931 1.137 3.591 0.02339 Cohort 1 2.301 1.520 3.672 0.00019 HLA.C_ESR1 IGG_LUM Cohort 1 2.142 1.447 3.278 0.00024 Cohort 3 2.505 1.461 4.605 0.00154 Cohort 2 4.239 2.176 9.843 0.00014 IGJ_AFF3 IGG_LUM Cohort 1 1.792 1.221 2.720 0.00402 Cohort 3 2.191 1.242 4.273 0.01223 Cohort 2 3.314 1.762 7.130 0.00070 IGJ_AGR3 IGG_LUM Cohort 1 1.818 1.239 2.754 0.00315 Cohort 2 2.453 1.448 4.468 0.00164 Cohort 3 2.948 1.641 5.821 0.00074 IGJ_BCL2 IGG_LUM Cohort 1 2.155 1.428 3.410 0.00051 Cohort 3 2.270 1.266 4.655 0.01258 Cohort 2 2.307 1.324 4.398 0.00597 IGJ_DNAJC12 IGG_LUM Cohort 3 1.776 1.055 3.225 0.04151 Cohort 2 1.846 1.106 3.307 0.02674 Cohort 1 2.371 1.563 3.782 0.00012 IGJ-ESRl IGG_LUM Cohort 1 2.253 1 500 3.532 0.00018 Cohort 3 2.721 1.522 5.364 0.00167 Cohort 2 3.352 1.821 6.962 0.00035 IGKC_AFF3 IGG_LUM Cohort 1 1.921 1.297 2.970 0.00186 Cohort 3 2.099 1.198 4.055 0.01639 Cohort 2 3.476 1.858 7.443 0.00037 IGKC_AGR3 IGG_LUM Cohort 1 1.937 1.311 2.972 0.00143 Cohort 2 2.636 1.542 4.868 0.00085 Cohort 3 2.812 1.567 5.536 0.00121 IGKC_BCL2 IGG_LUM Cohort 3 2.215 1.248 4.379 0.01247 Cohort 1 2.218 1.469 3.511 0.00031 Cohort 2 2.800 1.566 5.508 0.00124 IGKC_DNAJC12 IGG_LUM Cohort 3 1.790 1.059 3.271 0.04102 Cohort 2 2.203 1.288 4.103 0.00697 Cohort 1 2.398 1.565 3.896 0.00015 IGKC-ESRl IGG_LUM Cohort 1 2.394 1.573 3.843 0.00011 Cohort 3 2.557 1.448 4.934 0.00248 Cohort 2 3.772 2.012 8.113 0.00016 IGL_AFF3 IGG_LUM Cohort 1 2.011 1.352 3.122 0.00098 Cohort 3 2.092 1.223 3.877 0.01130 Cohort 2 4.654 2 373 10.857 0.00006 IGL_AGR3 IGG_LUM Cohort 1 2.045 1.373 3.175 0.00076 Cohort 3 2.817 1.590 5.474 0.00092 Cohort 2 3.319 1.877 6.508 0.00013 IGL_BCL2 IGG_LUM Cohort 3 2.215 1 283 4.205 0.00781 Cohort 1 2.260 1.496 3.580 0.00023 Cohort 2 3.928 2.087 8.538 0.00012 IGL_DNAJC12 IGG_LUM Cohort 3 1.825 1.090 3.269 0.02987 Cohort 1 2.446 1.593 3.988 0.00012 Cohort 2 2.984 1.675 5.963 0.00064 IGL-ESRl IGG_LUM Cohort 1 2.426 1.594 3.888 0.00009 Cohort 3 2.612 1.495 4.981 0.00158 Cohort 2 5.184 2.580 12.565 0.00004 IGLV3.25_AFF3 IGG_LUM Cohort 1 1.784 1.217 2.703 0.00422 Cohort 3 1.846 1.104 3.286 0.02589 Cohort 2 2.976 1.691 5.866 0.00050 IGLV3.25_AGR3 IGG_LUM Cohort 1 1.850 1.258 2.813 0.00255 Cohort 2 2.483 1.490 4.477 0.00103 Cohort 3 2.510 1.444 4.753 0.00222 IGLV3.25_BCL2 IGG_LUM Cohort 3 1.826 1.093 3.254 0.02842 Cohort 1 1.837 1.250 2.789 0.00281 Cohort 2 2.195 1.335 3.860 0.00335 IGLV3.25_DNAJC12 IGG_LUM Cohort 2 2.003 1.228 3.467 0.00793 Cohort 1 2.054 1.380 3.180 0.00067 IGLV3.25_ESR1 IGG_LUM Cohort 1 2.137 1.431 3.330 0.00039 Cohort 3 2.250 1 325 4.084 0.00432 Cohort 2 3.300 1.837 6.738 0.00026 IL2RG_AFF3 IGG_LUM Cohort 1 2.057 1.377 3.216 0.00079 Cohort 3 2.301 1.325 4.373 0.00569 Cohort 2 3.172 1.728 6.632 0.00066 IL2RG_AGR3 IGG_LUM Cohort 1 1.954 1.322 2.990 0.00121 Cohort 2 2.286 1.382 4.005 0.00211 Cohort 3 3.141 1.747 6.271 0.00038 IL2RG_BCL2 IGG_LUM Cohort 3 2.590 1.472 5.019 0.00210 Cohort 2 2.625 1.437 5.445 0.00423 Cohort 1 2.741 1.741 4.606 0.00004 IL2RG_DNAJC12 IGG_LUM Cohort 2 1.839 1.095 3.351 0.03102 Cohort 3 1.900 1.128 3.443 0.02250 Cohort 1 2.615 1.679 4.349 0.00007 IL2RG_ESRI IGG_LUM Cohort 1 2.458 1.621 3.907 0.00006 Cohort 3 2.909 1.641 5.671 0.00065 Cohort 2 3.363 1.836 7.010 0.00032 LAX1_AFF3 IGG_LUM Cohort 1 1.943 1.312 2.988 0.00145 Cohort 3 2.283 1.320 4.296 0.00562 Cohort 2 4.253 2.166 9.911 0.00016 LAX1_AGR3 IGG_LUM Cohort 1 1.892 1 287 2.869 0.00171 Cohort 2 2.735 1.615 4.991 0.00041 Cohort 3 2.993 1.685 5.837 0.00047 LAX1_BCL2 IGG_LUM Cohort 1 2.526 1.637 4.119 0.00008 Cohort 3 2.836 1 564 5.852 0.00169 Cohort 2 3.777 2.001 8.114 0.00017 LAX1_DNAJC12 IGG_LUM Cohort 3 1.918 1.136 3.491 0.02124 Cohort 2 2.440 1.395 4.711 0.00371 Cohort 1 2.581 1.667 4.245 0.00006 LAX1_ESRI IGG_LUM Cohort 1 2.341 1.557 3.671 0.00009 Cohort 3 2.788 1.590 5.331 0.00079 Cohort 2 4.615 2.314 11.252 0.00011 NTN3_AFF3 IGG_LUM Cohort 1 1.684 1.157 2.518 0.00822 Cohort 3 2.018 1.194 3.658 0.01287 Cohort 2 3.202 1.774 6.473 0.00037 NTN3_AGR3 IGG_LUM Cohort 1 1.701 1.171 2.526 0.00647 Cohort 2 2.504 1.494 4.483 0.00095 Cohort 3 2.691 1.541 5.129 0.00110 NTN3_BCL2 IGG_LUM Cohort 1 1.994 1.346 3.071 0.00096 Cohort 3 2.261 1.302 4.349 0.00718 Cohort 2 3.061 1.692 6.149 0.00061 NTN3_DNAJC12 IGG_LUM Cohort 2 2.100 1.212 4.039 0.01468 Cohort 1 2.362 1.552 3.801 0.00015 NTN3_ESR1 IGG_LUM Cohort 1 2.065 1.402 3.136 0.00039 Cohort 3 2.493 1.451 4.608 0.00175 Cohort 2 3.802 2 007 8.443 0.00021 PIM2_AFF3 IGG_LUM Cohort 1 1.908 1.295 2.908 0.00162 Cohort 3 2.115 1.247 3.843 0.00836 Cohort 2 3.874 2.061 8.385 0.00013 PIM2_AGR3 IGG_LUM Cohort 1 1.861 1.267 2.819 0.00219 Cohort 2 2.691 1.596 4.869 0.00045 Cohort 3 2.807 1.599 5.379 0.00075 PIM2_BCL2 IGG_LUM Cohort 1 2.348 1.559 3.696 0.00010 Cohort 3 2.452 1.406 4.712 0.00324 Cohort 2 3.217 1.811 6.327 0.00022 PIM2_DNAJC12 IGG_LUM Cohort 3 1.792 1.078 3.149 0.03101 Cohort 2 2.285 1.342 4.242 0.00442 Cohort 1 2.453 1.610 3.941 0.00008 PIM2_ESR1 IGG_LUM Cohort 1 2.332 1.555 3.645 0.00009 Cohort 3 2.634 1.521 4.934 0.00112 Cohort 2 4.345 2.260 9.840 0.00007 POU2AF1_AFF3 IGG_LUM Cohort 1 1.819 1.237 2.775 0.00347 Cohort 3 2.138 1.256 3.912 0.00814 Cohort 2 4.304 2.196 10.007 0.00013 POU2AF1_AGR3 IGG_LUM Cohort 1 1.809 1.234 2.734 0.00328 Cohort 3 2.693 1 545 5.101 0.00102 Cohort 2 2.801 1.641 5.169 0.00038 POU2AF1_BCL2 IGG_LUM Cohort 1 2.126 1.417 3.337 0.00051 Cohort 3 2.461 1.409 4.721 0.00316 Cohort 2 3.388 1 882 6.817 0.00017 POU2AF1_DNAJC12 IGG_LUM Cohort 3 1.834 1.099 3.253 0.02661 Cohort 1 2.246 1.486 3.567 0.00027 Cohort 2 2.373 1.388 4.423 0.00312 POU2AF1_ESR1 IGG_LUM Cohort 1 2.220 1.484 3.462 0.00021 Cohort 3 2.557 1.483 4.754 0.00142 Cohort 2 4.491 2.305 10.380 0.00007 TNFRSF17_AFF3 IGG_LUM Cohort 1 1.929 1.305 2.960 0.00156 Cohort 3 2.044 1.207 3.715 0.01170 Cohort 2 4.519 2.287 10.661 0.00010 TNFRSF17_AGR3 IGG_LUM Cohort 1 1.878 1.279 2.843 0.00187 Cohort 3 2.666 1.535 5.013 0.00103 Cohort 2 2.832 1.661 5.223 0.00032 TNFRSF17_BCL2 IGG_LUM Cohort 3 2.336 1.333 4.549 0.00608 Cohort 1 2.435 1.595 3.916 0.00009 Cohort 2 3.504 1.933 7.108 0.00014 TNFRSF17_DNAJC12 IGG_LUM Cohort 3 1.724 1.039 3.032 0.04356 Cohort 2 2.458 1.429 4.623 0.00241 Cohort 1 2.573 1.666 4.225 0.00006 TNFRSF17_ESR1 IGG_LUM Cohort 1 2.338 1.558 3.658 0.00009 Cohort 3 2.492 1.450 4.610 0.00178 Cohort 2 4.715 2 373 11.338 0.00008 AFF3_ESR1 LUM_LUM Cohort 1 1.673 1.141 2.547 0.01135 Cohort 3 1.733 1.041 3.071 0.04385 BCL2_AGR3 LUM-LUM Cohort 2 1.820 1.116 3.103 0.02058 Cohort 3 2.251 1.323 4.107 0.00454 BCL2_ESR1 LUM_LUM Cohort 1 1.738 1.192 2.599 0.00520 Cohort 3 2.123 1.262 3.778 0.00659 Cohort 2 2.927 1.601 6.082 0.00142 DNAJC12_AGR3 LUM-LUM Cohort 2 1.811 1.115 3.087 0.02080 Cohort 3 2.580 1.471 4.966 0.00202 DNAJC12_ESRI LUM-LUM Cohort 2 2.253 1.312 4.235 0.00603 Cohort 3 2.288 1.328 4.299 0.00519 ASPM_AFF3 PROLIF_LUM Cohort 1 2.147 1.435 3.356 0.00039 Cohort 3 2.330 1.350 4.391 0.00442 Cohort 2 3.625 1.944 7.673 0.00020 ASPM_AGR3 PROLIF_LUM Cohort 1 2.036 1.377 3.113 0.00059 Cohort 2 2.597 1.552 4.637 0.00056 Cohort 3 3.458 1.856 7.424 0.00037 ASPM_BCL2 PROLIF_LUM Cohort 3 2.399 1.383 4.554 0.00358 Cohort 1 2.671 1.726 4.396 0.00003 Cohort 2 3.367 1 754 7.408 0.00087 ASPM_DNAJC12 PROLIF_LUM Cohort 3 1.934 1.151 3.472 0.01792 Cohort 2 2.346 1.292 4.837 0.01088 Cohort 1 2.906 1.848 4.903 0.00002 ASPM_ESR1 PROLIF_LUM Cohort 1 2.439 1 621 3.829 0.00004 Cohort 3 3.227 1.782 6.512 0.00034 Cohort 2 5.082 2.447 13.141 0.00011 EX01_AFF3 PROLIF_LUM Cohort 1 2.010 1.358 3.084 0.00079 Cohort 3 2.453 1.409 4.694 0.00306 Cohort 2 3.079 1.725 6.113 0.00043 EXO1_AGR3 PROLIF_LUM Cohort 1 1.938 1.319 2.932 0.00109 Cohort 2 2.408 1.453 4.230 0.00114 Cohort 3 3.598 1.920 7.777 0.00027 EXO1_BCL2 PROLIF_LUM Cohort 2 2.348 1.351 4.472 0.00481 Cohort 1 2.460 1.618 3.938 0.00007 Cohort 3 2.790 1.568 5.507 0.00120 EXO1_DNAJC12 PROLIF_LUM Cohort 2 1.958 1.137 3.727 0.02520 Cohort 3 2.078 1.223 3.810 0.01072 Cohort 1 2.784 1.788 4.624 0.00002 EXO1_ESRI PROLIF_LUM Cohort 1 2.347 1.569 3.649 0.00007 Cohort 3 3.373 1.853 6.861 0.00023 Cohort 2 3.903 2.040 8.796 0.00021 KIF23_AFF3 PROLIF_LUM Cohort 1 2.105 1.413 3.265 0.00046 Cohort 3 2.198 1.286 4.063 0.00663 Cohort 2 3.347 1.860 6.711 0.00019 KIF23_AGR3 PROLIF_LUM Cohort 1 1.979 1 343 3.007 0.00084 Cohort 2 2.595 1.553 4.610 0.00053 Cohort 3 3.156 1.743 6.407 0.00046 KIF23_BCL2 PROLIF_LUM Cohort 3 2.299 1.336 4.299 0.00476 Cohort 1 2.740 1.760 4.552 0.00003 Cohort 2 3.016 1.649 6.179 0.00096 KIF23_DNAJC12 PROLIF_LUM Cohort 3 1.804 1.086 3.158 0.02861 Cohort 2 2.160 1.235 4.191 0.01279 Cohort 1 2.935 1.860 4.977 0.00002 KIF23_ESR1 PROLIF_LUM Cohort 1 2.422 1.612 3.793 0.00005 Cohort 3 2.956 1.669 5.749 0.00052 Cohort 2 4.577 2.298 11.098 0.00011 NEK2_AFF3 PROLIF_LUM Cohort 1 1.983 1.340 3.043 0.00099 Cohort 3 2.071 1.221 3.788 0.01072 Cohort 2 3.116 1.749 6.182 0.00036 NEK2_AGR3 PROLIF_LUM Cohort 1 1.931 1.314 2.922 0.00116 Cohort 2 2.503 1.502 4.443 0.00082 Cohort 3 3.200 1.752 6.614 0.00050 NEK2_BCL2 PROLIF_LUM Cohort 3 1.945 1.155 3.517 0.01777 Cohort 1 2.422 1.587 3.900 0.00010 Cohort 2 2.602 1 464 5.129 0.00257 NEK2_DNAJC12 PROLIF_LUM Cohort 2 2.006 1.164 3.815 0.02033 Cohort 1 2.769 1.772 4.633 0.00003 NEK2_ESR1 PROLIF_LUM Cohort 1 2.368 1.577 3.706 0.00007 Cohort 3 2.986 1 673 5.889 0.00057 Cohort 2 4.569 2.282 11.042 0.00012 ASPM_NEK2 PROLIF_PROLIF Cohort 1 1.514 1.036 2.298 0.03967 Cohort 2 1.685 1.038 2.870 0.04229 Cohort 3 2.071 1.210 3.860 0.01283 Table 9B. List of 146 combination scores significantly associated with lack of pCR across the 3 datasets. Gene combination signatures Dataset Odds ratio 95% CI lower limit 95% CI higher limit p-value CXCL8_GRB7 IGG_HER2 Cohort 1 0.667 0.447 0.967 0.03802 Cohort 3 0.589 0.338 0.974 0.04697 HLA.C_ERBB2 IGG_HER2 Cohort 1 0.538 0.354 0.791 0.00234 Cohort 2 0.575 0.326 0.951 0.04058 Cohort 3 0.523 0.297 0.873 0.01730 HLA.C_GRB7 IGG_HER2 Cohort 1 0.502 0.328 0.742 0.00087 Cohort 3 0.544 0.310 0.904 0.02412 HLA.C_TCAP IGG_HER2 Cohort 2 0.602 0.352 0.968 0.04539 Cohort 3 0.537 0.309 0.892 0.02046 NTN3_ERBB2 IGG_HER2 Cohort 1 0.462 0.297 0.690 0.00030 Cohort 3 0.422 0.219 0.740 0.00504 NTN3_GRB7 IGG_HER2 Cohort 1 0.433 0.276 0.650 0.00012 Cohort 3 0.466 0.251 0.800 0.00932 NTN3_STARD3 IGG_HER2 Cohort 1 0.478 0.306 0.714 0.00058 Cohort 3 0.532 0.289 0.903 0.02818 NTN3_TCAP IGG_HER2 Cohort 1 0.667 0.451 0.964 0.03543 Cohort 2 0.562 0.326 0.911 0.02597 Cohort 3 0.445 0.237 0.764 0.00595 CD27JGL IGGJGG Cohort 1 0.662 0.444 0.962 0.03545 Cohort 2 0.369 0.192 0.637 0.00099 HLA.CJGKC IGGJGG Cohort 1 0.658 0.442 0.954 0.03179 Cohort 2 0.597 0.349 0.972 0.04592 HLA.CJGL IGGJGG Cohort 1 0.613 0.407 0.895 0.01426 Cohort 2 0.378 0.200 0.652 0.00112 HLA.C-LAXl IGGJGG Cohort 1 0.673 0.452 0.975 0.04140 Cohort 2 0.595 0.353 0.954 0.03831 Cohort 3 0.581 0.324 0.970 0.04877 IGJJGL IGGJGG Cohort 1 0.648 0.433 0.942 0.02744 Cohort 2 0.332 0.165 0.590 0.00062 LAX1JGL IGGJGG Cohort 1 0.676 0.455 0.979 0.04370 Cohort 2 0.445 0 243 0.749 0.00441 NTN3JGL IGGJGG Cohort 1 0.686 0.465 0.991 0.04886 Cohort 2 0.458 0.257 0.762 0.00433 PIM2JGL IGGJGG Cohort 1 0.625 0.414 0.913 0.01903 Cohort 2 0.364 0 184 0.641 0.00134 POU2AF1JGL IGGJGG Cohort 1 0.631 0.418 0.921 0.02127 Cohort 2 0.483 0.267 0.805 0.00889 TNFRSF17JGL IGGJGG Cohort 1 0.658 0.441 0.956 0.03307 Cohort 2 0.480 0.265 0.805 0.00893 AFF3_ERBB2 LUMHER2 Cohort 1 0.446 0.285 0.668 0.00018 Cohort 2 0.259 0.119 0.489 0.00015 Cohort 3 0.358 0.182 0.635 0.00112 AFF3-GRB7 LUM_HER2 Cohort 1 0.424 0.269 0.639 0.00009 Cohort 2 0.333 0.170 0.589 0.00046 Cohort 3 0.388 0.200 0.681 0.00217 AFF3_STARD3 LUM_HER2 Cohort 1 0.434 0.275 0.654 0.00014 Cohort 2 0.308 0.157 0.545 0.00017 Cohort 3 0.380 0.193 0.671 0.00199 AFF3_TCAP LUMHER2 Cohort 1 0.527 0.346 0.777 0.00182 Cohort 2 0.219 0.098 0.420 0.00003 Cohort 3 0.379 0.195 0.667 0.00173 AGR3_ERBB2 LUMHER2 Cohort 1 0.440 0.281 0.659 0.00014 Cohort 2 0.329 0.176 0.567 0.00016 Cohort 3 0.291 0.139 0.536 0.00027 AGR3_GRB7 LUM_HER2 Cohort 1 0.420 0.266 0.632 0.00007 Cohort 2 0.363 0 198 0.617 0.00041 Cohort 3 0.309 0.151 0.562 0.00039 AGR3_STARD3 LUM_HER2 Cohort 1 0.451 0.290 0.673 0.00019 Cohort 2 0.371 0.204 0.627 0.00047 Cohort 3 0.292 0.139 0.539 0.00031 AGR3_TCAP LUMHER2 Cohort 1 0.521 0.343 0.766 0.00137 Cohort 2 0.320 0.168 0.554 0.00015 Cohort 3 0.308 0.150 0.562 0.00040 BCL2_ERBB2 LUMHER2 Cohort 1 0.397 0.250 0.602 0.00003 Cohort 2 0.290 0.122 0.576 0.00162 Cohort 3 0.343 0.173 0.613 0.00080 BCL2_GRB7 LUMHER2 Cohort 1 0.376 0.235 0.572 0.00001 Cohort 2 0.405 0.203 0.723 0.00492 Cohort 3 0.373 0.192 0.659 0.00156 BCL2_STARD3 LUMHER2 Cohort 1 0.361 0.220 0.560 0.00002 Cohort 2 0.394 0.204 0.691 0.00250 Cohort 3 0.368 0.190 0.649 0.00124 BCL2_TCAP LUMHER2 Cohort 1 0.474 0.303 0.709 0.00053 Cohort 2 0.281 0.130 0.527 0.00033 Cohort 3 0.340 0.172 0.608 0.00073 DNAJC12_ERBB2 LUMHER2 Cohort 1 0.334 0 199 0.524 0.00001 Cohort 2 0.347 0.161 0.649 0.00266 Cohort 3 0.436 0.238 0.744 0.00390 DNAJC12_GRB7 LUMHER2 Cohort 1 0.319 0.189 0.503 0.00000 Cohort 2 0.462 0 245 0.793 0.00913 Cohort 3 0.454 0.250 0.772 0.00558 DNAJC12_STARD3 LUMHER2 Cohort 1 0.318 0.185 0.505 0.00001 Cohort 2 0.448 0.238 0.774 0.00715 Cohort 3 0.473 0.261 0.800 0.00798 DNAJC12_TCAP LUMHER2 Cohort 1 0.418 0.260 0.635 0.00012 Cohort 2 0.332 0.160 0.607 0.00105 Cohort 3 0.440 0.238 0.754 0.00488 ESR1_ERBB2 LUMHER2 Cohort 1 0.406 0.259 0.610 0.00003 Cohort 2 0.225 0.096 0.440 0.00010 Cohort 3 0.326 0.166 0.579 0.00037 ESR1_GRB7 LUM_HER2 Cohort 1 0.392 0.248 0.591 0.00002 Cohort 2 0.290 0.140 0.528 0.00021 Cohort 3 0.345 0.177 0.609 0.00062 ESR1-STARD3 LUMHER2 Cohort 1 0.398 0.253 0.600 0.00003 Cohort 2 0.253 0.118 0.472 0.00008 Cohort 3 0.334 0.171 0.592 0.00046 ESR1_TCAP LUMHER2 Cohort 1 0.466 0.304 0.692 0.00026 Cohort 2 0.189 0.074 0.389 0.00006 Cohort 3 0.334 0.171 0.593 0.00047 AFF3_CD27 LUMJGG Cohort 1 0.505 0.328 0.747 0.00105 Cohort 2 0.255 0 111 0.498 0.00031 Cohort 3 0.419 0.222 0.726 0.00358 AFF3_CD79A LUMJGG Cohort 1 0.518 0.337 0.767 0.00162 Cohort 2 0.295 0.140 0.545 0.00037 Cohort 3 0.467 0.254 0.798 0.00848 AFF3_CXCL8 LUMJGG Cohort 1 0.583 0.387 0.852 0.00697 Cohort 2 0.271 0.128 0.502 0.00015 Cohort 3 0.493 0.271 0.835 0.01271 AFF3_HLA.C LUMJGG Cohort 1 0.586 0.392 0.854 0.00688 Cohort 2 0.301 0.144 0.550 0.00038 Cohort 3 0.504 0.278 0.851 0.01516 AFF3JGJ LUMJGG Cohort 1 0.558 0.368 0.819 0.00402 Cohort 2 0.302 0.140 0.568 0.00070 Cohort 3 0.456 0.234 0.805 0.01223 AFF3JGKC LUMJGG Cohort 1 0.520 0.337 0.771 0.00186 Cohort 2 0.288 0.134 0.538 0.00037 Cohort 3 0.476 0.247 0.835 0.01639 AFF3JGL LUMJGG Cohort 1 0.497 0.320 0.740 0.00098 Cohort 2 0.215 0.092 0.421 0.00006 Cohort 3 0.478 0.258 0.818 0.01130 AFF3JGLV3.25 LUMJGG Cohort 1 0.561 0 370 0.822 0.00422 Cohort 2 0.336 0.170 0.591 0.00050 Cohort 3 0.542 0.304 0.906 0.02589 AFF3JL2RG LUMJGG Cohort 1 0.486 0.311 0.726 0.00079 Cohort 2 0.315 0 151 0.579 0.00066 Cohort 3 0.435 0.229 0.755 0.00569 AFF3_LAX1 LUMJGG Cohort 1 0.515 0.335 0.762 0.00145 Cohort 2 0.235 0.101 0.462 0.00016 Cohort 3 0.438 0.233 0.757 0.00562 AFF3_NTN3 LUMJGG Cohort 1 0.594 0.397 0.865 0.00822 Cohort 2 0.312 0.154 0.564 0.00037 Cohort 3 0.496 0.273 0.837 0.01287 AFF3_PIM2 LUMJGG Cohort 1 0.524 0.344 0.772 0.00162 Cohort 2 0.258 0.119 0.485 0.00013 Cohort 3 0.473 0.260 0.802 0.00836 AFF3_POU2AF1 LUMJGG Cohort 1 0.550 0.360 0.809 0.00347 Cohort 2 0.232 0.100 0.455 0.00013 Cohort 3 0.468 0.256 0.796 0.00814 AFF3_TNFRSF17 LUMJGG Cohort 1 0.519 0.338 0.766 0.00156 Cohort 2 0.221 0.094 0.437 0.00010 Cohort 3 0.489 0.269 0.828 0.01170 AGR3_CD27 LUMJGG Cohort 1 0.534 0.353 0.783 0.00192 Cohort 2 0.406 0.228 0.679 0.00110 Cohort 3 0.328 0.168 0.584 0.00041 AGR3_CD79A LUMJGG Cohort 1 0.518 0.338 0.766 0.00150 Cohort 2 0.399 0.221 0.672 0.00110 Cohort 3 0.370 0.195 0.647 0.00104 AGR3_CXCL8 LUMJGG Cohort 1 0.577 0.386 0.843 0.00561 Cohort 2 0.355 0.194 0.602 0.00030 Cohort 3 0.382 0.203 0.663 0.00129 AGR3_HLA.C LUMJGG Cohort 1 0.575 0.385 0.838 0.00499 Cohort 2 0.407 0.227 0.682 0.00122 Cohort 3 0.363 0.191 0.635 0.00086 AGR3JGJ LUMJGG Cohort 1 0.550 0.363 0.807 0.00315 Cohort 2 0.408 0.224 0.691 0.00164 Cohort 3 0.339 0.172 0.610 0.00074 AGR3JGKC LUMJGG Cohort 1 0.516 0.337 0.763 0.00143 Cohort 2 0.379 0.205 0.648 0.00085 Cohort 3 0.356 0.181 0.638 0.00121 AGR3JGL LUMJGG Cohort 1 0.489 0.315 0.729 0.00076 Cohort 2 0.301 0.154 0.533 0.00013 Cohort 3 0.355 0.183 0.629 0.00092 AGR3_IGLV3.25 LUMJGG Cohort 1 0.540 0.355 0.795 0.00255 Cohort 2 0.403 0.223 0.671 0.00103 Cohort 3 0.398 0.210 0.693 0.00222 AGR3JL2RG LUMJGG Cohort 1 0.512 0 334 0.756 0.00121 Cohort 2 0.437 0.250 0.724 0.00211 Cohort 3 0.318 0.159 0.572 0.00038 AGR3_LAX1 LUMJGG Cohort 1 0.529 0.349 0.777 0.00171 Cohort 2 0.366 0 200 0.619 0.00041 Cohort 3 0.334 0.171 0.593 0.00047 AGR3_NTN3 LUMJGG Cohort 1 0.588 0.396 0.854 0.00647 Cohort 2 0.399 0.223 0.670 0.00095 Cohort 3 0.372 0.195 0.649 0.00110 AGR3_PIM2 LUMJGG Cohort 1 0.537 0.355 0.789 0.00219 Cohort 2 0.372 0.205 0.627 0.00045 Cohort 3 0.356 0.186 0.625 0.00075 AGR3_POU2AF1 LUMJGG Cohort 1 0.553 0.366 0.810 0.00328 Cohort 2 0.357 0.193 0.609 0.00038 Cohort 3 0.371 0.196 0.647 0.00102 AGR3_TNFRSF17 LUMJGG Cohort 1 0.532 0.352 0.782 0.00187 Cohort 2 0.353 0.191 0.602 0.00032 Cohort 3 0.375 0.199 0.651 0.00103 BCL2_CD27 LUMJGG Cohort 1 0.361 0.216 0.568 0.00003 Cohort 2 0.281 0.124 0.556 0.00089 Cohort 3 0.303 0.140 0.566 0.00070 BCL2_CD79A LUMJGG Cohort 1 0.452 0.288 0.680 0.00027 Cohort 2 0.400 0.215 0.684 0.00171 Cohort 3 0.417 0.219 0.725 0.00375 BCL2_CXCL8 LUMJGG Cohort 1 0.533 0.345 0.788 0.00256 Cohort 2 0.320 0 161 0.571 0.00037 Cohort 3 0.479 0.254 0.823 0.01293 BCL2_HLA.C LUMJGG Cohort 1 0.497 0.324 0.735 0.00075 Cohort 2 0.339 0.152 0.641 0.00290 Cohort 3 0.465 0.248 0.802 0.00990 BCL2JGJ LUMJGG Cohort 1 0.464 0.293 0.700 0.00051 Cohort 2 0.433 0.227 0.755 0.00597 Cohort 3 0.441 0.215 0.790 0.01258 BCL2JGKC LUMJGG Cohort 1 0.451 0.285 0.681 0.00031 Cohort 2 0.357 0.182 0.639 0.00124 Cohort 3 0.452 0.228 0.802 0.01247 BCL2JGL LUMJGG Cohort 1 0.442 0.279 0.668 0.00023 Cohort 2 0.255 0.117 0.479 0.00012 Cohort 3 0.451 0.238 0.780 0.00781 BCL2JGLV3.25 LUMJGG Cohort 1 0.544 0.359 0.800 0.00281 Cohort 2 0.456 0.259 0.749 0.00335 Cohort 3 0.548 0.307 0.915 0.02842 BCL2JL2RG LUMJGG Cohort 1 0.365 0.217 0.575 0.00004 Cohort 2 0.381 0.184 0.696 0.00423 Cohort 3 0.386 0.199 0.679 0.00210 BCL2_LAX1 LUMJGG Cohort 1 0.396 0 243 0.611 0.00008 Cohort 2 0.265 0.123 0.500 0.00017 Cohort 3 0.353 0.171 0.639 0.00169 BCL2_NTN3 LUMJGG Cohort 1 0.501 0.326 0.743 0.00096 Cohort 2 0.327 0 163 0.591 0.00061 Cohort 3 0.442 0.230 0.768 0.00718 BCL2_PIM2 LUMJGG Cohort 1 0.426 0.271 0.642 0.00010 Cohort 2 0.311 0.158 0.552 0.00022 Cohort 3 0.408 0.212 0.711 0.00324 BCL2_POU2AF1 LUMJGG Cohort 1 0.470 0.300 0.706 0.00051 Cohort 2 0.295 0.147 0.531 0.00017 Cohort 3 0.406 0.212 0.710 0.00316 BCL2_TNFRSF17 LUMJGG Cohort 1 0.411 0.255 0.627 0.00009 Cohort 2 0.285 0.141 0.517 0.00014 Cohort 3 0.428 0.220 0.750 0.00608 DNAJC12_CD27 LUMJGG Cohort 1 0.383 0.232 0.594 0.00006 Cohort 2 0.511 0.277 0.866 0.01974 Cohort 3 0.498 0.274 0.842 0.01386 DNAJC12_CD79A LUMJGG Cohort 1 0.437 0.276 0.661 0.00019 Cohort 2 0.503 0.282 0.837 0.01231 Cohort 3 0.548 0.309 0.915 0.02819 DNAJC12_CXCL8 LUMJGG Cohort 1 0.465 0.294 0.701 0.00051 Cohort 2 0.306 0.138 0.586 0.00120 Cohort 3 0.580 0.333 0.961 0.04178 DNAJC12_HLA.C LUMJGG Cohort 1 0.435 0.272 0.658 0.00019 Cohort 2 0.518 0 279 0.880 0.02339 DNAJC12JGJ LUMJGG Cohort 1 0.422 0.264 0.640 0.00012 Cohort 2 0.542 0.302 0.904 0.02674 Cohort 3 0.563 0.310 0.948 0.04151 DNAJC12JGKC LUMJGG Cohort 1 0.417 0.257 0.639 0.00015 Cohort 2 0.454 0.244 0.776 0.00697 Cohort 3 0.559 0.306 0.944 0.04102 DNAJC12JGL LUMJGG Cohort 1 0.409 0.251 0.628 0.00012 Cohort 2 0.335 0.168 0.597 0.00064 Cohort 3 0.548 0.306 0.918 0.02987 DNAJC12JGLV3.25 LUMJGG Cohort 1 0.487 0.314 0.725 0.00067 Cohort 2 0.499 0.288 0.814 0.00793 DNAJC12JL2RG LUMJGG Cohort 1 0.382 0.230 0.595 0.00007 Cohort 2 0.544 0.298 0.913 0.03102 Cohort 3 0.526 0.290 0.887 0.02250 DNAJC12_LAX1 LUMJGG Cohort 1 0.387 0.236 0.600 0.00006 Cohort 2 0.410 0.212 0.717 0.00371 Cohort 3 0.521 0.286 0.880 0.02124 DNAJC12_NTN3 LUMJGG Cohort 1 0.423 0.263 0.644 0.00015 Cohort 2 0.476 0.248 0.825 0.01468 DNAJC12_PIM2 LUMJGG Cohort 1 0.408 0 254 0.621 0.00008 Cohort 2 0.438 0.236 0.745 0.00442 Cohort 3 0.558 0.318 0.928 0.03101 DNAJC12_POU2AF1 LUMJGG Cohort 1 0.445 0.280 0.673 0.00027 Cohort 2 0.421 0 226 0.720 0.00312 Cohort 3 0.545 0.307 0.910 0.02661 DNAJC12_TNFRSF17 LUMJGG Cohort 1 0.389 0.237 0.600 0.00006 Cohort 2 0.407 0.216 0.700 0.00241 Cohort 3 0.580 0.330 0.963 0.04356 ESR1_CD27 LUMJGG Cohort 1 0.420 0.268 0.632 0.00007 Cohort 2 0.243 0.104 0.474 0.00020 Cohort 3 0.345 0.180 0.608 0.00055 ESRJCD79A LUMJGG Cohort 1 0.423 0.266 0.640 0.00011 Cohort 2 0.285 0.138 0.520 0.00017 Cohort 3 0.385 0.205 0.668 0.00139 ESR1_CXCL8 LUMJGG Cohort 1 0.504 0.331 0.742 0.00082 Cohort 2 0.241 0.106 0.463 0.00012 Cohort 3 0.413 0.225 0.707 0.00224 ESR1_HLA.C LUMJGG Cohort 1 0.467 0.305 0.691 0.00024 Cohort 2 0.236 0.102 0.460 0.00014 Cohort 3 0.399 0.217 0.684 0.00154 ESR1JGJ LUMJGG Cohort 1 0.444 0.283 0.667 0.00018 Cohort 2 0.298 0.144 0.549 0.00035 Cohort 3 0.368 0.186 0.657 0.00167 ESR1JGKC LUMJGG Cohort 1 0.418 0.260 0.636 0.00011 Cohort 2 0.265 0 123 0.497 0.00016 Cohort 3 0.391 0.203 0.691 0.00248 ESR1JGL LUMJGG Cohort 1 0.412 0.257 0.627 0.00009 Cohort 2 0.193 0.080 0.388 0.00004 Cohort 3 0.383 0.201 0.669 0.00158 ESRI JGLV3.25 LUMJGG Cohort 1 0.468 0.300 0.699 0.00039 Cohort 2 0.303 0.148 0.544 0.00026 Cohort 3 0.445 0.245 0.755 0.00432 ESR1JL2RG LUMJGG Cohort 1 0.407 0.256 0.617 0.00006 Cohort 2 0.297 0.143 0.545 0.00032 Cohort 3 0.344 0.176 0.609 0.00065 ESR1_LAX1 LUMJGG Cohort 1 0.427 0.272 0.642 0.00009 Cohort 2 0.217 0.089 0.432 0.00011 Cohort 3 0.359 0.188 0.629 0.00079 ESR1_NTN3 LUMJGG Cohort 1 0.484 0.319 0.713 0.00039 Cohort 2 0.263 0.118 0.498 0.00021 Cohort 3 0.401 0.217 0.689 0.00175 ESR1_PIM2 LUMJGG Cohort 1 0.429 0.274 0.643 0.00009 Cohort 2 0.230 0.102 0.442 0.00007 Cohort 3 0.380 0.203 0.658 0.00112 ESR1_POU2AF1 LUMJGG Cohort 1 0.450 0 289 0.674 0.00021 Cohort 2 0.223 0.096 0.434 0.00007 Cohort 3 0.391 0.210 0.674 0.00142 ESRI JTNFRSF17 LUMJGG Cohort 1 0.428 0.273 0.642 0.00009 Cohort 2 0.212 0 088 0.421 0.00008 Cohort 3 0.401 0.217 0.690 0.00178 AGR3_BCL2 LUM_LUM Cohort 2 0.549 0.322 0.896 0.02058 Cohort 3 0.444 0.243 0.756 0.00454 AGR3_DNAJC12 LUM_LUM Cohort 2 0.552 0.324 0.897 0.02080 Cohort 3 0.388 0.201 0.680 0.00202 ESR1_AFF3 LUM-LUM Cohort 1 0.598 0.393 0.876 0.01135 Cohort 3 0.577 0.326 0.961 0.04385 ESR1_BCL2 LUM-LUM Cohort 1 0.575 0.385 0.839 0.00520 Cohort 2 0.342 0.164 0.624 0.00142 Cohort 3 0.471 0.265 0.792 0.00659 ESR1_DNAJC12 LUM-LUM Cohort 2 0.444 0.236 0.762 0.00603 Cohort 3 0.437 0.233 0.753 0.00519 AFF3_ASPM LUM-PROLIF Cohort 1 0.466 0.298 0.697 0.00039 Cohort 2 0.276 0.130 0.514 0.00020 Cohort 3 0.429 0.228 0.741 0.00442 AFF3_EXO1 LUM-PROLIF Cohort 1 0.498 0.324 0.736 0.00079 Cohort 2 0.325 0.164 0.580 0.00043 Cohort 3 0.408 0.213 0.710 0.00306 AFF3_KIF23 LUM_PROLIF Cohort 1 0.475 0.306 0.708 0.00046 Cohort 2 0.299 0.149 0.538 0.00019 Cohort 3 0.455 0 246 0.778 0.00663 AFF3_NEK2 LUM_PROLIF Cohort 1 0.504 0.329 0.746 0.00099 Cohort 2 0.321 0.162 0.572 0.00036 Cohort 3 0.483 0.264 0.819 0.01072 AGR3_ASPM LUM_PROLIF Cohort 1 0.491 0.321 0.726 0.00059 Cohort 2 0.385 0.216 0.644 0.00056 Cohort 3 0.289 0.135 0.539 0.00037 AGR3_EXO1 LUM_PROLIF Cohort 1 0.516 0.341 0.758 0.00109 Cohort 2 0.415 0.236 0.688 0.00114 Cohort 3 0.278 0.129 0.521 0.00027 AGR3_KIF23 LUMPROLIF Cohort 1 0.505 0.333 0.744 0.00084 Cohort 2 0.385 0.217 0.644 0.00053 Cohort 3 0.317 0.156 0.574 0.00046 AGR3_NEK2 LUM-PROLIF Cohort 1 0.518 0.342 0.761 0.00116 Cohort 2 0.400 0.225 0.666 0.00082 Cohort 3 0.313 0.151 0.571 0.00050 BCL2_ASPM LUM-PROLIF Cohort 1 0.374 0.227 0.580 0.00003 Cohort 2 0.297 0.135 0.570 0.00087 Cohort 3 0.417 0.220 0.723 0.00358 BCL2_EXO1 LUM_PROLIF Cohort 1 0.407 0.254 0.618 0.00007 Cohort 2 0.426 0 224 0.740 0.00481 Cohort 3 0.358 0.182 0.638 0.00120 BCL2_KIF23 LUM_PROLIF Cohort 1 0.365 0.220 0.568 0.00003 Cohort 2 0.332 0.162 0.606 0.00096 Cohort 3 0.435 0 233 0.749 0.00476 BCL2_NEK2 LUM_PROLIF Cohort 1 0.413 0.256 0.630 0.00010 Cohort 2 0.384 0.195 0.683 0.00257 Cohort 3 0.514 0.284 0.866 0.01777 DNAJC12_ASPM LUM_PROLIF Cohort 1 0.344 0.204 0.541 0.00002 Cohort 2 0.426 0.207 0.774 0.01088 Cohort 3 0.517 0.288 0.869 0.01792 DNAJC12_EXO1 LUM_PROLIF Cohort 1 0.359 0.216 0.559 0.00002 Cohort 2 0.511 0.268 0.879 0.02520 Cohort 3 0.481 0.262 0.818 0.01072 DNAJC12_KIF23 LUM_PROLIF Cohort 1 0.341 0.201 0.538 0.00002 Cohort 2 0.463 0.239 0.810 0.01279 Cohort 3 0.554 0.317 0.921 0.02861 DNAJC12_NEK2 LUM_PROLIF Cohort 1 0.361 0.216 0.564 0.00003 Cohort 2 0.499 0.262 0.859 0.02033 ESRl-ASPM LUM_PROLIF Cohort 1 0.410 0.261 0.617 0.00004 Cohort 2 0.197 0.076 0.409 0.00011 Cohort 3 0.310 0.154 0.561 0.00034 ESR1_EXO1 LUM_PROLIF Cohort 1 0.426 0.274 0.637 0.00007 Cohort 2 0.256 0.114 0.490 0.00021 Cohort 3 0.296 0.146 0.540 0.00023 ESR1_KIF23 LUM_PROLIF Cohort 1 0.413 0 264 0.621 0.00005 Cohort 2 0.218 0.090 0.435 0.00011 Cohort 3 0.338 0.174 0.599 0.00052 ESR1_NEK2 LUM_PROLIF Cohort 1 0.422 0.270 0.634 0.00007 Cohort 2 0.219 0.091 0.438 0.00012 Cohort 3 0.335 0.170 0.598 0.00057 ASPM_ERBB2 PROLIF_HER2 Cohort 1 0.663 0.445 0.962 0.03519 Cohort 3 0.558 0.318 0.928 0.03096 ASPM_GRB7 PROLIF_HER2 Cohort 1 0.607 0.403 0.886 0.01241 Cohort 3 0.584 0.336 0.966 0.04338 EXO1_ERBB2 PROLIF_HER2 Cohort 1 0.626 0.419 0.909 0.01676 Cohort 2 0.515 0.276 0.876 0.02271 Cohort 3 0.595 0.346 0.981 0.04878 EXO1_TCAP PROLIF_HER2 Cohort 2 0.512 0.289 0.844 0.01328 Cohort 3 0.599 0.351 0.985 0.04950 KIF23_ERBB2 PROLIF_HER2 Cohort 1 0.615 0.410 0.895 0.01399 Cohort 2 0.534 0.284 0.912 0.03332 Cohort 3 0.481 0.268 0.810 0.00867 KIF23_GRB7 PROLIF_HER2 Cohort 1 0.561 0.371 0.823 0.00428 Cohort 3 0.512 0.288 0.858 0.01525 KIF23_STARD3 PROLIF_HER2 Cohort 1 0.679 0461 0.980 0.04271 Cohort 3 0.562 0.320 0.934 0.03285 KIF23_TCAP PROLIF_HER2 Cohort 2 0.551 0.316 0.895 0.02283 Cohort 3 0.501 0.281 0.841 0.01242 NEK2_ERBB2 PROLIF_HER2 Cohort 1 0.613 0 411 0.890 0.01235 Cohort 2 0.507 0.264 0.875 0.02449 Cohort 3 0.459 0.252 0.780 0.00638 NEK2_GRB7 PROLIF_HER2 Cohort 1 0.565 0.375 0.826 0.00434 Cohort 3 0.488 0.272 0.822 0.01010 NEK2_STARD3 PROLIF_HER2 Cohort 1 0.669 0.454 0.967 0.03629 Cohort 3 0.527 0.293 0.885 0.02135 NEK2_TCAP PROLIF_HER2 Cohort 2 0.522 0.291 0.862 0.01771 Cohort 3 0.483 0.270 0.813 0.00896 NEK2_ASPM PROLIF_PROLIF Cohort 1 0.660 0.435 0.965 0.03967 Cohort 2 0.594 0.348 0.964 0.04229 Cohort 3 0.483 0.259 0.826 0.01283 The combination scores predictive of pCR represent different combinations of the 4 signatures (i.e., immune-luminal, proliferation-luminal, HER2-immune, HER2-proliferation, 5 HER2-luminal, immune-immune, luminal-luminal, proliferation-proliferation). Specifically, 48% (n=70) are pairs composed of genes coming from the immune-luminal signatures, 14% (n=20) are pairs composed of genes from the proliferation-luminal signatures, 6% (n=8) are pairs composed of genes from the HER2-immune signatures, 8% (n=12) are pairs composed of genes from the HER2-proliferation signatures, and 14% (n=20) are pairs composed of 10 genes from the HER2-luminal signatures (Table 10). The combination scores predictive of lack of pCR represent different combinations of the 4 signatures (i.e., luminal-immune, luminal-proliferation, immune-HER2, proliferation-HER2, luminal-HER2, immune-immune, luminal-luminal and proliferation-proliferation). Specifically, 48% (n=70) are pairs composed of genes coming from the luminal-immune signatures, 14% (n=20) are pairs composed of 5 genes from the luminal-proliferation signatures, 6% (n=8) are pairs composed of genes from the immune-HER2 signatures, 8% (n=12) are pairs composed of genes from the proliferation-HER2 signatures, and 14% (n=20) are pairs composed of genes from the luminal-HERZ signatures (Table 10). 10          Table 10. Number of significant combination scores from each signature Gene 2 IGG LUM PROLIF HER2 j r* 1 c 3 IGG 10 70 0 8 LUM 70 5 20 20 PROLIF 0 20 1 -.2 HER2 8 20 12 0 * IGG: Immune signature, LUM: luminal signature, PROL1F: proliferation signature, HER2: HER2 amplicon 15

Claims

1. In vitro method for identifying biomarker signatures for the prognosis of patients suffering from HER2+ breast cancer, which comprises:a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESR1], 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; andc. 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 the prognosis of patients suffering from HER2+ breast cancer.

2. In vitro method for the prognosis of patients suffering from HER2+ breast cancer, according to claim 1, which comprises:a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESR1], 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; andc. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, this is indicative of the prognosis of patients suffering from HER2+ breast cancer.

3. In vitro method for the prognosis of patients suffering from HER2+ breast cancer, according to claim 1 or claim 2, which comprises:a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESR1], in a biological sample obtained from the patient;2022419002   26 Jun 2026b. Determining a combination score value by calculating the ratio of the expression of the 2 genes, wherein the ratio is calculated by:i. Combining a first gene comprised in the immune signature with a second gene comprised in the tumor cell proliferation signature; orii. Combining a first gene comprised in the immune signature with a second gene comprised in the luminal differentiation signature; oriii. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the tumor cell proliferation signature; oriv. Combining a first gene comprised in the immune signature selected from the group consisting of: CD79A, CD27, IGJ, POU2AF1, TNFRSF17, IL2RG, PIM2 or IGL with a second gene comprised in the immune signature selected from the group consisting of: CD27, CXCL8, HLA-C, IGLV3-25, IL2RG, LAX1, NTN3, PIM2 or POU2AF1;c. Wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23] and the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESR1]; andd. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, is indicative of good prognosis.

4. In vitro method for the prognosis of patients suffering from HER2+ breast cancer, according to any one of claims 1 to 3, which comprises:a. Measuring the level of expression of at least two genes selected from the gene combinations of Table 7A, 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; andc. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, is indicative of good prognosis.

5. In vitro method for the prognosis of patients suffering from HER2+ breast cancer, according to claim 1 or claim 2, which comprises:a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2,2022419002   26 Jun 2026KIF23, BCL2, DNAJC12, AGR3, AFF3 or ESR1], 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, wherein the ratio is calculated by:i. Combining a first gene comprised in the tumor cell proliferation signature with a second gene comprised in the immune signature; orii. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the immune signature; oriii. Combining a first gene comprised in the tumor cell proliferation signature with a second gene comprised in the luminal differentiation signature; oriv. Combining a first gene comprised in the immune signature selected from the group consisting of: CD27, CXCL8, HLA-C, IGLV3-25, IL2RG, LAX1, NTN3, PIM2 or POU2AF1 with a second gene comprised in the immune signature selected from the group consisting of: CD79A, CD27, IGJ, POU2AF1, TNFRSF17, IL2RG, PIM2 or IGL;c. Wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23] and the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESR1]; andd. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, is indicative of poor prognosis.

6. In vitro method for the prognosis of patients suffering from HER2+ breast cancer, according to claim 1, 2, or 5, which comprises:a. Measuring the level of expression of at least two genes selected from the gene combinations of Table 7B, 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; andc. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, is indicative of poor prognosis.

7. In vitro method for the prognosis of patients suffering from HER2+ breast cancer, according to any one of the preceding claims, which comprises measuring the level of expression of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL,2022419002   26 Jun 2026IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and ESR1].

8. In vitro method for identifying biomarker signatures for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies, which comprises:a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESR1, ERBB2, GRB7, STARD3 or TCAP], 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; andc. 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 the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies.

9. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or nonresponder patients to anti-HER2 therapies, according to claim 8, which comprises:a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESR1, ERBB2, GRB7, STARD3 or TCAP], 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; andc. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, this is indicative of the response to anti-HER2 therapies in patients suffering from HER2+ breast cancer.

10. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-2022419002   26 Jun 2026responder patients to anti-HER2 therapies, according to claim 8 or claim 9, which comprises:a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESR1, ERBB2, GRB7, STARD3 or TCAP], 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, wherein the ratio is calculated by:i. Combining a first gene comprised in the immune signature with a second gene comprised in the luminal differentiation signature; orii. Combining a first gene comprised in the tumor cell proliferation signature with a second gene comprised in the luminal differentiation signature; oriii. Combining a first gene comprised in the HER2 amplicon signature with a second gene comprised in the immune signature; oriv. Combining a first gene comprised in the HER2 amplicon signature with a second gene comprised in the tumor cell proliferation signature; orv. Combining a first gene comprised in the HER2 amplicon signature with a second gene comprised in the luminal differentiation signature; orvi. Combining a first gene comprised in the immune signature selected from the group consisting of: IGKC, IGL or LAX1 with a second gene comprised in the immune signature selected from the group consisting of: HLA-C, CD27, IGJ, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17; orvii. Combining a first gene comprised in the luminal differentiation signature selected from the group consisting of: AFF3, BCL2 or DNAJC12, with a second gene comprised in the luminal differentiation signature selected from the group consisting of: ESR1 or AGR3; orviii. Combining the first gene ASPM comprised in the tumor cell proliferation signature with the second gene NEK2 comprised in the tumor cell proliferation signature; andc. Wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF12022419002   26 Jun 2026or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23], the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESR1] and the HER2 amplicon signature comprises the genes: [ERBB2, GRB7, STARD3 or TCAP], andd. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, is an indication that the patients suffering from HER2+ breast cancer may respond to anti-HER2 therapies.

11. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or nonresponder patients to anti-HER2 therapies, according to any one of claims 8 to 10, which comprises:a. Measuring the level of expression of at least two genes selected from the gene combinations of Table 9A, 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; andc. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, is an indication that the patients suffering from HER2+ breast cancer may respond to anti-HER2 therapies.

12. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or nonresponder patients to anti-HER2 therapies, according to claim 8 or claim 9, which comprises:a. Measuring the level of expression of at least two genes selected from the group consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESR1, ERBB2, GRB7, STARD3 or TCAP], 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, wherein the ratio is calculated by:i. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the immune signature; or2022419002   26 Jun 2026ii. Combining a first gene comprised in the luminal differentiation signature with a second gene comprised in the tumor cell proliferation signature; oriii. Combining a first gene comprised in the immune differentiation signature with a second gene comprised in the HER2 ampliconsignature; oriv. Combining a first gene comprised in the tumor cell proliferationsignature with a second gene comprised in the HER2 ampliconsignature; orv. Combining a first gene comprised in the luminal differentiationsignature with a second gene comprised in the HER2 amplicon signature; orvi. Combining a first gene comprised in the immune signature selected from the group consisting of: HLA-C, CD27, IGJ, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17 with a second gene comprised in the immune signature selected from the group consisting of: IGKC, IGL or LAX1; orvii. Combining a first gene comprised in the luminal differentiation signature selected from the group consisting of: ESR1 or AGR3 with a second gene comprised in the luminal differentiation signature selected from the group consisting of: AFF3, BCL2, or DNAJC12; orviii. Combining the first gene NEK2 comprised in the tumor cell proliferation signature with the second gene ASPM comprised in the tumor cell proliferation signature; andc. Wherein the immune signature comprises the genes [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1 or TNFRSF17], the tumor cell proliferation signature comprises the genes [EXO1, ASPM, NEK2 or KIF23], the luminal differentiation signature comprises the genes: [BCL2, DNAJC12, AGR3, AFF3 or ESR1] and the HER2 amplicon signature comprises the genes: [ERBB2, GRB7, STARD3 or TCAP], andd. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, is an indication that the patients suffering from HER2+ breast cancer may not respond to anti-HER2 therapies.2022419002   26 Jun 202613. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or nonresponder patients to anti-HER2 therapies, according to claim 8, 9, or 12, which comprises:a. Measuring the level of expression of at least two genes selected from the gene combinations of Table 9B, 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; andc. Wherein if a deviation of the combination score value is identified, as compared with a pre-established reference value, is an indication that the patients suffering from HER2+ breast cancer may not respond to anti-HER2 therapies.

14. In vitro method for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or nonresponder patients to anti-HER2 therapies, according to any one of claims 8 to 13, which comprises measuring the level of expression of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESR1, ERBB2, GRB7, STARD3 and TCAP].

15. In vitro method, according to any one of the preceding claims, which further comprises identifying the nodal status (pN1) and / or tumor staging (pT2-4) wherein the identification of nodal status N1-3 and / or tumor status T2-4 is indicative of bad prognosis or that the patient is a non-responder patient to anti-HER2 therapies.

16. In vitro method, according to any one of the preceding claims, wherein the patient is suffering from HER2+ breast cancer.

17. In vitro method, according to any one of the preceding claims, wherein the sample is selected from: tissue, blood, serum, or plasma.

18. In vitro method, according to any one of the preceding claims, wherein the anti-HER2 therapy is a drug selected from: trastuzumab, pertuzumab, lapatinib, pyrotinib, poziotinib, tucatinib, neratinib, trastuzumab deruxtecan, SYD985 or ado-trastuzumab emtansine.

19. In vitro use of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3 and ESR1] for the prognosis of patients suffering from HER2+ breast cancer.2022419002   26 Jun 202620. In vitro use of a group of genes consisting of: [CD27, CD79A, HLA-C, IGJ, IGKC, IGL, IGLV3-25, IL2RG, CXCL8, LAX1, NTN3, PIM2, POU2AF1, TNFRSF17, EXO1, ASPM, NEK2, KIF23, BCL2, DNAJC12, AGR3, AFF3, ESR1, ERBB2, GRB7, STARD3 and TCAP] for the prediction of response to anti-HER2 therapies in patients suffering from HER2+ breast cancer, or for classifying patients into responder or non-responder patients to anti-HER2 therapies.

21. Anti-HER2 therapy, or any pharmaceutical composition comprising thereof, optionally including pharmaceutically acceptable excipients or carriers, for use in the treatment of patients suffering from HER2+ breast cancer, wherein the method comprises predicting the response to anti-HER2 therapies in the patients suffering from HER2+ breast cancer or classifying patients into responder or non-responder patients to anti-HER2 therapies, by following the method of any one of claims 8 to 18.

22. Anti-HER2 therapy, or any pharmaceutical composition comprising thereof, optionally including pharmaceutically acceptable excipients or carriers, for use in the treatment of patients suffering from HER2+ breast cancer, according to claim 21, wherein the anti-HER2 therapy is optionally selected from: trastuzumab, pertuzumab, lapatinib, pyrotinib, poziotinib, tucatinib, neratinib, trastuzumab deruxtecan, SYD985 or ado-trastuzumab emtansine.

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