DNA copy number alterations for predicting treatment response in breast cancer patients

Abstract

The present invention relates to the in vitro use of DNA copy number alterations (CNAs) for predicting the response of HR+ / HER2- breast cancer patients to treatments, including targeted therapies, such as CDK4 / 6 inhibitors, and / or endocrine therapy; for prognosing HR+ / HER2- breast cancer patients; for monitoring HR+ / HER2- breast cancer patients; or for classifying HR+ / HER2- breast cancer patients into responders or non-responders to treatments, including targeted therapies, such as CDK4 / 6 inhibitors, and / or endocrine therapy.

Description

[Technical field]

[0001] The present invention relates to the field of medicine.In particular, the present invention relates to the in vitro use of DNA copy number alterations (CNAs) for predicting the response of HR+ / HER2- breast cancer patients to treatment, including targeted therapy such as CDK4 / 6 inhibitors and / or endocrine therapy, for prognosis of HR+ / HER2- breast cancer patients, for monitoring HR+ / HER2- breast cancer patients, or for classifying HR+ / HER2- breast cancer patients into responders or non-responders to treatment, including targeted therapy and / or endocrine therapy. [Background technology]

[0002] Sequencing of tumor DNA has brought many new biomarkers and the potential for precision oncology. Detection of somatic gene mutations, amplifications and gene fusions allows for the delivery of targeted therapies in multiple cancer types such as lung, colorectal, melanoma and breast cancer. Furthermore, detection of a large number of somatic mutations (i.e., tumor mutational burden) or microsatellite instability high phenotypes can help to identify candidates for anti-PD1 / PDL1 immune checkpoint inhibitors. Importantly, sequencing of circulating tumor DNA in blood samples (i.e., so-called liquid biopsies, hereafter referred to as "ctDNA") allows easy access to several tumor-based genetic information at any given time point and can in some cases replace tumor tissue biopsies, thus avoiding the delays and complications of solid tumor invasive biopsy procedures, which can be very challenging in the metastatic setting.

[0003] Identification of single tumor DNA alterations can be clinically useful. However, cancer is highly complex and additional biological information is likely required to improve prediction of patient prognosis and / or treatment benefit. Breast cancer is a perfect example, as RNA-based signature profiling tests provide clinically and biologically useful information beyond individual somatic gene mutations or amplification of genes such as PIK3CA or ERBB2. In early disease, multigene RNA-based prognostic assays (e.g., OncotypeDX, Mammaprint and Prosigna) are available and recommended by clinical guidelines. In advanced disease, RNA-based profiling is becoming a promising prognostic tool. Unfortunately, tissue samples in patients with advanced disease are not readily available, and even so, the type of metastatic organ or site may compromise expression patterns obtained from bulk RNA and may not reflect intrapatient tumor heterogeneity.

[0004] The present invention focuses on solving this technical and clinical problem, and herein it is proposed to use DNA sequencing in plasma, serum, breast milk, cerebrospinal fluid or blood, preferably DNA sequencing of ctDNA, to capture clinically relevant information beyond simple single gene alterations. This approach may be highly relevant in the metastatic setting, where ctDNA may be the only genetic material readily available from the tumor.

[0005] Although the ability of ctDNA to capture complex data with clinical value is unknown in the metastatic breast cancer setting, the present invention demonstrates that complex, clinically relevant tumor phenotypic traits can be identified in DNA, specifically ctDNA. Summary of the Invention

[0006] Brief description of the invention The present invention relates to in vitro uses of CNAs for predicting the response of HR+ / HER2- breast cancer to treatments, including targeted therapies such as CDK4 / 6 inhibition and / or endocrine therapy, for prognosis of HR+ / HER2- breast cancer patients, for monitoring HR+ / HER2- breast cancer patients, or for classifying HR+ / HER2- breast cancer patients into responders or non-responders to treatments, including targeted therapies such as CDK4 / 6 inhibition and / or endocrine therapy.

[0007] In particular, the present invention focuses on the use of CNAs, preferably from ctDNA, to capture complex and clinically relevant tumor phenotypes in breast cancer.

[0008] The inventors herein demonstrate that machine learning multigene signatures derived from DNA, preferably ctDNA, sense many parts of a given pathway and identify several complex biological features, including measures of tumor growth and estrogen receptor signaling, similar to those achieved using direct tumor RNA profiling. For example, it is demonstrated herein that a ctDNA-based genomic signature tracking retinoblastoma loss of heterozygosity (RB-LOH) is significantly associated with poor prognosis and drug response in metastatic breast cancer patients treated with CDK4 / 6 inhibitors and / or endocrine therapy, independent of tumor cell fraction and other clinicopathological variables.

[0009] It should be noted that the contribution of the present invention to the prior art is the possibility of using CNAs spanning multiple DNA segments to predict the complex phenotype of HR+ / HER2- breast cancer patients, for example to predict whether the patient will respond to targeted therapy and / or endocrine therapy.The special technical feature that gives the unity of the present invention would therefore be the assessment of the presence of CNAs spanning multiple DNA segments on multiple chromosomes, which is accurate in this clinical situation, preferably deviating for blood, serum, breast milk, cerebrospinal fluid or plasma samples.

[0010] 150 CNA-based signatures [Xia Y, Fan C, Hoadley KA, Parker JS, Perou CM. Genetic determinants of the molecular portraits of epithelial cancers. Nature Communications 2019;10(1):5666 doi 10.1038 / s41467-019-13588-2] (see Figure 1) were investigated in 87 plasma samples with >3% tumor fraction (TF) from hormone receptor positive / HER2 negative (HR+ / HER2-) metastatic breast cancer (mBC) patients (hereafter CDK Plasma-1 cohort) treated with CDK4 / 6 inhibitors plus endocrine therapy (CDK4 / 6i+ET). Cox regression models revealed the association of each individual signature with progression-free survival (PFS) and overall survival (OS), and 27 prognostic signatures were identified for both PFS and OS. The 27 signatures were investigated in the MSKCC-CDK (n=381, PFS) and METABRIC-HR+ / HER2- (n=1131, DFS, OS) cohorts. The prognostic value of the 27 signatures was validated in at least one of the validation cohorts.

[0011] [Table 1]

[0012] A total of 514 segments were present in 27 signatures, and we selected segments that were included in at least 15 signatures, which meant 75 segments. Duplicate segments (segments in the same chromosomal region with the same signal) were excluded.

[0013] Sixteen segments (Table 2) were initially identified as the primary drivers of the 27 signatures (Table 3).

[0014] [Table 2]

[0015] [Table 3]

[0016] [Table 4]

[0017] [Table 5]

[0018] Using the CDKPlasma-1 (n=87, PFS, OS), MSKCC-CDK (n=381, PFS) and METABRIC-HR+ / HER2- (n=1131, DFS, OS) cohorts, we also demonstrate that the % of prognostic combined scores is higher than the % of prognostic segments.

[0019] [Table 6]

[0020] In a preferred embodiment of the invention, the presence of CNAs is assessed in any or all of the DNA segments of Table 7, Table 8 or Table 9.

[0021] [Table 7] TIFF2025514884000008.tif207170TIFF2025514884000009.tif208170TIFF2025514884000010.tif209170TIFF2025514884000011.tif209170TIFF2025514884000012.tif209170TIFF2025514884000013.tif209170TIFF2025514884000014.tif209170TIFF2025514884000015.tif209170TIFF2025514884000016.tif209170TIFF2025514884000017.tif213170

[0022]

Table 8

[0023]

Table 9

[0024] The methodology involves calculating segment-level CNA scores instead of gene-level CNAs. First, segmented files from CNVkit output (for tumor DNA) and ichorCNA output (ctDNA) are mapped to gene-level features. Each segment score is then calculated as the average copy number score across genes in the segment. The prognostic significance of each segment score is tested as a continuous variable using univariate and multivariate Cox models for PFS and OS. DNA-based signature scores were calculated as the weighted average of DNA segment values ​​for each sample. The coefficients of DNA segments for predicting gene signatures are those reported in Xia et al. Nat Comms 2019. The prognostic significance of each signature score is tested as a continuous variable or a categorical variable (low, intermediate, high defined by tertiles) using univariate and multivariate Cox models for PFS and OS.

[0025] Thus, a first embodiment of the present invention relates to an in vitro method for predicting the response of a HR+ / HER2- breast cancer patient to treatment, including targeted therapy such as CDK4 / 6 inhibitors and / or endocrine therapy, comprising: a) assessing the presence of CNAs across multiple DNA segments on multiple chromosomes in a biological sample obtained from the patient; b) processing the measured CNAs to obtain a score; and c) if a deviation or variation in the score value is identified compared to a pre-established reference value, this indicates that the HR+ / HER2- breast cancer patient will respond or be resistant to the treatment.

[0026] A second embodiment of the present invention relates to an in vitro method for prognosis of a patient with HR+ / HER2- breast cancer, comprising: a) assessing the presence of CNAs across multiple DNA segments on multiple chromosomes in a biological sample obtained from the patient; b) processing the measured CNAs to obtain a score; and c) if a deviation or variation in the score value is identified compared to a pre-established reference value, this indicates the prognosis of the patient with HR+ / HER2- breast cancer.

[0027] A third embodiment of the present invention relates to an in vitro method for monitoring HR+ / HER2- breast cancer patients to assess whether they are responding to treatment, including targeted therapy such as CDK4 / 6 inhibitors and / or endocrine therapy, comprising: a) assessing the presence of CNAs across multiple DNA segments on multiple chromosomes in a biological sample obtained from the patient; b) processing the measured CNAs to obtain a score; and c) if a deviation or variation in the score value is identified compared to a pre-established reference value, this indicates that the HR+ / HER2- breast cancer patient is responding or resistant to the treatment.

[0028] A fourth embodiment of the present invention relates to an in vitro method for classifying HR+ / HER2- breast cancer patients into biologically and clinically relevant groups associated with different responses to treatments including targeted therapies such as CDK4 / 6 inhibitors and / or endocrine therapies, comprising: a) assessing the presence of CNAs across multiple DNA segments on multiple chromosomes in a biological sample obtained from the patient; b) processing the measured CNAs to obtain multiple scores; c) classifying the breast cancer samples into different groups based on their scoring profile in comparison with pre-established reference values; and d) each group is indicative of whether the HR+ / HER2- breast cancer patient will respond to the treatment.

[0029] In a preferred embodiment of the present invention, the method is a computer-implemented method comprising: a) receiving a plurality of CNA data sets from a patient; b) processing the information according to step a) to find statistically significant variations or deviations; and c) providing results by a computer system based on the information received according to a) and pre-established standards already stored in the computer.

[0030] In a preferred embodiment, a "score" is obtained after evaluating the presence of CNAs in tumor DNA segments present in a biological sample. The signal of each segment is calculated by averaging the signal of each gene in each segment. The final score is calculated by multiplying the signal of each DNA segment by a pre-established coefficient or weight and summing them all. After processing all the values, a single score is obtained. This single score is compared to a "pre-established reference value" to finally make a clinical decision. The "pre-established reference value" is a threshold value obtained after evaluating the presence of CNAs in "normal DNA" segments (i.e., non-tumor DNA) segments present in a biological sample. In particular, if a deviation or variation in the "score" value, typically a "score" value higher or lower than the "pre-established reference value", is identified, this indicates that the HR+ / HER2- breast cancer patient is responsive or resistant to treatment, or that the patient has a good or bad prognosis.

[0031] In a preferred embodiment of the invention, the presence of CNAs is determined by the following DNA segments (see Tables 2, 4 and 5) (segment nomenclature is as in the NCBI genome data): chr20:33386980-33969561, chr13:46362859-48209064, chr17:63942109-65847254, chr17:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, are evaluated in either chr2:32460827-55039898, chr7:16017926-18944036, chr2:1-93300000, chr8:128774432-128849112, chr12:1-1311104, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607.

[0032] In a preferred embodiment of the invention, the presence of CNAs is determined by the following DNA segments (see Tables 2, 4 and 5): chr20: 33386980-33969561, chr13: 46362859-48209064, chr17: 63942109-65847254, chr17: 1-22200000, chr10: 129812260-135374737, chr17: 7471230-7717938, chr2: 32460827-55 Evaluated in all of the following locations: 039898, chr7:16017926-18944036, chr2:1-93300000, chr8:128774432-128849112, chr12:1-1311104, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607.

[0033] In a preferred embodiment, the biological sample is selected from a plasma, serum, breast milk, cerebrospinal fluid or blood sample.

[0034] In a preferred embodiment, the patient is afflicted with breast cancer.

[0035] In a preferred embodiment, the breast cancer subtype is selected from HR+ / HER2-, HER2+ and triple negative.

[0036] A fifth embodiment of the invention is a kit suitable for carrying out the above method, comprising the steps of: , chr2:1-93300000, chr8:128774432-128849112, chr12:1-1311104, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 and / or chr3:58626894-61524607, preferably in the DNA segments of Table 7, Table 8 or Table 9.

[0037] A sixth embodiment of the invention relates to the use of a kit as defined above for predicting the response of HR+ / HER2- breast cancer patients to treatments including targeted therapies such as CDK4 / 6 inhibitors and / or endocrine therapies, for prognosis of HR+ / HER2- breast cancer patients, for monitoring HR+ / HER2- breast cancer patients or for classifying HR+ / HER2- breast cancers into biologically relevant groups related to their response to treatments including targeted therapies such as CDK4 / 6 inhibitors and / or endocrine therapies.

[0038] A final embodiment of the invention relates to a targeted therapy, such as a CDK4 / 6 inhibitor, and / or an endocrine therapy, for use in treating a patient with HR+ / HER2- breast cancer, where the patient has been identified as a responder patient according to the method according to any of the above embodiments. Alternatively, the invention relates to a method of treating a patient with HR+ / HER2- breast cancer, comprising administering a therapeutically effective dose or amount of a targeted therapy, such as a CDK4 / 6 inhibitor, and / or an endocrine therapy, once the patient has been identified as a responder by the method according to any of the above embodiments.

[0039] In preferred embodiments, endocrine therapy includes selective estrogen receptor degraders such as letrozole, anastrozole, exemestane, tamoxifen, fulvestrant, and targeted therapy includes CDK4 / 6 inhibitors (palbociclib, abemaciclib, ribociclib, or trilaciclib), PI3K / mTOR inhibitors (alpelisib, everolimus), and antibody-drug conjugates targeting: HER2 (trastuzumab deruxtecan, trastuzumab duocarmazine, dicitamab vedotin, ARX788, or BAT8001), HER3 (patrituzumab or deruxtecan), and TROP2 (sactuzumab govitecan, datopotamab deruxtecan, or SKB264), and LIV-1 (radilatuzumab vedotin).

[0040] Furthermore, it is important to note that the prognostic value of the 16-segment score was evaluated in the CDK Plasma-1 (n=87, PFS, OS), MSKCC-CDK (n=381, PFS), and METABRIC-HR+ / HER2- (n=1131, DFS, OS) cohorts.

[0041] A total of 11 segments (68.75%) were prognostic in at least one cohort: four segments were associated with poor prognosis and eight segments were associated with good prognosis.

[0042] [Table 10]

[0043] [Table 11]

[0044] [Table 12]

[0045] Furthermore, it is important to note that signals from at least two segments were combined and their individual association with prognosis was evaluated. The score of each combination including two of the 16 segments identified above was calculated as combination score = adjusted score segment 2 - adjusted score segment 1. The prognostic value of 240 possible combination scores was evaluated in the CDK Plasma-1 (n=87, PFS, OS), MSKCC-CDK (n=381, PFS), and METABRIC-HR+ / HER2- (n=1131, DFS, OS) cohorts.

[0046] [Table 13]

[0047] A total of 184 combinations (76.7%) were prognostic: 92 combinations were associated with poor prognosis and 92 combinations were associated with good prognosis.

[0048] [Table 14] TIFF2025514884000045.tif209170TIFF2025514884000046.tif209170TIFF2025514884000047.tif20917 0TIFF2025514884000048.tif209170TIFF2025514884000049.tif209170TIFF2025514884000050.tif36170

[0049]

Table 15

[0050] Thus, the present invention also relates to an in vitro method for prognosing or predicting the response of a patient with HR+ / HER2- breast cancer to a treatment selected from a targeted therapy, including a CDK4 / 6 inhibitor, and / or an endocrine therapy, comprising: a) detecting in a biological sample obtained from the patient the following DNA segments selected from the group consisting of: chr20: 33386980-33969561, chr13: 46362859-48209064, chr1 7:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr8:128774432-128849112, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607 b) the presence of a CNA spanning any of the following DNA segments selected from the group consisting of: chr20:33386980-33969561, chr8:128774432-128849112, or chr16:1-38200000 is indicative of a poor prognosis or poor response; or c) the presence of a CNA spanning any of the following DNA segments selected from the group consisting of: chr13:46362859 The presence of CNAs spanning any of the following is an indication of a good prognosis or response: -48209064, chr17:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607.

[0051] The present invention also relates to an in vitro method for monitoring a patient with HR+ / HER2- breast cancer to assess whether they are responding to a treatment selected from a targeted therapy comprising a CDK4 / 6 inhibitor, and / or an endocrine therapy, comprising: a) detecting in a biological sample obtained from the patient the following DNA segments selected from the group consisting of: chr20:33386980-33969561, chr13:46362859-48209064, c Either hr17:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr8:128774432-128849112, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607 b) the presence of a CNA spanning any of the following DNA segments selected from the group consisting of: chr20:33386980-33969561, chr8:128774432-128849112 or chr16:1-38200000 is indicative of a poor prognosis or poor response; or c) the presence of a CNA spanning any of the following DNA segments selected from the group consisting of: chr13:4636285 The presence of CNAs spanning any of the following is an indication of a good prognosis or response: chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607.

[0052] The present invention also relates to an in vitro method for classifying HR+ / HER2- breast cancer patients into groups associated with different responses to a treatment selected from a targeted therapy, including a CDK4 / 6 inhibitor, and / or an endocrine therapy, comprising: a) detecting in a biological sample obtained from the patient the following DNA segments selected from the group consisting of: chr20: 33386980-33969561, chr13: 46362859-48209064, chr1 7:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr8:128774432-128849112, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607 b) the presence of a CNA spanning any of the following DNA segments selected from the group consisting of: chr20:33386980-33969561, chr8:128774432-128849112, or chr16:1-38200000 is indicative of a poor prognosis or poor response; or c) the presence of a CNA spanning any of the following DNA segments selected from the group consisting of: chr13:46362859 The presence of CNAs spanning any of the following is an indication of a good prognosis or response: -48209064, chr17:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607.

[0053] In a preferred embodiment, the method further comprises assessing the presence of CNAs across any of the following DNA segments selected from the group consisting of: chr17:63942109-65847254, chr2:32460827-55039898, chr7:16017926-18944036, chr2:1-93300000 or chr12:1-1311104.

[0054] In a preferred embodiment, the method comprises: a) assessing the presence of CNAs across any of the combinations of DNA segments in Table 14 and Table 15 in a biological sample obtained from a patient; b) processing the measured CNAs to obtain a score; c) if a deviation or variation in the score value is identified in any of the combinations of DNA segments in Table 14 compared to a pre-established reference value, this indicates a poor prognosis or poor response; or d) if a deviation or variation in the score value is identified in any of the combinations of DNA segments in Table 15 compared to a pre-established reference value, this indicates a good prognosis or good response.

[0055] In a preferred embodiment, the method is characterized in that it is a computer-implemented method comprising: a) receiving a plurality of CNA data sets from a patient; b) processing the information according to step a) to find statistically significant variations or deviations; and c) providing results by a computer system based on the information received according to a) and pre-established standards already stored in the computer.

[0056] In a preferred embodiment, the method comprises the steps of: 1) cloning the following DNA segments: chr20:33386980-33969561, chr13:46362859-48209064, chr17:63942109-65847254, chr17:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr2:32460827-55039898, chr7:1601 This included assessing the presence of CNAs in all of the following genomic regions: 7926-18944036, chr2:1-93300000, chr8:128774432-128849112, chr12:1-1311104, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 and chr3:58626894-61524607.

[0057] In a preferred embodiment, the method comprises assessing the presence of CNAs in any or all of the DNA segments of Table 7, Table 8 or Table 9.

[0058] In a preferred embodiment, the biological sample is selected from a plasma, serum, breast milk, cerebrospinal fluid or blood sample.

[0059] In a preferred embodiment, the cancer subtype is selected from HR+ / HER2-, HER2+ and triple negative.

[0060] The present invention also relates to a method for predicting the response of a patient with HR+ / HER2- breast cancer to a treatment selected from a targeted therapy comprising a CDK4 / 6 inhibitor and / or an endocrine therapy; for prognosticating a patient with HR+ / HER2- breast cancer; for monitoring a patient with HR+ / HER2- breast cancer to assess whether they are responding to a treatment selected from a targeted therapy comprising a CDK4 / 6 inhibitor and / or an endocrine therapy; or for classifying patients into groups associated with different responses to a treatment selected from a targeted therapy comprising a CDK4 / 6 inhibitor and / or an endocrine therapy, chr20:33386980-33969561, ch chr13:46362859-48209064, chr17:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr8:128774432-128849112, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607, or any combination of the DNA segments of Table 14 or Table 15.

[0061] The present invention also relates to chr20: 33386980-33969561, chr13: 46362859-48209064, chr17: 1-22200000, chr10: 129812260-135374737, chr17: 7471230-7717938, chr8: 128774432-128849112, chr16: 1-38200000, chr4: 83634873 The present invention relates to a kit suitable for carrying out the method of the present invention, comprising tools and reagents for assessing the presence of CNAs in any of the segments selected from the group consisting of: -83961360, chr5:76408288-81082828, chr19:1-526082 or chr3:58626894-61524607, or the combined DNA segments of Table 14 or Table 15.

[0062] The present invention also relates to the use of the kit for predicting the response of HR+ / HER2- breast cancer patients to a treatment selected from a targeted therapy comprising a CDK4 / 6 inhibitor and / or endocrine therapy; for prognosing HR+ / HER2- breast cancer patients; for monitoring HR+ / HER2- breast cancer patients to assess whether they are responding to a treatment selected from a targeted therapy comprising a CDK4 / 6 inhibitor and / or endocrine therapy; or for classifying patients into groups associated with different responses to a treatment selected from a targeted therapy comprising a CDK4 / 6 inhibitor and / or endocrine therapy.

[0063] The present invention also relates to targeted therapies, including CDK4 / 6 inhibitors, and / or endocrine therapies, for use in treating patients with HR+ / HER2- breast cancer, where the patient has been identified as a responder patient according to the methods of the present invention.

[0064] Alternatively, the present invention relates to a method of treating a patient suffering from HR+ / HER2- breast cancer with targeted therapy, including a CDK4 / 6 inhibitor, and / or endocrine therapy, comprising identifying the patient as a responder patient according to any of the methods of the present invention.

[0065] Finally, the present invention relates to an in vitro method for classifying HR+ / HER2- breast cancer patients according to their survival probability, comprising the steps of: a) detecting in a biological sample obtained from said patient the following DNA segments selected from the group consisting of: chr20: 33386980-33969561, chr13: 46362859-48209064, chr17: 63942109-65847254, chr17: 1-22200000, chr10: 129812260-135374 737, chr17:7471230-7717938, chr2:32460827-55039898, chr7:16017926-18944036, chr2:1-93300000, chr8:128774432-128849112, chr12:1-1311104, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 and and chr3:58626894-61524607; b) processing the measured CNA to obtain a score value; c) if a deviation or variation in the score value is identified compared to a first pre-established reference value, this indicates that the patient belongs to cluster 1 characterized by the highest survival probability; or d) if a deviation or variation in the score value is identified compared to a second pre-established reference value, this indicates that the patient belongs to cluster 2 characterized by the second highest survival probability; or e) if a deviation or variation in the score value is identified compared to a third pre-established reference value, this indicates that the patient belongs to cluster 3 characterized by the second worst survival probability; or f) if a deviation or variation in the score value is identified compared to a fourth pre-established reference value, this indicates that the patient belongs to cluster 4 characterized by the worst survival probability. For the purposes of the present invention, the following terms are defined. Copy Number Alterations (CNAs): are a phenomenon in which sections of the genome are repeated and the number of repeats in the genome varies between individuals. CNAs are a type of structural mutation, specifically a type of duplication or deletion event that affects a significant number of base pairs. CNAs can generally be classified into two main groups: short repeats and long repeats. However, there is no clear boundary between the two groups and the classification depends on the nature of the locus of interest. Short repeats include mainly dinucleotide repeats (two repeated nucleotides, e.g. ACACAC...) and trinucleotide repeats. Long repeats include repeats of the entire gene. This classification based on the size of the repeats is the most obvious type of classification, as size is an important factor when investigating the type of mechanism most likely to cause the repeats (and therefore the possible effects of these repeats on the phenotype). The expression "pre-established reference value" refers to a threshold value obtained after evaluating the presence of CNAs in "normal DNA" segments (i.e., non-tumor DNA) present in a biological sample. The expression "score" refers to the value obtained after evaluating the presence of CNAs in tumor DNA segments present in a biological sample. The signal of each segment is calculated by averaging the signals of each gene in each segment. The final score is calculated by multiplying the signal of each DNA segment by its weight and summing all of them. After processing all the values, a single score is obtained. This single value is compared to a "pre-established reference value" to finally make a clinical decision. In particular, if a deviation or variation in the "score" value is identified, typically a "score" value higher or lower than the "pre-established reference value", this indicates that the HR+ / HER2- breast cancer patient will respond or not respond to the treatment, or that the patient has a good or poor prognosis. "Comprising" means including, but not limited to, what follows the word "comprising." Thus, use of the word "comprising" indicates that the listed elements are required or mandatory, but other elements are optional and may or may not be present. "Consisting of" means "including, and limited to" what 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. By "therapeutically effective dose or amount" is intended an amount which, when administered as described herein, results in a positive therapeutic response in a subject suffering from 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, the mode of administration, and the like. [Brief description of the drawings]

[0066] [Figure 1]Circulating tumor DNA (ctDNA) in metastatic breast cancer. (a) Plasma samples were obtained from 207 patients (174 HR+ / HER2-, 16 HER2+, 16 TNBC, 1 N / A). After purification of plasma cell-free DNA, shallow whole genome sequencing (shWGS) was performed. Using ctDNA-based sequencing data from 514 DNA segments, 150 previously developed DNA copy number-based signatures [Xia Y, Fan C, Hoadley KA, Parker JS, Perou CM. Genetic determinants of the molecular portraits of epithelial cancers. Nature Communications 2019;10(1):5666 doi 10.1038 / s41467-019-13588-2] tracking of various biological processes were applied to patients with tumor cell fraction (TF) ≥ 3%. Individual scores for each signature were obtained. (b) Relationship between TFs and the number of altered DNA segments detected across 246 plasma ctDNA samples before (left) and after (right) adjusting for DNA copy number signals by TFs and ploidy using the ichorCNA tool. (c) Example of correlation between scores of two DNA-based signatures when determined in plasma vs. tumor tissue. Of note, the tissue samples were obtained at different time points than the plasma samples. (d) Association between ER (left) and HER2 (right) tumor tissue status and expression of two cDNA-based signatures tracking ER- and HER2-related biology, respectively. [Diagram 2]ctDNA-based RB-LOH signature predicts clinical outcome in advanced HR+ / HER2- breast cancer treated with endocrine therapy and CDK4 / 6 inhibitors. (a) Plasma samples were obtained from 124 patients within 48 hours of starting endocrine therapy and CDK4 / 6 inhibition. The ctDNA-based signature was applied to plasma samples with TF ≥ 3% (n = 87). (b) RB-LOH ctDNA-based signature scores in patients with complete or partial response (CR / PR), stable disease and progressive disease (PD). (c) Kaplan-Meier curves of PFS (left) and OS (right) for the RB-LOH ctDNA-based signature. Each patient group is based on tertiles. (d) Average ctDNA signal of 16 features of the original RB-LOH DNA-based signature (left column) as well as the weight and direction of each feature (right column) as previously reported in Xia et al [Xia Y, Fan C, Hoadley KA, Parker JS, Perou CM. Genetic determinants of the molecular portraits of epithelial cancers. Nature Communications 2019;10(1):5666 doi 10.1038 / s41467-019-13588-2]. (e) Forest plot of hazard ratios (HRs) for PFS (left) and OS (right) of the RB-LOH DNA-based signature when assessed in plasma alone (i.e., plasma-univariate; n=87), plasma when adjusted for TFs (n=87), plasma when adjusted for PAM50 RNA-based subtypes (n=53), plasma when adjusted for TFs+PAM50+clinical variables (n=53), tissue alone (i.e., tissue-univariate; n=63) and plasma when adjusted for tissue and vice versa (n=28). (f) ctDNA-based signature scores for the RB-LOH signature, Luminal A signature, 13q14.2 RB1 locus and TFs across seven patients with paired plasma samples (baseline vs. post-CDK4 / 6 inhibitor treatment). P-values ​​(p) were determined by two-tailed paired t-test. [Diagram 3]ctDNA-based profiling of metastatic breast cancer. (a) Unsupervised cluster analysis of 178 plasma samples with TFs ≥ 3% (columns) and scores of 150 ctDNA-based signatures (rows). Orange and purple represent scores above and below the median score of the signature across the dataset. Below the array tree, the IHC subtype and PAM50 molecular subtype are shown for each sample. Four clusters of samples were identified (clusters 1–4). Within cluster 2, two subgroups of samples were also identified (clusters 2A and 2B). (b) Expression of two tissue PAM50 RNA-based signatures (i.e., Luminal A and HER2 enrichment) in cluster 3 versus the other clusters. This analysis was performed on 107 paired plasma and tumor tissue samples. Of note, 58 tumor tissue samples were obtained at the same time point as the plasma samples and 49 tumor tissue samples were obtained at different time points before obtaining the plasma samples. (c) Unsupervised cluster heatmap analysis of Pearson's correlation coefficients obtained by comparing the top individual ctDNA-based signature scores versus each individual PAM50 RNA-based tissue signature score across 58 matched timepoint paired plasma-tissue cases. (d) Details of unsupervised cluster heatmap analysis showing correlation coefficients obtained by comparing luminal-associated and proliferation-associated ctDNA-based signature scores against the log2 values ​​of mRNA expression for each of the 771 genes. [Figure 4]DNA-based tumor profiles in tissue samples and associations with clinical outcomes. (a) Unsupervised cluster analysis (columns) and 150 DNA-based signature scores (rows) of 1,689 tumor samples from the METABRIC dataset. Orange and purple represent scores above and below the median of the signature across the dataset. Below the array tree, the InctClust classification and PAM50 molecular subtype are shown for each sample. The four clusters are shown below the data matrix. (b) PAM50 molecular subtype distribution across the four DNA-based clusters in 1,517 breast tumors from the METABRIC dataset. (c) TP53 mutation distribution across the four DNA-based clusters in 1,517 breast tumors from the METABRIC dataset. (d) Kaplan-Meier curves of DFS (left) and OS (right) for the four DNA-based clusters evaluated in all tumors (n=1,683) and HR+ / HER2-negative tumors (n=1,131) from the METABRIC database. [Diagram 5] Kaplan-Meier curves for PFS (Progression Free Survival). Kaplan-Meier curves for PFS of the four ctDNA-based clusters (determined by the 16 segments in Table 5) in 152 patients with HR+ / HER2- metastatic breast cancer treated with CDK4 / 6 inhibitors plus endocrine therapy. [Figure 6] Kaplan-Meier curves for DFS (METABRIC). Kaplan-Meier curves for DFS of the four ctDNA-based clusters (determined by the 16 segments in Table 5) in 1,131 HR+ / HER2-negative tumors from the METABRIC database. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0067] Detailed Description of the Invention The present invention is illustrated by the examples set out below, without intending to limit its scope of protection. EXAMPLES

[0068] Example 1. Materials and Methods Example 1.1. Study Participants and Samples Baseline pre-treatment plasma samples were collected from 124 patients with HR+ / HER2 advanced breast cancer treated with endocrine therapy in combination with a CDK4 / 6 inhibitor (i.e., palbociclib, ribociclib or abemaciclib) at the Hospital Clinic of Barcelona between 2018 and 2021. All plasma samples were obtained before the start of treatment. In seven patients, we obtained additional plasma samples after progression during treatment.

[0069] To complement the plasma dataset of 124 patients treated with endocrine therapy and CDK4 / 6 inhibitors, we collected 121 additional plasma samples from patients treated at the Hospital Clinic of Barcelona at different stages of the disease: 85 plasmas from 77 patients with advanced HR+ / HER2- breast cancer, 19 plasmas from 16 patients with HER2+ advanced breast cancer, 17 plasmas from 16 patients with advanced TNBC, and 1 plasma from 1 patient with unknown ER and HER2 status. In addition, we collected FFPE tumor tissues from 110 patients with available plasma samples, including 71 patients treated with endocrine therapy and CDK4 / 6 inhibitors. Finally, we collected FFPE tumors from 17 patients with HR+ / HER2- advanced breast cancer who did not have plasma samples but were treated with endocrine therapy and CDK4 / 6 inhibitors.

[0070] The hospital's institutional ethics committee approved the study in accordance with the principles of Good Clinical Practice, the Declaration of Helsinki, and other applicable local regulations. Written informed consent was obtained from all patients before enrollment. Medical records were retrospectively reviewed to obtain the necessary clinical data.

[0071] Example 1.2. DNA Sequencing of Plasma Samples Approximately 30 mL of peripheral blood was collected in K2-EDTA Vacutainer tubes (Becton Dickinson) and plasma isolation was performed within 2 h of blood collection by two centrifugation steps. Plasma was separated from peripheral blood cells by centrifugation at 1,600xg for 10 min at 4°C. Approximately 12 mL of plasma was obtained per patient and subsequently centrifuged at 16,000xg for 10 min at 4°C to remove residual supernatant and any residual contaminants including cells. Plasma samples were then aliquoted into 1.5 mL tubes and immediately stored at -80°C. cfDNA was obtained from 3 mL of plasma using the QIAamp Circulating Nucleic Acid Kit (QIAGEN Inc.) according to the manufacturer's instructions and quantified with the Qubit dsDNA High Sensitivity Assay Kit and Qubit 4.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). cfDNA was concentrated using a SpeedVac to meet the requirements for library preparation. Library preparation was performed by ligating unique dual index (UDI) custom adapters to a minimum of 10 ng of isolated cfDNA (10–50 ng dsDNA). More specifically, fragment ends of cfDNA were blunt-ended, 5' phosphorylated, and then A-tailed at the 3' end to support adapter ligation. Adapters were 10bp-UDI, recommended to mitigate errors introduced by index hopping or switching in Illumina instruments with patterned flow cells such as the NovaSeq 6000. Indexed libraries were quantified by qPCR using the KAPA Library Quantification Kit (Roche Sequencing Solutions), pooled, and sequenced on a NovaSeq 6000 Illumina at 0.5× average coverage with a read length of 2× 150 bp.ShWGS were analyzed using hmmcopy_utils (https: / / github.com / shahcompbio / hmmcopy_utils) and ichorCNA v0.2.0 (https: / / github.com / broadinstitute / ichorCNA) with a bin size of 500 kb and default parameters.

[0072] Example 1.3. DNA Sequencing of FFPE Tumor Samples DNA obtained from FFPE-derived tissues was purified using the QIAamp DNA FFPE Tissue kit (QIAGEN Inc.) according to the manufacturer's instructions for all available samples. Quantification was performed using the Qubit dsDNA broad-spectrum assay kit and Qubit 4.0 fluorometer (Life Technologies, Carlsbad, CA, USA). A minimum of 100 ng of extracted DNA was processed for library preparation using a custom hybridization-based capture panel (VHIO-300 v4 panel) targeting 435 genes with reported somatic mutations in different tumor types, performed with the Agilent SureSelectXT Low Input Target Enrichment System (Agilent Technologies, Inc). Indexed libraries were quantified by qPCR using the KAPA Library Quantification Kit (Roche Sequencing Solutions), pooled, and sequenced on a HiSeq 2500 Illumina (2 × 100 bp) at 500x average coverage. Reads were aligned to the hg19 reference genome using BWA, GATK base quality score recalibration, indel realignment, duplicate removal were applied, and variant calling was performed using VarScan2 (v2.4.3) and Mutect2 (v4.1.0.0) with the following parameters: minimum variant allele frequency (VAF) of 5% for single nucleotide variants (SNVs) and 10% for indels. Germline variants were excluded by filtering using a single nucleotide polymorphism (SNP) database.

[0073] Example 1.4. DNA-based signature estimation For both tumor DNA sequencing and plasma cell-free ctDNA sequencing, segmented files from CNVkit output (for tumor DNA) and ichorCNA output (ctDNA) were first mapped to gene-level features. Values ​​from 514 DNA segments were then determined as described in Xia et al [Xia Y, Fan C, Hoadley KA, Parker JS, Perou CM. Genetic determinants of the molecular portraits of epithelial cancers. Nature Communications 2019;10(1):5666 doi 10.1038 / s41467-019-13588-2]. Briefly, each segment score was calculated as the average copy number score across genes within the segment. Coefficients of DNA segments for predicting gene signatures were obtained from Xia et al. DNA-based signature scores were calculated as the weighted average of DNA segment values ​​for each sample.

[0074] For ctDNA, TFs and tumor ploidy were estimated by ichorCNA. For ctDNA samples with TFs>0, TF and tumor ploidy adjusted signature scores were calculated by first adjusting the copy number values ​​in the ichorCNA segmented files: adjusted_copy_number_ratio=log2(logR_copy_number / tumor_ploidy). Then, DNA-based signature scores were derived in the same way as described for tumor tissue. To calculate the number of altered segments, we used arbitrary gain / loss thresholds of + / -0.07 for unadjusted segment values ​​and 0.32 / -0.42 for adjusted segment values ​​[Xia Y, Fan C, Hoadley KA, Parker JS, Perou CM. Genetic determinants of the molecular portraits of epithelial cancers. Nature Communications 2019;10(1):5666 doi 10.1038 / s41467-019-13588-2]. Segments with values ​​above the gain threshold or below the loss threshold were called altered.

[0075] Example 1.5. Gene Expression Analysis of FFPE Tumor Samples RNA was extracted using the High Pure FFPET RNA Isolation Kit (Roche, Indianapolis, IN, USA) according to the manufacturer's protocol. One to five 10 μm FFPE slides were used for each tumor sample depending on tumor cellularity, and macrodissection was performed when necessary to avoid contamination of normal tissue. A minimum of approximately 100 ng of total RNA was analyzed on the nCounter platform (Nanostring Technologies, Seattle, USA) using the 770-gene Breast Cancer 360™ Gene Panel, which includes 50 PAM50 genes. Gene expression for each sample was independently normalized to the geometric mean of five housekeeping genes (ACTB, MRPL19, PSMC4, RPLP0, SF3A1). Research-based PAM50 subtyping was performed as previously described.

[0076] Example 1.6. METABRIC Breast Cancer Dataset Clinicopathological data were obtained from cbioportal. Processed DNA segment values ​​were downloaded and DNA-based signature scores were calculated for each sample as a weighted average of the DNA segment values.

[0077] Example 1.7. Four DNA-based subtype predictors To identify the four subtype clusters using DNA-based data, we used multiclass significance analysis of microarrays (SAM) with an FDR of less than 5% to select signatures that were significantly differentially expressed across the four clusters identified in ctDNA. We then used the selected gene list to calculate four centroids from the training data. For each new sample in METABRIC, we calculated the Euclidean distance to the four centroids and assigned a cluster class to each sample based on the closest centroid.

[0078] Example 1.8. General statistical procedures Categorical variables are expressed as number (%), χ 2Comparisons were made by the ANOVA or Fisher's exact test. Two-class unpaired SAM with FDR<5% was used to identify differentially expressed signatures between the two groups. Two-class paired SAM with FDR<5% was used to identify differentially expressed signatures between the two time points (i.e., baseline vs. post-progression on endocrine therapy and CDK4 / 6 inhibitors). Survival estimates were from Kaplan-Meier curves and tests of differences by log-rank test. Univariate and multivariate Cox models for PFS and OS were used to test the prognostic significance of each variable. The Bonferroni correction method was used to control the family-wise error rate in case of multiple comparisons. PFS was defined as the period from the start of endocrine therapy and CDK4 / 6 inhibitors to disease progression or the date of last follow-up. OS was defined as the period from the start of endocrine therapy and CDK4 / 6 inhibitors to death or the date of last follow-up. All cluster analyses were displayed using Java Treeview version 1.1.3. Average linkage hierarchical clustering was performed using Cluster v3.0. A two-sided p-value <0.05 was considered statistically significant. Statistical calculations were performed with R 4.0.3 (http: / / cran.r-project.org).

[0079] Example 2. Results To demonstrate that ctDNA can capture complex tumor phenotypes, shallow whole genome sequencing (shWGS) was performed on 209 plasma samples from 174 patients with advanced hormone receptor positive and HER2 negative breast cancer (HR+ / HER2-). Additional samples from other clinical subtypes were also assayed, including 19 plasma samples from 16 patients with HER2 positive (HER2+) breast cancer, 17 plasma samples from 16 patients with triple negative breast cancer (TNBC), and 1 plasma sample from 1 patient with unknown HR and HER2 status.

[0080] Example 2.1. Plasma Tumor Fraction From 246 plasma samples (Fig. 1a), 178 (72.4%) had a tumor cell fraction (TF) of ≥ 3% (range 4-84%; median 9.4%) according to ichorCNA. In plasma samples with TF ≥ 3%, we calculated the scores of each of 150 previously reported elastic net regression analysis-based DNA signatures predicting tumor RNA and protein phenotypes [Xia Y, Fan C, Hoadley KA, Parker JS, Perou CM. Genetic determinants of the molecular portraits of epithelial cancers. Nature Communications 2019;10(1):5666 doi 10.1038 / s41467-019-13588-2], note that all signatures / models were applied exactly as previously reported. Thus, in these cases, the 246 samples can be considered as a "test / validation" dataset. TFs as continuous variables were found to be strongly correlated with the number of altered DNA copy number segments found in each sample (Pearson's ρ = 0.76; Fig. 1b). Strong correlations with TFs (i.e., Pearson's ρ ≥ 0.70 or ≤ -0.70) were also identified in 46 of 150 (31.0%) ctDNA-based signatures, most of which tracked biological processes related to Luminal B (i.e., high TFs) versus Luminal A (i.e., low TFs) disease. This result reaffirms the hypothesis that TFs reflect not only the amount of disease burden in each patient but also its biological aggressiveness. As expected, adjustment for the tumor copy number signal detected in plasma by TFs in each sample reduced the strength of association between TFs and the number of altered copy number segments, and between TFs and each ctDNA-based signature score (Fig. 1b).

[0081] Example 2.2. Plasma vs. Tissue DNA-Based Signatures Next, we examined the correlation of each of the 150 DNA-based signatures determined using plasma ctDNA versus tumor DNA across 54 patients with available paired sample types obtained at different time points (Figure 1c). Tumor DNA sequencing was performed from formalin-fixed paraffin-embedded (FFPE) tumors, but not ctDNA shWGS from FFPE DNA, using a capture-based approach covering the entire chromosomal landscape. Across all 150 signatures, the average correlation coefficient was 0.40 (range 0.02-0.66), with 40 signatures (26.7%) having a correlation coefficient of 0.50 or higher. When correlation was evaluated in 27 cases in which plasma and tumor were obtained within a time window of <8.0 weeks, the number of signatures with a correlation coefficient ≥0.50 was 63 (42% vs. 19.3% in 27 cases in which plasma and tumor were obtained >8.0 weeks; p-value <0.001). Overall, these results suggest a moderate association between ctDNA-based and tumor DNA-based signatures across time points and DNA sequencing approaches (i.e., ctDNA shWGS vs. capture-based using FFPE DNA).

[0082] Example 2.3. ctDNA-based signatures versus tissue ER and HER2 status Estrogen receptor (ER) expression by immunohistochemistry (IHC), and HER2 overexpression by IHC and / or amplification by in situ hybridization are key biological features of breast cancer. To evaluate the relationship between ctDNA-based information and ER or HER2 tumor clinical biomarker status, we evaluated the association of each of the 150 ctDNA-based signatures with either ER clinical status (i.e., positive vs. negative) or HER2 status (i.e., positive vs. negative) in 177 samples that had TF>3% and tumor ER and HER2 IHC were available. As expected, ctDNA-based signatures that track luminal biological processes (e.g., luminal cluster signatures and GSEA-median-GP7-estrogen-signaling) were found to be enriched in ER+ disease (p<0.001; false discovery rate [FDR]<1%; highest AUC=0.77) compared to ER-negative disease (Fig. 1d). Similarly, ctDNA-based signatures tracking HER2 expression or amplification (e.g., HER2 signature and HER2-amplified HER2 amplicon) were found to be significantly enriched in HER2+ disease compared to HER2-negative disease (p<0.001; FDR<1%; highest AUC=0.72) (Figure 1d). Overall, these results suggest that ctDNA-based profiling captures and predicts specific phenotypic tumor traits.

[0083] Example 2.4. Prognostic value of cDNA-based signatures To evaluate the association of ctDNA-based signatures with prognosis, we evaluated baseline pretreatment plasma samples from 124 patients with advanced HR+ / HER2- breast cancer treated with endocrine therapy and CDK4 / 6 inhibitors (Figure 2a). 87 plasma samples had TF>3%. Median follow-up was 12.5 months (range 1.0-56.7 months), with most patients defined as endocrine sensitive (83.9%) and treated in the first-line setting (59.8%) (Table 16).

[0084] [Table 16]

[0085] From 150 ctDNA-based signatures, 36 (24%) and 37 (25%) were found to be significantly associated with progression-free survival (PFS) and overall survival (OS), respectively, and 27 (18%) signatures were found to be significantly associated with both PFS and OS. In general, signatures associated with poor survival outcomes were those hypothesized to track proliferation and non-ER+ / non-luminal associated biological processes, such as MM_p53null.Luminal (i.e., TP53 deficiency) and MM_Myc signatures (i.e., high MYC / MYC amplification). Conversely, ctDNA signatures associated with better outcomes tracked luminal A associated biological processes.

[0086] Consistent with known resistance mechanisms of CDK4 / 6 inhibitors, high enrichment of signature-tracking RB-LOH was associated with poor outcome and treatment response (Figure 2b-c). The DNA-based RB-LOH signature consists of 224 copy number features, including amplifications of 2p (e.g., ETV6), 3q (e.g., PIK3CA), 8q (e.g., MYC), 20q (e.g., AURKA) and 21q (e.g., TMPRSS2 and ERG), as well as deletions of 2q (e.g., PARD3B), 4q, 5q, 12q, 13q (e.g., RB1), 15q and 17p. As expected, the direction (i.e., amplification or deletion) and intensity (i.e., coefficient) of 48 main features of the original tissue-based DNA RB-LOH signature [Xia Y, Fan C, Hoadley KA, Parker JS, Perou CM. Genetic determinants of the molecular portraits of epithelial cancers. Nature Communications 2019;10(1):5666 doi 10.1038 / s41467-019-13588-2] were adequately detected in ctDNA (correlation coefficient = 0.75, p-value < 0.001; Fig. 2d). Finally, the association of ctDNA RB-LOH signature with PFS and OS was independent of TF (as a continuous variable), type of CDK4 / 6 inhibitor, treatment line (first line vs. second line vs. subsequent lines), presence of visceral disease and number of metastases (Fig. 2e).

[0087] Example 2.5. ctDNA RB-LOH signatures versus ctDNA RB1 individual regions The DNA-based RB-LOH signature considers signals at the RB1 locus (13q14.2) among 224 other features [Xia Y, Fan C, Hoadley KA, Parker JS, Perou CM. Genetic determinants of the molecular portraits of epithelial cancers. Nature Communications 2019;10(1):5666 doi 10.1038 / s41467-019-13588-2]. The correlation coefficient between ctDNA signals at 13q14.2 and the ctDNA RB-LOH signature score was -0.12 across 178 samples with TF>3%. In a previous cohort of patients with advanced HR+ / HER2- breast cancer treated with endocrine therapy and CDK4 / 6 inhibitors, ctDNA signals of individual 13q14.2 segments were not significantly associated with PFS (p-value=0.061), but were significantly associated with OS (p-value=0.020). However, the RB-LOH signature was the only variable significantly associated with PFS and OS in a bivariate Cox model. Overall, the RB-LOH ctDNA-based signature captured clinical behavior better than individual DNA regions looking at RB1 alone, thus highlighting the ability of multi-feature algorithms to sense pathway activity.

[0088] Example 2.6. Signals from individual DNA segments as prognostic drivers To further understand the prognostic value of individual DNA segments, we focused on 27 ctDNA-based signatures (see Tables 3 and 4 above) that were significantly associated with both PFS and OS. From each signature, we evaluated the signals from 534 DNA segments and their original weights. We identified 16 DNA segments with high weights (i.e., defined as >0.10 or <-0.10) in at least 11 (40%) of the 40 signatures. Among these, deletions of 13q14.2 (where RB1 is located) and 17p13.1 (e.g., TP53) and amplifications of 8q24.21 (e.g., MYC) and 12p13.33 (e.g., FOXM1) were identified. Next, we evaluated the association of each of the 16 DNA segments with PFS in 87 patients with advanced HR+ / HER2- breast cancer treated with endocrine therapy and CDK4 / 6 inhibitors. The signals from 3 of 16 (18.8%) DNA segments were associated with PFS. Then, we combined the signals from two segments (i.e., a total of 120 different combinations) and evaluated their individual association with PFS. A total of 40 different combinations (30%) out of 120 were significantly associated with PFS, and all 16 DNA segments were found in at least one combination (see Table 2, Table 4 and Table 5 above).

[0089] Example 2.7. Prognostic Implications of RB-LOH in Tumor vs. Plasma To compare the prognostic value of DNA-based RB-LOH signatures when determined in tumor vs. plasma, 63 of 124 (51.0%) patients with advanced HR+ / HER2- breast cancer treated with endocrine therapy and CDK4 / 6 inhibitors had paired tumor DNA samples (Fig. 2e). In univariate analysis, both RB-LOH tumor and ctDNA plasma signatures (as continuous variables) were significantly associated with PFS and OS. When both signatures were directly evaluated in a bivariate COX model, the RB-LOH ctDNA plasma signature was found to be significantly associated with PFS, whereas the RB-LOH tumor signature was not (Fig. 2e). Overall, baseline pretreatment ctDNA captures patient prognosis better than archived tumor tissue DNA.

[0090] Example 2.8. Capturing biological characteristics before and after endocrine therapy and CDK4 / 6 inhibition Scores from 150 ctDNA-based signatures were evaluated in paired plasma samples (i.e., baseline vs. post-treatment after progressive disease) across seven patients with advanced HR+ / HER2- breast cancer treated with endocrine therapy and CDK4 / 6 inhibitors (Fig. 2f). Of these, 103 signatures (57.2%) were found to be differentially enriched between the two time points (FDR<5%). As might be expected, enrichment of signatures tracking non-luminal / proliferation-related biological processes (e.g., RB-LOH) and luminal A-related biological processes was found to be significantly increased and decreased, respectively, in post-treatment samples compared to pre-treatment samples (Fig. 2f). Of note, TFs did not change significantly between the two time points across the seven patients (Fig. 2f), with one patient with substantial reduction in TFs still showing increased RB-LOH scores and reduced luminal A signatures. These biological changes identified in ctDNA are consistent with similar biological changes identified across 18 patients with paired tumor-based RNA expression prior to and at progression on endocrine therapy and CDK4 / 6 inhibitors. Specifically, PAM50 Luminal A and proliferation signatures were found to be significantly decreased and increased, respectively, in progression samples.

[0091] Example 2.9. cDNA-Based Tumor Profiling To explore the biology identified by the 150 ctDNA-based signatures, we performed an unsupervised hierarchical cluster analysis of all 150 signatures across 178 plasma samples with TFs ≥ 3% (Figure 3a). Using consensus clustering plus, we identified four major sample clusters. Clusters 3 and 4 showed high scores of ctDNA-based proliferation-related signatures and low scores of differentiation state and luminal A-related signatures. Compared to cluster 4, cluster 3 showed high expression of basal-like gene expression subtype-related biology (p-value < 0.001). Cluster 2 showed high enrichment of differentiation and luminal B-related signatures, as well as low enrichment of basal-like-related biology. Visually, cluster 2 could be further subdivided into cluster 2A and cluster 2B (minimum 20 samples and correlation coefficient > 0.75), both of which showed differential enrichment of ctDNA-based proliferation features and luminal A-related signatures. Consistent with the Luminal A-associated biology identified in Cluster 2A, this group was characterized by 16p amplifications and 16q deletions, both of which are known hallmarks of low-grade and slow-proliferative breast cancer. Finally, Cluster 1 showed low enrichment for proliferation and Luminal B-associated signatures and high enrichment for Luminal A subtype-associated signatures. Plasma TFs in Cluster 1 were significantly lower compared to the other clusters combined (mean 6.8% vs. 9.8%, p-value < 0.001; Figure 3a), which may be predicted for slow-proliferating Luminal A tumors.

[0092] Example 2.10. cDNA-Based Data vs. Tissue RNA-Based Expression Data RNA-based expression data from FFPE tissues using a research-based PAM50 intrinsic subtype assay were available for 108 cases with TFs ≥ 3% in plasma. Tissue samples were obtained at various time points. As expected, cluster 3 was enriched for tumors with PAM50 nonluminal subtypes (i.e., HER2-enriched or basal-like) compared to other clusters (85.7% vs. 25%, p-value < 0.001). Consistent with this finding, PAM50 Luminal A and HER2-enriched signatures (as continuous variables) were found to be differentially expressed in cluster 3 versus other clusters (Figure 3b). Furthermore, we observed that cluster 2B was enriched for PAM50 Luminal B tumors compared to cluster 2A (53.3% vs. 18.8%, p-value = 0.044).

[0093] To further explore the association between ctDNA enrichment and RNA expression data, we evaluated the correlation of the six PAM50 RNA-based tissue signatures with each of the 150 ctDNA-based signatures. To summarize these results, we plotted the correlation coefficients of the most correlated signatures across 58 matched time-point paired plasma-tissue cases in an unsupervised cluster analysis (Fig. 3c). In general, the ctDNA-based signatures were positively and negatively correlated with the known biology that each PAM50 subtype signature is hypothesized to track. For example, a ctDNA-based signature that is enriched in E2F target genes and tracks tumor growth rate, a RB-LOH gene expression signature, was found to be positively correlated with the PAM50 RNA-based basal-like HER2 enrichment and proliferation tumor signature, and negatively correlated with the PAM50 RNA-based Luminal A tumor signature (Fig. 3c).

[0094] Expression of 771 genes in tumors using the nCounter Breast Cancer 360 panel was determined in 107 cases with TFs ≥ 3% in plasma. Correlation coefficients between mRNA expression of each individual gene and each of the 150 ctDNA-based signature scores were also determined. Similar to the PAM50 RNA tumor signature, mRNA expression of luminal genes (e.g., ESR1 and GATA3) was positively correlated with the luminal ctDNA-based signature, and mRNA-driven proliferation and cell cycle-related genes (e.g., MKI67, AURKA, TTK, E2F1, and CCNE1) were positively correlated with the proliferation-related ctDNA-based signature (Figure 3d). Overall, these findings confirm that DNA copy number-based signatures derived from ctDNA can track major breast cancer phenotypes and their known gene expression features.

[0095] Example 2.11. ctDNA-Based Tumor Subtype Prognosis This study demonstrates that tumor profiles identify samples with similar expression patterns and that these patterns are associated with clinically relevant genotypes. We then hypothesized that ctDNA-defined subtypes, representing recurrently observed combinations of these patterns, may explain the variation in clinical outcomes. To investigate this, we evaluated the prognostic value of four ctDNA-based tumor groups (Figure 3a). Compared to clusters 1-2-4 (as groups), cluster 3 was significantly associated with worse PFS (median 2.4 vs. 11.6 months; hazard ratio = 9.23; 95% confidence interval [CI] 3.1 to 27.3; p-value < 0.001). Regarding OS, a trend that did not reach statistical significance was observed (median 16.8 vs. 55.4 months; hazard ratio = 2.73; 95% CI 0.79 to 9.38; p-value = 0.112). As expected, expression of the ctDNA-based RB-LOH signature was significantly higher in cluster 3 compared to other clusters (p<0.001).

[0096] Example 2.12. DNA-Based Tumor Subtyping in Tissue Samples To explore how tumor subtypes identified in ctDNA data perform when applied to tumor tissue DNA, we scored and evaluated 150 DNA-based signatures using tumor DNA from 1,038 patients with early breast cancer from the publicly available METABRIC dataset (Fig. 4a). We developed a four-class subtype classifier from the ctDNA group (Fig. 3a) and applied this predictor to the tumor DNA data from METABRIC. Overall, four major clusters were identified in METABRIC, including cluster 1, suggesting that the profile identified in ctDNA is observed in primary tumors. Consistent with this finding, PAM50 nonluminal subtypes in the METABRIC cohort were enriched in cluster 3 compared to other clusters (79.6% vs. 11.6%, p-value < 0.001) (Fig. 4b). Furthermore, TP53 somatic mutations in METABRIC were also enriched in cluster 3 compared to other clusters (80.7% vs. 51.2%, p-value < 0.001) (Fig. 4c). Finally, the four major clusters were found to be significantly associated with disease-free survival (DFS) and OS in all patients and in patients with HR+ / HER2-negative breast cancer (Fig. 4d).

[0097] Example 2.13. Analysis to identify clustering of the four subtypes We also performed an analysis to identify four subtype clusterings using 16 segments (see Table 2) that were significantly differentially expressed across the four clusters identified in ctDNA using multiclass significance analysis (SAM) of microarrays with an FDR of less than 5%. We then used the selected gene list to calculate four centroids from the training data. For each new sample, we calculated the Euclidean distance to the four centroids and assigned a cluster class to each sample based on the closest centroid.

[0098] We performed an analysis comparing the clusters obtained using the 150 gene signature with the clusters obtained using the 16 segments. An independent chi-square test was performed to evaluate the association between these two sets of clusters. The results of the chi-square test showed a p-value of <2.2e-16, indicating a statistically significant association between the two sets of clusters.

[0099] Prognostic value of four ctDNA-based tumor groups (n=152) combining CDKPlasma-1 and CDKPlasma-2. Compared to cluster 1 (median PFS=27.6 months), clusters 2, 3 and 4 were found to be significantly associated with worse PFS (median PFS 9.5, 5.8 and 7.4 months, respectively) (p-value<0.0005). See Figure 5 showing Kaplan-Meier curves for PFS. The prognostic value of DNA subtypes was validated in DNA from tumor tissue using METABRIC-HR+ / HER2- (n=1131, DFS) (Figure 6).

Claims

1. An in vitro method to assist in prognosis diagnosis or response prediction for HR+ / HER2- breast cancer patients to treatment selected from targeted therapy including CDK4 / 6 inhibitors and / or endocrine therapy, a. DNA segments selected from the following group in the biological sample obtained from the patient: chr20: 33386980-33969561, chr13: 46362859-48209064, chr17: 1-22200000, chr10: 129812260-135374737, chr17: 7471230-7717938, chr This includes evaluating the presence of DNA copy number variations (CNAs) across any of the following: 8:128774432–128849112, chr16:1–38200000, chr4:83634873–83961360, chr5:76408288–81082828, chr19:1–526082, or chr3:58626894–61524607. b. The presence of a CNA in any of the following DNA segments selected from the group: chr20:33386980–33969561, chr8:128774432–128849112, or chr16:1–38200000 is an indicator of poor prognosis or poor response, or c. The presence of CNA in any of the following DNA segments selected from the group below: chr13:46362859–48209064, chr17:1–22200000, chr10:129812260–135374737, chr17:7471230–7717938, chr4:83634873–83961360, chr5:76408288–81082828, chr19:1–526082, or chr3:58626894–61524607 is an indicator of a favorable prognosis or good response. In vitro method.

2. An in vitro method for monitoring HR+ / HER2- breast cancer patients and evaluating whether they are responding to targeted therapy including CDK4 / 6 inhibitors and / or endocrine therapy, a. DNA segments selected from the following group in the biological sample obtained from the patient: chr20: 33386980-33969561, chr13: 46362859-48209064, chr17: 1-22200000, chr10: 129812260-135374737, chr17: 7471230-7717938, chr This includes evaluating the presence of DNA copy number variations (CNAs) across any of the following: 8:128774432–128849112, chr16:1–38200000, chr4:83634873–83961360, chr5:76408288–81082828, chr19:1–526082, or chr3:58626894–61524607. b. The presence of a CNA in any of the following DNA segments selected from the group: chr20:33386980–33969561, chr8:128774432–128849112, or chr16:1–38200000 is an indicator of poor response, or c. The presence of CNAs in any of the following DNA segments selected from the group below: chr13:46362859–48209064, chr17:1–22200000, chr10:129812260–135374737, chr17:7471230–7717938, chr4:83634873–83961360, chr5:76408288–81082828, chr19:1–526082, or chr3:58626894–61524607 is an indicator of a good response. In vitro method.

3. An in vitro method for classifying HR+ / HER2- breast cancer patients into groups associated with different responses to targeted therapy and / or endocrine therapy selected from CDK4 / 6 inhibitors, a. DNA segments selected from the following group in the biological sample obtained from the patient: chr20: 33386980-33969561, chr13: 46362859-48209064, chr17: 1-22200000, chr10: 129812260-135374737, chr17: 7471230-7717938, chr This includes evaluating the presence of DNA copy number variations (CNAs) across any of the following: 8:128774432–128849112, chr16:1–38200000, chr4:83634873–83961360, chr5:76408288–81082828, chr19:1–526082, or chr3:58626894–61524607. b. The presence of a CNA in any of the following DNA segments selected from the group: chr20:33386980–33969561, chr8:128774432–128849112, or chr16:1–38200000 is an indicator of poor response, or c. The presence of CNAs in any of the following DNA segments selected from the group below: chr13:46362859–48209064, chr17:1–22200000, chr10:129812260–135374737, chr17:7471230–7717938, chr4:83634873–83961360, chr5:76408288–81082828, chr19:1–526082, or chr3:58626894–61524607 is an indicator of a good response. In vitro method.

4. An in vitro method according to any one of claims 1 to 3, further comprising evaluating the presence of CNAs in any of the following DNA segments selected from the group consisting of: chr17:63942109-65847254, chr2:32460827-55039898, chr7:16017926-18944036, chr2:1-93300000, or chr12:1-1311104.

5. a. Evaluate the presence of DNA copy number variations (CNAs) across any of the DNA segment combinations in Tables 14 and 15 in the biological sample obtained from the patient. b. Processing the measured CNA in order to obtain a score, Includes, c. If the deviation or variation in the score value is identified in any of the DNA segment combinations in Table 14 compared to a pre-established reference value, this indicates a poor prognosis or poor response, or d. If the deviation or variation in the score value is identified in any of the DNA segment combinations in Table 15 compared to a pre-established reference value, this indicates a favorable prognosis or good response. The in vitro method according to any one of claims 1 to 3.

6. a. Receiving multiple CNA datasets from the aforementioned patient, b. To process the information in accordance with step a) in order to find statistically significant variation or deviation. c. The computer system provides results based on the information received in accordance with step a) and pre-established standards already stored in the computer. An in vitro method according to any one of claims 1 to 3, characterized in that it is a computer implementation method including

7. The presence of CNAs is observed in the following DNA segments: chr20: 33386980–33969561, chr13: 46362859–48209064, chr17: 63942109–65847254, chr17: 1–22200000, chr10: 129812260–135374737, chr17: 7471230–7717938, chr2: 32460827–55039898, chr7: 16017926–189440 An in vitro method according to any one of claims 1 to 3, evaluated in all of the following: 36, chr2:1-93300000, chr8:128774432-128849112, chr12:1-1311104, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 and chr3:58626894-61524607.

8. The in vitro method according to any one of claims 1 to 3, wherein the presence of CNA is evaluated in any or all of the DNA segments in Table 7, Table 8, or Table 9.

9. The in vitro method according to any one of claims 1 to 3, wherein the biological sample is selected from plasma, serum, breast milk, cerebrospinal fluid, or blood sample.

10. The in vitro method according to any one of claims 1 to 3, wherein the cancer subtype is selected from HR+ / HER2-, HER2+, and triple-negative.

11. For predicting the response of HR+ / HER2- breast cancer patients to treatment selected from targeted therapy including CDK4 / 6 inhibitors and / or endocrine therapy; for prognosis diagnosis of HR+ / HER2- breast cancer patients; for monitoring HR+ / HER2- breast cancer patients to assess whether they are responding to treatment selected from targeted therapy including CDK4 / 6 inhibitors and / or endocrine therapy; or for assessing whether patients are responding to treatment selected from targeted therapy including CDK4 / 6 inhibitors and / or endocrine therapy. To classify into related groups, chr20:33386980-33969561, chr13:46362859-48209064, chr17:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr8:128774432-128849112, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 In vitro use of CNAs across DNA segments selected from the group consisting of chr3:58626894-61524607, or across any combination of DNA segments in Table 14 or Table 15, wherein the presence of CNAs across any of the following DNA segments selected from the group consisting of: chr20:33386980-33969561, chr8:128774432-128849112, or chr16:1-38200000 is an indicator of poor prognosis or poor response; The presence of CNA in any of the following DNA segments selected from the group below: chr13:46362859–48209064, chr17:1–22200000, chr10:129812260–135374737, chr17:7471230–7717938, chr4:83634873–83961360, chr5:76408288–81082828, chr19:1–526082, or chr3:58626894–61524607 is an indicator of a favorable prognosis or good response;If the deviation or variation in the score value is identified in any of the DNA segment combinations in Table 14 compared to a pre-established reference value, this indicates a poor prognosis or poor response; or if the deviation or variation in the score value is identified in any of the DNA segment combinations in Table 15 compared to a pre-established reference value, this indicates a good prognosis or good response, for in vitro use.

12. For predicting the response of HR+ / HER2- breast cancer patients to treatment selected from targeted therapy and / or endocrine therapy comprising a CDK4 / 6 inhibitor; for prognosis diagnosis of HR+ / HER2- breast cancer patients; for monitoring HR+ / HER2- breast cancer patients to assess whether the patient is responding to treatment selected from targeted therapy and / or endocrine therapy comprising a CDK4 / 6 inhibitor; or relating the patient to different responses to treatment selected from targeted therapy and / or endocrine therapy comprising a CDK4 / 6 inhibitor. To classify into the following groups: chr20:33386980-33969561, chr13:46362859-48209064, chr17:1-22200000, chr10:129812260-135374737, chr17:7471230-7717938, chr8:128774432-128849112, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 or chr3: The use of a kit containing tools and reagents for evaluating the presence of CNAs across segments selected from the group consisting of 58626894-61524607, or across combinations of DNA segments in Table 14 or 15, wherein the presence of CNAs across any of the following DNA segments selected from the group consisting of: chr20: 33386980-33969561, chr8: 128774432-128849112, or chr16: 1-38200000 is an indicator of poor prognosis or poor response. Yes; the presence of CNA in any of the following DNA segments selected from the group consisting of: chr13:46362859–48209064, chr17:1–22200000, chr10:129812260–135374737, chr17:7471230–7717938, chr4:83634873–83961360, chr5:76408288–81082828, chr19:1–526082, or chr3:58626894–61524607 is an indicator of a good prognosis or good response;If the deviation or variation in the score value is identified in any of the DNA segment combinations in Table 14 compared to a pre-established reference value, this indicates a poor prognosis or poor response; or if the deviation or variation in the score value is identified in any of the DNA segment combinations in Table 15 compared to a pre-established reference value, this indicates a good prognosis or good response, according to the use of the kit.

13. Targeted therapy comprising a CDK4 / 6 inhibitor and / or endocrine therapy for use in the treatment of HR+ / HER2- breast cancer patients, wherein the patient is identified as a responder patient according to the method of any one of claims 1 to 3.

14. An in vitro method for classifying HR+ / HER2- breast cancer patients according to their survival probability, a) DNA segments selected from the following group in the biological sample obtained from the patient: hr20: 33386980-33969561, chr13: 46362859-48209064, chr17: 63942109-65847254, chr17: 1-22200000, chr10: 129812260-135374737, chr17: 7471230-7717938, chr2: 32460827-55039898, chr7: 16 Evaluate the presence of DNA copy number variations (CNAs) across any of the following: 017926-18944036, chr2:1-93300000, chr8:128774432-128849112, chr12:1-1311104, chr16:1-38200000, chr4:83634873-83961360, chr5:76408288-81082828, chr19:1-526082 and / or chr3:58626894-61524607. b) Processing the measured CNA in order to obtain a score value, Includes, c) If the deviation or variation in the score value is identified by comparison with a first pre-established reference value, this indicates that the patient belongs to cluster 1, which is characterized by the highest survival probability, or c) If the deviation or variation in the score value is identified by comparison with a second pre-established reference value, this indicates that the patient belongs to cluster 2, which is characterized by the second highest survival probability, or c) If the deviation or variation in the score value is identified by comparison with a third pre-established reference value, this indicates that the patient belongs to cluster 3, which is characterized by the second worst survival probability, or c) If the deviation or variation in the score value is identified by comparison with a fourth pre-established reference value, this indicates that the patient belongs to cluster 4, which is characterized by the worst survival probability. In vitro method.

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