Method for predicting prognosis of breast cancer by using immunosuppressive fibroblast activity measurement data

Abstract

The present disclosure relates to an information provision method for predicting the prognosis of breast cancer by using immunosuppressive fibroblast activity measurement data. The information provision method for predicting the prognosis of breast cancer, according to the present invention, can predict the prognosis of breast cancer patients, and also provide various pieces of information that can further understand the breast cancer tumor ecosystem.

Description

A method for predicting breast cancer prognosis using immunosuppressive fibroblast activity measurement data.

[0001] The present disclosure relates to a method for providing information for predicting the prognosis of breast cancer using immunosuppressive fibroblast activity measurement data, and more particularly, to a method for providing information for predicting the prognosis of breast cancer using cell state scores and MYC target activity levels of immunosuppressive fibroblasts, myofibroblasts, or immunosuppressive fibroblasts and myofibroblasts.

[0002] Breast cancer is one of the most prevalent diseases, affecting nearly one in eight women in the United States. Furthermore, it is the most commonly diagnosed cancer in women worldwide and the leading cause of cancer death in women. Both the incidence and mortality rates of breast cancer have been increasing over the past several decades. Risk factors for breast cancer include genetics, lifestyle, obesity, hormones, and age. Surprisingly, breast cancer incidence is closely related to age, with the highest incidence reported in Korea among women aged 40-49.

[0003] Breast cancer is a heterogeneous disease, with significant differences between individual patients (intertumoral heterogeneity) and even within individual tumors (intratumoral heterogeneity). Intertumoral heterogeneity contributes to differences in disease stage and histopathologic characteristics. These differences are reflected in subtype classification, providing important insights into prognosis and treatment approaches. Breast cancer is generally classified into four subtypes: luminal A, luminal B, HER2-positive, and triple-negative breast cancer (TNBC).

[0004] Immunohistochemistry (IHC) and Prediction analysis of microarray 50 (PAM50) methodologies are commonly used to distinguish the above breast cancer subtypes. The IHC-based subtypes depend on the expression of estrogen and progesterone nuclear hormone receptors (ER / PR) and human epidermal growth factor receptor-2 (HER-2). Luminal A, the most prevalent subtype, accounting for approximately 40% of cases, is characterized by ER and / or PR positivity, while being HER-2 negative. Luminal B, accounting for approximately 20% of cases, is characterized by ER, PR, and HER-2 positivity. Additionally, HER2-positive breast cancers represent approximately 10-15% of cases and are ER and PR negative, but HER-2 positive. Finally, TNBC is characterized by ER, PR, and HER-2 negativity and accounts for approximately 15-20% of cases. In contrast, subtypes based on the PAM50 method are unique subtypes of breast cancer based on the expression of 50 genes. PAM50-based subtypes offer improvements in risk prediction and disease management compared to IHC-based subtypes. Notably, treatment options vary depending on the subtype. For example, Luminal A tumors respond better to endocrine therapy, whereas TNBC responds better to chemotherapy. Therefore, accurate subtype classification is essential for selecting the most effective treatment strategy. Nevertheless, accurate subtype classification remains a challenge.

[0005] Breast cancer diagnosed at a younger age is associated with more aggressive characteristics and worse survival outcomes. Furthermore, patients with young breast cancer (YBC) have a higher risk of metastasis and recurrence compared to patients with older breast cancer (OBC). However, the underlying mechanisms related to age and tumor malignancy remain unknown. Furthermore, the biological properties of cancer stem from complex interactions among numerous cellular components, including DNA, RNA, proteins, and small molecules. In particular, since the actual mediators and regulators of cell signaling pathways in cancer are mostly observed at the protein level, elucidating the biological mechanisms of OBC requires expanding beyond the transcriptional level to the translational level. Consequently, relying solely on genomic alterations poses significant challenges due to the complexity of tumorigenesis, and a more concise understanding of these mechanisms requires integrating multi-layered omics data, including genomic, transcriptomic, epigenomic, and proteomic domains.

[0006] In this study, we generated and analyzed multi-omics data for 289 Korean breast cancer samples, including 178 YBC and 111 OBC samples. The multi-omics data in this study encompasses six data types, ranging from genome, exome, and transcriptome sequencing to methylome, proteome, and phosphorylome data. This integrated multi-omics study will provide novel, clinically relevant insights that may be difficult to uncover when examining single-omics data alone.

[0007] The purpose of the present invention is to provide a method for providing information for predicting the prognosis of breast cancer, comprising the steps of: obtaining a biological sample from a cancer patient; measuring a cell status score of immunosuppressed fibroblasts, myofibroblasts, or immunosuppressed fibroblasts and myofibroblasts from the obtained sample; and predicting the prognosis of breast cancer using the measured cell status score.

[0008] In addition, another object of the present invention is to provide a method for providing information for predicting the prognosis of breast cancer, including the steps of: obtaining multi-omics data from a separated sample of a cancer patient; performing a hierarchical clustering analysis to classify each sample into a sub-class of breast cancer based on the multi-omics data; and predicting the prognosis based on the classified sub-class.

[0009] However, the problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by a person having ordinary skill in the relevant technical field from the description below.

[0010] A method for providing information for predicting breast cancer prognosis according to one embodiment of the present invention comprises the steps of: a) obtaining a biological sample from a cancer patient; b) measuring a cell status score of immunosuppressed fibroblasts, myofibroblasts, or immunosuppressed fibroblasts and myofibroblasts from the obtained sample; and c) predicting breast cancer prognosis using the measured cell status score.

[0011] According to one embodiment of the present invention, the genes specifying the immunosuppressive fibroblasts are composed of GEM, CXCL8, CXCL3, CXCL2, CXCL1, IL6, TNFAIP6, MT1A, THAP2, AKR1C1, FOSL1, CEBPB, IER3, LIF, SOD2, C11orf96, CD44, GPRC5A, KDM6B, TNFRSF12A, DDX21, NFKBIA, MAFF, UAP1, SLC3A2, WTAP, PPP1R15A, RND3, MYC, ADAMTS4, HSPD1, PTGS2, CREM, PLIN2, CYCS, BAG3, REL, NAMPT, CYTOR, ZC3H12A, BTG3, XBP1, HSPH1, HSPA9, EIF4A3, EIF5 and ERRFI1. There may be one or more types selected from the military.

[0012] According to one embodiment of the present invention, the immunosuppressive fibroblasts may be IL6+ inflammatory fibroblasts.

[0013] According to one embodiment of the present invention, the step of predicting the prognosis of breast cancer using the cell status score of step (c) may be performed by comparing the cell status score with a given reference value.

[0014] According to one embodiment of the present invention, if the cell status score of the measured fibroblasts is evaluated to be higher than the median, it may be evaluated as a poor prognosis.

[0015] According to one embodiment of the present invention, if the cell status score of the measured myofibroblasts is evaluated to be higher than the median, it may be evaluated as a good prognosis.

[0016]

[0017] In addition, the present invention provides a method for providing information for predicting the prognosis of breast cancer, comprising the steps of: obtaining multi-omics data from a separated sample of a cancer patient; performing a hierarchical clustering analysis to classify each sample into a sub-class of breast cancer based on the multi-omics data; and predicting the prognosis based on the classified sub-class.

[0018] According to one embodiment of the present invention, the multi-omics data may include at least one of genetic variation, tumor microenvironment components, immune cell ratio, immune characteristic score, fibroblast activity, TP53 mutation status, and MYC activity score.

[0019] According to one embodiment of the present invention, the method for providing information for predicting the prognosis of breast cancer may further include, after the step of classifying into subclasses, a step of comparing the age group, TP53 mutation status, and existing breast cancer subtype distribution of the subclasses to confirm the heterogeneity of breast cancer subtypes between each cluster.

[0020] According to one embodiment of the present invention, the method for providing information for predicting the prognosis of breast cancer may further include, after the step of classifying into subclasses, a step of analyzing tumor microenvironment (TME) and immune components by subclass to identify molecular characteristics of each subclass.

[0021] According to one embodiment of the present invention, the method for providing information for predicting breast cancer prognosis may further include a step of analyzing the immune cell ratio according to age by sub-classification to confirm the immune cell composition according to age.

[0022] According to one embodiment of the present invention, the sub-classification of breast cancer derived through the clustering may be classified into nine types.

[0023] A method for providing information for predicting the prognosis of breast cancer according to one embodiment of the present invention can provide information related to predicting the prognosis of a breast cancer patient.

[0024] The method for providing information for predicting breast cancer prognosis according to one embodiment of the present invention may help to further understand the breast cancer tumor ecosystem and, in particular, to elucidate the underlying mechanisms for the more aggressive characteristics observed in breast cancer diagnosed at a young age (YBC).

[0025] Additionally, multi-omics-based clustering analysis can systematically understand the various subtypes of breast cancer, enabling more accurate prognostic assessment and personalized treatment strategies.

[0026] However, the effects of the present invention are not limited to the above-described effects, and should be understood to include all effects that can be inferred from the composition of the invention described in the detailed description or claims of the present invention.

[0027] Figure 1. Differences in the tumor microenvironment between tumors with high and low immunosuppressive fibroblast activity: (A) UMAP visualization of single-cell transcriptomes colored by cell type, (B) UMAP visualization of single-cell transcriptomes colored by mesenchymal cell state, (C) multiple comparisons of protein cell state scores between MYC-high and MYC-low samples (statistical analysis was performed using the Wilcoxon signed-rank test, with a false discovery rate (FDR) of <0.1), (D) correlation analysis investigating the influence of MYC target activity and age on IL6+ inflammatory fibroblast (top) and myofibroblast (bottom) protein scores (left) and comparison of cell state scores between MYC-high and MYC-low tumors (middle) and between YBC and OBC (right), (E) correlation of IL6+ inflammatory fibroblast (top) and myofibroblast (bottom) scores with multiple immunoscores, (F) Correlation between IL6+ inflammatory fibroblast (top) and myofibroblast (bottom) scores and several immune cell fractions is shown.

[0028] Figure 2 shows the prognostic effects of IL6+ inflammatory fibroblasts and myofibroblasts, showing the survival analysis between high and low score groups based on (A) IL6+ inflammatory fibroblast (left) and myofibroblast (right) scores in a breast cancer cohort (Kaplan-Meier curves showing progression-free survival (PFS) (top) and overall survival (OS) (bottom)), (B) survival analysis between high and low IL6+ inflammatory fibroblast score groups in a breast cancer TCGA cohort (left) and a multi-cancer CPTAC (right), and (C) the evaluation of IL6+ inflammatory fibroblast and myofibroblast scores in predicting innate immune responses and clinical outcomes of immunotherapy by comparing IL6+ inflammatory fibroblast (left) and myofibroblast (right) scores between durable clinical benefit (DCB) and no clinical benefits (NCB) in an immunotherapy cohort.

[0029] Figure 3a shows a new unsupervised hierarchical clustering of breast cancer subtypes into nine subcategories using multi-omics data. The distribution of age group, TP53 mutation status, and PAM50 subtype for each multi-omics subcategory is shown at the top, and sample information is shown along with the levels of MYC expression, MYC target activity, IL6+ inflammatory fibroblasts, and myofibroblasts for each subcategory. The bottom shows markers normalized to z-scores, including genetic mutations, tumor microenvironment components, and immune scores including immune cell fraction and immune signature. Meanwhile, Figure 3b is an enlarged view of the classification name (indicated by 'a') described on the right side of Figure 3a.

[0030] Figure 4 shows the analysis of multi-omics clustering, showing (A) the results of a survival analysis for a new subtype using progression-free survival (PFS) within the TNBC (left), luminal (middle), and HER2 (right) subclasses, and (B) the flow of markers such as the proportion of immune cells, fibroblasts, MYC target activity, and TP53 mutation status for each multi-omics subtype.

[0031] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, the embodiments may be modified in various ways, and the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, or alternatives to the embodiments are included within the scope of the patent application.

[0032]

[0033] The terms used in the examples are for illustrative purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to indicate the presence of a feature, number, step, operation, component, or combination thereof described in the specification, but should be understood to not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, or combinations thereof.

[0034] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments pertain. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0035] In this specification, the term '+' or 'positive' may mean that a marker capable of characterizing the cell is expressed.

[0036] In this specification, the term 'MYC' may refer to a proto-oncogene that is located at the intersection of several cell growth-promoting signaling pathways and immediately responds to downstream genes of several ligand-membrane receptor complexes to regulate cell growth and proliferation.

[0037] When describing an embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description is omitted.

[0038]

[0039] The method for providing information for predicting breast cancer prognosis of the present invention comprises the steps of: a) obtaining a biological sample from a cancer patient; b) measuring a cell status score of immunosuppressed fibroblasts, myofibroblasts, or immunosuppressed fibroblasts and myofibroblasts from the obtained sample; and c) predicting breast cancer prognosis using the measured cell status score.

[0040] In the present invention, the method for providing information for predicting the prognosis of breast cancer may be to identify various mesenchymal cell states of immunosuppressed fibroblasts, myofibroblasts, or immunosuppressed fibroblasts and myofibroblasts through non-negative matrix factorization (NMF) analysis.

[0041] In the present invention, the genes specifying the immunosuppressive fibroblasts are selected from the group consisting of GEM, CXCL8, CXCL3, CXCL2, CXCL1, IL6, TNFAIP6, MT1A, THAP2, AKR1C1, FOSL1, CEBPB, IER3, LIF, SOD2, C11orf96, CD44, GPRC5A, KDM6B, TNFRSF12A, DDX21, NFKBIA, MAFF, UAP1, SLC3A2, WTAP, PPP1R15A, RND3, MYC, ADAMTS4, HSPD1, PTGS2, CREM, PLIN2, CYCS, BAG3, REL, NAMPT, CYTOR, ZC3H12A, BTG3, XBP1, HSPH1, HSPA9, EIF4A3, EIF5 and ERRFI1. It may be one or more selected, and more preferably, the gene specifying the immunosuppressive fibroblasts may be one or more selected from the group consisting of AKR1C1, IL-6, FOSL1, CXCL1, CXCL3, and CXCL8, but is not limited thereto.

[0042] In the present invention, the immunosuppressive fibroblasts may be IL6+ inflammatory fibroblasts, and the IL6+ inflammatory fibroblasts may show a strong positive correlation and a negative correlation, respectively, between MYC target activity and patient age.

[0043] In addition, in the present invention, the IL6+ inflammatory fibroblast state may have a negative correlation with macrophages, γδ T cells, and DCs in correlation with immune cells, and may have a positive correlation with neutrophils.

[0044] In the present invention, the method for providing information for breast cancer prognosis and prediction may be to measure the activity of each cell state from a breast cancer sample, and then evaluate the score by distinguishing between high and low based on the median value of each score in the cohort used for analysis.

[0045] In the present invention, the step of predicting the prognosis of breast cancer using the cell status score of step (c) may be performed by comparing the cell status score with a reference value, i.e., the median of each score in the cohort used for analysis, and more specifically, if the cell status score of the measured immunosuppressed fibroblasts is evaluated to be higher than the median, it may be evaluated as a poor prognosis, and if the cell status score of the measured myofibroblasts is evaluated to be higher than the median, it may be evaluated as a good prognosis.

[0046] In the present invention, the method for providing information for predicting the prognosis of breast cancer may be to evaluate a specific cell state, more specifically, a case in which the proportion of IL6+ inflammatory fibroblasts is high, as immunotherapy resistance and poor prognosis.

[0047] In addition, in the present invention, the prognosis and prediction of breast cancer can be evaluated using the measured IL6+ inflammatory fibroblast level and myofibroblast level. In this case, if the measured immunosuppressive fibroblast activity is evaluated to be higher than the median, it can be evaluated as a poor prognosis, and if the measured myofibroblast activity is higher than the median, it can be evaluated as a good prognosis.

[0048]

[0049] The method for providing information for predicting the prognosis of breast cancer of the present invention may include the steps of: obtaining multi-omics data from a separate sample of a cancer patient; performing a hierarchical clustering analysis to classify each sample into a sub-class of breast cancer based on the multi-omics data; and predicting the prognosis based on the classified sub-class.

[0050] In the present invention, the step of obtaining the multi-omics data may be collecting multi-omics data such as genes, transcripts, proteins, and metabolites from a sample of a breast cancer patient, and the data may include genetic mutations, tumor microenvironment components, immune cell ratios, immune characteristic scores, fibroblast activity, TP53 mutation status, and MYC activity scores.

[0051] In the present invention, the method for providing information for predicting the prognosis of breast cancer may be to integrate the collected multi-omics data and then analyze the data using a hierarchical clustering technique to classify breast cancer subtypes. In this case, more detailed subclassification of breast cancer may be possible, and the detailed classification may enable the development of a customized treatment strategy. More specifically, when classifying breast cancer subtypes using the multi-omics clustering data according to the present invention, heterogeneity between tumors may be confirmed by assigning them to different subclassifications.

[0052] In the present invention, the sub-classification of breast cancer derived through the clustering may be classified into 9 types, and the 9 sub-classifications may be referred to as C1 to C9.

[0053] In the present invention, the method for providing information for prognosis and prediction of breast cancer may further include a step of evaluating the consistency between the results of multi-omics-based clustering and the existing PAM50 or IHC subtypes, and comparing the age group, TP53 mutation status, and the distribution of existing breast cancer subtypes of each classification to confirm heterogeneity between tumors, thereby confirming heterogeneity of breast cancer subtypes between each cluster. At this time, although samples with the same PAM50 or IHC subtype generally belong to the same cluster, in some cases, the same subtype may be assigned to a different subclass, thereby confirming heterogeneity between tumors.

[0054] In the present invention, the method for providing information for prognosis and prediction of breast cancer may further include a step of analyzing genetic mutations, tumor microenvironment (TME), and immune components to confirm unique molecular characteristics of each subclass, and through the analysis, the complex heterogeneity of breast cancer can be more deeply understood.

[0055] In the present invention, the method for providing information for prognosis and prediction of breast cancer may further include a step of comparing the proportion of immune cells correlated with age by multi-omics sub-classification in order to understand the relationship between age and breast cancer subtype by confirming the difference in immune cell composition according to age.

[0056] Hereinafter, preferred examples are presented to aid in understanding the present invention. However, the following examples are provided solely to facilitate a more readily understanding of the present invention, and the scope of the present invention is not limited by the examples.

[0057] Example 1: Analysis of the correlation between MYC activity and immune cells in the tumor ecosystem of breast cancer.

[0058] To systematically characterize the tumor ecology of breast cancer associated with MYC activity, we analyzed single-cell transcriptomes of 450,877 cells obtained from 106 tumor samples and 28 normal samples to define various cell states. Specifically, non-negative matrix factorization (NMF) analysis was applied to decompose gene expression into multiple cell states characterized by up to 50 marker genes for each cell type. For example, this analysis method identified various mesenchymal cell states, including IL6+ inflammatory fibroblasts, CCL19+ fibroblasts, and myofibroblasts. Based on the protein levels of these markers, a cell state score was calculated for each breast cancer sample. Meanwhile, IL6+ inflammatory fibroblasts can be identified by the expression of marker genes including AKR1C1, IL-6, FOSL1, and CXCL1 / 3 / 8.

[0059] Next, we calculated the correlation between the above cell status score and MYC target activity or the age at diagnosis across the cohort samples. Consistent with MYC target activity and patient age, the IL6+ inflammatory fibroblast status and the myofibroblast status showed strong positive and negative correlations, respectively (Figures 1C and 1D). The IL6+ inflammatory fibroblasts were identified by marker genes including AKR1C1, IL-6, FOSL1, and CXCL1 / 3 / 8. AKR1C1, which encodes a member of the aldo / keto reductase superfamily, regulates the metabolism of hormones such as estrogen and progesterone. Indeed, this fibroblast status was overexpressed in organs associated with reproductive hormones, including the breast, uterus, fallopian tubes, ovaries, and prostate. In endometrial cancer, estrogen and progesterone levels are regulated by AKR1C1 and AKR1C3. MYC protein synthesis is influenced by post-transcriptional regulation involving the internal ribosome entry site in the 5'-untranslated region. Notably, IL-6, a marker gene for IL6+ inflammatory fibroblast status, has been reported to enhance MYC translation through this mechanism. This IL-6-mediated translational control of MYC has been proposed as a pathway linking inflammation and cancer. Therefore, it was suggested that IL-6 released from IL6+ inflammatory fibroblasts could potentially contribute to increased MYC signaling in young breast cancer patients, potentially related to hormone metabolism.

[0060] Unlike YBC, which exhibits enhanced MYC targeting, OBC exhibits a more enriched myofibroblast state. The negative correlations between myofibroblast state and MYC target activity and patient age are consistent with these findings. Notably, cell state scores calculated using transcriptomes rather than proteome data showed a reduced correlation between IL6+ inflammatory fibroblasts and myofibroblasts and MYC target activity. Therefore, we confirmed the importance of measuring protein activity in understanding the molecular epidemiology of breast cancer and that utilizing proteomic data can more accurately characterize the tumor ecosystem.

[0061] Next, we investigated the correlation between the two fibroblast statuses identified across breast cancer samples and the immune scores and immune cell fractions. Both immune activity parameters showed an overall negative correlation with the IL6+ inflammatory fibroblast score, but a positive correlation with the myofibroblast score (Figures 1E and 1F). More specifically, various types of T cells showed a negative correlation with the IL6+ inflammatory fibroblast status, unlike macrophages, γδ T cells, and DCs (Figure 1F). Macrophages are known to promote breast cancer tumorigenesis through processes such as angiogenesis, immunosuppression, tumor invasion, and metastasis. Tumor-infiltrating γδ T cells and DCs have been identified as predictors of adverse survival outcomes in breast cancer. Macrophages, γδ T cells, and DCs were identified as the three immune cell types that were inversely correlated with the myofibroblast status (Figure 1F). Neutrophils showed a positive correlation with the IL6+ inflammatory fibroblast status (Figure 1F). CXCL1 / 2 / 3, markers of IL6+ inflammatory fibroblast status, are known to recruit neutrophils. These genes may contribute to poor prognosis and resistance to immunotherapy in breast cancer by protecting tumor cells from cytotoxic T cells.

[0062] Meanwhile, the evaluation criteria for IL6+ inflammatory fibroblast and myofibroblast scores were categorized as high or low based on the median score of each score in the cohort used for analysis. Furthermore, a score higher than the median for IL6+ inflammatory fibroblast status was assessed as a poor prognosis, while a score higher than the median for myofibroblast status was assessed as a good prognosis.

[0063]

[0064] In conclusion, these findings suggest that IL6+ inflammatory fibroblasts may contribute to T cell exclusion by attracting pro-tumorigenic immune cells such as macrophages, γδ T cells, DCs, and neutrophils. The activity of MYC target genes showed a similar correlation pattern with immune cell fractions, particularly in terms of T cell repulsion, which was consistent with the T cell exclusion signature observed in these MYC-activated tumors (Fig. 1C). Thus, we confirmed that IL6+ inflammatory fibroblasts may play a role in generating an inflammatory, immunosuppressive tumor microenvironment in MYC-activated breast cancer.

[0065]

[0066] Example 2: Clinical significance of age-related molecular features

[0067] The prognostic potential of age-related features was evaluated in terms of progression-free survival (PFS) and overall survival (OS) data from the cohort. First, all samples were divided into high and low groups based on the median value of each feature. For both fibroblast statuses, IL6+ inflammatory fibroblasts were associated with worse survival, whereas myofibroblasts were correlated with better survival (Figure 2A). This could be partially explained by their relationship with the immune microenvironment (Figures 1D and 1E). In summary, markers identified as associated with age at diagnosis appeared to have a significant impact on survival outcomes. This was also confirmed in the breast cancer TCGA cohort and the multi-cancer CPTAC cohort, where groups with a high IL6+ inflammatory fibroblast score had worse overall survival (OS) (Figure 2B).

[0068] To further investigate the immunological aspects of the two fibroblast states, we collected transcriptome data from a diverse cohort of patients across various cancer types treated with immune checkpoint blockade (ICB). After classifying samples into response groups as either durable clinical benefit (DCB) or no clinical benefit (NCB), we assessed the predictive power of the two fibroblast states on ICB response. The IL6+ inflammatory fibroblast state was more prevalent in the NCB group, whereas the myofibroblast state was more prevalent in the DCB group (Fig. 2C). These findings suggest that fibroblast state serves as an important modulator of the immune microenvironment, potentially explaining both the innate immune response and the response to ICB treatment. Given the proven predictive power of these markers across various cancers, they have the potential to be utilized as biomarkers to guide immunotherapy strategies.

[0069]

[0070] Example 3: Breast cancer subtype classification using multi-omics data

[0071] To investigate whether multi-omics data could more precisely subclassify breast cancer subtypes, we performed hierarchical clustering using 6,000 data points from 142 samples for which all data types were available. This identified nine distinct subtypes (C1 to C9) (Fig. 3). Analysis of the distribution of age groups, TP53 mutations, and PAM50 subtypes revealed that while samples with the same PAM50 or IHC subtype generally belonged to the same cluster, in some cases, the multi-omics clustering revealed heterogeneity between tumors, with the same subtype being assigned to different subtypes. Specifically, TNBC samples were primarily concentrated in C1, C2, and C3, but C1 and C2 were comprised primarily of YBC samples harboring TP53 mutations, whereas C3 was comprised primarily of TP53 wild-type OBC samples. Furthermore, the luminal A / B-enriched subclasses showed a high proportion of TP53 wild-type samples. In particular, OBC samples were abundant in C4, C7, and C8, whereas YBC samples were prominent in C5 and C6. C9 consisted solely of TP53 mutant samples and was classified mostly as HER2 subtype based on PAM50 criteria. These results demonstrate the complex molecular diversity of breast cancer and emphasize that multi-omics-based clustering can provide a deeper understanding of its heterogeneity. This approach can provide new insights into the subclassification of breast cancer and the development of personalized treatment strategies. Multi-omics clustering is expected to improve the predictive power of breast cancer severity and guide more personalized treatment strategies.

[0072] Furthermore, to comprehensively understand the molecular characteristics of each subclass, we analyzed various markers, including genetic mutations, tumor microenvironment (TME) components, immune cell fraction, and immune signature. To explore functional differences, we clustered markers based on their expression patterns to identify unique molecular characteristics for each multi-omics subclass. Specifically, TME markers formed an independent cluster with myofibroblasts and vascular CAF (vCAF), and were expressed at high levels in C3, C4, and C7, whereas they were expressed at low levels in C1, C5, and C9. Conversely, IL6+ inflammatory fibroblasts clustered with tumor-like CAF (tCAF), stromal CAF (mCAF), and dividing CAF (dCAF) and were mainly expressed in C1 and C9. These were a group dominated by TP53 mutant YBC, and their expression was low in C3 and C4.

[0073] To verify age-related differences in immune cell composition, we compared the proportion of immune cells correlated with age (Fig. 1F) across multi-omics subgroups. Clustering analysis revealed that immune cells enriched in YBC (macrophages, neutrophils, monocytes) were clustered into a single cluster, while immune cells enriched in OBC formed two independent clusters, each consisting of CD4+ T cells and helper T cells. Furthermore, subgroups enriched in TNBC (C1-C3) showed a lower proportion of helper T cells, which play a crucial role in promoting anti-tumor responses, suggesting that these cells may promote immune evasion in TNBC tumors.

[0074] Nonetheless, significant heterogeneity was observed even within these TNBC-enriched subtypes. For example, C3 showed the highest levels of myofibroblasts and vCAFs, suggesting a relatively stable and less aggressive nature, and despite being classified as TNBC, its characteristics were different. Furthermore, CD4+ T cells were abundant in C2, C3, and C7, whereas immune cells, which are abundant in YBC, were less abundant in these subtypes, suggesting that the active immune activity of C2 and C3 may induce a more potent antitumor response than C1. Furthermore, we confirmed that immune-related cell state markers and interferon CAF (ifnCAF) were highly expressed in C2 and C3. These subtypes exhibited high immune signature scores, suggesting a robust immune response, including strong lymphocyte infiltration. These results support the robust immune activity characteristic of these subtypes.

[0075] In other words, the multi-omics clustering analysis according to the present invention confirmed the complex heterogeneity of breast cancer in the tumor microenvironment and immune environment. These complex interactions characterize breast cancer subtypes, confirming the importance of molecular profiling to capture this complexity. These findings demonstrate that a multi-omics approach is an essential component of cancer research, enabling a more precise understanding of tumor dynamics and the development of more effective, personalized treatment strategies.

[0076]

[0077] Example 4: Clinical prognostic evaluation using multi-omics data

[0078] To confirm the clinical significance of the multi-omics subclassifications of Example 4 above, the clinical prognosis of each subclass was evaluated, and the results are shown in Figure 4A. The evaluation results showed that while TNBC is generally associated with a poor prognosis, TNBC tumors belonging to C3 showed a good prognosis, reflecting non-malignant molecular characteristics (left). A significant difference in survival outcomes was observed among the luminal A / B-enriched subclasses (C4-C7). In particular, C5, the most malignant luminal A / B subclass, showed the worst survival rate, while C4, the least malignant, showed the best survival rate (middle). In addition, the prognosis of C9 was worse than that of C8, which is interpreted as being due to the high MYC score and low myofibroblast score in C9 (right).

[0079] Furthermore, we summarized the molecular characteristics of each breast cancer subtype by comprehensively visualizing the distribution of key markers by subtype, including age group and PAM50 subtype (Figure 4B). This analysis effectively captured the complex heterogeneity within breast cancer samples, including key factors such as age-related immune cell proportions, fibroblast status, MYC target activity, and TP53 mutation status. These results provide valuable insights into the unique molecular characteristics defining each subtype and the interplay between age, immune cell composition, fibroblast activity, and key oncogenic pathways such as MYC. Notably, a strong correlation was observed between age group and fibroblast status, suggesting that this interaction may influence tumor aggressiveness and clinical outcome. In conclusion, the multi-omics approach according to the present invention enables precise subtype classification of breast cancer and provides insight into the molecular heterogeneity driving disease progression, which may provide guidance for more accurate prognostic assessment and the development of personalized treatment strategies.

[0080]

[0081] Although the embodiments described above have been described with limited drawings, those skilled in the art will appreciate that various technical modifications and variations can be applied based on the above. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or the described components are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0082] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

Claims

1. a) A step of obtaining a biological sample from a cancer patient; b) a step of measuring the cell status score of immunosuppressed fibroblasts, myofibroblasts, or immunosuppressed fibroblasts and myofibroblasts from the obtained sample; and c) a step of predicting the prognosis of breast cancer using the measured cell status score; including; A method for providing information for predicting the prognosis of breast cancer.

2. In paragraph 1, The gene specifying the above-mentioned immunosuppressive fibroblasts is one selected from the group consisting of GEM, CXCL8, CXCL3, CXCL2, CXCL1, IL6, TNFAIP6, MT1A, THAP2, AKR1C1, FOSL1, CEBPB, IER3, LIF, SOD2, C11orf96, CD44, GPRC5A, KDM6B, TNFRSF12A, DDX21, NFKBIA, MAFF, UAP1, SLC3A2, WTAP, PPP1R15A, RND3, MYC, ADAMTS4, HSPD1, PTGS2, CREM, PLIN2, CYCS, BAG3, REL, NAMPT, CYTOR, ZC3H12A, BTG3, XBP1, HSPH1, HSPA9, EIF4A3, EIF5, and ERRFI1. A method for providing information for predicting the prognosis of breast cancer.

3. In paragraph 1, A method for providing information for predicting the prognosis of breast cancer, wherein the above-mentioned immunosuppressive fibroblasts are IL6+ inflammatory fibroblasts.

4. In paragraph 1, A method for providing information for predicting the prognosis of breast cancer, characterized in that the step of predicting the prognosis of breast cancer using the cell status score of step (c) is performed by comparing the cell status score with a given reference value.

5. In paragraph 1, A method for providing information for predicting the prognosis of breast cancer, wherein a cell status score of the above-mentioned measured immunosuppressive fibroblasts is evaluated as higher than the median, which is evaluated as a poor prognosis.

6. In paragraph 1, A method for providing information for predicting the prognosis of breast cancer, wherein a good prognosis is assessed when the cell status score of the measured myofibroblasts is evaluated higher than the median. 7.1) Step of obtaining multi-omics data from isolated samples of cancer patients; 2) A step of performing hierarchical clustering analysis based on the above multi-omics data to classify each sample into a sub-classification of breast cancer; and 3) A method for providing information for predicting the prognosis of breast cancer, comprising: a step of predicting the prognosis based on the classified sub-classification groups.

8. In paragraph 7, A method for providing information for predicting the prognosis of breast cancer, wherein the multi-omics data includes at least one of genetic variation, tumor microenvironment components, immune cell proportion, immune characteristics, fibroblast activity, TP53 mutation status, and MYC activity score.

9. In paragraph 7, After the step of classifying into subcategories of the above 3), A method for providing information for predicting the prognosis of breast cancer, comprising: a step of comparing the age group of the above subclassification, the presence of TP53 mutation, and the distribution of existing breast cancer subtypes to confirm the heterogeneity of breast cancer subtypes between each cluster; 10. In paragraph 7, After the step of classifying into subcategories of the above 3), A method for providing information for predicting the prognosis of breast cancer, comprising: a step of analyzing tumor microenvironment (TME) and immune components by each subclassification to identify molecular characteristics of each subclassification; 11. In paragraph 7, After the step of classifying into subcategories of the above 3), A method for providing information for predicting the prognosis of breast cancer, comprising: a step of analyzing the immune cell ratio by age in each of the above subcategories to confirm the immune cell composition by age; 12. In Article 7 A method for providing information for predicting the prognosis of breast cancer, wherein the subcategories of breast cancer derived through the above clustering are classified into nine types.

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