WO2025127679A1 - Method for predicting prognosis of breast cancer by using immunosuppressive fibroblast activity measurement data - Google Patents

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

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

The present disclosure relates to a method for providing information for predicting the prognosis of breast cancer using data measuring the activity of immunosuppressive fibroblasts, and more specifically, 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.

Breast cancer is one of the most prevalent diseases, affecting nearly 1 in 8 women in the United States. It is also 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, the incidence of breast cancer is closely related to age, and in Korea, it is reported to occur most frequently in women aged 40 to 49 years.

Breast cancer is a heterogeneous disease with significant differences between individual patients (inter-tumor heterogeneity) and even within each individual tumor (intra-tumor heterogeneity). Inter-tumor 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, and triple negative breast cancer (TNBC).

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. In addition, HER2-positive breast cancers account for approximately 10-15% of cases and are ER and PR negative, but HER-2 positive. Finally, TNBC is characterized by being negative for ER, PR, and HER-2, and accounts for about 15-20% of cases. In contrast, the subtype based on the PAM50 method is a unique subtype of breast cancer based on the gene expression of 50 genes. The PAM50-based subtype offers improvements in risk prediction and disease management compared to the IHC-based subtype. In particular, the choice of treatment varies depending on the subtype. For example, Luminal A responds better to endocrine therapy, whereas TNBC responds better to chemotherapy. Therefore, accurate subtype classification is essential to select the most effective treatment strategy. Nevertheless, accurate subtype classification still remains a challenge.

Breast cancer diagnosed at a younger age is associated with more aggressive characteristics and worse survival outcomes. In addition, young breast cancer (YBC) patients have a higher risk of metastasis and recurrence compared to old breast cancer (OBC) patients. However, the underlying mechanisms related to age and tumor malignancy remain unknown. Furthermore, the biological characteristics of cancer arise from complex interactions among numerous components that constitute cells, including DNA, RNA, proteins, and small molecules. In particular, since most of the actual mediators and regulators of cell signaling pathways in cancer are observed at the protein level, it is necessary to expand beyond the transcriptional level to the translational level to elucidate the biological mechanisms of YBC. In conclusion, relying solely on genomic alterations poses significant challenges due to the complexity of tumorigenesis, and a more concise understanding of these mechanisms requires the integration of multiple layers of omics data, including genomic, transcriptomic, epigenomic, and proteomic domains.

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 invention includes six data types, ranging from genome, exome, and transcriptome sequencing to methylome, proteome, and phosphorylome data. The integrated multi-omics study will provide novel, clinically relevant insights that may be difficult to obtain when examining only single-omics data.

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 immunosuppressive fibroblasts, myofibroblasts, or immunosuppressive fibroblasts and myofibroblasts from the obtained sample; and predicting the prognosis of breast cancer using the measured cell status score.

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 subclass of breast cancer based on the multi-omics data; and predicting the prognosis based on the classified subclass.

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.

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.

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.

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

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

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.

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.

In addition, the present invention provides 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 subclass of breast cancer based on the multi-omics data; and predicting the prognosis based on the classified subclass.

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 proportion, immune signature score, fibroblast activity, TP53 mutation status, and MYC activity score.

According to one embodiment of the present invention, the method for providing information for predicting breast cancer prognosis 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.

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

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.

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

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.

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

In addition, multi-omics-based clustering analysis can systematically understand the various subcategories of breast cancer, which can be used to develop more accurate prognostic assessments and personalized treatment strategies.

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

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 effect 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 immune scores, (F) Correlation between IL6+ inflammatory fibroblast (top) and myofibroblast (bottom) scores and several immune cell fractions.

Figure 2. Prognostic effects of IL6+ inflammatory fibroblasts and myofibroblasts: (A) survival analysis between high and low score groups based on IL6+ inflammatory fibroblast (left) and myofibroblast (right) scores in 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 breast cancer TCGA cohort (left) and multi-cancer CPTAC (right); (C) evaluation of IL6+ inflammatory fibroblast and myofibroblast scores in predicting innate immune response and clinical outcome of immunotherapy by comparing IL6+ inflammatory fibroblast (left) and myofibroblast (right) scores between durable clinical benefit (DCB) and no clinical benefits (NCB) in immunotherapy cohort.

Fig. 3a shows a new unsupervised hierarchical clustering of breast cancer subtypes into nine detailed 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 each subcategory of MYC expression, MYC target activity, IL6+ inflammatory fibroblasts, and myofibroblasts. 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, Fig. 3b is an enlarged view of the classification name (indicated by 'a') described on the right side of Fig. 3a.

Figure 4 shows the analysis of multi-omics clustering, (A) the results of survival analysis for new subtypes using progression-free survival (PFS) within the TNBC (left), luminal (middle), and HER2 (right) subcategories, 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.

Hereinafter, embodiments will be described in detail with reference to the attached drawings. However, since various modifications may be made to the embodiments, the scope of the patent application rights is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, or substitutes to the embodiments are included in the scope of the rights.

The terms used in the examples are for the purpose of description only and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this specification, the terms "comprises" or "has" and the like are intended to specify the presence of a feature, number, step, operation, component or combination thereof described in the specification, but should be understood to not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms defined in commonly used dictionaries, such as those defined in common usage dictionaries, should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant art, and will not be interpreted in an idealized or overly formal sense unless expressly defined in this application.

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

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

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

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.

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.

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 at least one selected, and more preferably, the gene specifying the immunosuppressive fibroblasts may be at least one selected from the group consisting of AKR1C1, IL-6, FOSL1, CXCL1, CXCL3 and CXCL8, but is not limited thereto.

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 between MYC target activity and patient age, respectively.

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.

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.

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 immunosuppressive 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.

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 a poor prognosis.

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 as 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.

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 separated sample of a cancer patient; performing a hierarchical clustering analysis to classify each sample into a subclass of breast cancer based on the multi-omics data; and predicting the prognosis based on the classified subclass.

In the present invention, the step of obtaining the multi-omics data may be collecting multi-omics data such as genes, transcriptomes, 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.

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. At this time, 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, it may be possible to confirm heterogeneity between tumors by assigning them to different subclassifications.

In the present invention, the subclassification of breast cancer derived through the clustering may be classified into nine types, and the nine subclassifications may be referred to as C1 to C9.

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 existing PAM50 or IHC subtypes, and confirming heterogeneity between tumors by comparing the age group of each classification, the presence or absence of TP53 mutations, and the distribution of existing breast cancer subtypes, thereby confirming the 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 subclassification, thereby confirming heterogeneity between tumors.

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.

In the present invention, the method for providing information for prognosis and prediction of breast cancer may further include a step of comparing the ratio 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.

Hereinafter, preferred examples are presented to help understand the present invention. However, the following examples are provided only to help understand the present invention more easily, and the content of the present invention is not limited by the examples.

Example 1: Correlation Analysis of MYC Activity and Immune Cells in the Tumor Ecosystem of Breast Cancer

To systematically characterize the tumor ecology of breast cancer associated with MYC activity, single-cell transcriptomes of 450,877 cells obtained from 106 tumor samples and 28 normal samples were analyzed 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, the analysis method identified various mesenchymal cell states including IL6+ inflammatory fibroblasts, CCL19+ fibroblasts, and myofibroblasts. Based on the protein levels of the above markers, a cell state score was calculated for each breast cancer sample. Meanwhile, the IL6+ inflammatory fibroblasts can be identified by the expression of marker genes including AKR1C1, IL-6, FOSL1, and CXCL1/3/8.

Next, we calculated the correlation between the above cell status score and MYC target activity or 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 posttranscriptional regulation involving the internal ribosome entry site in the 5'-untranslated region. In particular, IL-6, one of the marker genes for the IL6+ inflammatory fibroblast state, has been reported to enhance MYC translation through this mechanism. This translational control of MYC by IL-6 has been proposed as a pathway linking inflammation and cancer. Therefore, it was suggested that IL-6 released from IL6+ inflammatory fibroblasts could potentially be the cause of increased MYC signaling in relation to hormone metabolism in young breast cancer patients.

Unlike YBCs, which are enriched for MYC targeting, OBCs appear to be more enriched for myofibroblast status. The negative correlations between myofibroblast status and MYC target activity and patient age are consistent with the above findings. In particular, cell state scores calculated using transcriptomes instead of proteome data showed a decreased correlation between IL6+ inflammatory fibroblasts and myofibroblasts and MYC target activity. Thus, we confirm the importance of protein activity measurements in understanding the molecular epidemiology of breast cancer and that utilizing protein data can more accurately characterize the tumor ecosystem.

Next, we investigated the correlation between the two fibroblast statuses identified across breast cancer samples and the immune score and immune cell fraction. As a result, 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 tumorigenesis in breast cancer 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 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 state, 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.

Meanwhile, the evaluation criteria for the scores of IL6+ inflammatory fibroblasts and myofibroblasts were categorized as high and low based on the median value of each score in the cohort used for analysis. In addition, if the IL6+ inflammatory fibroblast status value was higher than the median value, it was evaluated as a case with a poor prognosis, and if the myofibroblast status value was higher than the median value, it was evaluated as a case with a good prognosis.

In conclusion, these findings suggest that IL6+ inflammatory fibroblasts may contribute to T cell exclusion by recruiting 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, especially in terms of T cell repulsion, which was consistent with the T cell exclusion signature observed in the 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.

Example 2: Clinical significance of age-related molecular features

The prognostic potential of age-related features was evaluated in terms of progression-free survival (PFS) and overall survival (OS) data of the cohort. First, all samples were divided into high and low groups based on the median value of each feature. In the two fibroblast states, IL6+ inflammatory fibroblasts were associated with worse survival, whereas myofibroblasts were correlated with better survival (Fig. 2A). This could be partly explained by the relationship with the immune microenvironment (Figs. 1D and 1E). Taken together, markers identified as being 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 the group with a high IL6+ inflammatory fibroblast score showed a worse overall survival (OS) (Fig. 2B).

To further investigate the immunological aspects of the two fibroblast states, we collected transcriptome data from different cohorts across different cancer types treated with immune checkpoint blockade (ICB). After classifying samples according to response as either sustained clinical benefit (DCB) or no clinical benefit (NCB), we evaluated 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 the fibroblast state acts as an important modulator of the immune microenvironment, which may explain both the innate immune response and the response to ICB treatment. Given the proven predictive power of these markers in a variety of cancers, they have the potential to be utilized as biomarkers to guide immunotherapy strategies.

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

To investigate whether multi-omics data can further refine breast cancer subtypes, we performed hierarchical clustering using 6,000 data from 142 samples for which all data types were available, and identified nine distinct subtypes (C1 to C9) (Fig. 3). Analysis of the distribution of age groups, TP53 mutations, and PAM50 subtypes revealed that samples with the same PAM50 or IHC subtype generally belonged to the same cluster, but in some cases, the same subtype was assigned to different subtypes in the multi-omics clustering, revealing heterogeneity between tumors. Specifically, TNBC samples were mainly concentrated in C1, C2, and C3, but C1 and C2 were composed mostly of YBC samples with TP53 mutations, whereas C3 was mainly composed of TP53 wild-type OBC samples. In addition, the luminal A/B-enriched subclasses mainly 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 only of TP53 mutant samples and was mostly classified as HER2 subtype according to 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 the heterogeneity of breast cancer. 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.

In addition, 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 the markers according to 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. In contrast, 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 mainly composed of TP53 mutant YBC, and their expression was low in C3 and C4.

To verify the differences in immune cell composition by age, we compared the proportion of immune cells correlated with age (Fig. 1F) by multi-omics subclassification. The clustering analysis results showed that immune cells (macrophages, neutrophils, monocytes) abundant in YBC were clustered into one cluster, while immune cells abundant in OBC formed two independent clusters consisting of CD4+ T cells and helper T cells, respectively. In addition, the TNBC-enriched subclassification (C1-C3) showed a low proportion of helper T cells, which play an important role in promoting anti-tumor responses, suggesting that they may promote immune evasion in TNBC tumors.

Nonetheless, considerable heterogeneity was observed even within these TNBC-enriched subclasses. 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. In addition, CD4+ T cells were abundant in C2, C3, and C7, whereas immune cells that were high in YBC were less abundant in these subclasses, suggesting that the active immune activity of C2 and C3 may induce a stronger antitumor response than C1. In addition, immune-related cell status markers and interferon CAF (ifnCAF) were highly expressed in C2 and C3. These subclasses showed high immune signature scores, suggesting an active immune response such as strong lymphocytic infiltration. These results support the strong immune activity that characterizes these subclasses.

That is, through the multi-omics clustering analysis according to the present invention, the complex heterogeneity of breast cancer in the tumor microenvironment and immune environment was confirmed. Their complex interactions characterize breast cancer subtypes, and the importance of molecular profiling to capture this complexity was confirmed. These research results indicate that the multi-omics approach is an essential element in cancer research, and it was confirmed that it can more precisely understand the dynamic changes of tumors and develop more effective customized treatment strategies.

Example 4: Clinical prognosis assessment using multi-omics data

To confirm the clinical significance of the multi-omics subclassification of Example 4 above, the clinical prognosis of each subclassification was evaluated, and the results are shown in Fig. 4A. The evaluation results showed that TNBC is generally associated with a poor prognosis, but TNBC tumors belonging to C3 showed a good prognosis reflecting non-malignant molecular characteristics (left). A large difference in survival outcomes was observed in the luminal A/B-enriched subclassifications (C4–C7). In particular, C5, the most malignant luminal A/B subclass, showed the worst survival rate, and C4, the most non-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).

In addition, the distribution of key markers by subtype, including age group and PAM50 subtype, was comprehensively visualized to summarize the molecular characteristics of each breast cancer subtype (Fig. 4B). This analysis effectively captured the complex heterogeneity within breast cancer samples, including key factors such as age-related immune cell proportion, fibroblast status, MYC target activity, and TP53 mutation status. These results provide important insights into the unique molecular characteristics defining each subtype and the interactions between age, immune cell composition, fibroblast activity, and key oncogenic pathways such as MYC. In particular, a strong correlation was observed between age group and fibroblast status, suggesting that the interactions may affect 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 an understanding of the molecular heterogeneity driving disease progression, which may provide guidance for more accurate prognostic assessment and development of personalized therapeutic strategies.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, even if the described techniques are performed in a different order than the described method, and/or the described components are combined or combined in a different form than the described method, or are replaced or substituted by other components or equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims are also included in the scope of the claims described below.

Claims (12)

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