Immunogenomic Landscape in Breast Cancer Reveals Immunotherapeutically Relevant Gene Signatures

Abstract

Breast cancer is characterized by some types of heterogeneity, high aggressive behaviour, and low immunotherapeutic efficiency. Detailed immune stratification is a prerequisite for interpreting resistance to treatment and escape from immune control. Hence, the immune landscape of breast cancer needs further understanding. We systematically clustered breast cancer into six immune subtypes based on the mRNA expression patterns of immune signatures and comprehensively depicted their characteristics. The immunotherapeutic benefit score (ITBscore) was validated to be a superior predictor of the response to immunotherapy in cohorts from various datasets. Six distinct immune subtypes related to divergences in biological functions, signatures of immune or stromal cells, extent of the adaptive immune response, genomic events, and clinical prognostication were identified. These six subtypes were characterized as immunologically quiet, chemokine dominant, lymphocyte depleted, wounding dominant, innate immune dominant, and IFN-γ dominant and exhibited features of the tumor microenvironment (TME). The high ITBscore subgroup, characterized by a high proportion of M1 macrophages:M2 macrophages, an activated inflammatory response, and increased mutational burden (such as mutations in TP53, CDH1 and CENPE), indicated better immunotherapeutic benefits. A low proportion of tumor-infiltrating lymphocytes (TILs) and an inadequate response to immune treatment were associated with the low ITBscore subgroup, which was also associated with poor survival. Analyses of four cohorts treated with immune checkpoint inhibitors (ICIs) suggested that patients with a high ITBscore received significant therapeutic advantages and clinical benefits. Our work may facilitate the understanding of immune phenotypes in shaping different TME landscapes and guide precision immuno-oncology and immunotherapy strategies.

Oncogenic signaling pathway-related long non-coding RNAs for predicting prognosis and immunotherapy response in breast cancer

“…Therefore, we can conclude that the Oncosig, resembling the immunotherapeutic benefit score developed by Wang et al. ( 59 ), has a distinctive characteristic of predicting response to immunotherapy.…”

“…Furthermore, the prognosis of patients with similar OncoSig is, to some extent, influenced by the differential expression of immune checkpoint genes. Therefore, we can conclude that the Oncosig, resembling the immunotherapeutic benefit score developed by Wang et al (59), has a distinctive characteristic of predicting response to immunotherapy.…”

Multivariate analysis of the molecular mechanism related to breast cancer and depression

“…In recent years, with the rapid development of immunotherapy, the research on immune invasion of breast cancer has gradually attracted extensive attention. There are signi cant differences between breast cancer with different subtypes and pathological characteristics [24] .The immune microenvironment is mainly composed of immune cells, extracellular matrix, various growth factors, in ammatory factors, and special physicochemical characteristics, which signi cantly affect the diagnosis and clinical treatment sensitivity of tumors. The key immune cell subsets in the tumor microenvironment of breast cancer: CD4+Th1 cells produce interferon γ Mediate the expansion, differentiation, and activation of CD8+tumor in ltrating lymphocytes (TIL); CD4+FOXP3+TIL represents immunosuppressive mediators by inhibiting CD8+T cells, CD4+Th1 cells, APCs, and natural killer cells (NKs); M2 TAMs help activate Th2 immune response, thereby exhibiting immunosuppressive effects (such as inhibiting T cell function); NKs are cytotoxic members of the innate immune system (releasing cytotoxic cytokines and directly killing cancer cells); Dendritic cells (DCs) are antigen-presenting cells and play a key role in the adaptive immune system; Myeloid derived inhibitory cells (MDSCs) represent immature myeloid cells (possibly originating from bone marrow precursor cells) that exhibit immunosuppressive function by inhibiting T cells, B cells, NKs, M1-TAMs, and DCs.The recruitment and accumulation of immunosuppressive mediators are caused by the secretion of cytokines and chemokines by tumor cells, such as IL-6 and IL-1-β Transforming Growth Factor-1 β CCl_ 2) Mediated [25] .Chronic stress can affect immune function and promote tumor growth by reducing T cell-mediated immunity and reducing lymphocyte count [26] .By analyzing the relationship between key genes and immune invasion, we further explore the potential molecular mechanism of key genes affecting the progress of breast cancer.…”

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature

“…Furthermore, we conducted immunohistochemistry (IHC) on the paraffin-embedded tissue specimens, employing methodologies that have been outlined in previous publications ( 34 , 35 ). The antibodies utilized in this process are comprehensively listed in Supplementary Table S2 .…”