Jul 2026· Cancer Immunology and Immunotherapy· 0 citations
Medicine
TL;DR
The comprehensive evaluation determined that high-risk cohort's immune microenvironment exhibited a greater inclination toward immune suppression, whereas the low-risk cohort was markedly predisposed to anti-tumor immune responses, underscores cellular heterogeneity inherent in BRCA TME.
Abstract
BACKGROUNDS
Breast cancer (BRCA) represents the most prevalent malignancy globally, with projections indicating 3.2 million new cases anticipated by 2050. Current treatment modalities, encompassing surgical intervention, chemotherapy, and immunotherapy, are markedly hindered by treatment resistance and the inherent complexity of BRCA, thereby impeding effective disease management. Consequently, this study is designed to elucidate cellular heterogeneity within the tumor microenvironment (TME) and to identify prospective therapeutic targets, thus enabling individualized treatment approaches for individuals with BRCA, which remains crucial.
Methods
GEO and TCGA databases were the sources of all analytical data employed in this investigation. Single-cell RNA sequencing data were employed to identify macrophage-associated genes in BRCA, followed by the development of a machine-learning-based prognostic model (PM) integrating data from TCGA and GEO databases. This model stratifies individuals into cohorts of either low or high risk, enabling a disparity evaluation in survival outcomes and tumor immune microenvironment characteristics. Gene co-expression patterns were examined through an analysis of gene co-expression network weighted by high dimension for screening pivotal central genes implicated in tumor immunity. Clinical PMs were subsequently constructed utilizing machine learning algorithms, with validation performed on training and test sets. Furthermore, XGBoost and LightGBM machine learning algorithms were implemented to pinpoint potential biomarkers. Ultimately, validation of these biomarkers was conducted through ELISA, CCK-8 assay, flow cytometry, Western blotting, and immunoprecipitation assays.
Results
Findings indicated a pronounced elevation of TAMs in BRCA patients, with their phenotypic attributes exhibiting a strong correlation with prognosis. The developed clinical PM demonstrated high accuracy and robust predictive capability concerning survival rates of 1, 3, or 5 years. The comprehensive evaluation determined that high-risk cohort's immune microenvironment exhibited a greater inclination toward immune suppression, whereas the low-risk cohort was markedly predisposed to anti-tumor immune responses. Notably, the trifolium factor 1 (TFF1) gene was identified as a key determinant, with its overexpression being markedly associated with tumor invasiveness and immune evasion.
Conclusions
The study underscores cellular heterogeneity inherent in BRCA TME, while PM, predicated on macrophage subpopulation-related genes, offers a novel approach for risk stratification and personalized therapeutic interventions for BRCA patients. Moreover, TFF1 has been established as a prospective therapeutic target, yielding important directions for developing specific treatment interventions.
Tumor-infiltrating lymphocytes (TILs) represent a crucial component of the tumor microenvironment in breast cancer (BC), playing a significant role in tumor progression, immune surveillance, and therapeutic response. This review provides a comprehensive synthesis of current evidence regarding the prognostic and predictive significance of TILs across breast cancer molecular subtypes, with particular emphasis on luminal breast cancer and recent advances in artificial intelligence (AI)-based assessment approaches. A narrative review with a structured literature search was conducted using major biomedical databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published between 2014 and 2025. Current evidence demonstrates that high stromal TIL levels are strongly associated with improved overall survival, disease-free survival, and pathological complete response in triple-negative breast cancer (TNBC) and HER2-positive breast cancer. In contrast, the prognostic role of TILs in luminal breast cancer remains controversial, largely due to lower baseline immune infiltration, biological heterogeneity, and methodological variability across studies. Novel computational approaches, including deep learning-based TIL assessment systems, may provide more consistent quantification of TIL density and spatial distribution patterns, with potential to improve prognostic stratification. Emerging evidence suggests that not only TIL density but also spatial organization, including aggregated and diffuse immune infiltration patterns, significantly influences clinical outcomes. Compared with conventional histopathological evaluation, AI-driven models have shown potential to improve reproducibility, quantitative precision, and spatial characterization. This review highlights the need for standardized TIL assessment methodologies and supports the integration of digital pathology and AI-based tools into clinical practice to improve prognostic accuracy and facilitate personalized therapeutic decision-making in breast cancer.
Salem Harsha· AlQalam journal of medical a...· 0 citations
Immunotherapy has emerged as a pivotal approach in cancer treatment, yet its efficacy is influenced by the interactions within the tumor microenvironment (TME). Angiogenesis, the formation of new blood vessels, is a hallmark feature of the TME, particularly in aggressive malignancies such as pancreatic ductal adenocarcinoma (PDAC). This study aims to elucidate angiogenesis patterns in PDAC and investigate their associations with clinical characteristics, TME features, and the response to immunotherapy. By analyzing 40 angiogenesis‐related genes, 28 angiogenesis‐associated immune genes (AIGs) were identified, enabling classification of PDAC patients into two subclusters with distinct clinical and TME profiles. Afterwards, we constructed an AIGScore risk model using least absolute shrinkage and selection operator regression in PDAC, and its reliable predictive ability was confirmed in both training and validation sets. Functional analyses suggested that a high AIGScore may be associated with worse prognosis, reduced tumor immune cycle activity, elevated immune and stromal scores, and diminished response to anti‐PD‐1 immunotherapy. Pan‐cancer analyses further indicated a potential correlation between the seven AIGs and tumor progression, as well as patient outcomes across other malignancies. In summary, this study proposes a novel AIGScore model with significant prognostic utility for PDAC. The findings may provide insights into TME characteristics and offer a potential framework for optimizing immunotherapeutic strategies in patients with PDAC.
Qing Chang, Qian Wang, Xiumei Jiang et al.· Visual Information Expert Wo...· 0 citations
Background: Ovarian cancer remains the most lethal gynecologic cancer, with limited improvements in patient survival despite targeted therapies and a high recurrence rate (~80%). Current standard-of-care for frontline treatment involves platinum-based chemotherapy, but the emergence of resistant clones limits long-term efficacy. Existing models often overlook critical interactions between cancer cells and their microenvironment. Therefore, we investigated the ovarian cancer microenvironment to identify cell populations and markers driving treatment resistance. Methods: We employed a multi-modal systems biology approach, integrating multiplex immunohistochemistry, bulk, and single-cell RNA sequencing to characterize the ovarian cancer microenvironment. Cell type composition was quantified using ImageJ (mIF) and computational deconvolution tools (CIBERSORTx, singleR) for benign (n=6–13) and cancer (n=7–20) samples. Platinum-sensitivity was determined by mapping single-cell data to a clinically annotated reference. Differential expression analysis and pathway enrichment were performed to identify key biological processes between benign vs cancer and sensitive vs resistant phenotypes. Additionally, a combinatorial marker identification tool (COMET) was used to determine a resistant signature in the sc-RNAseq dataset, which was validated using pseudotime in sc-RNAseq and the TCGA-OV bulk-RNAseq cohort. Results: Across modalities, results showed an increase in macrophage and T cell marker expression with an upregulation of inflammatory and immune pathways, alongside decreased fibroblast abundance, in cancer compared to benign tissues. Resistant samples also showed high expression of macrophage and fibroblast markers paired with an enrichment of the epithelial-to-mesenchymal transition pathway while sensitive samples showed high expression of T and NK cell markers and the upregulation of immune pathways. COMET identified two distinct resistant programs: an EMT-associated fibroblast signature characterized by INHBA, TIMP3 and NNMT; and a canonical epithelial ovarian cancer signature characterized by SLPI, MMP7, and WFDC2. Resistant signature scoring of bulk data from the TCGA-OV cohort predicted shorter treatment-free intervals for patients with higher signature scores and longer treatment-free intervals for patients with lower scores. Conclusions: These findings highlight the importance of tumor microenvironment components, particularly macrophages and fibroblasts, as key contributors to resistance in ovarian cancer and establish a potential resistant signature for biomarker discovery.
Adriana Del Pino Herrera, Miguel A. Martínez, Monica Kim et al.· Research Square· 0 citations
A dual-gene model based on neutrophil heterogeneity demonstrated strong predictive performance and functioned as an independent prognostic indicator, and distinct immune and prognostic differences were identified among molecular subtypes.
