Spatial Distribution and Densities of CD103+ and FoxP3+ Tumor Infiltrating Lymphocytes by Digital Analysis for Outcome Prediction in Breast Cancer

The prognostic value of the tumor-stroma ratio compared to tumor-infiltrating lymphocytes in triple-negative breast cancer: a review

Previous literature extensively explored biomarkers to personalize treatment for breast cancer patients. The clinical need is especially high in patients with triple-negative breast cancer (TNBC) due to its aggressive nature and limited treatment modalities. This review aims to evaluate the value of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) as prognostic biomarkers in TNBC patients and assess their clinical potential. A literature search was conducted in PubMed, Embase, Emcare, Web of Science, and Cochrane Library. Papers comparing survival outcomes of TNBC patients with low/high or negative/positive TSR and immune cells were included. The most frequently mentioned subgroups of TILs were selected and reported in this review. Data from 43 articles on TILs and eight articles on TSR were included. Among TNBC patients, high CD8 expression was generally associated with better survival. Notable, the poor survival outcomes were related to high intra-tumoral PD-L1 expression, whereas high stromal PD-L1 expression more often was correlated with favorable outcomes. For the TSR, a high amount of stroma in the primary tumor of TNBC patients was consistently associated with worse survival. This review highlights that a high number of CD8-positive T-cells is a promising prognostic factor for TNBC patients. PD-L1 expression analyzed for intra-tumoral and stromal expression separately reports strong but contrasting information. Finally, the TSR shows potential to be an important prognostic marker, especially for TNBC patients. Utilizing both biomarkers, either on itself or combined, could enhance clinical decision-making and personalization of treatment.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

The potential role of CD8+ cytotoxic T lymphocytes and one branch connected with tissue-resident memory in non-luminal breast cancer

Advances in understanding the pathological mechanisms of breast cancer have resulted in the emergence of novel therapeutic strategies. However, triple-negative breast cancer (TNBC), a molecular subtype of breast cancer with a poor prognosis, lacks classical and general therapeutic targets, hindering the clinical application of several therapies to breast cancer. As insights into the unique immunity and molecular mechanisms of TNBC have become more extensive, immunotherapy has gradually become a valuable complementary approach to classical radiotherapy and chemotherapy. CD8+ cells are significant actors in the tumor immunity cycle; thus, research on TNBC immunotherapy is increasingly focused in this direction. Recently, CD8+ tissue-resident memory (TRM) cells, a subpopulation of CD8+ cells, have been explored in relation to breast cancer and found to seemingly play an undeniably important role in tumor surveillance and lymphocytic infiltration. In this review, we summarize the recent advances in the mechanisms and relative targets of CD8+ T cells, and discuss the features and potential applications of CD8+ TRM cells in non-luminal breast cancer immunotherapy.

Prognostic Significance of CD163+ and/or CD206+ Tumor-Associated Macrophages Is Linked to Their Spatial Distribution and Tumor-Infiltrating Lymphocytes in Breast Cancer

Tumor-associated macrophages (TAMs) is a key element in the breast tumor microenvironment. CD163 and CD206 have been utilized for TAM identification, but the clinical implications of TAMs identified by these markers have not been thoroughly explored. This study conducted a comparative analysis of CD163 and CD206 TAMs using digital image analysis, focusing on their spatial distribution and prognostic significance in relation to tumor-infiltrating lymphocytes (TILs). Distinct clinico-pathological and prognostic characteristics were noted between the two types of TAMs. CD163 TAMs were linked to high-grade tumors (p = 0.006), whereas CD206 TAMs were associated with a higher incidence of nodal metastasis (p = 0.033). CD206 TAMs were predominantly found in the stroma, with more cases being stromal CD206-high (sCD206-high) than tumoral CD206-high (tCD206-high) (p = 0.024). Regarding prognostication, patients stratified according to stromal and tumoral densities of CD163 showed different disease-free survival (DFS) time. Specifically, those that were sCD163-low but tCD163-high exhibited the poorest DFS (chi-square = 10.853, p = 0.013). Furthermore, a high sCD163-to-stromal-TILs ratio was identified as an independent predictor of unfavorable survival outcomes (DFS: HR = 3.477, p = 0.018). The spatial distribution and interactions with TILs enhanced the prognostic value of CD163 TAMs, while CD206 TAMs appeared to have limited prognostic utility in breast cancer cases.

The cellular composition of the tumor microenvironment is an important marker for predicting therapeutic efficacy in breast cancer

At present, the incidence rate of breast cancer ranks first among new-onset malignant tumors in women. The tumor microenvironment is a hot topic in tumor research. There are abundant cells in the tumor microenvironment that play a protumor or antitumor role in breast cancer. During the treatment of breast cancer, different cells have different influences on the therapeutic response. And after treatment, the cellular composition in the tumor microenvironment will change too. In this review, we summarize the interactions between different cell compositions (such as immune cells, fibroblasts, endothelial cells, and adipocytes) in the tumor microenvironment and the treatment mechanism of breast cancer. We believe that detecting the cellular composition of the tumor microenvironment is able to predict the therapeutic efficacy of treatments for breast cancer and benefit to combination administration of breast cancer.