No abstract

Understanding the tumor immune microenvironment (TIME) promises to be key for optimal cancer therapy, especially in triple-negative breast cancer (TNBC). Integrating spatial resolution of immune cells with laser capture microdissection gene expression profiles, we defined distinct TIME stratification in TNBC, with implications for current therapies including immune checkpoint blockade. TNBCs with an immunoreactive microenvironment exhibited tumoral infiltration of granzyme B + CD8 + T cells (GzmB + CD8 + T cells), a type 1 IFN signature, and elevated expression of multiple immune inhibitory molecules including indoleamine 2,3-dioxygenase (IDO) and programmed cell death ligand 1 (PD-L1), and resulted in good outcomes. An "immune-cold" microenvironment with an absence of tumoral CD8 + T cells was defined by elevated expression of the immunosuppressive marker B7-H4, signatures of fibrotic stroma, and poor outcomes. A distinct poor-outcome immunomodulatory microenvironment, hitherto poorly characterized, exhibited stromal restriction of CD8 + T cells, stromal expression of PD-L1, and enrichment for signatures of cholesterol biosynthesis. Metasignatures defining these TIME subtypes allowed us to stratify TNBCs, predict outcomes, and identify potential therapeutic targets for TNBC.

Stromal stiffening accompanies malignancy, compromises treatment, and promotes tumor aggression. Clarifying the molecular nature and the factors that regulate stromal stiffening in tumors should identify biomarkers to stratify patients for therapy and interventions to improve outcome. We profiled lysyl hydroxylase- and lysyl oxidase-mediated collagen crosslinks and quantified the greatest abundance of total and complex collagen crosslinks in aggressive human breast cancer subtypes with the stiffest stroma. These tissues harbor the highest number of tumor-associated macrophages (TAM), whose therapeutic ablation in experimental models reduced metastasis, and decreased collagen crosslinks and stromal stiffening. Epithelial-targeted expression of the crosslinking enzyme, lysyl oxidase, had no impact on collagen crosslinking in PyMT mammary tumors, whereas stromal cell targeting did. Stromal cells in microdissected human tumors expressed the highest level of collagen crosslinking enzymes. Immunohistochemical analysis of a cohort of breast cancer patient biopsies revealed that stromal expression of lysyl hydroxylase two, an enzyme that induces hydroxylysine aldehyde-derived collagen crosslinks and stromal stiffening, correlated significantly with disease specific mortality. The findings link tissue inflammation, stromal cell-mediated collagen crosslinking and stiffening to tumor aggression and identify lysyl hydroxylase two as a stromal biomarker.

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.

Anti‐cancer drugs often increase reactive oxygen species (ROS) and cause DNA damage. Here, we highlight a new cross talk between chronic oxidative stress and the histone variant H2AX, a key player in DNA repair. We observe that persistent accumulation of ROS, due to a deficient JunD‐/Nrf2‐antioxidant response, reduces H2AX protein levels. This effect is mediated by an enhanced interaction of H2AX with the E3 ubiquitin ligase RNF168, which is associated with H2AX poly‐ubiquitination and promotes its degradation by the proteasome. ROS‐mediated H2AX decrease plays a crucial role in chemosensitivity. Indeed, cycles of chemotherapy that sustainably increase ROS reduce H2AX protein levels in Triple‐Negative breast cancer (TNBC) patients. H2AX decrease by such treatment is associated with an impaired NRF2‐antioxidant response and is indicative of the therapeutic efficiency and survival of TNBC patients. Thus, our data describe a novel ROS‐mediated regulation of H2AX turnover, which provides new insights into genetic instability and treatment efficacy in TNBC patients.

The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their "black-box" characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics.

Background Recent efforts of gene expression profiling analyses recognized at least four different triple-negative breast cancer (TNBC) molecular subtypes. However, little is known regarding their tumor microenvironment (TME) heterogeneity. Methods Here, we investigated TME heterogeneity within each TNBC molecular subtype, including immune infiltrate localization and composition together with expression of targetable immune pathways, using publicly available transcriptomic and genomic datasets from a large TNBC series totaling 1512 samples. Associations between molecular subtypes and specific features were assessed using logistic regression models. All statistical tests were two-sided. Results We demonstrated that each TNBC molecular subtype exhibits distinct TME profiles associated with specific immune, vascularization, stroma, and metabolism biological processes together with specific immune composition and localization. The immunomodulatory subtype was associated with the highest expression of adaptive immune-related gene signatures and a fully inflamed spatial pattern appearing to be the optimal candidate for immune check point inhibitors. In contrast, most mesenchymal stem-like and luminal androgen receptor tumors showed an immunosuppressive phenotype as witnessed by high expression levels of stromal signatures. Basal-like, luminal androgen receptor, and mesenchymal subtypes exhibited an immune cold phenotype associated with stromal and metabolism TME signatures and enriched in margin-restricted spatial pattern. Tumors with high chromosomal instability and copy number loss in the chromosome 5q and 15q regions, including genomic loss of major histocompatibility complex related genes, showed reduced cytotoxic activity as a plausible immune escape mechanism. Conclusions Our results demonstrate that each TNBC subtype is associated with specific TME profiles, setting the ground for a rationale tailoring of immunotherapy in TNBC patients.

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.