Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns from Breast Multiplexed Digital Pathology.

The tumor microenvironment is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. Despite extensive research efforts dedicated to characterizing this complex and heterogeneous environment, considerable challenges persist. In this study, we introduce a data-driven approach for identifying patterns of cell organizations in the tumor microenvironment that are associated with patient prognoses. Our methodology relies on the construction of a bi-level graph model: (i) a cellular graph, which models the intricate tumor microenvironment, and (ii) a population graph that captures inter-patient similarities, given their respective cellular graphs, by means of a soft Weisfeiler-Lehman subtree kernel. This systematic integration of information across different scales enables us to identify patient subgroups exhibiting unique prognoses while unveiling tumor microenvironment patterns that characterize them. We demonstrate our approach in a cohort of breast cancer patients, where the identified tumor microenvironment patterns result in a risk stratification system that provides complementary, new information with respect to alternative standards. Our results, which are validated in a completely independent cohort, allow for new insights into the prognostic implications of the breast tumor microenvironment, and this methodology could be applied to other cancer types more generally.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Epithelial Expressed B7-H4 Drives Differential Immunotherapy Response in Murine and Human Breast Cancer.

Combinations of immune checkpoint inhibitors (ICI, including anti-PD-1/PD-L1) and chemotherapy have been FDA approved for metastatic and early-stage triple-negative breast cancer (TNBC), but most patients do not benefit. B7-H4 is a B7 family ligand with proposed immunosuppressive functions being explored as a cancer immunotherapy target and may be associated with anti-PD-L1 resistance. However, little is known about its regulation and effect on immune cell function in breast cancers. We assessed murine and human breast cancer cells to identify regulation mechanisms of B7-H4 in vitro. We used an immunocompetent anti-PD-L1-sensitive orthotopic mammary cancer model and induced ectopic expression of B7-H4. We assessed therapy response and transcriptional changes at baseline and under treatment with anti-PD-L1. We observed B7-H4 was highly associated with epithelial cell status and transcription factors and found to be regulated by PI3K activity. EMT6 tumors with cell-surface B7-H4 expression were more resistant to immunotherapy. In addition, tumor-infiltrating immune cells had reduced immune activation signaling based on transcriptomic analysis. Paradoxically, in human breast cancer, B7-H4 expression was associated with survival benefit for patients with metastatic TNBC treated with carboplatin plus anti-PD-L1 and was associated with no change in response or survival for patients with early breast cancer receiving chemotherapy plus anti-PD-1. While B7-H4 induces tumor resistance to anti-PD-L1 in murine models, there are alternative mechanisms of signaling and function in human cancers. In addition, the strong correlation of B7-H4 to epithelial cell markers suggests a potential regulatory mechanism of B7-H4 independent of PD-L1. SIGNIFICANCE: This translational study confirms the association of B7-H4 expression with a cold immune microenvironment in breast cancer and offers preclinical studies demonstrating a potential role for B7-H4 in suppressing response to checkpoint therapy. However, analysis of two clinical trials with checkpoint inhibitors in the early and metastatic settings argue against B7-H4 as being a mechanism of clinical resistance to checkpoints, with clear implications for its candidacy as a therapeutic target.

Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.