Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma.

Single-cell technologies have elucidated mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are not amenable to a clinical diagnostic setting. In contrast, bulk RNA sequencing (RNA-seq) is now routine for research and clinical applications. Our workflow uses transcription factor (TF)-directed coexpression networks (regulons) inferred from single-cell RNA-seq data to deconvolute immune functional states from bulk RNA-seq data. Regulons preserve the phenotypic variation in CD45+ immune cells from metastatic melanoma samples (n = 19, discovery dataset) treated with ICIs, despite reducing dimensionality by >100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells were associated with therapy response, and were characterized by differentially active and cell state-specific regulons. Clustering of bulk RNA-seq melanoma samples from four independent studies (n = 209, validation dataset) according to regulon-inferred scores identified four groups with significantly different response outcomes (P < 0.001). An intercellular link was established between exhausted T cells and monocyte lineage cells, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. The ligand-receptor expression analysis suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative costimulation. Together, our results demonstrate how regulon-based characterization of cell states provide robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

CRABP2 - A novel biomarker for high-risk endometrial cancer.

OBJECTIVE: Investigate the clinical and functional implications of elevated CRABP2 expression in endometrial cancer (EC) patients. METHODS: Patients were stratified into high and low CRABP2 expression groups using a decision tree classifier. Univariate and multivariate statistical analyses determined the prognostic and clinicopathological consequences of increased CRABP2 expression. A CRABP2 gene signature was generated using differential expression analysis, and analyzed using network-based approaches. The findings were validated in The Clinical Proteomic Tumor Analysis Consortium (CPTAC), a newly generated cohort of 120 endometrial tissues, and The Cancer Dependency Map (DepMap). RESULTS: 60 (11%) patients in TCGA had high CRABP2 expression, whilst 468 (89%) had low expression. High expression was associated with serous EC, reduced overall survival, advanced stage and grade. Downstream retinoic acid receptors (RARG and RARA) were correlated with CRABP2 expression and were associated with worse prognosis in serous EC. The CRABP2 gene signature was enriched for Polycomb target gene sets, and was regulated by ELP3 and BMP7. BMP7 expression was increased in the CRABP2-high group, was associated with worse prognosis, and CRISPR-Cas9 screens revealed correlations in its cell-fitness score with CRABP2 following gene knockout. The opposite was true for ELP3, suggesting opposing effects from both master regulators. CONCLUSIONS: CRABP2 expression is associated with poor prognosis and advanced EC. The expression of RARA and RARG correlates with CRABP2 and are associated with worse prognosis in advanced histological subtypes. Polycomb target gene sets and two master regulators, ELP3 and BMP7, were identified as functionally relevant mechanisms driving aberrant CRABP2 expression.