While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi’s utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients’ drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

CAR T-cell toxicities resembling hemophagocytic lymphohistiocytosis (HLH) occur in a subset of patients with cytokine release syndrome (CRS). As a variant of conventional CRS, a comprehensive characterization of CAR T-cell associated HLH (carHLH) and investigations into associated risk factors are lacking. In the context of 59 patients infused with CD22 CAR T-cells where a substantial proportion developed carHLH, we comprehensively describe the manifestations and timing of carHLH as a CRS variant and explore factors associated with this clinical profile. Amongst 52 subjects with CRS, 21 (40.4%) developed carHLH. Clinical features of carHLH included hyperferritinemia, hypertriglyceridemia, hypofibrinogenemia, coagulopathy, hepatic transaminitis, hyperbilirubinemia, severe neutropenia, elevated lactate dehydrogenase and occasionally hemophagocytosis. Development of carHLH was associated with pre-infusion NK-cell lymphopenia and higher bone marrow T/NK-cell ratio, which was further amplified with CAR T-cell expansion. Following CRS, more robust CAR T-cell and CD8 T-cell expansion in concert with pronounced NK-cell lymphopenia amplified pre-infusion differences in those with carHLH without evidence for defects in NK-cell mediated cytotoxicity. CarHLH was further characterized by persistent elevation of HLH-associated inflammatory cytokines, which contrasted with declining levels in those without carHLH. In the setting of CAR T-cell mediated expansion, clinical manifestations and immunophenotypic profiling in those with carHLH overlap with features of secondary HLH, prompting consideration of an alternative framework for identification and management of this toxicity profile to optimize outcomes following CAR T-cell infusion.

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell type–specific gene expression in each sample from bulk expression, and LIRICS (Ligand–Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand–receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method CIBERSORTx. Second, analyzing The Cancer Genome Atlas, we uncover cell type–specific ligand–receptor interactions uniquely associated with mismatch-repair deficiency across different cancer types, providing additional insights into their enhanced sensitivity to anti–programmed cell death protein 1 (PD-1) therapy compared with other tumors with high neoantigen burden. Finally, we identify a subset of cell type–specific ligand–receptor interactions in the melanoma TME that stratify survival of patients receiving anti–PD-1 therapy better than some recently published bulk transcriptomics-based methods. Significance: This work presents two new computational methods that can deconvolve a large collection of bulk tumor gene expression profiles into their respective cell type–specific gene expression profiles and identify cell type–specific ligand–receptor interactions predictive of response to immune-checkpoint blockade therapy. This article is highlighted in the In This Issue feature, p. 873