A landscape of response to drug combinations in non-small cell lung cancer.

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

SULT1A1-dependent sulfonation of alkylators is a lineage-dependent vulnerability of liver cancers.

Adult liver malignancies, including intrahepatic cholangiocarcinoma and hepatocellular carcinoma, are the second leading cause of cancer-related deaths worldwide. Most individuals are treated with either combination chemotherapy or immunotherapy, respectively, without specific biomarkers for selection. Here using high-throughput screens, proteomics and in vitro resistance models, we identify the small molecule YC-1 as selectively active against a defined subset of cell lines derived from both liver cancer types. We demonstrate that selectivity is determined by expression of the liver-resident cytosolic sulfotransferase enzyme SULT1A1, which sulfonates YC-1. Sulfonation stimulates covalent binding of YC-1 to lysine residues in protein targets, enriching for RNA-binding factors. Computational analysis defined a wider group of structurally related SULT1A1-activated small molecules with distinct target profiles, which together constitute an untapped small-molecule class. These studies provide a foundation for preclinical development of these agents and point to the broader potential of exploiting SULT1A1 activity for selective targeting strategies.

Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms.

Cancer-associated fibroblasts (CAFs) are highly heterogeneous. With the lack of a comprehensive understanding of CAFs' functional distinctions, it remains unclear how cancer treatments could be personalized based on CAFs in a patient's tumor. We have established a living biobank of CAFs derived from biopsies of patients' non-small lung cancer (NSCLC) that encompasses a broad molecular spectrum of CAFs in clinical NSCLC. By functionally interrogating CAF heterogeneity using the same therapeutics received by patients, we identify three functional subtypes: (1) robustly protective of cancers and highly expressing HGF and FGF7; (2) moderately protective of cancers and highly expressing FGF7; and (3) those providing minimal protection. These functional differences among CAFs are governed by their intrinsic TGF-ß signaling, which suppresses HGF and FGF7 expression. This CAF functional classification correlates with patients' clinical response to targeted therapies and also associates with the tumor immune microenvironment, therefore providing an avenue to guide personalized treatment.