The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000-Genomes Project. The challenge participants developed algorithms to predict inter-individual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against a blinded experimental dataset. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson’s r<0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r<0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

Emerging evidence indicates that various cancers can gain resistance to targeted therapies by acquiring lineage plasticity. Although various genomic and transcriptomic aberrations correlate with lineage plasticity, the molecular mechanisms enabling the acquisition of lineage plasticity have not been fully elucidated. We reveal that Janus kinase (JAK)–signal transducer and activator of transcription (STAT) signaling is a crucial executor in promoting lineage plasticity-driven androgen receptor (AR)-targeted therapy resistance in prostate cancer. Importantly, ectopic JAK–STAT activation is specifically required for the resistance of stem-like subclones expressing multilineage transcriptional programs but not subclones exclusively expressing the neuroendocrine-like lineage program. Both genetic and pharmaceutical inhibition of JAK–STAT signaling resensitizes resistant tumors to AR-targeted therapy. Together, these results suggest that JAK–STAT are compelling therapeutic targets for overcoming lineage plasticity-driven AR-targeted therapy resistance.

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy. Tumor neoantigens are critical targets of the host antitumor immune response, and their presence correlates with the efficacy of immunotherapy treatment. Many studies involving assessment of tumor neoantigens principally focus on total neoantigen load, which simplistically treats all neoantigens equally. Neoantigen load has been linked with treatment response and prognosis in some studies but not others. We developed a Cauchy-Schwarz index of Neoantigens (CSiN) score to better account for the degree of concentration of immunogenic neoantigens in truncal mutations. Unlike total neoantigen load determinations, CSiN incorporates the effect of both clonality and MHC binding affinity of neoantigens when characterizing tumor neoantigen profiles. By analyzing the clinical responses in 501 treated patients with cancer (with most receiving checkpoint inhibitors) and the overall survival of 1978 patients with cancer at baseline, we showed that CSiN scores predict treatment response to checkpoint inhibitors and prognosis in patients with melanoma, lung cancer, and kidney cancer. CSiN score substantially outperformed prior genetics-based prediction methods of responsiveness and fills an important gap in research involving assessment of tumor neoantigen burden.

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