Immune checkpoint blockade (ICB) therapy provides remarkable clinical gains and has been very successful in treatment of melanoma. However, only a subset of patients with advanced tumors currently benefit from ICB therapies, which at times incur considerable side effects and costs. Constructing predictors of patient response has remained a serious challenge because of the complexity of the immune response and the shortage of large cohorts of ICB-treated patients that include both 'omics' and response data. Here we build immuno-predictive score (IMPRES), a predictor of ICB response in melanoma which encompasses 15 pairwise transcriptomics relations between immune checkpoint genes. It is based on two key conjectures: (i) immune mechanisms underlying spontaneous regression in neuroblastoma can predict melanoma response to ICB, and (ii) key immune interactions can be captured via specific pairwise relations of the expression of immune checkpoint genes. IMPRES is validated on nine published datasets and on a newly generated dataset with 31 patients treated with anti-PD-1 and 10 with anti-CTLA-4, spanning 297 samples in total. It achieves an overall accuracy of AUC = 0.83, outperforming existing predictors and capturing almost all true responders while misclassifying less than half of the nonresponders. Future studies are warranted to determine the value of the approach presented here in other cancer types.

Highlights d Novel mouse system to uncouple tumor mutational load and tumor heterogeneity d Lower tumor heterogeneity leads to decreased tumor growth because of immune rejection d Both clone numbers and their genetic diversity mediate tumor growth and rejection d Tumor heterogeneity is linked to patient survival and checkpoint blockade response

The urea cycle (UC) is the main pathway by which mammals dispose of waste nitrogen. We find that specific alterations in the expression of most UC enzymes occur in many tumors, leading to a general metabolic hallmark termed "UC dysregulation" (UCD). UCD elicits nitrogen diversion toward carbamoyl-phosphate synthetase2, aspartate transcarbamylase, and dihydrooratase (CAD) activation and enhances pyrimidine synthesis, resulting in detectable changes in nitrogen metabolites in both patient tumors and their bio-fluids. The accompanying excess of pyrimidine versus purine nucleotides results in a genomic signature consisting of transversion mutations at the DNA, RNA, and protein levels. This mutational bias is associated with increased numbers of hydrophobic tumor antigens and a better response to immune checkpoint inhibitors independent of mutational load. Taken together, our findings demonstrate that UCD is a common feature of tumors that profoundly affects carcinogenesis, mutagenesis, and immunotherapy response.

We have developed a cost-effective and portable graphene-enabled biosensor to detect Zika virus with a highly specific immobilized monoclonal antibody. Field Effect Biosensing (FEB) with monoclonal antibodies covalently linked to graphene enables real-time, quantitative detection of native Zika viral (ZIKV) antigens. The percent change in capacitance in response to doses of antigen (ZIKV NS1) coincides with levels of clinical significance with detection of antigen in buffer at concentrations as low as 450pM. Potential diagnostic applications were demonstrated by measuring Zika antigen in a simulated human serum. Selectivity was validated using Japanese Encephalitis NS1, a homologous and potentially cross-reactive viral antigen. Further, the graphene platform can simultaneously provide the advanced quantitative data of nonclinical biophysical kinetics tools, making it adaptable to both clinical research and possible diagnostic applications. The speed, sensitivity, and selectivity of this first-of-its-kind graphene-enabled Zika biosensor make it an ideal candidate for development as a medical diagnostic test.

Highlights d Innovative bioinformatics pipeline reveals FAK as candidate synthetic lethal with Gaq d FAK is a central mediator of the GNAQ-driven oncogenic signaling circuitry d FAK activates YAP by MOB1 phosphorylation resulting in Hippo pathway inhibition d FAK represents a potential precision therapeutic target for uveal melanoma treatment

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.

IMPORTANCE Therapies to inhibit programmed cell death 1 and its ligand (anti-PD-1/PD-L1) provide significant survival benefits in many cancers, but the efficacy of these treatments varies considerably across different cancer types. Identifying the underlying variables associated with this cancer type-specific response remains an important open research challenge.OBJECTIVE To evaluate systematically a multitude of neoantigen-, checkpoint-, and immune response-related variables to determine the key variables that accurately predict the response to anti-PD-1/PD-L1 therapy across different cancer types. DESIGN, SETTING, AND PARTICIPANTS This analysis of a broad range of data used whole-exome and RNA sequencing of 7187 patients from the publicly available Cancer Genome Atlas and the objective response rate (ORR) data of 21 cancer types obtained from a collection of clinical trials. Thirty-six variables of 3 distinct classes considered were associated with (1) tumor neoantigens, (2) tumor microenvironment and inflammation, and (3) the checkpoint targets. The performance of each class of variables and their combinations in predicting the ORR to anti-PD-1/PD-L1 therapy was evaluated. Accuracy of predictions was quantified with Spearman correlation measured using a standard leave-one-out cross-validation, a statistical method of evaluating a statistical model by dividing data into 2 segments: one to train the model and the other to validate the model. Data were collected from October 19 through 31, 2018, and were analyzed from November 1 through December 14, 2018. MAIN OUTCOMES AND MEASURESResponse to anti-PD-1/PD-1 therapy. RESULTS Among the 36 variables, estimated CD8 + T-cell abundance was the most predictive of the response to anti-PD-1/PD-L1 therapy across cancer types (Spearman R = 0.72; P < 2.3 × 10 −4 ), followed by the tumor mutational burden (Spearman R = 0.68; P < 6.2 × 10 −4 ), and the fraction of samples with high PD1 gene expression (Spearman R = 0.68; P < 6.9 × 10 −4 ). Notably these top 3 variables cover the 3 classes considered, and their combination is highly correlated with response (Spearman R = 0.90; P < 4.1 × 10 −8 ), explaining more than 80% of the ORR variance observed across different tumor types.CONCLUSIONS AND RELEVANCE That we know of, this is the first systematic evaluation of the different variables associated with anti-PD-1/PD-L1 therapy response across different tumor types. The findings suggest that the 3 key variables can explain most of the observed cross-cancer response variability, but their relative explanatory roles may vary in specific cancer types.

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