Summary Immune checkpoint inhibitors significantly improve clinical outcomes in numerous malignancies, but high-grade immune-related adverse events can occur, particularly with combination immunotherapy. Herein, we report two melanoma patients who developed fatal myocarditis following treatment with ipilimumab and nivolumab. Both patients developed myositis with rhabdomyolysis, early progressive and refractory cardiac electrical instability, and myocarditis with robust T-cell and macrophage infiltrates. Selective clonal T-cell populations infiltrating the myocardium were identical to those present in tumor and skeletal muscle. Pharmacovigilance data revealed that myocarditis occurred in 0.27% of patients treated with ipilimumab/nivolumab, suggesting this is a rare, potentially fatal, T-cell-driven drug reaction.

By impairing both function and survival, the severe reduction in oxygen availability associated with high-altitude environments is likely to act as an agent of natural selection. We used genomic and candidate gene approaches to search for evidence of such genetic selection. First, a genome-wide allelic differentiation scan (GWADS) comparing indigenous highlanders of the Tibetan Plateau (3,200-3,500 m) with closely related lowland Han revealed a genome-wide significant divergence across eight SNPs located near EPAS1. This gene encodes the transcription factor HIF2α, which stimulates production of red blood cells and thus increases the concentration of hemoglobin in blood. Second, in a separate cohort of Tibetans residing at 4,200 m, we identified 31 EPAS1 SNPs in high linkage disequilibrium that correlated significantly with hemoglobin concentration. The sex-adjusted hemoglobin concentration was, on average, 0.8 g/dL lower in the major allele homozygotes compared with the heterozygotes. These findings were replicated in a third cohort of Tibetans residing at 4,300 m. The alleles associating with lower hemoglobin concentrations were correlated with the signal from the GWADS study and were observed at greatly elevated frequencies in the Tibetan cohorts compared with the Han. High hemoglobin concentrations are a cardinal feature of chronic mountain sickness offering one plausible mechanism for selection. Alternatively, as EPAS1 is pleiotropic in its effects, selection may have operated on some other aspect of the phenotype. Whichever of these explanations is correct, the evidence for genetic selection at the EPAS1 locus from the GWADS study is supported by the replicated studies associating function with the allelic variants.chronic mountain sickness | high altitude | human genome variation | hypoxia | hypoxia-inducible factor

We introduce quanTIseq, a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data. quanTIseq was extensively validated in blood and tumor samples using simulated, flow cytometry, and immunohistochemistry data. quanTIseq analysis of 8000 tumor samples revealed that cytotoxic T cell infiltration is more strongly associated with the activation of the CXCR3/CXCL9 axis than with mutational load and that deconvolution-based cell scores have prognostic value in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture and to reveal immune-cell types that underlie differential patients’ responses to checkpoint blockers. Availability: quanTIseq is available at http://icbi.at/quantiseq . Electronic supplementary material The online version of this article (10.1186/s13073-019-0638-6) contains supplementary material, which is available to authorized users.

Therapeutic antibodies blocking programmed death-1 and its ligand (PD-1/PD-L1) induce durable responses in a substantial fraction of melanoma patients. We sought to determine whether the number and/or type of mutations identified using a next generation sequencing (NGS) panel available in the clinic were correlated with response to anti–PD-1 in melanoma. Using archival melanoma samples from anti–PD-1/PD-L1-treated patients, we performed hybrid capture-based NGS on 236–315 genes and T-cell receptor (TCR) sequencing on initial and validation cohorts from two centers. Patients who responded to anti–PD-1/PD-L1 had higher mutational loads in an initial cohort (median 45.6 vs. 3.9 mutations/MB; P = 0.003), and a validation cohort (37.1 vs. 12.8 mutations/MB; P = 0.002) compared to nonresponders. Response rate, progression-free survival, and overall survival was superior in the high, compared to intermediate and low, mutation load groups. Melanomas with NF1 mutations harbored high mutational loads (median 62.7 mutations/MB) and high response rates (74%) whereas BRAF/NRAS/NF1 wild-type melanomas had a lower mutational load. In these archival samples, TCR clonality did not predict response. Mutation numbers in the 315 genes in the NGS platform strongly correlated with those detected by whole exome sequencing in The Cancer Genome Atlas samples, but was not associated with survival. In conclusion, mutational load, as determined by an NGS platform available in the clinic, effectively stratified patients by likelihood of response. This approach may provide a clinically feasible predictor of response to anti–PD-1/PD-L1.

Stroma-specific loss of heterozygosity or allelic imbalance is associated with somatic TP53 mutations and regional lymph-node metastases in sporadic breast cancer but not in hereditary breast cancer.

We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.Cancer immunotherapy with antibodies targeting immune checkpoints has shown durable benefit or even curative potential in various cancers 1,2 . As only a fraction of patients are responsive to immune checkpoint blockers, efforts are underway to identify predictive markers as well as mechanistic rationale for combination therapies with synergistic potential. Thus, comprehensive and quantitative immunological characterization of tumors in a large number of clinical samples is of utmost importance, but it is currently hampered by the lack of simple and efficient methods. Therefore, we developed quanTIseq, a computational pipeline for the quantification of the Tumor Immune contexture using RNA-seq data and images of haematoxylin and eosin (H&E)-stained tissue slides (Fig. 1a). As part of quanTIseq, we first developed a deconvolution algorithm based on constrained least squares regression 12 . We then designed a signature matrix from a compendium of 51 RNA-seq data sets (Supplementary (Fig. 1c).To validate quanTIseq we first used both simulated data and published data. We simulated 1,700RNA-seq data sets from human breast tumors by mixing various numbers of reads from tumor and immune-cell RNA-seq data, considering different immune compositions and sequencing depths.quanTIseq obtained a high correlation between the true and the estimated fractions and accurately quantified tumor content (measured by the fraction of "other" cells) (Supplementary Figure 1). We then validated quanTIseq using experimental data from a previous study 13 , in which peripheral blood mononuclear cell (PBMC) mixtures were subjected to both, RNA-seq and flow cytometry. A high accuracy of quanTIseq estimates was also observed with this data set ( Fig. 1d and Supplementary Figure 2). Additionally, we successfully validated quanTIseq using two previous published data sets (Supplementary Figures 3 and 4).As most of the validation data sets available in the literature are based on microarray data or consider a limited number of phenotypes, we generated RNA-seq and flow cytometry data from mixtures of peripheral-blood immune cells collected from nine healthy donors. Flow cytometry was used to quantify all the immune sub-populations considered by quanTIseq signature matrix except macrophages, which are not present in blood. Comparison between quanTIseq cell estimates and flow cytometry fractions showed a high correlation at a single and multiple cell-type level ( Fig. 1e and Supplementary Figure 5).We then validated quanTIseq using two independent data sets. The first data...

Introduction The pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer. Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature. Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature. Objectives Develop an exhaled breath biosignature of lung cancer using a colorimetric sensor array. Determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors. Methods The exhaled breath of 229 study subjects, 92 with lung cancer and 137 controls, was drawn across a colorimetric sensor array. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and COPD were incorporated in the prediction models. Results The validated prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects (C-statistic 0.811). The accuracy improved when the model focused on only one histology (C-statistic 0.825 – 0.890). Individuals with different histologies could be accurately distinguished from one another (C-statistic 0.889 for adenocarcinoma vs. squamous cell carcinoma). Moderate accuracies were noted for validated breath biosignatures of stage and survival (C-statistic 0.793, 0.770 respectively). Conclusions A colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer. The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors.

Checkpoint inhibitors produce durable responses in numerous metastatic cancers, but immune-related adverse events (irAEs) complicate and limit their benefit. IrAEs can affect organ systems idiosyncratically; presentations range from mild and self-limited to fulminant and fatal. The molecular mechanisms underlying irAEs are poorly understood. Here, we report a fatal case of encephalitis arising during anti-PD-1 therapy. Histologic analyses revealed robust T-cell infiltration and prominent PD-L1 expression. We identified 209 reported cases in global pharmacovigilance databases—across multiple cancer types—of encephalitis associated with checkpoint inhibitor regimens, with a 19% fatality rate. We performed further analyses from the index case and two additional cases to shed light on this recurrent and fulminant irAE. Spatial and multi-omic analyses pinpointed activated memory CD4+ T cells as highly enriched in the inflamed, affected region. We identified a highly oligoclonal T cell receptor (TCR) repertoire, which we localized to activated memory cytotoxic (CD45RO+GZMB+) CD4 cells. We also identified Epstein-Barr-virus-specific T cell receptors and EBV+ lymphocytes in the affected region, which we speculate contributed to neural inflammation in the index case. Collectively, the 3 cases studied here identify CD4+ and CD8+ T cells as culprits of checkpoint inhibitor-associated immune encephalitis.