Summary Combination immune checkpoint blockade has demonstrated promising benefit in lung cancer, but predictors of response to combination therapy are unknown. Using whole exome sequencing to examine non-small cell lung cancer (NSCLC) treated with PD-1 plus CTLA-4 blockade, we found that high tumor mutation burden (TMB) predicted improved objective response, durable benefit, and progression-free survival. TMB was independent of PD-L1 expression and the strongest feature associated with efficacy in multivariable analysis. The low response rate in TMB low NSCLCs demonstrates that combination immunotherapy does not overcome the negative predictive impact of low TMB. This study demonstrates the association between TMB and benefit to combination immunotherapy in NSCLC. TMB should be incorporated in future trials examining PD-(L)1 with CTLA-4 blockade in NSCLC.

Predicting the binding affinity of major histocompatibility complex I (MHC I) proteins and their peptide ligands is important for vaccine design. We introduce an open-source package for MHC I binding prediction, MHCflurry. The software implements allele-specific neural networks that use a novel architecture and peptide encoding scheme. When trained on affinity measurements, MHCflurry outperformed the standard predictors NetMHC 4.0 and NetMHCpan 3.0 overall and particularly on non-9-mer peptides in a benchmark of ligands identified by mass spectrometry. The released predictor, MHCflurry 1.2.0, uses mass spectrometry datasets for model selection and showed competitive accuracy with standard tools, including the recently released NetMHCpan 4.0, on a small benchmark of affinity measurements. MHCflurry's prediction speed exceeded 7,000 predictions per second, 396 times faster than NetMHCpan 4.0. MHCflurry is freely available to use, retrain, or extend, includes Python library and command line interfaces, may be installed using package managers, and applies software development best practices.

BackgroundInhibition of programmed death-ligand 1 (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens, and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.Methods and findingsThe goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pretreatment tumor samples as well as TCR-seq of matched, serially collected peripheral blood, collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression-free survival [PFS] >6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state.Patients with DCB displayed a higher proportion of tumor-infiltrating T lymphocytes (TIL) (n = 24, Mann-Whitney p = 0.047). Pretreatment peripheral blood TCR clonality below the median was associated with improved PFS (n = 29, log-rank p = 0.048) and OS (n = 29, log-rank p = 0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n = 22, Mann-Whitney p = 0.022). The combination of high pretreatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n = 10, hazard ratio (HR) (mean) = 89.88, HR (median) = 23.41, 95% CI [2.43, 506.94], p(HR > 1) = 0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which, in turn, impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load, and expressed neoantigen load did not demonstrate significant association with DCB (n = 25, Mann-Whitney p = 0.22, n = 25, Mann-Whitney p = 0.55, and n = 25, Mann-Whitney p = 0.29, respectively). Instead, we found evidence of time-varying effects of somatic mutation load on PFS in this cohort (...

Highlights d Affinity-tagging protocol enables proteomic profiling of individual HLA-II alleles d Even in ''hot'' tumors, professional APCs-not cancer cellsdrive HLA-II expression d Cellular localization influences which phagocytosed cancer proteins get presented d Machine-learning models for binding and processing improve HLA-II prediction

Protein kinases represent one of the largest gene families in eukaryotes and play roles in a wide range of cell signaling processes and human diseases. Current tools for visualizing kinase data in the context of the human kinome superfamily are limited to encoding data through the addition of nodes to a low-resolution image of the kinome tree. We present Coral, a user-friendly interactive web application for visualizing both quantitative and qualitative data. Unlike previous tools, Coral can encode data in three features (node color, node size, and branch color), allows three modes of kinome visualization (the traditional kinome tree as well as radial and dynamic force networks), and generates high-resolution scalable vector graphics files suitable for publication without the need for refinement using graphics editing software. Due to its user-friendly, interactive, and highly customizable design, Coral is broadly applicable to high-throughput studies of the human kinome. The source code and web application are available at github.com/dphansti/CORAL and phanstiel-lab.med.unc.edu/Coral, respectively.

Immune checkpoint inhibitors are promising treatments for patients with a variety of malignancies. Toward understanding the determinants of response to immune checkpoint inhibitors, it was previously demonstrated that the presence of somatic mutations is associated with benefit from checkpoint inhibition. A hypothesis was posited that neoantigen homology to pathogens may in part explain the link between somatic mutations and response. To further examine this hypothesis, we reanalyzed cancer exome data obtained from our previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of these patients. We found that the ability to accurately predict patient benefit did not increase as the analysis narrowed from somatic mutation burden, to inclusion of only those mutations predicted to be MHC Class I neoantigens, to only including those neoantigens that were expressed or that had homology to pathogens. The only association between somatic mutation burden and response was found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden were also associated with response, but neither is more predictive than somatic mutation burden. Neither the previously-described tetrapeptide signature, nor an updated method to evaluate neoepitope homology to pathogens, were more predictive than mutation burden.

Supplementary Materials:We have developed user-friendly software for fitting the model described in the paper. The software is publically available as part of the rstanarm package, downloadable from the Comprehensive R Archive Network (https://cran.r-project.org/). The supplementary materials include an example of the code required to fit the model and additional details about the model estimation. However, the Iressa Pan-Asia Study (IPASS) dataset used in our application is not publicly available. AbstractJoint modelling of longitudinal and time-to-event data has received much attention recently.Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progressionfree survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.

Antibodies targeting CTLA-4 induce durable responses in some patients with melanoma and are being tested in a variety of human cancers. However, these therapies are ineffective for a majority of patients across tumor types. Further understanding the immune alterations induced by these therapies may enable the development of novel strategies to enhance tumor control and biomarkers to identify patients most likely to respond. In several murine models, including colon26, MC38, CT26, and B16 tumors cotreated with GVAX, anti-CTLA-4 efficacy depends on interactions between the Fc region of CTLA-4 antibodies and Fc receptors (FcR). Anti-CTLA-4 binding to FcRs has been linked to depletion of intratumoral T regulatory cells (Treg). In agreement with previous studies, we found that Tregs infiltrating CT26, B16-F1, and autochthonous Braf V600E Pten À/À melanoma tumors had higher expression of surface CTLA-4 (sCTLA-4) than other T-cell subsets, and anti-CTLA-4 treatment led to FcR-dependent depletion of Tregs infiltrating CT26 tumors. This Treg depletion coincided with activation and degranulation of intratumoral natural killer cells. Similarly, in non-small cell lung cancer (NSCLC) and melanoma patient-derived tumor tissue, Tregs had higher sCTLA-4 expression than other intratumoral T-cell subsets, and Tregs infiltrating NSCLC expressed more sCTLA-4 than circulating Tregs. Patients with cutaneous melanoma who benefited from ipilimumab, a mAb targeting CTLA-4, had higher intratumoral CD56 expression, compared with patients who received little to no benefit from this therapy. Furthermore, using the murine CT26 model we found that combination therapy with anti-CTLA-4 plus IL15/IL15Ra complexes enhanced tumor control compared with either monotherapy.