Retained introns in long RNA-seq reads are not reliably detected in sample-matched short reads.

BACKGROUND: There is growing interest in retained introns in a variety of disease contexts including cancer and aging. Many software tools have been developed to detect retained introns from short RNA-seq reads, but reliable detection is complicated by overlapping genes and transcripts as well as the presence of unprocessed or partially processed RNAs. RESULTS: We compared introns detected by 8 tools using short RNA-seq reads with introns observed in long RNA-seq reads from the same biological specimens. We found significant disagreement among tools (Fleiss' [Formula: see text]) such that 47.7% of all detected intron retentions were not called by more than one tool. We also observed poor performance of all tools, with none achieving an F1-score greater than 0.26, and qualitatively different behaviors between general-purpose alternative splicing detection tools and tools confined to retained intron detection. CONCLUSIONS: Short-read tools detect intron retention with poor recall and precision, calling into question the completeness and validity of a large percentage of putatively retained introns called by commonly used methods.

Androgen receptor activity in T cells limits checkpoint blockade efficacy.

Immune checkpoint blockade has revolutionized the field of oncology, inducing durable anti-tumour immunity in solid tumours. In patients with advanced prostate cancer, immunotherapy treatments have largely failed1-5. Androgen deprivation therapy is classically administered in these patients to inhibit tumour cell growth, and we postulated that this therapy also affects tumour-associated T cells. Here we demonstrate that androgen receptor (AR) blockade sensitizes tumour-bearing hosts to effective checkpoint blockade by directly enhancing CD8 T cell function. Inhibition of AR activity in CD8 T cells prevented T cell exhaustion and improved responsiveness to PD-1 targeted therapy via increased IFNγ expression. AR bound directly to Ifng and eviction of AR with a small molecule significantly increased cytokine production in CD8 T cells. Together, our findings establish that T cell intrinsic AR activity represses IFNγ expression and represents a novel mechanism of immunotherapy resistance.

pepsickle rapidly and accurately predicts proteasomal cleavage sites for improved neoantigen identification.

MOTIVATION: Proteasomal cleavage is a key component in protein turnover, as well as antigen processing and presentation. Although tools for proteasomal cleavage prediction are available, they vary widely in their performance, options and availability. RESULTS: Herein, we present pepsickle, an open-source tool for proteasomal cleavage prediction with better in vivo prediction performance (area under the curve) and computational speed than current models available in the field and with the ability to predict sites based on both constitutive and immunoproteasome profiles. Post hoc filtering of predicted patient neoepitopes using pepsickle significantly enriches for immune-responsive epitopes and may improve current epitope prediction and vaccine development pipelines. AVAILABILITY AND IMPLEMENTATION: pepsickle is open source and available at https://github.com/pdxgx/pepsickle. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic analysis of multiple primary melanomas arising within the boundaries of congenital nevi depigmentosa.

Here, we present a rare case of a patient who developed multiple primary melanomas within the boundaries of two nevi depigmentosa. The melanomas were excised, and as a preventive measure, the remainder of the nevi depigmentosa were removed. We performed whole-exome sequencing on excised tissue from the nevus depigmentosus, adjacent normal skin, and saliva to explain this intriguing phenomenon. We also performed a GeneTrails Comprehensive Solid Tumor Panel analysis on one of the melanoma tissues. Genetic analysis revealed germline MC1R V92M and TYR R402Q polymorphisms and a MET E168D germline mutation that may have increased the risk of melanoma development. This genetic predisposition, combined with a patient-reported history of substantial sun exposure and sunburns, which were more severe within the boundaries of the nevi depigmentosa due to the lack of photoprotective melanin, produced numerous somatic mutations in the melanocytes of the nevi depigmentosa. Fitting with this paradigm for melanoma development in chronically sun-damaged skin, the patient's melanomas harbored somatic mutations in CDKN2A (splice site), NF1, and ATRX and had a tumor mutation burden in the 90-95th percentile for melanoma.

A phase II single-arm study of pembrolizumab with enzalutamide in men with metastatic castration-resistant prostate cancer progressing on enzalutamide alone.

BACKGROUND: Checkpoint inhibitors can induce profound anticancer responses, but programmed cell death protein-1 (PD-1) inhibition monotherapy has shown minimal activity in prostate cancer. A published report showed that men with prostate cancer who were resistant to the second-generation androgen receptor inhibitor enzalutamide had increased programmed death-ligand 1 (PD-L1) expression on circulating antigen-presenting cells. We hypothesized that the addition of PD-1 inhibition in these patients could induce a meaningful cancer response. METHODS: We evaluated enzalutamide plus the PD-1 inhibitor pembrolizumab in a single-arm phase II study of 28 men with metastatic castration-resistant prostate cancer (mprogressing on enzalutamide alone. Pembrolizumab 200 mg intravenous was given every 3 weeks for four doses with enzalutamide. The primary endpoint was prostate-specific antigen (PSA) decline of ≥50%. Secondary endpoints were objective response, PSA progression-free survival (PFS), time to subsequent treatment, and time to death. Baseline tumor biopsies were obtained when feasible, and samples were sequenced and evaluated for the expression of PD-L1, microsatellite instability (MSI), mutational and neoepitope burdens. RESULTS: Five (18%) of 28 patients had a PSA decline of ≥50%. Three (25%) of 12 patients with measurable disease at baseline achieved an objective response. Of the five responders, two continue with PSA and radiographic response after 39.3 and 37.8 months. For the entire cohort, median follow-up was 37 months, and median PSA PFS time was 3.8 months (95% CI: 2.8 to 9.9 months). Time to subsequent treatment was 7.21 months (95% CI: 5.1 to 11.1 months). Median overall survival for all patients was 21.9 months (95% CI: 14.7 to 28 .4 months), versus 41.7 months (95% CI: 22.16 to not reached (NR)) in the responders. Of the three responders with baseline biopsies, one had MSI high disease with mutations consistent with DNA-repair defects. None had detectable PD-L1 expression. CONCLUSIONS: Pembrolizumab has activity in mCRPC when added to enzalutamide. Responses were deep and durable and did not require tumor PD-L1 expression or DNA-repair defects. TRIAL REGISTRATION NUMBER: clinicaltrials.gov (NCT02312557).

Human Leukocyte Antigen Susceptibility Map for Severe Acute Respiratory Syndrome Coronavirus 2.

Genetic variability across the three major histocompatibility complex (MHC) class I genes (human leukocyte antigen A [HLA-A], -B, and -C genes) may affect susceptibility to and severity of the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). We performed a comprehensive in silico analysis of viral peptide-MHC class I binding affinity across 145 HLA-A, -B, and -C genotypes for all SARS-CoV-2 peptides. We further explored the potential for cross-protective immunity conferred by prior exposure to four common human coronaviruses. The SARS-CoV-2 proteome was successfully sampled and was represented by a diversity of HLA alleles. However, we found that HLA-B*46:01 had the fewest predicted binding peptides for SARS-CoV-2, suggesting that individuals with this allele may be particularly vulnerable to COVID-19, as they were previously shown to be for SARS (M. Lin, H.-T. Tseng, J. A. Trejaut, H.-L. Lee, et al., BMC Med Genet 4:9, 2003, https://bmcmedgenet.biomedcentral.com/articles/10.1186/1471-2350-4-9). Conversely, we found that HLA-B*15:03 showed the greatest capacity to present highly conserved SARS-CoV-2 peptides that are shared among common human coronaviruses, suggesting that it could enable cross-protective T-cell-based immunity. Finally, we reported global distributions of HLA types with potential epidemiological ramifications in the setting of the current pandemic.IMPORTANCE Individual genetic variation may help to explain different immune responses to a virus across a population. In particular, understanding how variation in HLA may affect the course of COVID-19 could help identify individuals at higher risk from the disease. HLA typing can be fast and inexpensive. Pairing HLA typing with COVID-19 testing where feasible could improve assessment of severity of viral disease in the population. Following the development of a vaccine against SARS-CoV-2, the virus that causes COVID-19, individuals with high-risk HLA types could be prioritized for vaccination.

Burden of tumor mutations, neoepitopes, and other variants are weak predictors of cancer immunotherapy response and overall survival.

BACKGROUND: Tumor mutational burden (TMB; the quantity of aberrant nucleotide sequences a given tumor may harbor) has been associated with response to immune checkpoint inhibitor therapy and is gaining broad acceptance as a result. However, TMB harbors intrinsic variability across cancer types, and its assessment and interpretation are poorly standardized. METHODS: Using a standardized approach, we quantify the robustness of TMB as a metric and its potential as a predictor of immunotherapy response and survival among a diverse cohort of cancer patients. We also explore the additive predictive potential of RNA-derived variants and neoepitope burden, incorporating several novel metrics of immunogenic potential. RESULTS: We find that TMB is a partial predictor of immunotherapy response in melanoma and non-small cell lung cancer, but not renal cell carcinoma. We find that TMB is predictive of overall survival in melanoma patients receiving immunotherapy, but not in an immunotherapy-naive population. We also find that it is an unstable metric with potentially problematic repercussions for clinical cohort classification. We finally note minimal additional predictive benefit to assessing neoepitope burden or its bulk derivatives, including RNA-derived sources of neoepitopes. CONCLUSIONS: We find sufficient cause to suggest that the predictive clinical value of TMB should not be overstated or oversimplified. While it is readily quantified, TMB is at best a limited surrogate biomarker of immunotherapy response. The data do not support isolated use of TMB in renal cell carcinoma.

neoepiscope improves neoepitope prediction with multivariant phasing.

MOTIVATION: The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels). RESULTS: Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools. AVAILABILITY AND IMPLEMENTATION: neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2-8. Supplementary File 2 contains Supplementary Tables 2-6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Population-level distribution and putative immunogenicity of cancer neoepitopes.

BACKGROUND: Tumor neoantigens are drivers of cancer immunotherapy response; however, current prediction tools produce many candidates requiring further prioritization. Additional filtration criteria and population-level understanding may assist with prioritization. Herein, we show neoepitope immunogenicity is related to measures of peptide novelty and report population-level behavior of these and other metrics. METHODS: We propose four peptide novelty metrics to refine predicted neoantigenicity: tumor vs. paired normal peptide binding affinity difference, tumor vs. paired normal peptide sequence similarity, tumor vs. closest human peptide sequence similarity, and tumor vs. closest microbial peptide sequence similarity. We apply these metrics to neoepitopes predicted from somatic missense mutations in The Cancer Genome Atlas (TCGA) and a cohort of melanoma patients, and to a group of peptides with neoepitope-specific immune response data using an extension of pVAC-Seq (Hundal et al., pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med 8:11, 2016). RESULTS: We show neoepitope burden varies across TCGA diseases and HLA alleles, with surprisingly low repetition of neoepitope sequences across patients or neoepitope preferences among sets of HLA alleles. Only 20.3% of predicted neoepitopes across TCGA patients displayed novel binding change based on our binding affinity difference criteria. Similarity of amino acid sequence was typically high between paired tumor-normal epitopes, but in 24.6% of cases, neoepitopes were more similar to other human peptides, or bacterial (56.8% of cases) or viral peptides (15.5% of cases), than their paired normal counterparts. Applied to peptides with neoepitope-specific immune response, a linear model incorporating neoepitope binding affinity, protein sequence similarity between neoepitopes and their closest viral peptides, and paired binding affinity difference was able to predict immunogenicity (AUROC = 0.66). CONCLUSIONS: Our proposed prioritization criteria emphasize neoepitope novelty and refine patient neoepitope predictions for focus on biologically meaningful candidate neoantigens. We have demonstrated that neoepitopes should be considered not only with respect to their paired normal epitope, but to the entire human proteome, and bacterial and viral peptides, with potential implications for neoepitope immunogenicity and personalized vaccines for cancer treatment. We conclude that putative neoantigens are highly variable across individuals as a function of cancer genetics and personalized HLA repertoire, while the overall behavior of filtration criteria reflects predictable patterns.