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Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor–normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Combining PD-L1 blockade with inhibition of oncogenic mitogen-activated protein kinase (MAPK) signaling may result in long-lasting responses in patients with advanced melanoma. This phase 1, open-label, dose-escalation and -expansion study (NCT02027961) investigated safety, tolerability and preliminary efficacy of durvalumab (anti–PD-L1) combined with dabrafenib (BRAF inhibitor) and trametinib (MEK inhibitor) for patients with BRAF-mutated melanoma (cohort A, n = 26), or durvalumab and trametinib given concomitantly (cohort B, n  = 20) or sequentially (cohort C, n = 22) for patients with BRAF -wild type melanoma. Adverse events and treatment discontinuation rates were more common than previously reported for these agents given as monotherapy. Objective responses were observed in 69.2% (cohort A), 20.0% (cohort B) and 31.8% (cohort C) of patients, with evidence of improved tumor immune infiltration and durable responses in a subset of patients with available biopsy samples. In conclusion, combined MAPK inhibition and anti–PD-L1 therapy may provide treatment options for patients with advanced melanoma. Immune checkpoints inhibitors or MAPK inhibitors are currently used as standard of care therapies for patients with advanced melanoma. Here the authors report a phase 1 clinical trial testing the anti-PD-L1 antibody durvalumab in combination with the BRAF inhibitor dafrafenib and the MEK inhibitor trametinib in patients with BRAFV600-mutant melanoma.

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53 , CDKN2A , and B2M , and other genes. Analysing the regulatory consequences of mutations and splice variants at large scale in cancer requires efficient computational tools. Here, the authors develop RegTools, a software package that can identify splice-associated variants from large-scale genomics and transcriptomics data with efficiency and flexibility.

Background Interferon-gamma (IFNγ) exerts potent growth inhibitory effects on a wide range of cancer cells through unknown signaling pathways. We pursued complementary screening approaches to characterize the growth inhibition pathway. Methods We performed chemical genomics and whole genome targeting CRISPR/Cas9 screens using patient-derived melanoma lines to uncover essential nodes in the IFNγ-mediated growth inhibition pathway. We used transcriptomic profiling to identify cell death pathways activated upon IFNγ exposure. Live imaging experiments coupled with apoptosis assays confirmed the involvement of these pathways in IFNγ-mediated cell death. Results We show that IFNγ signaling activated ERK. Blocking ERK activation rescued IFNγ-mediated apoptosis in 17 of 23 (~ 74%) cell lines representing BRAF, NRAS, NF1 mutant, and triple wild type subtypes of cutaneous melanoma. ERK signaling induced a stress response, ultimately leading to apoptosis through the activity of DR5 and NOXA proteins. Conclusions Our results provide a new understanding of the IFNγ growth inhibition pathway, which will be crucial in defining mechanisms of immunotherapy response and resistance.

BackgroundImmune checkpoint blockade (ICB) response in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) is limited to 15%–20% of patients and underpinnings of resistance remain undefined.MethodsStarting with an anti-PD1 sensitive murine HNSCC cell line, we generated an isogenic anti-PD1 resistant model. Mass cytometry was used to delineate tumor microenvironments of both sensitive parental murine oral carcinoma (MOC1) and resistant MOC1esc1 tumors. To examine heterogeneity and clonal dynamics of tumor infiltrating lymphocytes (TILs), we applied paired single-cell RNA and TCR sequencing in three HNSCC models.ResultsAnti-PD1 resistant MOC1esc1 line displayed a conserved cell intrinsic immune evasion signature. Immunoprofiling showed distinct baseline tumor microenvironments of MOC1 and MOC1esc1, as well as the remodeling of immune compartments on ICB in MOC1esc1 tumors. Single cell sequencing analysis identified several CD8 +TIL subsets including Tcf7 +Pd1− (naïve/memory-like), Tcf7 +Pd1+ (progenitor), and Tcf7-Pd1+ (differentiated effector). Mapping TCR shared fractions identified that successful anti-PD1 or anti-CTLA4 therapy-induced higher post-treatment T cell lineage transitions.ConclusionsThese data highlight critical aspects of CD8 +TIL heterogeneity and differentiation and suggest facilitation of CD8 +TIL differentiation as a strategy to improve HNSCC ICB response.

BackgroundGenetically engineered T-cell immunotherapies for adoptive cell transfer (ACT) have emerged as a promising form of cancer treatment, but many of these patients develop recurrent disease. Furthermore, delineating mechanisms of resistance may be challenging since the analysis of bulk tumor profiling can be complicated by spatial heterogeneity.MethodsTumor samples were collected from a patient with synovial sarcoma who developed acquired resistance to ACT targeting NY-ESO-1. Biopsies (primary, progressive metastasis, and recurrence) were subjected to bulk tumor DNA and RNA sequencing, as well as high-dimensional spatial profiling of RNA and protein targets. Untreated and progressive lesions were compared with identified patterns associated with acquired resistance to ACT.ResultsGene expression patterns due to immune activity and infiltration were diluted in bulk tumor sequencing. The metastasis was enriched for tumor regions with increased CTNNB1 (encoding beta-catenin), which were negatively associated with the expression of T-cell surface proteins and antigen presentation machinery. Spatial profiling was most highly concordant with bulk sequencing in the lesions with decreased spatial heterogeneity.ConclusionsComplementary use of bulk and spatial profiling enables more accurate interrogation of tumor specimens, particularly to address complex questions regarding immunotherapeutic mechanisms. Our study uses this approach to demonstrate a mechanism of T-cell exclusion and resistance to cellular immunotherapy in synovial sarcoma.

T cell receptors (TCRs) enable T cells to specifically recognize mutations in cancer cells 1 – 3 . Here we developed a clinical-grade approach based on CRISPR–Cas9 non-viral precision genome-editing to simultaneously knockout the two endogenous TCR genes TRAC (which encodes TCRα) and TRBC (which encodes TCRβ). We also inserted into the TRAC locus two chains of a neoantigen-specific TCR (neoTCR) isolated from circulating T cells of patients. The neoTCRs were isolated using a personalized library of soluble predicted neoantigen–HLA capture reagents. Sixteen patients with different refractory solid cancers received up to three distinct neoTCR transgenic cell products. Each product expressed a patient-specific neoTCR and was administered in a cell-dose-escalation, first-in-human phase I clinical trial ( NCT03970382 ). One patient had grade 1 cytokine release syndrome and one patient had grade 3 encephalitis. All participants had the expected side effects from the lymphodepleting chemotherapy. Five patients had stable disease and the other eleven had disease progression as the best response on the therapy. neoTCR transgenic T cells were detected in tumour biopsy samples after infusion at frequencies higher than the native TCRs before infusion. This study demonstrates the feasibility of isolating and cloning multiple TCRs that recognize mutational neoantigens. Moreover, simultaneous knockout of the endogenous TCR and knock-in of neoTCRs using single-step, non-viral precision genome-editing are achieved. The manufacture of neoTCR engineered T cells at clinical grade, the safety of infusing up to three gene-edited neoTCR T cell products and the ability of the transgenic T cells to traffic to the tumours of patients are also demonstrated. A first-in-human phase I clinical trial demonstrates the feasibility and safety of non-viral precision genome-engineering of a personalized adoptive cell transfer anticancer therapeutic.

In this randomized phase 2 trial, blockade of cytotoxic T-lymphocyte protein 4 (CTLA-4) with continuation of programmed death protein 1 (PD-1) blockade in patients with metastatic melanoma who had received front-line anti-PD-1 or therapy against programmed cell death 1 ligand 1 and whose tumors progressed was tested in comparison with CTLA-4 blockade alone. Ninety-two eligible patients were randomly assigned in a 3:1 ratio to receive the combination of ipilimumab and nivolumab, or ipilimumab alone. The primary endpoint was progression-free survival. Secondary endpoints included the difference in CD8 T cell infiltrate among responding and nonresponding tumors, objective response rate, overall survival and toxicity. The combination of nivolumab and ipilimumab resulted in a statistically significant improvement in progression-free survival over ipilimumab (hazard ratio = 0.63, 90% confidence interval (CI) = 0.41–0.97, one-sided P  = 0.04). Objective response rates were 28% (90% CI = 19–38%) and 9% (90% CI = 2–25%), respectively (one-sided P  = 0.05). Grade 3 or higher treatment-related adverse events occurred in 57% and 35% of patients, respectively, which is consistent with the known toxicity profile of these regimens. The change in intratumoral CD8 T cell density observed in the present analysis did not reach statistical significance to support the formal hypothesis tested as a secondary endpoint. In conclusion, primary resistance to PD-1 blockade therapy can be reversed in some patients with the combination of CTLA-4 and PD-1 blockade. Clinicaltrials.gov identifier: NCT03033576 . Patients with stage 4 or unresectable stage 3 melanoma refractory to first-line anti-programmed death protein 1 (PD-1) or anti-programmed cell death 1 ligand 1 have longer progression-free survival when treated with a combination of anti-cytotoxic T-lymphocyte protein 4 (CTLA-4) and anti-PD-1 versus anti-CTLA-4 alone.