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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.

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.

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.

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.

The emerging field of soundscape ecology focuses on biological, geophysical, and anthropogenic sounds, and provides a non-invasive method to inventory ecosystems. Most of the work on freshwater soundscapes focuses on larger fishes in deeper water, or on insects. We suggest the possibility that such studies have either missed or misidentified photosynthetic oxygen bubble sounds (POBS) produced by bubble streams from damaged macrophytes in sunny shallow water. These contribute significantly to local soundscapes. We recorded such sounds in the shallows of Gull Lake, Alberta, Canada, where POBS from sago pondweed ( Stuckenia pectinata ), along with water boatman stridulations (Hemiptera: Corixidae), comprised almost all of the sounds we encountered. These sounds attenuate rapidly with distance, and the POBS constitute a remarkable acoustic diversity, resulting in a patchwork of very different soundscapes in these shallows. Recognition of POBS has important consequences for acoustic bioinventories in shallow water, rapid ecosystem assessments involving indices of primary production, and bioacoustics studies of such organisms as corixid bugs, communicating against a cacophonous background of POBS.

CIViC is an expert-crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer describing the therapeutic, prognostic, diagnostic and predisposing relevance of inherited and somatic variants of all types. CIViC is committed to open-source code, open-access content, public application programming interfaces (APIs) and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

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.

Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutations were also a poor outcome driver that was validated in METABRIC. For MA12, poor outcome associated with PIK3R1 mutation was also reproducible. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false discovery correction ( q  = 0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typifies ER+ breast cancer. Unravelling the link between somatic mutation and prognosis in estrogen positive (ER+) breast cancer requires the use of long-term follow-up data. Here, combining archival formalin-fixed paraffin embedded tissue and targeted sequencing in three cohorts of ER+ breast cancer, the authors find associations with clinical outcome for NF1 frame-shift nonsense mutations, PIK3R1 mutation, and DDR1 mutations.

Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consuming, costly, poorly standardized, and non-reproducible. Here, we systematized and standardized somatic variant refinement using a machine learning approach. The final model incorporates 41,000 variants from 440 sequencing cases. This model accurately recapitulated manual refinement labels for three independent testing sets (13,579 variants) and accurately predicted somatic variants confirmed by orthogonal validation sequencing data (212,158 variants). The model improves on manual somatic refinement by reducing bias on calls otherwise subject to high inter-reviewer variability. A machine learning approach for refinement of somatic variant calls automates this process and reduces bias stemming from inter-reviewer variability.