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      Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer

      research-article
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      Nature
      Nature Publishing Group UK
      Cancer immunotherapy, Pancreatic cancer

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          Abstract

          Pancreatic ductal adenocarcinoma (PDAC) is lethal in 88% of patients 1 , yet harbours mutation-derived T cell neoantigens that are suitable for vaccines 2, 3 . Here in a phase I trial of adjuvant autogene cevumeran, an individualized neoantigen vaccine based on uridine mRNA–lipoplex nanoparticles, we synthesized mRNA neoantigen vaccines in real time from surgically resected PDAC tumours. After surgery, we sequentially administered atezolizumab (an anti-PD-L1 immunotherapy), autogene cevumeran (a maximum of 20 neoantigens per patient) and a modified version of a four-drug chemotherapy regimen (mFOLFIRINOX, comprising folinic acid, fluorouracil, irinotecan and oxaliplatin). The end points included vaccine-induced neoantigen-specific T cells by high-threshold assays, 18-month recurrence-free survival and oncologic feasibility. We treated 16 patients with atezolizumab and autogene cevumeran, then 15 patients with mFOLFIRINOX. Autogene cevumeran was administered within 3 days of benchmarked times, was tolerable and induced de novo high-magnitude neoantigen-specific T cells in 8 out of 16 patients, with half targeting more than one vaccine neoantigen. Using a new mathematical strategy to track T cell clones (CloneTrack) and functional assays, we found that vaccine-expanded T cells comprised up to 10% of all blood T cells, re-expanded with a vaccine booster and included long-lived polyfunctional neoantigen-specific effector CD8 + T cells. At 18-month median follow-up, patients with vaccine-expanded T cells (responders) had a longer median recurrence-free survival (not reached) compared with patients without vaccine-expanded T cells (non-responders; 13.4 months, P = 0.003). Differences in the immune fitness of the patients did not confound this correlation, as responders and non-responders mounted equivalent immunity to a concurrent unrelated mRNA vaccine against SARS-CoV-2. Thus, adjuvant atezolizumab, autogene cevumeran and mFOLFIRINOX induces substantial T cell activity that may correlate with delayed PDAC recurrence.

          Abstract

          A phase I clinical trial of an adjuvant personalized mRNA neoantigen vaccine, autogene cevumeran, in patients with pancreatic ductal carcinoma demonstrates that the vaccine can induce T cell activity that may correlate with delayed recurrence of disease.

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          Most cited references61

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

            The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

                Author and article information

                Contributors
                greenbab@mskcc.org
                balachav@mskcc.org
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                10 May 2023
                10 May 2023
                : 1-7
                Affiliations
                [1 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Immuno-Oncology Service, Human Oncology and Pathogenesis Program, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [2 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Hepatopancreatobiliary Service, Department of Surgery, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [3 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, David M. Rubenstein Center for Pancreatic Cancer Research, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [4 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Computational Oncology Service, Department of Epidemiology and Biostatistics, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [5 ]GRID grid.434484.b, ISNI 0000 0004 4692 2203, BioNTech, ; Mainz, Germany
                [6 ]GRID grid.418158.1, ISNI 0000 0004 0534 4718, Genentech, ; San Francisco, CA USA
                [7 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Center for Cell Engineering, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [8 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Immunology Program, , Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [9 ]GRID grid.516104.7, ISNI 0000 0004 0408 1530, Department of Oncological Sciences, , Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [10 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Surgery, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [11 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Pathology, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [12 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Epidemiology and Biostatistics, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [13 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Radiology, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [14 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Medicine, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [15 ]GRID grid.5386.8, ISNI 000000041936877X, Meyer Cancer Center, Weill Cornell Medicine, , Weill Cornell Medical College, ; New York, NY USA
                [16 ]HI-TRON, Helmholtz Institute for Translational Oncology, Mainz, Germany
                [17 ]GRID grid.5386.8, ISNI 000000041936877X, Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, , Weill Cornell Medical College, ; New York, NY USA
                Author information
                http://orcid.org/0000-0001-9218-4086
                http://orcid.org/0000-0002-0406-017X
                http://orcid.org/0000-0002-3655-916X
                http://orcid.org/0000-0003-4153-4036
                http://orcid.org/0000-0002-4708-5211
                http://orcid.org/0000-0002-9751-0677
                http://orcid.org/0000-0002-9031-8025
                http://orcid.org/0000-0003-3947-2585
                http://orcid.org/0000-0003-2747-1366
                http://orcid.org/0000-0002-8006-3102
                http://orcid.org/0000-0002-2505-959X
                http://orcid.org/0000-0002-6132-7299
                http://orcid.org/0000-0002-1518-5111
                http://orcid.org/0000-0003-0363-1564
                http://orcid.org/0000-0001-6153-8793
                http://orcid.org/0000-0002-8076-9199
                http://orcid.org/0000-0002-2956-223X
                Article
                6063
                10.1038/s41586-023-06063-y
                10171177
                37165196
                b50765e4-de98-4cfc-80f6-b99805613e78
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 January 2023
                : 6 April 2023
                Categories
                Article

                Uncategorized
                cancer immunotherapy,pancreatic cancer
                Uncategorized
                cancer immunotherapy, pancreatic cancer

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