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      RNA neoantigen vaccines prime long-lived CD8 + T cells in pancreatic cancer

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      Nature
      Nature Publishing Group UK
      Pancreatic cancer, RNA vaccines

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          Abstract

          A fundamental challenge for cancer vaccines is to generate long-lived functional T cells that are specific for tumour antigens. Here we find that mRNA–lipoplex vaccines against somatic mutation-derived neoantigens may solve this challenge in pancreatic ductal adenocarcinoma (PDAC), a lethal cancer with few mutations. At an extended 3.2-year median follow-up from a phase 1 trial of surgery, atezolizumab (PD-L1 inhibitory antibody), autogene cevumeran 1 (individualized neoantigen vaccine with backbone-optimized uridine mRNA–lipoplex nanoparticles) and modified (m) FOLFIRINOX (chemotherapy) in patients with PDAC, we find that responders with vaccine-induced T cells ( n = 8) have prolonged recurrence-free survival (RFS; median not reached) compared with non-responders without vaccine-induced T cells ( n = 8; median RFS 13.4 months; P  =  0.007). In responders, autogene cevumeran induces CD8 + T cell clones with an average estimated lifespan of 7.7 years (range 1.5 to roughly 100 years), with approximately 20% of clones having latent multi-decade lifespans that may outlive hosts. Eighty-six percent of clones per patient persist at substantial frequencies approximately 3 years post-vaccination, including clones with high avidity to PDAC neoepitopes. Using PhenoTrack, a novel computational strategy to trace single T cell phenotypes, we uncover that vaccine-induced clones are undetectable in pre-vaccination tissues, and assume a cytotoxic, tissue-resident memory-like T cell state up to three years post-vaccination with preserved neoantigen-specific effector function. Two responders recurred and evidenced fewer vaccine-induced T cells. Furthermore, recurrent PDACs were pruned of vaccine-targeted cancer clones. Thus, in PDAC, autogene cevumeran induces de novo CD8 + T cells with multiyear longevity, substantial magnitude and durable effector functions that may delay PDAC recurrence. Adjuvant mRNA–lipoplex neoantigen vaccines may thus solve a pivotal obstacle for cancer vaccination.

          Abstract

          In a phase 1 trial, patients with pancreatic ductal adenocarcinoma who were treated with surgery and bespoke neoantigen mRNA vaccines combined with anti-PD-L1 and chemotherapy exhibited marked long-lived persistence of neoantigen-specific CD8 + T cell clones, which correlated with prolonged recurrence-free survival at a 3.2-year follow-up.

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          Most cited references45

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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.
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            Cancer statistics, 2023

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes using incidence data collected by central cancer registries and mortality data collected by the National Center for Health Statistics. In 2023, 1,958,310 new cancer cases and 609,820 cancer deaths are projected to occur in the United States. Cancer incidence increased for prostate cancer by 3% annually from 2014 through 2019 after two decades of decline, translating to an additional 99,000 new cases; otherwise, however, incidence trends were more favorable in men compared to women. For example, lung cancer in women decreased at one half the pace of men (1.1% vs. 2.6% annually) from 2015 through 2019, and breast and uterine corpus cancers continued to increase, as did liver cancer and melanoma, both of which stabilized in men aged 50 years and older and declined in younger men. However, a 65% drop in cervical cancer incidence during 2012 through 2019 among women in their early 20s, the first cohort to receive the human papillomavirus vaccine, foreshadows steep reductions in the burden of human papillomavirus-associated cancers, the majority of which occur in women. Despite the pandemic, and in contrast with other leading causes of death, the cancer death rate continued to decline from 2019 to 2020 (by 1.5%), contributing to a 33% overall reduction since 1991 and an estimated 3.8 million deaths averted. This progress increasingly reflects advances in treatment, which are particularly evident in the rapid declines in mortality (approximately 2% annually during 2016 through 2020) for leukemia, melanoma, and kidney cancer, despite stable/increasing incidence, and accelerated declines for lung cancer. In summary, although cancer mortality rates continue to decline, future progress may be attenuated by rising incidence for breast, prostate, and uterine corpus cancers, which also happen to have the largest racial disparities in mortality.
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              SCANPY : large-scale single-cell gene expression data analysis

              Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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                Author and article information

                Contributors
                balachav@mskcc.org
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                19 February 2025
                19 February 2025
                2025
                : 639
                : 8056
                : 1042-1051
                Affiliations
                [1 ]Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [2 ]Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [3 ]The Olayan Center for Cancer Vaccines, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [4 ]Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [5 ]BioNTech, ( https://ror.org/04fbd2g40) Mainz, Germany
                [6 ]Genentech, ( https://ror.org/04gndp242) San Francisco, CA USA
                [7 ]Meyer Cancer Center, Weill Cornell Medicine, Weill Cornell Medical College, ( https://ror.org/02r109517) New York, NY USA
                [8 ]Department of Pathology, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [9 ]Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [10 ]Department of Medicine, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [11 ]David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [12 ]HI-TRON, Helmholtz Institute for Translational Oncology, ( https://ror.org/054qg2939) Mainz, Germany
                [13 ]Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, ( https://ror.org/02r109517) New York, NY USA
                Author information
                http://orcid.org/0000-0002-3655-916X
                http://orcid.org/0009-0004-6449-8335
                http://orcid.org/0000-0003-4170-8796
                http://orcid.org/0000-0001-9218-4086
                http://orcid.org/0000-0003-4153-4036
                http://orcid.org/0000-0002-4708-5211
                http://orcid.org/0000-0003-2885-1133
                http://orcid.org/0009-0001-4436-2746
                http://orcid.org/0000-0002-4354-8864
                http://orcid.org/0000-0002-1518-5111
                http://orcid.org/0000-0003-2747-1366
                http://orcid.org/0000-0001-8683-8477
                http://orcid.org/0000-0002-8006-3102
                http://orcid.org/0000-0002-2505-959X
                http://orcid.org/0000-0002-0406-017X
                http://orcid.org/0000-0002-8076-9199
                http://orcid.org/0000-0002-6132-7299
                http://orcid.org/0000-0003-0363-1564
                http://orcid.org/0000-0001-6153-8793
                http://orcid.org/0000-0002-2956-223X
                Article
                8508
                10.1038/s41586-024-08508-4
                11946889
                39972124
                34152a0d-60f5-4685-83c4-f4e33694aa20
                © The Author(s) 2025

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

                History
                : 5 April 2024
                : 10 December 2024
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                © Springer Nature Limited 2025

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                pancreatic cancer,rna vaccines
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