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      Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition

      research-article
      1 , 3 , 13 , 2 , 3 , 13 , 1 , 13 , 1 , 3 , 3 , 3 , 1 , 1 , 1 , 1 , 1 , 1 , 4 , 5 , 5 , 6 , 7 , 7 , 5 , 8 , 9 , 10 , 4 , 6 , 11 , 6 , 12 , , PhD 2 , 3 , , 3 , ∗∗ , 1 , 3 , 14 , ∗∗∗
      Cell
      Cell Press
      neoantigen, mutation, immunogenicity, checkpoint inhibitors, immunotherapy, biomarkers, CXCL9, clonal TMB, meta-analysis

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          Summary

          Checkpoint inhibitors (CPIs) augment adaptive immunity. Systematic pan-tumor analyses may reveal the relative importance of tumor-cell-intrinsic and microenvironmental features underpinning CPI sensitization. Here, we collated whole-exome and transcriptomic data for >1,000 CPI-treated patients across seven tumor types, utilizing standardized bioinformatics workflows and clinical outcome criteria to validate multivariable predictors of CPI sensitization. Clonal tumor mutation burden (TMB) was the strongest predictor of CPI response, followed by total TMB and CXCL9 expression. Subclonal TMB, somatic copy alteration burden, and histocompatibility leukocyte antigen (HLA) evolutionary divergence failed to attain pan-cancer significance. Dinucleotide variants were identified as a source of immunogenic epitopes associated with radical amino acid substitutions and enhanced peptide hydrophobicity/immunogenicity. Copy-number analysis revealed two additional determinants of CPI outcome supported by prior functional evidence: 9q34 ( TRAF2) loss associated with response and CCND1 amplification associated with resistance. Finally, single-cell RNA sequencing (RNA-seq) of clonal neoantigen-reactive CD8 tumor-infiltrating lymphocytes (TILs), combined with bulk RNA-seq analysis of CPI-responding tumors, identified CCR5 and CXCL13 as T-cell-intrinsic markers of CPI sensitivity.

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          Highlights

          • Large-scale meta-analysis of >1,000 CPI-treated cases with exome/transcriptome data

          • Clonal TMB and CXCL9/CXCL13 expression are the strongest predictors of CPI response

          • A multivariable predictor of CPI response significantly outperforms TMB

          • 9q34 loss and CCND1 amplification are additional determinants of CPI response

          Abstract

          A whole-exome and transcriptome meta-analysis of over 1,000 patients treated with immune checkpoint blockade across seven tumor types highlights the potential of multivariable prediction models that consider both tumor- and T-cell-intrinsic mechanisms of response.

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          Most cited references118

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                04 February 2021
                04 February 2021
                : 184
                : 3
                : 596-614.e14
                Affiliations
                [1 ]Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
                [2 ]Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6BT, UK
                [3 ]Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6BT, UK
                [4 ]Stem Cell Group, Cancer Institute, University College London, London WC1E 6DD, UK
                [5 ]Bill Lyons Informatics Centre, University College London Cancer Institute, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6BT, UK
                [6 ]Renal and Skin Units, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
                [7 ]Department of Health Technology, Technical University of Denmark, Copenhagen, Denmark
                [8 ]Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
                [9 ]Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
                [10 ]Cancer Genomics Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
                [11 ]Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, Weill Cornell Medical College, and Parker Center for Cancer Immunotherapy, 885 2nd Avenue, New York, NY 10017, USA
                [12 ]Cancer Dynamics Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
                Author notes
                []Corresponding author s.quezada@ 123456ucl.ac.uk
                [∗∗ ]Corresponding author nicholas.mcgranahan.10@ 123456ucl.ac.uk
                [∗∗∗ ]Corresponding author charles.swanton@ 123456crick.ac.uk
                [13]

                These authors contributed equally

                [14]

                Lead contact

                Article
                S0092-8674(21)00002-7
                10.1016/j.cell.2021.01.002
                7933824
                33508232
                610d58a3-92ab-4ece-9e4f-2a7e10dde2e2
                © 2021 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 February 2020
                : 26 June 2020
                : 4 January 2021
                Categories
                Article

                Cell biology
                neoantigen,mutation,immunogenicity,checkpoint inhibitors,immunotherapy,biomarkers,cxcl9,clonal tmb,meta-analysis

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