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      Integrative Pan-Cancer Genomic and Transcriptomic Analyses of Refractory Metastatic Cancer

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          Abstract

          A clinically annotated cohort of 1,031 patients was profiled via whole-exome and RNA sequencing, which characterized novel RNA and DNA markers of refractory metastatic cancer and established the use of this cohort for investigation of resistance mechanisms and predictive analyses.

          Abstract

          Metastatic relapse after treatment is the leading cause of cancer mortality, and known resistance mechanisms are missing for most treatments administered to patients. To bridge this gap, we analyze a pan-cancer cohort (META-PRISM) of 1,031 refractory metastatic tumors profiled via whole-exome and transcriptome sequencing. META-PRISM tumors, particularly prostate, bladder, and pancreatic types, displayed the most transformed genomes compared with primary untreated tumors. Standard-of-care resistance biomarkers were identified only in lung and colon cancers—9.6% of META-PRISM tumors, indicating that too few resistance mechanisms have received clinical validation. In contrast, we verified the enrichment of multiple investigational and hypothetical resistance mechanisms in treated compared with nontreated patients, thereby confirming their putative role in treatment resistance. Additionally, we demonstrated that molecular markers improve 6-month survival prediction, particularly in patients with advanced breast cancer. Our analysis establishes the utility of the META-PRISM cohort for investigating resistance mechanisms and performing predictive analyses in cancer.

          Significance:

          This study highlights the paucity of standard-of-care markers that explain treatment resistance and the promise of investigational and hypothetical markers awaiting further validation. It also demonstrates the utility of molecular profiling in advanced-stage cancers, particularly breast cancer, to improve the survival prediction and assess eligibility to phase I clinical trials.

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          Most cited references86

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Is Open Access

              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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                Author and article information

                Journal
                Cancer Discov
                Cancer Discov
                Cancer Discovery
                American Association for Cancer Research
                2159-8274
                2159-8290
                04 May 2023
                02 March 2023
                : 13
                : 5
                : 1116-1143
                Affiliations
                [1 ]Université Paris-Saclay, CentraleSupélec, MICS lab, Gif-Sur-Yvette, France.
                [2 ]Gustave Roussy, Department of Medical Oncology, Villejuif, France.
                [3 ]Centre Hospitalier Régional Universitaire de Besançon, Department of Medical Oncology, Besançon, France.
                [4 ]Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.
                [5 ]Gustave Roussy, Université Paris-Saclay, Bioinformatics Core Facility, Inserm US23, CNRS UMS 3655, Villejuif, France.
                [6 ]Gustave Roussy, Early Drug Development Department (DITEP), Villejuif, France.
                [7 ]Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.
                [8 ]Gustave Roussy, Department of Biostatistics and Epidemiology, Villejuif, France.
                [9 ]Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France.
                [10 ]Université Paris-Saclay, Faculty of Medicine, Kremlin-Bicêtre, France.
                Author notes
                [#]

                Note: A.A. Yurchenko, K. Gunbin, L. Cerbone, and M. Deloger contributed equally to this article.

                [* ] Corresponding Authors: Daniel Gautheret, Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France. Phone: 0033-1698-26238; E-mail: daniel.gautheret@ 123456universite-paris-saclay.fr ; and Sergey I. Nikolaev, Gustave Roussy, INSERM U981, 114 rue Edouard Vaillant, 94805 Villejuif Cedex, France. Phone: 0033-1421-15775; E-mail: Sergey.NIKOLAEV@ 123456gustaveroussy.fr

                Cancer Discov 2023;13:1116–43

                Author information
                https://orcid.org/0000-0002-4647-5779
                https://orcid.org/0000-0001-7792-0110
                https://orcid.org/0000-0002-2239-6902
                https://orcid.org/0000-0003-1851-8169
                https://orcid.org/0000-0003-0663-244X
                https://orcid.org/0000-0002-6352-101X
                https://orcid.org/0000-0002-2784-8576
                https://orcid.org/0000-0002-5694-4022
                https://orcid.org/0000-0001-5483-4981
                https://orcid.org/0000-0003-4935-3248
                https://orcid.org/0000-0002-5770-7037
                https://orcid.org/0000-0002-6963-2968
                https://orcid.org/0000-0003-2869-7551
                https://orcid.org/0000-0002-6719-532X
                https://orcid.org/0000-0003-0936-7338
                https://orcid.org/0000-0001-5960-3955
                https://orcid.org/0000-0002-8338-1739
                https://orcid.org/0000-0001-5090-8189
                https://orcid.org/0000-0002-1129-4978
                https://orcid.org/0000-0001-5795-8357
                https://orcid.org/0000-0001-7679-6197
                https://orcid.org/0000-0002-1508-8469
                https://orcid.org/0000-0001-8587-2307
                Article
                CD-22-0966
                10.1158/2159-8290.CD-22-0966
                10157368
                36862804
                e7fc6b0a-7261-4878-b19a-861ff1ed5a64
                ©2023 The Authors; Published by the American Association for Cancer Research

                This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

                History
                : 31 August 2022
                : 02 January 2023
                : 27 February 2023
                Page count
                Pages: 28
                Funding
                Funded by: Agence Nationale de la Recherche (ANR), https://doi.org/10.13039/501100001665;
                Award ID: ANR-18-IBHU-0002
                Award Recipient :
                Funded by: Fondation ARC pour la Recherche sur le Cancer (ARC), https://doi.org/10.13039/501100004097;
                Award ID: RPT21145LLA
                Award Recipient :
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