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      Clinical sequencing of soft tissue and bone sarcomas delineates diverse genomic landscapes and potential therapeutic targets

      research-article
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      Nature Communications
      Nature Publishing Group UK
      Cancer genomics, Sarcoma, Tumour heterogeneity, Tumour biomarkers

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          Abstract

          The genetic, biologic, and clinical heterogeneity of sarcomas poses a challenge for the identification of therapeutic targets, clinical research, and advancing patient care. Because there are > 100 sarcoma subtypes, in-depth genetic studies have focused on one or a few subtypes. Herein, we report a comparative genetic analysis of 2,138 sarcomas representing 45 pathological entities. This cohort is prospectively analyzed using targeted sequencing to characterize subtype-specific somatic alterations in targetable pathways, rates of whole genome doubling, mutational signatures, and subtype-agnostic genomic clusters. The most common alterations are in cell cycle control and TP53, receptor tyrosine kinases/PI3K/RAS, and epigenetic regulators. Subtype-specific associations include TERT amplification in intimal sarcoma and SWI/SNF alterations in uterine adenosarcoma. Tumor mutational burden, while low compared to other cancers, varies between and within subtypes. This resource will improve sarcoma models, motivate studies of subtype-specific alterations, and inform investigations of genetic factors and their correlations with treatment response.

          Abstract

          Sarcomas are rare tumours with many different subtypes and clinical outcomes; a broader knowledge of their genetic features is required. Here, the authors analyse 2138 soft tissue and bone sarcomas across 45 subtypes using MSK-IMPACT targeted sequencing and find genomic groups that are distinct from histological subgroups.

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          Most cited references66

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          Cancer statistics, 2018

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2014, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2015, were collected by the National Center for Health Statistics. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2005-2014) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2006-2015) declined by about 1.5% annually in both men and women. The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak. Of the 10 leading causes of death, only cancer declined from 2014 to 2015. In 2015, the cancer death rate was 14% higher in non-Hispanic blacks (NHBs) than non-Hispanic whites (NHWs) overall (death rate ratio [DRR], 1.14; 95% confidence interval [95% CI], 1.13-1.15), but the racial disparity was much larger for individuals aged <65 years (DRR, 1.31; 95% CI, 1.29-1.32) compared with those aged ≥65 years (DRR, 1.07; 95% CI, 1.06-1.09) and varied substantially by state. For example, the cancer death rate was lower in NHBs than NHWs in Massachusetts for all ages and in New York for individuals aged ≥65 years, whereas for those aged <65 years, it was 3 times higher in NHBs in the District of Columbia (DRR, 2.89; 95% CI, 2.16-3.91) and about 50% higher in Wisconsin (DRR, 1.78; 95% CI, 1.56-2.02), Kansas (DRR, 1.51; 95% CI, 1.25-1.81), Louisiana (DRR, 1.49; 95% CI, 1.38-1.60), Illinois (DRR, 1.48; 95% CI, 1.39-1.57), and California (DRR, 1.45; 95% CI, 1.38-1.54). Larger racial inequalities in young and middle-aged adults probably partly reflect less access to high-quality health care. CA Cancer J Clin 2018;68:7-30. © 2018 American Cancer Society.
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            Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer.

            Immune checkpoint inhibitors, which unleash a patient's own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non-small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti-PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti-PD-1 therapy. Copyright © 2015, American Association for the Advancement of Science.
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              Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade

              The genomes of cancers deficient in mismatch repair contain exceptionally high numbers of somatic mutations. In a proof-of-concept study, we previously showed that colorectal cancers with mismatch repair deficiency were sensitive to immune checkpoint blockade with antibodies to programmed death receptor-1 (PD-1). We have now expanded this study to evaluate the efficacy of PD-1 blockade in patients with advanced mismatch repair-deficient cancers across 12 different tumor types. Objective radiographic responses were observed in 53% of patients, and complete responses were achieved in 21% of patients. Responses were durable, with median progression-free survival and overall survival still not reached. Functional analysis in a responding patient demonstrated rapid in vivo expansion of neoantigen-specific T cell clones that were reactive to mutant neopeptides found in the tumor. These data support the hypothesis that the large proportion of mutant neoantigens in mismatch repair-deficient cancers make them sensitive to immune checkpoint blockade, regardless of the cancers' tissue of origin.
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                Author and article information

                Contributors
                singers@mskcc.org
                tapw@mskcc.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                15 June 2022
                15 June 2022
                2022
                : 13
                : 3405
                Affiliations
                [1 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Medicine, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [2 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Medicine, , Weill Cornell Medical College, ; New York, 10065 NY USA
                [3 ]GRID grid.134907.8, ISNI 0000 0001 2166 1519, The Laboratory of Chromatin Biology and Epigenetics, , The Rockefeller University, ; New York, 10065 NY USA
                [4 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Surgery, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [5 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Marie-Josée and Henry R. Kravis Center for Molecular Oncology, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [6 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Pathology, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [7 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Epidemiology and Biostatistics, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [8 ]GRID grid.5386.8, ISNI 000000041936877X, Physiology, Biophysics and Systems Biology Graduate Program, , Weill Cornell Medical College, ; New York, 10065 NY USA
                [9 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Bioinformatics Core, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [10 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Human Oncology and Pathogenesis Program, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [11 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Pediatrics, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [12 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Surgery, , Weill Cornell Medical College, ; New York, 10065 NY USA
                [13 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Radiation Oncology, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                [14 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Present Address: Department of Epidemiology and Biostatistics, , Memorial Sloan Kettering Cancer Center, ; New York, 10065 NY USA
                Author information
                http://orcid.org/0000-0003-4991-2492
                http://orcid.org/0000-0002-2990-4893
                http://orcid.org/0000-0003-4597-9876
                http://orcid.org/0000-0002-9717-8205
                http://orcid.org/0000-0002-4383-2523
                http://orcid.org/0000-0001-5406-4104
                http://orcid.org/0000-0002-3580-8240
                http://orcid.org/0000-0003-0552-8544
                http://orcid.org/0000-0002-3736-3783
                http://orcid.org/0000-0002-4393-8088
                http://orcid.org/0000-0003-0724-8263
                http://orcid.org/0000-0001-5316-6440
                http://orcid.org/0000-0002-0802-1186
                http://orcid.org/0000-0003-3882-5000
                http://orcid.org/0000-0002-6614-802X
                http://orcid.org/0000-0002-0131-4904
                Article
                30453
                10.1038/s41467-022-30453-x
                9200818
                35705560
                ee4271fc-a8de-4aee-bb9a-a836525a5f67
                © The Author(s) 2022

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 October 2021
                : 2 May 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: P50 CA217694
                Award ID: P30 CA008748
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Categories
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                © The Author(s) 2022

                Uncategorized
                cancer genomics,sarcoma,tumour heterogeneity,tumour biomarkers
                Uncategorized
                cancer genomics, sarcoma, tumour heterogeneity, tumour biomarkers

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