1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Pathogenic germline variants are associated with poor survival in stage III/IV melanoma patients

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Patients with late stage resected cutaneous melanoma have poor overall survival (OS) and experience irreversible adverse events from systemic therapy. There is a clinical need to identify biomarkers to predict outcome. Performing germline/tumour whole-exome sequencing of 44 stage III/IV melanoma patients we identified pathogenic germline mutations in CDKN2A, CDK4, ATM, POLH, MRE11A, RECQL4 and XPC, affecting 7/44 patients. These mutations were associated with poor OS ( p = 0.0082). We confirmed our findings in The Cancer Genome Atlas (TCGA) human skin cutaneous melanoma cohort where we identified pathogenic variants in 40/455 patients ( p = 0.0203). Combining these cohorts (n = 499) further strengthened these findings showing germline carriers had worse OS ( p = 0.0009). Additionally, we determined whether tumour mutation burden (TMB) or BRAF status were prognostic markers of survival. Low TMB rate (< 20 Mut/Mb; p = 0.0034) and BRAF p.V600 mutation ( p = 0.0355) were associated with worse progression-free survival. Combining these biomarkers indicated that V600 mutant patients had significantly lower TMB ( p = 0.0155). This was confirmed in the TCGA (n = 443, p = 0.0007). Integrative analysis showed germline mutation status conferred the highest risk (HR 5.2, 95% CI 1.72–15.7). Stage IV (HR 2.5, 0.74–8.6) and low TMB (HR 2.3, 0.57–9.4) were similar, whereas BRAF V600 status was the weakest prognostic biomarker (HR 1.5, 95% CI 0.44–5.2).

          Related collections

          Most cited references61

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          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
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            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
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Cutadapt removes adapter sequences from high-throughput sequencing reads

                Bookmark

                Author and article information

                Contributors
                l.aoude@uq.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 October 2020
                19 October 2020
                2020
                : 10
                : 17687
                Affiliations
                [1 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, The University of Queensland Diamantina Institute, , The University of Queensland, ; Woolloongabba, QLD 4102 Australia
                [2 ]GRID grid.1049.c, ISNI 0000 0001 2294 1395, QIMR Berghofer Medical Research Institute, ; Herston, QLD 4006 Australia
                [3 ]GRID grid.412744.0, ISNI 0000 0004 0380 2017, Division of Cancer Services, , Princess Alexandra Hospital, ; Woolloongabba, QLD 4102 Australia
                [4 ]GRID grid.412744.0, ISNI 0000 0004 0380 2017, Queensland Melanoma Project, , Princess Alexandra Hospital, ; Woolloongabba, QLD 4102 Australia
                [5 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Faculty of Medicine, , University of Queensland, ; St Lucia, QLD 4067 Australia
                Article
                74956
                10.1038/s41598-020-74956-3
                7572377
                33077847
                06fcbee0-a4cb-4996-a327-118ba3accf90
                © The Author(s) 2020

                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
                : 22 June 2020
                : 8 October 2020
                Funding
                Funded by: National Health and Medical Research Council
                Award ID: APP1109048
                Award ID: APP1139071
                Award Recipient :
                Funded by: Cure Cancer Australia Foundation
                Award ID: 1144639
                Award Recipient :
                Funded by: The Gallipoli Medical Research Foundation
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                cancer genetics,cancer genomics,melanoma,tumour biomarkers
                Uncategorized
                cancer genetics, cancer genomics, melanoma, tumour biomarkers

                Comments

                Comment on this article