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      A comprehensive pan-cancer analysis of CD274 gene amplification, tumor mutation burden, microsatellite instability, and PD-L1 expression in Chinese cancer patients

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          Abstract

          Background

          Immune checkpoint inhibitors blocking programmed cell death 1 (PD-1) or programmed cell death ligand 1 (PD-L1) have emerged as effective treatment options for cancer. However, immunotherapy is only effective in a subset of patients. Identifying effective biomarkers to predict the treatment response to PD-1/PD-L1 inhibitors remains an unmet clinical need.

          Methods

          This study retrospectively analyzed clinical information and genetic profiling results of 16,013 samples from Chinese patients with various cancer types in order to investigate the prevalence of CD274 (also known as PD-L1) amplification in various cancer types and its association with existing PD-1/PD-L1 biomarkers, including tumor mutational burden (TMB), microsatellite instability (MSI), and PD-L1 expression.

          Results

          Amplification of CD274 was identified in 174 samples with an overall prevalence of 1.09% among all cancer types in the cohort. The prevalence of CD274 amplification in different cancer types and histological subtypes of lung cancer was varied, with cervical cancer having the highest prevalence. Distinct distributions of TMB, MSI, and PD-L1 expression between CD274-amplified and wild-type samples were observed in several cancer types as well as among different histological subtypes of lung cancer.

          Conclusions

          Although CD274 amplification was only observed in a small proportion of patients, it demonstrated an association with TMB, MSI, and PD-L1 expression in several common cancer types. The molecular features of CD274 in different cancer types are heterogeneous. The role of CD274 amplification as a novel biomarker of PD-1/PD-L1 inhibitors remains to be characterized in future prospective clinical studies.

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          Most cited references23

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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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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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                Author and article information

                Journal
                Ann Transl Med
                Ann Transl Med
                ATM
                Annals of Translational Medicine
                AME Publishing Company
                2305-5839
                2305-5847
                April 2021
                April 2021
                : 9
                : 8
                : 677
                Affiliations
                [1 ]deptDepartment of Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute , Tongji University School of Medicine , Shanghai, China;
                [2 ]Department of Medical Oncology, Affiliated Tumor Hospital of Nantong University, Nantong , China;
                [3 ]deptDepartment of Urology, The Sixth Affiliated Hospital , Sun Yat-sen University , Guangzhou, China;
                [4 ]Burning Rock Biotech , Guangzhou, China;
                [5 ]deptDepartment of Oncology , The First Affiliated Hospital of Soochow University , Suzhou, China;
                [6 ]deptDepartment of Oncology , Wuxi People’s Hospital , Wuxi, China
                Author notes

                Contributions: (I) Conception and design: K Chen, J Xu; (II) Administrative support: K Chen, J Xu; (III) Provision of study materials or patients: G Gao, XD Zhang, H Qu, B Yao; (IV) Collection and assembly of data: G Gao, XD Zhang, Y Zhou, J Xu, C Chen, T Hou; (V) Data analysis and interpretation: G Gao, XD Zhang, C Chen, T Hou; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

                [#]

                These authors contributed equally to this work.

                Correspondence to: Dr. Kai Chen. Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China. Email: cky9920@ 123456163.com ; Dr. Junying Xu. Department of Oncology, Wuxi People’s Hospital, Wuxi, China. Email: xujunyingletters@ 123456163.com .
                Article
                atm-09-08-677
                10.21037/atm-21-853
                8106035
                33987375
                d976915c-0e6c-4d9e-b80a-80ee46eb0888
                2021 Annals of Translational Medicine. All rights reserved.

                Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

                History
                : 24 January 2021
                : 17 April 2021
                Categories
                Original Article

                cd274,pd-l1,gene amplification,predictive biomarker,pan-cancer analysis

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