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      Hepatocellular carcinoma patients with high circulating cytotoxic T cells and intra-tumoral immune signature benefit from pembrolizumab: results from a single-arm phase 2 trial

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          Abstract

          Background

          A limited number of studies have characterized genomic properties of hepatocellular carcinoma (HCC) patients in response to anti-PD-1 immunotherapy.

          Methods

          Herein, we performed comprehensive molecular characterization of immediate (D-42 to D-1) pre-treatment tumor biopsy specimens from 60 patients with sorafenib-failed HCC in a single-arm prospective phase II trial of pembrolizumab. Objective response rate was the primary efficacy endpoint. We used whole-exome sequencing, RNA sequencing, and correlative analysis. In addition, we performed single-cell RNA sequencing using peripheral blood mononuclear cells.

          Results

          The overall response rate of pembrolizumab in sorafenib-failed HCC patients was 10% ([6/60] 95% CI, 2.4–17.6). In a univariate analysis using clinicopathological features, female gender, PD-L1 positivity, and low neutrophil-to-lymphocyte ratio (NLR) were identified as contributing factors to pembrolizumab response. Somatic mutations in CTNNB1 and genomic amplifications in MET were found only in non-responders. Transcriptional profiles through RNA sequencing identified that pembrolizumab responders demonstrated T cell receptor (TCR) signaling activation with expressions of MHC genes, indicating increased levels of T cell cytotoxicity. In single-cell sequencing from 10 pre- and post-treatment peripheral blood mononuclear cells (PBMCs), patients who achieved a partial response or stable disease exhibited immunological shifts toward cytotoxic CD8+ T cells. Conversely, patients with progressive disease showed an increased number of both CD14+ and CD16+ monocytes and activation of neutrophil-associated pathways.

          Conclusions

          Taken together, HCC patients with infiltration of cytotoxic T cells, along with increased active circulating CD8+ T cells during pembrolizumab treatment and down-regulation of neutrophil-associated markers, significantly benefited from pembrolizumab treatment.

          Trial registration

          NCT#03163992 (first posted: May 23, 2017)

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13073-021-00995-8.

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          Most cited references50

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              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
                yh.paik@skku.edu
                hoylim@skku.edu
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                6 January 2022
                6 January 2022
                2022
                : 14
                : 1
                Affiliations
                [1 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, , Sungkyunkwan University School of Medicine, ; Seoul, Republic of Korea
                [2 ]GRID grid.414964.a, ISNI 0000 0001 0640 5613, Innovative Therapeutic Research Center, Precision Medicine Research Institute, , Samsung Medical Center, ; Seoul, Republic of Korea
                [3 ]GRID grid.258803.4, ISNI 0000 0001 0661 1556, Current address: Department of Biomedical Convergence Science and Technology, , Kyungpook National University, ; Daegu, Republic of Korea
                [4 ]GRID grid.222754.4, ISNI 0000 0001 0840 2678, Department of Biomedical Sciences, , Korea University College of Medicine, ; Seoul, Republic of Korea
                [5 ]GRID grid.417993.1, ISNI 0000 0001 2260 0793, Merck & Co., Inc., ; Kenilworth, NJ USA
                [6 ]GRID grid.414964.a, ISNI 0000 0001 0640 5613, Department of Pathology and Translational Genomics, , Samsung Medical Center, Sungkyunkwan University School of Medicine, ; Seoul, Republic of Korea
                [7 ]GRID grid.414964.a, ISNI 0000 0001 0640 5613, Division of Gastroenterology, Department of Medicine, , Samsung Medical Center, Sungkyunkwan University School of Medicine, ; Seoul, Republic of Korea
                [8 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Department of Radiation Oncology, Samsung Medical Center, , Sungkyunkwan University School of Medicine, ; Seoul, Republic of Korea
                [9 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Department of Radiology and Center for Imaging Science, Samsung Medical Center, , Sungkyunkwan University School of Medicine, ; Seoul, Republic of Korea
                [10 ]GRID grid.255649.9, ISNI 0000 0001 2171 7754, Division of Hematology-Oncology, Department of Internal Medicine, , Ewha Womans University College of Medicine, ; Seoul, Republic of Korea
                [11 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Department of Health Science and Technology, Samsung Advanced Institute for Health Science and Technology, , Sungkyunkwan University, ; Seoul, Republic of Korea
                Author information
                http://orcid.org/0000-0003-1363-9332
                Article
                995
                10.1186/s13073-021-00995-8
                8734300
                34986867
                e5cfe581-c712-49fe-9735-4890233a2e43
                © The Author(s) 2021

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 4 April 2021
                : 20 October 2021
                Funding
                Funded by: ministry of health & welfare, republic of korea
                Award ID: HR20C0025
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Molecular medicine
                carcinoma,hepatocellular,pembrolizumab,biomarkers,tumor
                Molecular medicine
                carcinoma, hepatocellular, pembrolizumab, biomarkers, tumor

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