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      Clinical, Genomic, and Transcriptomic Data Profiling of Biliary Tract Cancer Reveals Subtype-Specific Immune Signatures

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          PURPOSE

          Biliary tract cancers (BTCs) are aggressive cancers that carry a poor prognosis. An enhanced understanding of the immune landscape of anatomically and molecularly defined subsets of BTC may improve patient selection for immunotherapy and inform immune-based combination treatment strategies.

          METHODS

          We analyzed deidentified clinical, genomic, and transcriptomic data from the Tempus database to determine the mutational frequency and mutational clustering across the three major BTC subtypes (intrahepatic cholangiocarcinoma [IHC], extrahepatic cholangiocarcinoma, and gallbladder cancer). We subsequently determined the relationship between specific molecular alterations and anatomical subsets and features of the BTC immune microenvironment.

          RESULTS

          We analyzed 454 samples of BTC, of which the most commonly detected alterations were TP53 (42.5%), CDKN2A (23.4%), ARID1A (19.6%), BAP1 (15.5%), KRAS (15%), CDKN2B (14.2%), PBRM1 (11.7%), IDH1 (11.7%), TERT (8.4%), KMT2C (10.4%) and LRP1B (8.4%), and FGFR2 fusions (8.7%). Potentially actionable molecular alterations were identified in 30.5% of BTCs including 39.1% of IHC. Integrative cluster analysis revealed four distinct molecular clusters, with cluster 4 predominately associated with FGFR2 rearrangements and BAP1 mutations in IHC. Immune-related biomarkers indicative of an inflamed tumor-immune microenvironment were elevated in gallbladder cancers and in cluster 1, which was enriched for TP53, KRAS, and ATM mutations. Multiple common driver genes, including TP53, FGFR2, IDH1, TERT, BRAF, and BAP1, were individually associated with unique BTC immune microenvironments.

          CONCLUSION

          BTC subtypes exhibit diverse DNA alterations, RNA inflammatory signatures, and immune biomarkers. The association between specific BTC anatomical subsets, molecular alterations, and immunophenotypes highlights new opportunities for therapeutic development.

          Abstract

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          Most cited references35

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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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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              Robust enumeration of cell subsets from tissue expression profiles

              We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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                Author and article information

                Journal
                JCO Precis Oncol
                JCO Precis Oncol
                po
                PO
                JCO Precision Oncology
                Wolters Kluwer Health
                2473-4284
                June 2022
                8 June 2022
                8 June 2022
                : 6
                : e2100510
                Affiliations
                [ 1 ]Mayo Clinic, Jacksonville, FL
                [ 2 ]Tempus Labs Inc, Chicago, IL
                [ 3 ]Johns Hopkins University, Baltimore, MD
                [ 4 ]Division of Hematology and Oncology, Department of Medicine, University of Arizona Cancer Center, Tucson, AZ
                [ 5 ]The University of California, San Francisco Medical Center, San Francisco, CA
                [ 6 ]USC Norris Comprehensive Cancer Center, Los Angeles, CA
                [ 7 ]Emory University School of Medicine, Winship Cancer Institute, Atlanta, GA
                Author notes
                Mark Yarchoan, MD, Johns Hopkins University, 1650 Orleans St, CRBI 4M08, Baltimore, MD 21287; e-mail: mark.yarchoan@ 123456jhmi.edu .
                Author information
                https://orcid.org/0000-0003-3254-2990
                https://orcid.org/0000-0003-2080-4172
                https://orcid.org/0000-0002-9613-3988
                https://orcid.org/0000-0003-0125-2405
                https://orcid.org/0000-0003-0066-2672
                https://orcid.org/0000-0002-1984-2430
                https://orcid.org/0000-0002-9053-3841
                https://orcid.org/0000-0001-9476-0104
                https://orcid.org/0000-0003-4401-0748
                Article
                PO.21.00510
                10.1200/PO.21.00510
                9200391
                35675577
                1a6e30a6-f1b1-429f-9183-1440f57cba2a
                © 2022 by American Society of Clinical Oncology

                Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: https://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 18 November 2021
                : 14 February 2022
                : 15 April 2022
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 35, Pages: 0
                Categories
                ORIGINAL REPORTS
                Precision Medicine
                Custom metadata
                TRUE

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