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      Molecular phenotyping of small cell lung cancer using targeted cfDNA profiling of transcriptional regulatory regions

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          Abstract

          We report an approach for cancer phenotyping based on targeted sequencing of cell-free DNA (cfDNA) for small cell lung cancer (SCLC). In SCLC, differential activation of transcription factors (TFs), such as ASCL1, NEUROD1, POU2F3, and REST defines molecular subtypes. We designed a targeted capture panel that identifies chromatin organization signatures at 1535 TF binding sites and 13,240 gene transcription start sites and detects exonic mutations in 842 genes. Sequencing of cfDNA from SCLC patient-derived xenograft models captured TF activity and gene expression and revealed individual highly informative loci. Prediction models of ASCL1 and NEUROD1 activity using informative loci achieved areas under the receiver operating characteristic curve (AUCs) from 0.84 to 0.88 in patients with SCLC. As non-SCLC (NSCLC) often transforms to SCLC following targeted therapy, we applied our framework to distinguish NSCLC from SCLC and achieved an AUC of 0.99. Our approach shows promising utility for SCLC subtyping and transformation monitoring, with potential applicability to diverse tumor types.

          Abstract

          Targeted cell-free DNA nucleosome profiling at transcriptional regulatory regions can distinguish lung cancer types and subtypes.

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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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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: Writing - original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: InvestigationRole: MethodologyRole: Validation
                Role: Formal analysisRole: InvestigationRole: SoftwareRole: Validation
                Role: Investigation
                Role: Formal analysis
                Role: InvestigationRole: Visualization
                Role: Formal analysis
                Role: Data curationRole: MethodologyRole: Resources
                Role: InvestigationRole: ResourcesRole: Supervision
                Role: ConceptualizationRole: InvestigationRole: ResourcesRole: SupervisionRole: Writing - review & editing
                Role: MethodologyRole: ResourcesRole: Writing - review & editing
                Role: Funding acquisitionRole: MethodologyRole: ResourcesRole: Writing - review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing - review & editing
                Role: ConceptualizationRole: Resources
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing - original draftRole: Writing - review & editing
                Journal
                Sci Adv
                Sci Adv
                sciadv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                12 April 2024
                10 April 2024
                : 10
                : 15
                : eadk2082
                Affiliations
                [ 1 ]Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA.
                [ 2 ]Veterans Affairs Puget Sound Healthcare System - Seattle Branch, Seattle, WA, USA.
                [ 3 ]Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA.
                [ 4 ]Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
                [ 5 ]Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA.
                [ 6 ]Medical Scientist Training Program, University of Washington, Seattle, WA, USA.
                [ 7 ]Genomics and Bioinformatics Shared Resource, Fred Hutchinson Cancer Center, Seattle, WA, USA.
                [ 8 ]Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
                [ 9 ]Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA.
                [ 10 ]Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK.
                [ 11 ]Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
                [ 12 ]Graduate Program in Pharmacology, Weill Cornell Medical College; New York, NY, USA.
                [ 13 ]Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
                [ 14 ]Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
                [ 15 ]Department of Genome Sciences, University of Washington, Seattle, WA, USA.
                Author notes
                [* ]Corresponding author. Email: gha@ 123456fredhutch.org (G.H.); dmacpher@ 123456fredhutch.org (D.M.)
                [†]

                These author contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-8050-5310
                https://orcid.org/0000-0002-3371-3352
                https://orcid.org/0000-0002-4551-0819
                https://orcid.org/0000-0003-1787-644X
                https://orcid.org/0000-0002-7234-3446
                https://orcid.org/0000-0001-5051-1755
                https://orcid.org/0000-0001-5025-2001
                https://orcid.org/0000-0002-1726-8850
                https://orcid.org/0000-0001-5204-3465
                https://orcid.org/0000-0003-3293-3115
                https://orcid.org/0000-0002-8970-3137
                https://orcid.org/0000-0001-7578-7272
                https://orcid.org/0000-0003-3729-907X
                Article
                adk2082
                10.1126/sciadv.adk2082
                11006233
                38598634
                ef869ed6-7029-4fbc-8250-a959d947d18b
                Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 08 August 2023
                : 27 February 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P50 CA228944
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32 HL007093
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: K12 CA076930
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: K22 CA237746
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R21 CA264383
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: DP2 CA280624
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P30 CA015704
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U24 CA213274
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R35 CA263816
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P50 CA228944
                Funded by: FundRef http://dx.doi.org/10.13039/100000982, Conquer Cancer Foundation;
                Award ID: Young Investigator Award
                Funded by: Kuni Foundation Discovery Grants for Cancer Research: Advancing Innovation;
                Funded by: Kuni Foundation Discovery Grants for Cancer Research: Advancing Innovation;
                Funded by: FundRef http://dx.doi.org/10.13039/501100020252, CRUK Lung Cancer Centre of Excellence;
                Funded by: FundRef http://dx.doi.org/10.13039/501100014679, CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester;
                Categories
                Research Article
                Biomedicine and Life Sciences
                SciAdv r-articles
                Cancer
                Genetics
                Cancer
                Custom metadata
                Jeanelle Ebreo

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