Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps

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SUMMARY

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by antigen-presentation differences associated with tumor cell-differentiation status. Microsatellite unstable CRCs contain distinct non-metaplastic regions where tumor cells acquire stem cell properties and cytotoxic immune cells are depleted. Our multi-omic atlas provides insights into malignant progression of colorectal polyps and their microenvironment, serving as a framework for precision surveillance and prevention of CRC.

In brief

A single-cell resolution atlas of human colorectal polyps maps out distinct paths for pre-cancer to cancer transformation, accompanied by differential immune microenvironment features.

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

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

A. Subramanian, P Tamayo, V K Mootha … (2005)

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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Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Paul Shannon, Andrew Markiel, Owen Ozier … (2003)

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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Author and article information

Journal

Journal ID (nlm-journal-id): 0413066

Journal ID (pubmed-jr-id): 2830

Journal ID (nlm-ta): Cell

Journal ID (iso-abbrev): Cell

Title: Cell

ISSN (Print): 0092-8674

ISSN (Electronic): 1097-4172

Publication date Nihms-submitted: 6 February 2022

Publication date (Print): 22 December 2021

Publication date (Electronic): 14 December 2021

Publication date PMC-release: 23 March 2022

Volume: 184

Issue: 26

Pages: 6262-6280.e26

Affiliations

[1 ]Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA

[2 ]Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA

[3 ]Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA

[4 ]Department of Biostatistics and Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

[5 ]Vanderbilt-Ingram Cancer Center, Nashville, TN, USA

[6 ]Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA

[7 ]Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Vanderbilt University Medical Center, Nashville, TN, USA

[8 ]Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA

[9 ]Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA

[10 ]Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA

[11 ]Vanderbilt Technologies for Advanced Genomics, Vanderbilt University Medical Center, Nashville, TN, USA

[12 ]Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

[13 ]Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA

[14 ]Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

[15 ]Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

[16 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

[17 ]Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

[18 ]Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

[19 ]Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

[20 ]Department of Immunology, Harvard Medical School, Boston, MA, USA

[21 ]Department of Organoid Medicine, Keio University School of Medicine, Tokyo, Japan

[22 ]Clinical Research Division, Fred Hutchinson Cancer Research Center, and Gastroenterology Division, University of Washington School of Medicine, Seattle, WA, USA

[23 ]Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA

[24 ]These authors contributed equally

[25 ]Present address: Genentech, 1 DNA Way, South San Francisco, CA, USA

[26 ]Present address: Department of Pathology, Yale School of Medicine, New Haven, CT, USA

[27 ]Lead contact

Author notes

AUTHOR CONTRIBUTIONS

Conceptualization, B.C., M.K.W., W.Z., C.L.S., R.J.C., M.J.S., and K.S.L.; data curation, B.C., C.R.S., X.Z., C.N.H., L.D.B., M.J.S., K.S.L., P.N.V., H.K., A. Rolong, J.R., K.K., K.P., M.H., J.H.C., S.S., K.N., M.G., G.M.B., A.J.A., and A.C.A.; formal analysis, B.C., E.T.M., M.A.R.-S., C.N.H., S.V., Q.L., and K.S.L.; investigation, B.C., E.T.M., A.J.S., X.Z., Y.X., A.N.S.-S., N.O.M., Q.S., J.L.D., C.N.H., Y.Z., F.R., L.D.B., Q.C., J.L.F., J.T.R., T.S., W.J.H., R.J.C., M.J.S., K.S.L., M.I., and H.N.; methodology, B.C., E.T.M., A.J.S., X.Z., A.N.S.-S., J.L.F., R.J.C., M.J.S., K.S.L., A.L.J., J.A.G., M.I., and H.N.; project administration, E.T.M., A.J.S., X.Z., Q.L., R.J.C., M.JS., and K.S.L.; Resources, M.K.W., W.Z., J.R.G., J.T.R., W.J.H., Q.L., R.J.C., M.J.S., and K.S.L.; software, B.C., C.N.H., Q.L., K.S.L., T.S., and W.M.G.; supervision, R.J.C., M.J.S., K.S.L., O.R.-R., A. Regev, and N.H.; validation, B.C. and C.N.H.; visualization, B.C., M.A.R.-S., Q.S., C.N.H., S.V., W.J.H., Q.L., R.J.C., M.J.S., and K.S.L.; writing – original draft, B.C., W.J.H., R.J.C., M.J.S., and K.S.L.; writing – reviewing & editing, B.C., E.T.M., A.J.S., M.A.R.-S., X.A., A.N.S.-S., N.O.M., Q.S., J.L.D., Y.X., C.N.H., Y.Z., M.K.W., F.R., L.D.B., W.Z., Q.C., C.L.S., J.R.G., J.L.F., S.V., J.T.R., T.S., W.J.H., J.A.G., Q.L., R.J.C., M.J.S., and K.S.L.

[* ]Correspondence: robert.coffey@ 123456vumc.org (R.J.C.), martha.shrubsole@ 123456vanderbilt.edu (M.J.S.), ken.s.lau@ 123456vanderbilt.edu (K.S.L.)

Article

Manuscript ID: NIHMS1758707

DOI: 10.1016/j.cell.2021.11.031

PMC ID: 8941949

PubMed ID: 34910928

SO-VID: 861c653c-7e3d-402a-bed8-d57ac32d135a

License:

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps

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SUMMARY

In brief

Graphical Abstract

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Evolutionary Cell Biology

Most cited references 152

STAR: ultrafast universal RNA-seq aligner.

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

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

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