Overall Colorectal cancer (CRC) is among the top three most prevalent cancers in global incidence and mortality. Most of these cancers develop from pre-cancerous adenomas. Colonoscopy is currently the most effective CRC prevention strategy. However, colonoscopy may fail to prevent carcinoma in as many as 24% of cases, is less effective at preventing proximal CRCs, is expensive for health care systems to implement, carries economic and psychosocial burdens for patients, and can be complicated by bleeding, perforation, and other adverse events. There is an unmet need to develop new preventive strategies and risk stratification models to address these and other issues. By analysis of whole human tissue, seminal work from Bert Vogelstein and co-workers demonstrated that CRC develops from an accumulation of genetic events as tumors evolve from small to large adenomas and, eventually, to cancers. More recently, our group reported the first comprehensive proteogenomic characterization of CRC, which also was from a bulk analysis of whole tissue Despite this wealth of data on CRC, we believe that the ability to provide the most effective precision diagnostics and preventive strategies can only be achieved with single-cell analysis. Through such a single-cell analysis, we propose to map spatial relationships across the spectrum of normal colon, early polyps, and late adenomas, including their unique stromal and microbial microenvironments.
Aim 1 : To construct a pre-cancer atlas of colorectal adenoma progression that depicts the spatial landscape of the tumor ecosystem, including the stroma and biofilm-associated microbiome, using single-cell (sc)RNA-seq, whole exome sequencing, multiplex immunofluorescence (MxIF), and species-specific bacterial fluorescence in situ hybridization (FISH).
Aim 2 : To integrate the activities and data from the Biospecimen, Tissue Characterization and Data Analysis Units for the prospective standardized collection and analysis of colorectal tissue, associated biospecimens, and related clinical and epidemiological data from 1,800 participants undergoing colonoscopy or surgical resection.
Aim 3 : To disseminate the pre-cancer atlas, related biospecimens, primary data sets and analytical tools to the Human Tumor Atlas Network (HTAN), the broader scientific community, and the lay public. To accomplish these aims, we have assembled a highly interactive and established team of investigators with complementary expertise (epidemiologists, gastroenterologists, pathologists, surgeons, systems biologists, bioinformaticians, cancer biologists, immunologists, and biofilms/infectious disease experts). To further optimize our novel methodologies for application to the prospectively collected samples from 1,800 atlas participants, we will leverage our existing large repository of colorectal adenomas and supporting biospecimens, generated and curated through an ongoing epidemiological project through 3 cycles of the Vanderbilt GI Special Programs of Research Excellence (SPORE). We are confident that our application, in toto, is greater than the sum of its parts, and we look forward to robust bi-directional interactions with HTAN.

Public Health Relevance

Overall Despite increased access and use to colonoscopy, colorectal cancer (CRC) remains the second most common cause of cancer death in the United States so it is critical to develop new primary and/or secondary preventive strategies for CRC, which are based on the unique molecular phenotypes that can be used to identify those individuals at high-risk for cost-effective surveillance and chemoprevention Through a single-cell analysis, we propose to map spatial relationships across the spectrum of normal colon, early polyps, advanced adenomas, and adenocarcinomas, including their unique stromal and microbial microenvironments, to identify these phenotypes for development of precision diagnostics and preventive strategies.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Multi-Component Projects and Centers Cooperative Agreements (U2C)
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Special Emphasis Panel (ZRG1)
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Kagan, Jacob
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Vanderbilt University Medical Center
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Chen, Bob; Herring, Charles A; Lau, Ken S (2018) pyNVR: Investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction. Bioinformatics :
Liu, Qi; Herring, Charles A; Sheng, Quanhu et al. (2018) Quantitative assessment of cell population diversity in single-cell landscapes. PLoS Biol 16:e2006687