Colorectal cancer (CRC) is the third highest cause of cancer death in the United States. Almost 80% of sporadic colorectal cancers have an APC gene mutation. Familial adenomatous polyposis (FAP), a hereditary colon cancer syndrome, is also caused by mutations in APC and affects children as young as 7 years of age. FAP causes hundreds of colonic polyps in affected individuals and a 100% lifetime risk of CRC. In preliminary efforts we have successfully collected hundreds of pre-cancerous colon polyps from individual FAP patients, applied genomic, epigenomic and other multi-omic analyses and begun to elucidate the impact of multiple types of “omic” alterations on precancerous colon polyp evolution toward CRC. We propose to use an integrated and collaborative approach to develop a PreCancer Atlas for colorectal adenocarcinoma using FAP as the disease model. We will: 1) Establish a biospecimen collection pipeline for procurement of longitudinal tissue samples during surveillance colonoscopy and during prophylactic surgical colectomy, including whole blood, serum, normal colonic tissue, colon microbiome, benign pre-cancerous polyps, dysplastic precancerous polyps and colon adenocarcinomas. The material will be used for our own center and will also be available to the Human Tumor Atlas Network (HTAN). Medical records, longitudinal samples and all relevant metadata will also be collected. 2) Establish a center to characterize the tissue samples with state-of-the-art omics and imaging technologies. These include but are not limited to whole genome sequencing, methylation, transcriptome, proteome, cytokine, metabolome, microbiome, and molecular imaging. 3) Establish an analysis core that analyzes and integrates results from -omics, imaging and medical information, builds a spatiotemporal, multidimensional, integrative multi-omics cancer atlas, and develops longitudinal and predictive models for PreCancer biology and progression, as well as data portal and visualization framework. 4) Establish multi-omics technologies on smaller number of samples. 5) Perform a “multiscale deep data analysis” on a large number of samples (57) from a few people and a fewer number of samples (6) from many people. Use this information to guide additional data collection. 6) Identify factors (e.g. germline genetics, microbiome, immune dysfunction) contributing to polyp heterogeneity between and across individuals. Build disease progression models based on these data. 7) Make all biospecimens, information, protocols and software available to the PCA, HTAN and the general scientific community. We expect our efforts will greatly facilitate understanding CRC at its earliest stages and serve as a model for understanding precancerous lesions of other solid tumor malignancies.