Ontario Familial Colon Cancer Registry (OFCCR)


  1. To determine the molecular (N=200) and histologic signatures present in population-based YOCRCs through somatic mutation, whole transcriptomics, and pathologist-supervised digital H&E tumor profiling.  

  1. To correlate unknown molecular and histologic signatures from Aim 1 with known etiologic (environmental and lifestyle) risk factors from our heavily curated, epidemiologically informed cohort to determine new YOCRC signatures. 

  1. To determine the molecular and/or histologic signatures that change in prevalence in YOCRCs diagnosed over a 10-year period i.e., identify temporal molecular and histologic signatures. These will be compared with existing publicly available database through the TCGA (The Cancer Genome Atlas). 



Recent work has shown that somatic profiling of cancer genomes and transcriptomes can provide a historical record of the mutational processes, both endogenous and exogenous, that were active in tumor initiation and progression, providing a characteristic “signatures” for each cancer. The potential of machine learning assessment of digital images of hematoxylin and eosin (H&E)-stained sections to identify histologic profiles or “signatures” offers novel approaches to detail features of the tumor microenvironment. We hypothesize that a comprehensive tumor profiling approach generating somatic mutation, and whole transcriptomics, and deep learning-derived “histologic signatures” (Aim 1) of 200 richly characterized YOCRCs diagnosed, excluded for known high penetrance monogenic CRC and polyposis genes, over a 10-year period will identify signatures that have increased over this timeframe (temporal signatures; Aim 2). These temporal signatures will be associated with established etiological mechanisms (e.g. pks+ E.coli somatic mutation signature or assessed for novel associations with modifiable environmental/lifestyle risk factors (Aim 3).