We introduce CAMM, a multi-scale interpretable deep learning framework that models regional SNV/indel density, helps map the most important chromatin features contributing, and points out mutation-enriched coding and non-coding loci. github.com/reimandlab/C... [3/3]
Using genomic and clinical data from nearly 10,000 primary tumors, we identified 57 prognostic genomic interactions (PGIs): pairs of alterations whose joint status was associated with patient outcome beyond either alteration alone. [2/4]
By integrating WGS data from primary and metastatic cancers with epigenomics profiles, we show that even in metastatic cancers, regional mutagenesis remains strongly informed by the chromatin accessibility of the primary tumor cell of origin. [2/3]
These PGIs refined prognostic stratification and were linked to distinct transcriptomic programs, while also highlighting candidate genes within recurrent copy-number regions and supported functional dependencies. [3/4]
New preprint from our lab out "Pairwise genomic alterations identify prognostic tumor states in multiple cancer types". Most genomic models of cancer prognosis focus on single alterations. Here, we asked what can be learned from combinations of events [1/4] doi.org/10.64898/202...
Enjoyed this recent review/perspective: many somatic mutations are already present in normal tissues, shifting attention from driver mutations to non-mutagenic promoters of oncogenesis. Also wild that it took 3 years from submission to publication. nature.com/articles/s41586-026-10386-x
OICR is hiring a junior/mid comp.bio PI! Interests include AI/ML for cancer biology, digital pathology, variant effects, drug target discovery, perturbation modeling, cancer evolution, +more. Come join the excellent research ecosystem of the UToronto discovery district. oicr.bamboohr.com/careers/442
This study builds on PACIFIC, our computational method for detecting clinically important multi-omics interactions (aacrjournals.org/mcr/article/...). Here, we extend this interaction-based framework to discovery of prognostic signatures defined by pairs of genomic alterations. [4/4]
⚠️ new preprint: Chromatin accessibility of primary cancers informs regional mutagenesis in metastases through multi-scale deep learning. led by @hanlijiang13.bsky.social @moleculargenetics.bsky.social @oicr.on.ca www.biorxiv.org/content/10.6... [1/3]
