CINner: A New Computational Framework Sheds Light on Cancer Evolution
In an innovative study recently published in PLOS Computational Biology, IICD associate research scientist Khanh N. Dinh, alongside IICD director Simon Tavaré, former IICD postdoctoral fellow Ignacio Vázquez-García—now assistant professor at Harvard Medical School, Massachusetts General Hospital and the Broad Institute—and SRP intern Andrew Chan, introduce CINner, a novel computational framework designed to investigate chromosomal instability (CIN), a critical driver of cancer growth.
Chromosomal instability, characterized by frequent structural and numerical chromosomal alterations, significantly impacts tumor development and adaptation. However, previously existing computational models have faced limitations, typically capturing only certain aspects of CIN.
“To address this challenge, CINner integrates multiple CIN mechanisms in tandem, including whole-genome duplications (WGD), chromosome missegregations, and focal amplifications or deletions,” explains Dinh. This broad modeling spectrum allows the researchers to better understand the combined effects of these genomic events on tumor fitness.
By applying CINner to datasets such as the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), the researchers identified chromosome-arm selection parameters that characterize the specific selection forces driving each cancer type. These parameters are also predictive of WGD prevalence in chromosomally unstable tumors. Their findings suggest that the selective advantage of WGD may relate to cancer cells' enhanced tolerance for aneuploidy and reduced risk of nullisomy. Further application of CINner across diverse cancer datasets confirmed its ability to reflect genetic imbalances between oncogenes and tumor suppressor genes, providing deeper insights into cancer-specific evolutionary pressures. Moreover, CINner can study both chromosomally unstable and stable cancer types, demonstrating its versatility.
“By using a selection model focused on driver gene mutations and focal genomic events, we can also accurately characterize cancers like chronic lymphocytic leukemia, which typically exhibit minimal chromosomal instability,” adds Dinh
Looking ahead, Dinh, Tavaré, Vázquez-García, and colleagues aim to couple CINner with advanced parameter inference methods to quantify occurrence rates and fitness consequences of CIN events, particularly leveraging the precision of single-cell DNA sequencing. They also plan further investigations into the broader cellular impacts of whole-genome duplication and the influence of extra-chromosomal DNA on cancer evolution.
Ultimately, CINner represents a significant advancement in computational oncology, poised to substantially enhance researchers' ability to unravel the complexities of tumor progression and adaptation, potentially leading to improved clinical strategies and outcomes.