Combining chromosomal arm status and significantly aberrant genomic locations reveals new cancer subtypes

TL;DR

This paper introduces a novel method combining chromosomal arm status and aberrant genomic locations to identify and visualize new cancer subtypes, improving detection of significant genomic alterations across tumor datasets.

Contribution

The study presents an innovative algorithm that quantifies aberrations using a volume measure and integrates it with chromosomal arm data to discover new cancer subtypes.

Findings

Detected known cancer-associated genomic regions

Identified potential new cancer subtypes

Validated method on multiple datasets

Abstract

Many types of tumors exhibit chromosomal losses or gains, as well as local amplifications and deletions. Within any given tumor type, sample specific amplifications and deletionsare also observed. Typically, a region that is aberrant in more tumors,or whose copy number change is stronger, would be considered as a more promising candidate to be biologically relevant to cancer. We sought for an intuitive method to define such aberrations and prioritize them. We define V, the volume associated with an aberration, as the product of three factors: a. fraction of patients with the aberration, b. the aberrations length and c. its amplitude. Our algorithm compares the values of V derived from real data to a null distribution obtained by permutations, and yields the statistical significance, p value, of the measured value of V. We detected genetic locations that were significantly aberrant and combined them with chromosomal arm status to create a succint fingerprint of the tumor genome. This genomic fingerprint is used to visualize the tumors, highlighting events that are co ocurring or mutually exclusive. We allpy the method on three different public array CGH datasets of Medulloblastoma and Neuroblastoma, and demonstrate its ability to detect chromosomal regions that were known to be altered in the tested cancer types, as well as to suggest new genomic locations to be tested. We identified a potential new subtype of Medulloblastoma, which is analogous to Neuroblastoma type 1.