Machine Learning for Detecting and Analyzing Chromoanagenesis Events

Roni Rasnic; Michal Linial

doi:10.1007/978-1-0716-4750-9_17

Machine Learning for Detecting and Analyzing Chromoanagenesis Events

Roni Rasnic^*, Michal Linial

^*Corresponding author for this work

Department of Biological Chemistry

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

A comprehensive analysis of chromoanagenesis pan-cancer features is crucial for a broad and deep understanding of the phenomena. In this chapter, we describe a cancer-type agnostic machine-learning algorithm for detecting chromoanagenesis. We leveraged data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct and test a predictive algorithm for chromoanagenesis detection based on CNA data, with an accuracy of 86%. This algorithm was applied to analyze data from over 10,000 TCGA cancer patients. The analysis identified cancer-type specific chromoanagenesis characteristics and revealed distinct sets of genes impacted by chromoanagenesis versus non-chromoanagenesis tumorigenesis.

Original language	English
Title of host publication	Methods in Molecular Biology
Publisher	Humana Press Inc.
Pages	291-310
Number of pages	20
DOIs	https://doi.org/10.1007/978-1-0716-4750-9_17
State	Published - 2025

Publication series

Name	Methods in Molecular Biology
Volume	2968
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2025.

Keywords

CNA
Chromothripsis
Feature engineering
Mutual exclusivity
TP53

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-1-0716-4750-9_17

Cite this

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abstract = "A comprehensive analysis of chromoanagenesis pan-cancer features is crucial for a broad and deep understanding of the phenomena. In this chapter, we describe a cancer-type agnostic machine-learning algorithm for detecting chromoanagenesis. We leveraged data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct and test a predictive algorithm for chromoanagenesis detection based on CNA data, with an accuracy of 86\%. This algorithm was applied to analyze data from over 10,000 TCGA cancer patients. The analysis identified cancer-type specific chromoanagenesis characteristics and revealed distinct sets of genes impacted by chromoanagenesis versus non-chromoanagenesis tumorigenesis.",

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AU - Linial, Michal

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Machine Learning for Detecting and Analyzing Chromoanagenesis Events

Abstract

Publication series

Bibliographical note

Keywords

UN SDGs

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