Chromothripsis Detection with Multiple Myeloma Patients Based on Deep Graph Learning

Motivation: Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma (MM). The catastrophic event is reported to be detectable prior to the progression of MM. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for MM patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole genome sequencing technology to retrieve both copy number variation (CNV) and structural variation (SV) data. Meanwhile, CNV data is much easier to obtain than SV data. Hence, in order to reduce the reliance on human experts’ efforts and SV data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data.

Results: To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph (DAG) of CNV features is inferred to derive a CNV embedding graph (i.e., CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and nonlinear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights. 
© The Author(s) 2023. Published by Oxford University Press.