Abstract 2171: Brain cancer map: A neural network-based clustering of brain cancer samples based on genome-wide CNV and LOH patterns

Abstract

Abstract Genomic based classification of cancers has been performed using gene expression and methylation patterns (e.g. well-established gene expression profiles used to sub-classify breast cancer). However, there has been very limited progress on classifying cancers based on their global CNV and/or LOH patterns. In this study, we present a novel neural network algorithm based on Self-Organizing Maps (SOMs) that creates a 2-dimensional map of cancer based on global CNV and LOH patterns. We applied this algorithm to 636 brain tumor samples from the TCGA project as part of the Glioblastoma (GBM) and Low-Grade Glioma (LGG) TCGA data sets. The CNV and LOH profiles for the samples was generated and manually curated to adjust for sample ploidy and call fragmentation due to normal cell contamination. Our algorithm generated a 25-node map of brain tumors, where different nodes represent a unique genome-wide profile and neighboring nodes are more similar to each other than more distant nodes. The generated map included nodes representing already known and prognostically important profiles such as 1p/19q co-deletion and combination of trisomy 7, monosomy 10, and homozygous deletion of p16. This is a significant validation of the approach since these profiles were generated completely automatically. In addition to these, new profiles were generated that were further investigated for prognostic value. We then projected all 636 samples back onto the learned map and were able to detect clusters of samples that generally fell within one cancer type, but also discovered some nodes shared by GBM and LGG samples indicating similarity in the CNV profile between these cancer types. We generated Kaplan-Meier survival plot for samples mapped to each node and were able to show statistically significant differences in survival based on the clustering. This approach has the possibility to create a unique CNV based classification of brain tumors for both prognostic and therapeutic applications. Citation Format: Megan Roytman, Soheil Shams. Brain cancer map: A neural network-based clustering of brain cancer samples based on genome-wide CNV and LOH patterns [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2171.