Abstract 2094: Improving cancer cytogenetic case review with a new machine learning system

Abstract

Abstract A novel machine learning system trained on The Cancer Genome Atlas (TCGA) data has been designed to help with classification, prognostic measures, and monitoring treatment responses in cancer management. The utility of this system will be demonstrated using a set of retrospective samples. Cytogenetic analysis has long been utilized in clinical cancer care with conventional cytogenetic methods such as chromosomal banding and FISH still standard protocols for hematologic malignancies. With the advent of microarrays, smaller and more complex variants have been discovered in cancer samples but identifying the potential impact of many alterations such as copy number variation (CNV) and loss of heterozygosity (LOH) has not been fully exploited. A typical cancer cytogenetic report can list many tens of variants with unclear clinical implications. To bridge this gap and assist with determining the clinical implications of such alterations, we have developed a novel machine learning system that has been trained using data from The Cancer Genome Atlas (TCGA). The TCGA data has been processed using the SNP-FASST2 segmentation algorithm followed by manual review for ploidy adjustment yielding high quality regions of CNV and LOH for over 6000 cases across 29 cancer types. Our novel neural network used this processed data to arrange samples based on their CNV and LOH profile across a two-dimensional map. The proximity of one sample to another on this map corresponds to its similarity in terms of CNV and LOH profile. To apply this system in a clinical setting, we have designed a case review system that aligns a new case with the learned cancer map to provide information on the similarity of the case with ones in TCGA. This enables rapid verification of tumor type or detection of a different origin for the sample than the one reported. In addition, the system can create a prognostic score using clinical survival data for the samples in TCGA. Here we will demonstrate the utility of the system in classifying new cases against the learned cancer map and estimating prognostic measures. Citation Format: Megan Roytman, Viren Wasnikar, Paul An, Raja Kashevan, Shalini Verma, Soheil Shams. Improving cancer cytogenetic case review with a new machine learning system [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2094.