Abstract 5046: DeepTumour: Identify tumor origin from whole genome sequences
Authors: Stein Lincoln David , Jiao Wei , Atwal Gurnit , Morris Quaid 
Published: June 2022

The DeepTumour algorithm predicts the tissue of origin of a tumor based on the pattern of passenger mutations identified by whole genome sequencing. "Passengers" are incidental mutations that accrue in the genome over time due to random mutational processes, and are functionally distinct from the "driver" mutations that are responsible for the cancer's malignant behavior. In adult cancers, passenger mutations typically outnumber drivers by a hundred or thousand-fold; critically, the vast majority of passengers arise in the normal cell lineage that precedes the malignant transformation event and hence reflects mutational processes existing in the cancer's precursor cell and its ancestors.

Passenger mutations are not uniformly distributed across the genome, but are concentrated in areas of the genome that have a locally high mutation rate. Mutation rates are highest at places in the genome where chromatin is tightly packed and less accessible to the DNA repair machinery. Each distinct cell type has a different pattern of chromatin packing due to epigenetic modifications. DeepTumour takes advantage of this to infer the chromatin state in the cell of origin from the distribution of passenger mutations in the tumor.

Another characteristic of passenger mutations is that the probability of a particular type of mutation occurring (e.g. replacement of C by T) depends on the mutational processes that were active in the cell of origin and its ancestors. Because certain cancers are associated with distinct mutational exposures (e.g. lung cancer and smoking), DeepTumour uses the tumor's distribution of passenger mutation type as well as position on the genome.

The DeepTumour algorithm itself is a fully connected, feed-forward neural network which we trained using 28 cohorts representing different tumor types from the Pan-Cancer Analysis of Whole Genomes project. When applied to independent sets of tumors, the algorithm is able to achieve an overall accuracy of 88% on primary tumors and 83% on metastatic tumors for distinguishing the 28 cancer types. Furthermore, DeepTumour provides estimates of the models uncertainty, allowing it to automatically detect rare cancer samples with an accuracy of 93%.

The DeepTumour algorithm is now available as a fast, convenient and secure web-based service at https://deeptumour.oicr.on.ca. It accepts uploads of VCF files containing somatic mutations from tumor whole genome sequencing, and returns a ranked list of tumor type matches and their relative probabilities. It can be used to provide leads when evaluating a cancer of uncertain primary, to assist in resolving diagnostic ambiguities, and as a research tool for understanding tumors of intermediate histology.

Citation Format: Lincoln David Stein, Wei Jiao, Gurnit Atwal, Quaid Morris. DeepTumour: Identify tumor origin from whole genome sequences [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5046.