Abstract 219: Development of TEAPOT algorithm to reconstruct individual ovarian tumors' evolutionary history based upon bulk and single cell whole exome sequencing data
Authors: Jianshu Zhang, Helaman Escobar, Harshmi Shah, Mickey Miller, Yang Wei, Kristen Schneider, Michelle Knirr, Kenny Day, Christopher Johnson, Baoli Yang, Eric Devor, Kristina Thiel, Lincoln Nadauld, Kimberly Leslie, Donghai Dai
Journal: Cancer Research (2018)

Abstract Background. Mutation detection through genetic testing is playing an increasingly important role in personalized precision medicine in cancer. However, current tests identifying driver mutations as therapeutic targets are based on detection of common mutations in cancer genes. These tests are not patient specific and do not address intra-tumor heterogeneity. Ubiquitous intra-tumor genetic heterogeneity is a mechanism of drug resistance and cancer recurrence. Methods. Approximately 16-24 microsamples are acquired to represent the entire cancer cell population for every ovarian tumor. Each microsample consists of a few cells within a clone and is selected to substitute for a single cell and overcome the large allele dropout rate commonly seen in single genome amplification and sequencing. TEAPOT (Tumor Evolution Assay for Personalized Oncology Therapy) algorithm has been developed to reconstruct a tumor's evolutionary history through integration of whole exome sequencing data from the bulk primary tumor and 16-24 microsamples taken from the bulk tumor. The evolutionary history for an individual tumor is expressed as a rooted and binary tumor developmental tree representing the mitotic process starting from an ancestral cancer cell. Individual mutations are assigned to the cells where they originally occur. The offspring size carrying a mutation was estimated based on tumor purity, variant allele frequency and the variant's copy number. Results. TEAPOT algorithm builds a tumor's evolutionary history with the following features: 1) a tumor's evolutionary history is unique for each ovarian cancer patient; 2) the size of a tree is proportional to the number of microsamples selected; 3) 16-24 microsamples builds a tree with 5 or more generations; 4) TEAPOT detects a driver mutation's occurrence at a specific developmental stage such as 1-cell, 2-cell, 4-cell, etc; 5) The size of offspring carrying a mutation thus the intra-tumor prevalence of the mutation can be estimated; 6) multiple driver mutations can be located separately in different clones. Therefore, TEAPOT provides a quantitative description of intra-tumor genetic heterogeneity and identifies sub-clonal driver mutations in a tumor. Conclusion. TEAPOT reconstructs a tumor's developmental process thus providing a patient-specific evolutionary history. Quantitation of intra-tumor prevalence of driver mutations may inform selection of an effective targeted agent and may provide rationale for cocktail treatment targeting multiple driver mutations simultaneously. TEAPOT can be also used for other solid and liquid cancers. A driver mutation's role in a patient may be functionally defined and quantitated based upon the growth advantage (fitness) it confers on its host cells in the reconstructed tumor evolutionary history. Citation Format: Jianshu Zhang, Helaman Escobar, Harshmi Shah, Mickey Miller, Yang Wei, Kristen Schneider, Michelle Knirr, Kenny Day, Christopher Johnson, Baoli Yang, Eric Devor, Kristina Thiel, Lincoln Nadauld, Kimberly Leslie, Donghai Dai. Development of TEAPOT algorithm to reconstruct individual ovarian tumors' evolutionary history based upon bulk and single cell whole exome sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 219.