Abstract 3139: In vivo functional analyses of cancer gene variants for cancer driver identification and drug discovery
Authors: Jiang Jingjing , Yu Tengfei , Yan Ying , Du Wei , Tan Tingting , Yang Xuqin , Gu Jiali , Qiu Ling , Ye Xin K. , Gu Zhenyu 
Published: July 2017

Precision oncology requires identifying and understanding of cancer genome changes in a patient tumor tissue and finding the best cancer therapy targeting the changes. Although many cancer gene targets have been validated so far, next-generation genomic profile analyses have uncovered much more cancer gene variants with unconfirmed functions. Developing methods to functionally evaluate mutations/variants and understand their roles in cancer development and drug responses, such as drug resistance or synthetic lethality, will be critical in cancer treatment decision support. In addition, in some clinical cases, multiple treatment choices such as multiple drug combinations exist. Developing cancer models which can test multiple treatments will provide direct comparison of those therapies and select the best options. At GenenDesign, we have performed drug tests on mouse “avatars”, which are also known as Patient-Derived Xenograft (PDX) models. They are personalized cancer models derived from patient tumor samples with cancer mutation profiles and drug responses very similar to the corresponding cancer patients. Drug screenings were carried out in avatars by testing chemotherapies or targeted drugs against specific cancer gene mutations and variants. Selected drugs or drug combinations from avatar studies have been applied to corresponding patients with highly consistent treatment outcome. From genomic profile analysis of our near 1500 PDX tumor models in cancer types such as lung, colorectal, gastric, liver, and esophageal, we are able to identify a large number of cancer gene mutations/variants, gene fusions, as well as gene copy number and RNA expression changes in major cancer signal pathways such as EGFR, Her2, c-Met/ALK, Ras/Raf, FGFRs, PI3K/Akt, Wnt, Notch, DNA repair, cell cycle regulation, angiogenesis. Many of these gene aberrations are potential drug targets and have been functionally tested in PDX models with approved drugs or clinical drug candidates. The mutation/variant information and drug response information generated from PDX models have been organized into our Precision Cancer Information Lab database. Patient tumor DNA test results have been used for searching genetically matched PDX models in our database. Once matched PDX models are identified, the available drug response information can be used as evidence for clinical treatment decision. In addition, the matched PDX models can also been used to test more treatment options such as different combinations and new clinical drug candidates.

Citation Format: Jingjing Jiang, Tengfei Yu, Ying Yan, Wei Du, Tingting Tan, Xuqin Yang, Jiali Gu, Ling Qiu, Xin K. Ye, Zhenyu Gu. In vivo functional analyses of cancer gene variants for cancer driver identification and drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3139. doi:10.1158/1538-7445.AM2017-3139