Author Interviews, Cancer Research, Columbia, Genetic Research, Personalized Medicine / 23.12.2016
AI plus Genetic Database Drives Personalized Cancer Treatment
MedicalResearch.com Interview with:
Dr. Kai Wang
Zilkha Neurogenetic Institute, University of Southern California
Institute for Genomic Medicine, Columbia University
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Cancer is a genetic disease caused by a small number of “driver mutations” in the cancer genome that drive disease initiation and progression. To understand such mechanism, there are increasing community efforts in interrogating cancer genomes, transcriptomes and proteomes by high-throughput technologies, generating huge amounts of data. For example, The Cancer Genome Atlas (TCGA) project has already made public 2.5 petabytes of data describing tumor and normal tissues from more than 11,000 patients. We were interested in using such publicly available genomics data to predict cancer driver genes/variants for individual patients, and design an "electronic brain” called iCAGES that learns from such information to provide personalized cancer diagnosis and treatment.
iCAGES is composed of three consecutive layers, to infer driver variants, driver genes and drug treatment, respectively. Unlike most other existing tools that infer driver genes from a cohort of patients with similar cancer, iCAGES attempts to predict drivers for individual patient based on his/her genomic profile.
What we have found is that iCAGES outperforms other tools in identifying driver variants and driver genes for individual patients. More importantly, a retrospective analysis on TCGA data shows that iCAGES predicts whether patients respond to drug treatment and predicts long-term survival. For example, we analyzed two groups of patients and found that using iCAGES recommend drugs can increase patients’ survival probability by 66%. These results suggest that whole-genome information, together with transcriptome and proteome information, may benefit patients in getting optimal and precise treatment. (more…)