The use of tumor transcriptomics for precision oncology has made significant advances, mainly by identifying cancer driver genes or actionable mutations for treatment with targeted therapies. However, this strategy misses out on broader genetic interactions that could reveal additional biologically testable biomarkers for therapy response prediction and inform the selection of more effective drugs for targeted treatment.
Scientists at the National Cancer Institute (NCI) have developed SELECT, a computational, precision-oncology framework that uses the tumor’s whole transcriptome to identify synthetic lethal and synthetic rescue genetic interactions. These genetic interactions provided actional information predicting therapeutic response in 28 of 35 published targeted and immunotherapy trials from 10 different cancer types. Also, it was predictive of patients’ response in 80 % of these clinical trials. SELECT, an excellent tool in developing new targeted therapies or enhancing patient stratification in transcriptomic multi-arm trials, is available for co-development or licensing opportunity.
- Predictive accuracy of patients’ response in many treatment options, including chemotherapy, targeted drugs and immunotherapy
- Predictive accuracy of patients’ response across cancer types
- Enhancing patient stratification for clinical trials and improved therapeutic strategies
- Increasing the number of patients that could benefit from precision-based treatments
- Patient stratification in clinical trials
- Identifying new actionable drug targets and treatments
- Analysis of genetic interactions that can provide actionable information for selecting effective treatment options for cancer patients