The combination of cytotoxic chemotherapy with radiotherapy (CRT) has resulted in significant improvements in clinical outcomes for patients with many locally advanced unresectable cancers. Only a small proportion of patients achieve pathological complete responses to CRT; combination of CRT with targeted agents offers the promise of further improving treatment responses. However, numerous clinical trials have failed to show an improvement in clinical outcomes with the addition of targeted agents. In part, the gap in translation to large, expensive and ultimately unsuccessful clinical trials is a reflection of the shortcomings of inconsistently designed, executed and reported preclinical data that these studies are based on. In an effort to standardize the selection of agents for future clinical testing, we have designed, optimized and validated a high throughput clonogenic assay platform for step-wise progression of preclinical studies from in vitro to in vivo in non-small cell lung cancer (NSCLC) and pancreatic ductal adenocarcinoma (PDAC). This highly stable in vitro method was standardized for identification of the most promising drugs that could best be combined with CRT from amongst as screen of multiple agents tested in an unbiased manner using 96-well plates. The methodology lends itself to seamless testing of multiple agents in a similar fashion allowing cross-comparisons, evaluation of CRT and/or radiation therapy alone, and testing multiple concentrations of test agents sequenced at different times before and after radiation. The method identified Trametinib (MEKi) as a strong CRT sensitizer in KRAS-mutant NSCLC and PDAC cell lines. To increase the accessibility of our screening method and accelerate the pace at which novel combinations with CRT are identified and incorporated into standard practices for treatments, we report details on screening method optimization, data generation, and downstream data analysis.Copyright © 2021. Published by Elsevier Inc.
About The Expert
Rui Ye
Yawei Qiao
Pankaj K Singh
Yifan Wang
Jianzhong He
Nan Li
Sunil Krishnan
Steven H Lin
References
PubMed