Mutation load estimation model as a predictor of the response to cancer immunotherapy
2018
National Yang-Ming University, Taipei, Taiwan
Although the efficacy of immunotherapy has been demonstrated, treatment response differs from patient to patient. One tool to predict an individual patient's responses and improve therapeutic efficiency is the identification of patient's specific point mutations also called the mutation load. As far as now, techniques to identify mutation load have been too expensive and time-consuming to be used in the clinic. In the present study, the researchers have used publically available cancer genomics data to generate mathematical predictive models of mutation load for lung adenocarcinoma based only on 24 genes instead of whole-exome sequencing. The same model can be adapted to predict mutation load for melanoma and colorectal cancer. The estimated mutation load can be used to predict the clinical outcome of cancer immunotherapy with high accuracy. Using this estimation model should reduce the cost and time needed for the assessment of the mutation load and facilitate the obtention of cancer immunotherapy response prediction in the standard clinical setting.
Mutation load estimation model as a predictor of the response to cancer immunotherapy
Yu-Chao Wang, Yi-Chen Yeh
Added on: 07-26-2021
[1] https://www.nature.com/articles/s41525-018-0051-x[2] https://data.jrc.ec.europa.eu/dataset/352f7dfd-05cf-434b-a96a-7e270dc76573
