Researchers have developed a novel artificial intelligence (AI) model that may accurately predict whether patients with cancer will respond to certain therapies, according to a recent study published by Sinha et al in Nature Cancer. The findings indicated that single-cell RNA sequencing data may be used to help physicians more precisely match patients with the most effective drugs for their cancer type.
Background
Current strategies used to match patients with cancer drugs rely on bulk sequencing of tumor DNA and RNA, which takes an average of all the cells in a tumor sample. However, tumors contain many different types of subpopulations of cells and include clones that may respond differently to specific cancer drugs or develop resistance to them.
In contrast to bulk sequencing, single-cell RNA sequencing provides much higher resolution data down to the single-cell level. Previous research has suggested that using this approach to identify and target individual clones may lead to more lasting drug responses. Nonetheless, single-cell gene-expression data could be much more costly to generate than bulk gene-expression data and are not yet widely available in clinical settings.
Study Methods and Results
In the recent proof-of-concept study, the researchers used widely available bulk RNA sequencing data and a machine learning technique known as transfer learning to train an AI model to predict responses to certain cancer drugs. They then fine-tuned the model with single-cell RNA sequencing data and used the novel approach on published cell-line data from large-scale drug screens to build AI models for 44 U.S. Food and Drug Administration–approved cancer drugs. The researchers noted that the AI models accurately predicted how individual cells would respond to both cancer monotherapies and combination therapies.
Further, the researchers tested the novel approach on published data involving 41 patients with multiple myeloma who received a combination of four drugs and 33 patients with breast cancer who received a combination of two drugs. They discovered that if just one of the clones was resistant to a particular cancer drug, the patient would not respond to therapy—even if all of the other clones responded. In addition, the novel AI model successfully predicted the development of treatment resistance in published data from 24 patients with non–small cell lung cancer who received targeted therapies.
Conclusions
The researchers emphasized that the accuracy of this novel approach could improve if single-cell RNA sequencing data become more widely available. They stated that as a result of their findings, they have developed a research website and a guide for how to use the novel AI model (called PERCEPTION) with new data sets.
Disclosure: For full disclosures of the study authors, visit nature.com.
