Study Shows New AI Tool Predicts Whether an Individual’s Cancer Will Respond to Specific Drugs

Researchers at the National Institutes of Health have developed an AI tool “that uses data from individual cells inside tumors to predict whether a person’s cancer will respond to a specific drug,” according to a new study and a release from NIH.

Current approaches to evaluating which drug a cancer patient should be prescribed “rely on bulk sequencing of tumor DNA and RNA, which takes an average of all the cells in a tumor sample. However, tumors contain more than one type of cell and in fact can have many different types of subpopulations of cells. Individual cells in these subpopulations are known as clones. Researchers believe these subpopulations of cells may respond differently to specific drugs, which could explain why some patients do not respond to certain drugs or develop resistance to them.”

A newer technology known as single-cell RNA sequencing “provides much higher resolution data, down to the single cell level. Using this approach to identify and target individual clones may lead to more lasting drug responses.” Thus, researchers in this new study wanted to investigate “whether they could use a machine learning technique called transfer learning to train an AI model to predict drug responses using widely available bulk RNA sequencing data but then fine-tune that model using single-cell RNA sequencing data.”

The researchers tested this approach on published data for 41 patients with multiple myeloma and 33 patients with breast cancer. The researchers discovered that if just one clone were resistant to a particular drug, the patient would not respond to that drug, even if all the other clones responded. In addition, the AI model successfully predicted the development of resistance in published data from 24 patients treated with targeted therapies for non-small cell lung cancer.”

The researchers suggest that “such single-cell RNA sequencing data could one day be used to help doctors more precisely match cancer patients with drugs that will be effective for their cancer,” and emphasize that more widespread availability of such data will only make these tools more accurate.