NIH Researchers Improve Cancer Drug Response Prediction Using scRNA-seq Data AI Model
In a proof-of-concept study, researchers at the National Institutes of Health (NIH) explored the feasibility of employing transfer learning, a machine learning method, to train an AI model to predict cancer drug responses. They aimed to utilise readily accessible bulk RNA sequencing data for initial training and subsequently refine the model using single-cell RNA sequencing data.
The NIH researchers 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. Researchers at the National Cancer Institute (NCI), part of NIH, published their study on April 18, 2024, in Nature Cancer, and suggest that such single-cell RNA sequencing (scRNA-seq) data could one day be used to help doctors more precisely match cancer patients with drugs that will be effective for their cancer.
Current approaches to matching patients to 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 more than one type of cell and may have numerous different types of subpopulations of cells. Individual cells in these subpopulations are known as clones. Researchers speculate that these subpopulations of cells might exhibit varied responses to particular drugs, potentially elucidating the reasons behind patients’ lack of response to certain medications or their development of drug resistance.
Unlike bulk sequencing, single-cell RNA sequencing provides much higher resolution data, down to the single-cell level. Leveraging this technology to pinpoint and address individual clones could yield more sustainable drug responses. Yet, single-cell gene expression data are much more costly to generate than bulk gene expression data and not yet widely available in clinical settings.
scRNA-seq Data Trained AI Models Accurately Predict Individual Cell Responses to Drugs
In the study, using bulk RNA sequencing data and fine-tuning with scRNA-seq data, the researchers built AI models for 44 Food and Drug Administration (FDA)–approved cancer drugs. The AI models accurately predicted how individual cells would respond to both single drugs and combinations of drugs.
The researchers then tested their approach on published data for 41 patients with multiple myeloma treated with a combination of four drugs, and 33 patients with breast cancer treated with a combination of two drugs. The researchers found 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 cautioned that the accuracy of this technique will improve if single-cell RNA sequencing data becomes more widely available. In the meantime, the researchers have developed a research website and a guide on how to use the AI model, called Personalized Single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION), with new datasets.
This work was conducted by NCI’s Center for Cancer Research and led by Alejandro Schaffer, Ph.D., and Sanju Sinha, Ph.D., previously at NCI, now at Sanford Burnham Prebys. Eytan Ruppin, M.D., Ph.D., supervised the work.
Who
Eytan Ruppin, M.D., Ph.D., Center for Cancer Research, National Cancer Institute
Reference
“Predicting patient response and resistance to treatment from single-cell transcriptomics of their tumors via the PERCEPTION computational pipeline(link is external)” DOI: 10.1038/s43018-024-00756-7 April 18, 2024, in Nature Cancer.
About the National Cancer Institute (NCI): NCI leads the National Cancer Program and NIH’s efforts to dramatically reduce the prevalence of cancer and improve the lives of people with cancer. NCI supports a wide range of cancer research and training extramurally through grants and contracts. NCI’s intramural research program conducts innovative, transdisciplinary basic, translational, clinical, and epidemiological research on the causes of cancer, avenues for prevention, risk prediction, early detection, and treatment, including research at the NIH Clinical Center—the world’s largest research hospital. Learn more about the intramural research done in NCI’s Center for Cancer Research.
For more information about cancer, please visit the NCI website at cancer.gov or call NCI’s contact center at 1-800-4-CANCER (1-800-422-6237).
About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases.
For more information about NIH and its programs, please visit www.nih.gov.
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