A false color-scanning election micrograph of lung cancer cells grown in culture. A new AI tool called PERCEPTION uses data at the level of single cells to help predict patients’ response to different therapies. Anne Weston, Francis Crick Institute/Wellcome Collection
With more than 200 types of cancer and every cancer individually unique, ongoing efforts to develop precision oncology treatments remain daunting. Most of the focus has been on developing genetic sequencing assays or analyses to identify mutations in cancer driver genes, and then trying to match treatments that may work against those mutations.But many, if not most, cancer patients do not benefit from these early targeted therapies.
Sinha says, “The ability to monitor the emergence of resistance is the most exciting part for me. It has the potential to allow us to adapt to the evolution of cancer cells and even modify our treatment strategy.”Sinha and colleagues used transfer learning—a branch of AI—to build PERCEPTION. PERCEPTION uses published bulk-gene expression from tumors to pre-train its models. Then, single-cell data from cell lines and patients, even though limited, was used to tune the models.PERCEPTION was successfully validated by predicting the response to monotherapy and combination treatment in three independent, recently published clinical trials for multiple myeloma, breast, and lung cancer.
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