A new study published in the journal npj Breast Cancer demonstrates that an artificial intelligence (AI) model offers more accurate predictions of recurrence risk in patients with early-stage breast cancer than the widely used 21-gene Recurrence Score. This research, conducted by a team from the ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) and Caris Life Sciences, specifically targets hormone receptor-positive (HR+) and HER2-negative disease, the most common subtype of breast cancer, which accounts for about half of all breast cancer cases in the United States.

Not all cancer cells are the same, and neither are their responses to drugs. Finding the right drug for the right cancer cell is a long battle in drug development and in designing effective treatments. A new AI tool called ProteinTalks makes this task easier by accurately predicting whether a drug will be effective against a cancer cell line, finding new drug combinations and identifying proteins linked to drug resistance.