Predicting which breast cancer patients will respond to chemotherapy remains a critical challenge that could spare thousands from ineffective treatments while identifying those who need more aggressive intervention. This computational analysis addresses that gap by developing a molecular signature based on cellular autophagy mechanisms. Researchers analyzed RNA sequencing data from major cancer databases to identify 11 chemotherapy-sensitive autophagy-related genes that collectively predict treatment outcomes. The resulting prognostic model demonstrated reasonable accuracy, with areas under the ROC curve ranging from 0.635 to 0.749 for predicting 1-, 3-, and 5-year survival across multiple patient cohorts. The genes selected regulate autophagy, the cellular recycling process that cancer cells exploit both for survival under stress and to resist chemotherapy drugs. This creates a biological paradox where the same pathways that help tumors survive also determine their vulnerability to treatment. The computational approach represents an incremental advance in precision oncology, though the modest predictive accuracy suggests these 11 genes capture only part of the chemotherapy response puzzle. Current clinical decision-making relies heavily on tumor staging and receptor status, often leading to overtreatment or undertreatment. While this autophagy-based signature shows promise for refining treatment selection, it would likely need combination with other molecular markers to achieve the high accuracy required for clinical implementation. The findings add to growing evidence that autophagy regulation is a key determinant of cancer treatment success, potentially opening new therapeutic strategies that manipulate these pathways alongside conventional chemotherapy.