Precision cancer therapies frequently fail due to tumors’ evolving clonal diversity rather than drug efficacy. Even when initial treatment succeeds, resistance often emerges, leading to relapse. Clinicians then find themselves in the same cycle of repeating the process of testing a new drug until therapeutic exhaustion. The cycle escalates with each new treatment until no further options are available. The real-life experience of precision therapy will undeniably lead to an upgrade – from biomarker testing to drug response prediction – accordingly to favor more effective treatment options, more clinical benefit, and more patient coverage to include non-responders. While biomarker tests (or companion diagnostics) advance precision medicine by identifying only a fraction of patients as responders, drug response prediction aims to expand treatment options – particularly for non-responders – by tailoring personalized therapies to optimize outcomes while minimizing side effects. Artificial intelligence-driven approaches (e.g., deep learning and predictive modeling) leverage large datasets to generate these predictions. However, such systems remain experimental, not yet ready for clinical use. Patient-derived gene expression-informed anticancer drug efficacy (PGA) is the ultimate answer to the unmet clinical need for a quick turnaround and cost-efficient drug response prediction technology. With PGA, therapeutic non-responders now are able to benefit from more drug options than ever before. Since the technology is fitted with patient testing, gene activity detection, data mapping, drug matching, and efficacy ranking capabilities, clinicians can be quickly notified of potentially effective drugs, winning the decisive time for decision-making.