Artificial intelligence for the next generation of precision oncology

The concept of precision oncology involves the prescription of therapies that target the molecular driver alterations of an individual patient’s tumor. This treatment paradigm has been accelerated in recent years through increasing integration of molecular profiling approaches into mainstream clinical oncology, and the approval of a variety of molecularly targeted agents that have improved the clinical outcomes of patients across multiple cancer types. Targeted therapies such as trastuzumab or vemurafenib have become the standards of care for patients with HER2-expressing breast cancers or BRAF-mutant melanomas, respectively, and immune checkpoint inhibitors recently received tissue-agnostic approval for treating patients whose tumors exhibit microsatellite instability. With the advent of personalized cancer medicine, the portfolio of anti-tumor agents and companion diagnostic assays has experienced an unprecedented expansion, and a plethora of clinical trials are exploring the most effective treatments for the best-matching patients. In this context, data-driven approaches for optimizing clinical matching, reducing the cost of diagnostic testing, and improving the prediction of clinical phenotypes hold great promise for enhancing the clinical management of patients and maximize the value of precision oncology. However, all stakeholders must be aware of the important challenges that need to be overcome to reach these goals. This editorial aims at raising awareness about these challenges.