Precision Oncology: Evolving Clinical Trials across Tumor Types

SIMPLE SUMMARY: Precision oncology is the use of anticancer drugs to specifically inhibit the function of aberrant oncogenic proteins driving a patient’s tumor. The application of molecular technologies and targeted therapeutics has led to significant advancements in precision oncology, resulting in favorable clinical outcomes for selected patients with cancer. This review focuses on selected precision oncology clinical trials that match patient- and tumor-specific aberrations with targeted therapies. These trials include the IMPACT, SHIVA, IMPACT2, NCI-MPACT, TAPUR, DRUP, and NCI-MATCH trials. Significant and impactful progress has been made towards the realization of precision oncology, and many matched targeted therapies are now available for patients with cancer. However, precision oncology remains inaccessible to many patients. The successes, challenges, and opportunities that have emerged—and the lessons learned—are highlighted. The use of artificial intelligence, machine learning, and bioinformatic analyses of complicated multi-omic data may improve the tumor characterization process and accelerate the implementation of precision oncology. ABSTRACT: Advances in molecular technologies and targeted therapeutics have accelerated the implementation of precision oncology, resulting in improved clinical outcomes in selected patients. The use of next-generation sequencing and assessments of immune and other biomarkers helps optimize patient treatment selection. In this review, selected precision oncology trials including the IMPACT, SHIVA, IMPACT2, NCI-MPACT, TAPUR, DRUP, and NCI-MATCH studies are summarized, and their challenges and opportunities are discussed. Brief summaries of the new ComboMATCH, MyeloMATCH, and iMATCH studies, which follow the example of NCI-MATCH, are also included. Despite the progress made, precision oncology is inaccessible to many patients with cancer. Some patients’ tumors may not respond to these treatments, owing to the complexity of carcinogenesis, the use of ineffective therapies, or unknown mechanisms of tumor resistance to treatment. The implementation of artificial intelligence, machine learning, and bioinformatic analyses of complex multi-omic data may improve the accuracy of tumor characterization, and if used strategically with caution, may accelerate the implementation of precision medicine. Clinical trials in precision oncology continue to evolve, improving outcomes and expediting the identification of curative strategies for patients with cancer. Despite the existing challenges, significant progress has been made in the past twenty years, demonstrating the benefit of precision oncology in many patients with advanced cancer.