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Precision Oncology: Evolving Clinical Trials across Tumor Types
I-Wen Song1, Henry Hiep Vo1, Ying-Shiuan Chen1
1Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA.
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.
Insights
Precision oncology uses molecular insights to tailor cancer treatments, improving outcomes for many. However, challenges remain in accessibility and treatment resistance, necessitating advanced AI and machine learning for future progress.
Area of Science:
- Oncology
- Genomics
- Biomarker Discovery
Background:
- Precision oncology advances treatment selection using molecular profiling and biomarkers.
- Next-generation sequencing (NGS) and immune assessments optimize patient stratification.
Purpose of the Study:
- To review precision oncology trials, discussing their challenges and opportunities.
- To summarize ongoing and emerging precision oncology studies.
Main Methods:
- Review of key precision oncology trials (IMPACT, SHIVA, NCI-MATCH, etc.).
- Discussion of challenges including tumor resistance and treatment accessibility.
- Exploration of AI, machine learning, and multi-omic data in precision medicine.
Main Results:
- Precision oncology has improved outcomes in selected patients.
- Despite progress, many patients lack access, and treatment resistance is a significant hurdle.
- Emerging trials like ComboMATCH, MyeloMATCH, and iMATCH build upon previous successes.
Conclusions:
- Precision oncology offers significant benefits but faces accessibility and resistance challenges.
- AI and advanced bioinformatics hold promise for enhancing tumor characterization and treatment strategies.
- Continued evolution of clinical trials is crucial for advancing precision medicine and identifying curative strategies.
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