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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.
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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