AI-integrated multi-omics platform to revolutionize anti-metastatic therapy development through circulating tumor...

Area of Science:

• Oncology
• Cancer Metastasis Research
• Translational Medicine

Background:

• Metastatic disease is the primary cause of cancer mortality, with current precision oncology focusing on primary tumors and cell-free tumor DNA (ctDNA).
• ctDNA analysis, while valuable, cannot capture the dynamic cellular states and adaptations driving metastasis.
• Integrating circulating tumor cell (CTC) profiling is proposed as the next frontier in precision oncology.

Purpose of the Study:

• To propose a framework for integrating CTC profiling into precision oncology to combat metastasis.
• To leverage the SCRUM-MONSTAR platform for multi-omics analysis of CTCs.
• To identify and target cellular plasticity programs that drive metastatic spread.

Main Methods:

• Utilizing the SCRUM-MONSTAR platform for nationwide multi-omics analysis and longitudinal biospecimen collection.
• Combining matched tissue profiling, serial ctDNA analysis, and single-cell CTC transcriptomics.
• Employing metabolomics and functional modeling (organoid and mouse) to study metastatic processes like adherent-to-suspension transition (AST).

Main Results:

• The SCRUM-MONSTAR-CTC framework facilitates a translational ecosystem for anti-metastatic drug discovery.
• Adherent-to-suspension transition (AST) is identified as a key plasticity program enabling tumor cell survival in circulation and distant organ colonization.
• The study highlights the potential for identifying metastatic vulnerabilities and developing targeted therapies.

Conclusions:

• Targeting AST and other metastatic plasticity programs offers a pathway to clinically actionable anti-metastatic therapies.
• This framework can guide the development of biomarker-guided anti-metastatic trials and accelerate reverse translation.
• Precision oncology must evolve to target metastasis as a dynamic process, beyond genomic profiling.