Emerging Genomic and Immunological Correlates Defining Oligometastatic Trajectories in Intermediate/High-Grade...

Area of Science:

• Oncology
• Cancer Biology
• Medical Informatics

Background:

• Soft-tissue sarcomas (STSs) are rare mesenchymal malignancies where histologic grade dictates prognosis and treatment.
• Intermediate/high-grade STSs often relapse distantly, but metastatic behavior varies, with an emerging oligometastatic phenotype characterized by limited, indolent spread.

Purpose of the Study:

• To define criteria for true oligometastatic STSs and explore biological correlates.
• To investigate the potential of integrating multi-dimensional data using AI for predicting metastatic behavior in STS.

Main Methods:

• Review of emerging molecular and immunological correlates associated with metastatic competence.
• Exploration of artificial intelligence and machine learning for integrating genomic, immune, imaging, and liquid biopsy data.
• Discussion of potential multimodal management strategies for oligometastatic STS.

Main Results:

• Low genomic complexity (low-risk CINSARC), B-cell/TLS-rich microenvironment, high immune-cytotoxic signatures, and low circulating tumor DNA (ctDNA) correlate with reduced metastatic potential.
• High chromosomal instability, immunosuppressive microenvironments, and elevated ctDNA suggest covertly polymetastatic disease despite limited visible metastases.

Conclusions:

• Oligometastatic STS is a biologically definable subset amenable to multimodal treatment, including local therapies, systemic agents, and immunotherapy.
• Prospective, biomarker-stratified trials are essential to validate selection criteria and optimize treatment sequencing.