Inferring cancer type-specific patterns of metastatic spread using Metient

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Metastasis, the spread of cancer, varies significantly between cancer types.
• Reconstructing metastatic spread from tumor sequencing data is crucial for understanding cancer progression.
• Existing computational methods for metastasis reconstruction face limitations in scalability and clinical relevance.

Purpose of the Study:

• To develop a novel computational method, Metient, for reconstructing cancer metastatic spread.
• To overcome the limitations of current methods in terms of scalability and reliance on outdated assumptions.
• To provide a translatable tool for both clinical sequencing data and preclinical lineage tracing.

Main Methods:

• Metient employs gradient-based, multi-objective optimization to generate and rescore hypotheses of metastatic spread.
• Hypotheses are rescoring using independent data on genetic distance and organotropism.
• The method is validated on clinical sequencing data from 169 patients and 490 tumors across different cancer types.

Main Results:

• Metient successfully identified cancer type-specific metastatic dissemination trends in melanoma, neuroblastoma, and non-small cell lung cancer.
• Reconstructions often align with expert analyses but reveal more plausible migration histories, including metastasis-to-metastasis seeding and polyclonal seeding.
• The tool demonstrated translatability across clinical and preclinical models.

Conclusions:

• Metient offers a scalable and clinically relevant approach to reconstructing cancer metastatic spread.
• The findings challenge existing assumptions about metastasis and enhance understanding of cancer-specific dissemination.
• Metient provides insights to inform future clinical treatment strategies for metastatic disease.