Inferring cancer type-specific patterns of metastatic spread using Metient

Area of Science:

• Computational Biology
• Oncology
• Genomics

Background:

• Cancer metastasis is a complex process with varied dissemination routes.
• Current methods for reconstructing metastatic spread from tumor sequencing data are limited by scalability and biological assumptions.

Purpose of the Study:

• To develop and validate a novel computational method, Metient, for reconstructing metastatic dissemination pathways.
• To overcome the limitations of existing methods in scalability and biological accuracy.

Main Methods:

• Metient employs gradient-based, multiobjective optimization to generate multiple metastatic spread hypotheses.
• Hypotheses are rescored using independent genetic distance and organotropism data.
• The method is applicable to both clinical sequencing data and preclinical lineage tracing.

Main Results:

• Metient successfully identified distinct metastatic dissemination trends in melanoma, neuroblastoma, and non-small cell lung cancer.
• Reconstructions align with expert analyses but also propose novel, plausible migration histories.
• The study suggests more polyclonal seeding and metastasis-to-metastasis spread than previously reported.

Conclusions:

• Metient provides a robust and versatile tool for analyzing cancer metastatic spread.
• Findings challenge established assumptions about metastatic dissemination patterns.
• The study offers new insights into cancer type-specific metastatic behaviors.