Collaborative intra-tumor heterogeneity detection.

Area of Science:

• Computational Biology
• Genomics
• Cancer Research

Background:

• Phylogenetic relationship inference of cancer mutations is challenging due to data noise and biological complexity.
• Existing methods often rely on bulk sequencing data from single tumor samples, facing theoretical limitations.
• Accurate phylogenetic ordering is crucial for understanding cancer evolution and developing targeted therapies.

Purpose of the Study:

• To introduce Hintra, a novel computational method for detecting intra-tumor heterogeneity.
• To enhance the accuracy of tumor phylogeny inference by leveraging evolutionary information across a cohort of tumors.
• To overcome limitations of existing methods in inferring mutation order from bulk sequencing data.

Main Methods:

• Hintra integrates sequencing data from multiple tumor samples within a cohort.
• It infers individual tumor phylogenies by identifying shared evolutionary patterns across tumors.
• An iterative process is employed to learn and utilize these patterns for resolving phylogenetic ambiguities.

Main Results:

• Hintra demonstrates improved performance compared to two state-of-the-art methods in synthetic experiments.
• Application to a Breast Cancer dataset yields results consistent with existing knowledge.
• The method provides potentially novel insights into breast cancer evolution.

Conclusions:

• Hintra offers a robust approach for intra-tumor heterogeneity detection and phylogenetic inference.
• Integrating cohort data effectively addresses limitations of single-sample analyses.
• The method has the potential to advance our understanding of cancer evolutionary dynamics.