MASTRO

MASTRO uncovers conserved evolutionary trajectories in cancer by analyzing collections of tumor phylogenetic trees to identify statistically significant, coherent sequences of genetic alterations.

Key Features:

• Phylogenetic tree analysis: Analyzes collections of tumor phylogenetic trees representing clonal evolution across a cohort.
• Conserved trajectory identification: Systematically identifies conserved evolutionary trajectories within those trees as sequences of genetic alterations shared among tumors.
• Conditional statistical test: Assesses significance using a conditional statistical test that evaluates coherence in the sequence of alterations across tumors.
• Novel algorithmic approach: Implements a novel algorithmic approach to detect and prioritize conserved evolutionary trajectories.
• Sequencing data integration: Operates on data derived from single-cell, multi-region, and bulk sequencing to account for intra-tumor heterogeneity and clonal architecture.

Scientific Applications:

• Conserved trajectory discovery: Identify statistically significant conserved sequences of genetic alterations across tumor cohorts.
• Clonal architecture analysis: Infer relationships among clones using phylogenetic trees reconstructed from single-cell, multi-region, and bulk sequencing data.
• Demonstrated datasets: Applied to nonsmall-cell lung cancer bulk sequencing and acute myeloid leukemia single-cell panel sequencing datasets.

Methodology:

Analyzes collections of phylogenetic trees, systematically identifies conserved evolutionary trajectories using a novel algorithmic approach, and assesses their significance with a conditional statistical test that evaluates coherence in the sequence of alterations across tumors.