EvoTraceR (Evolutionary lineage Tracing in R)

Our experimental barcode-based platforms generate rich clonal tracking data that we leverage through custom computational pipelines and statistical frameworks to reconstruct metastatic histories and identify evolutionary drivers of metastasis. EvoTraceR is our R package for analyzing CRISPR-based cell-lineage tracing data from sequencing amplicon experiments. The package processes paired-end FASTQ files from multiple tissues, trims and merges reads, collapses duplicates, and calls mutations to infer evolutionary trees relating individual clones. By integrating barcode information with multi-omic data and statistical inference, EvoTraceR reconstructs migration histories from primary to metastatic sites, revealing how individual tumor clones navigate the metastatic cascade. Our analyses reveal that metastatic prostate cancer is driven by infrequent expansion of a few aggressive clones that escape their tumor niche, highlighting critical windows for therapeutic intervention and identifying opportunities to block metastasis before it becomes established.

BEAM (Bayesian Evolutionary Analysis of Metastasis

BEAM is a probabilistic modeling and inference framework developed in collaboration with the Siepel Lab, built on the BEAST2 platform for Bayesian phylogenetics. BEAM integrates high-resolution clonal tracking data from our CRISPR barcode-based lineage tracing platforms (EvoCaP for prostate cancer and EvoCaB for bladder cancer) to infer a full posterior distribution over cell-lineage phylogenies and tissue migration graphs, complete with timing information. Unlike methods limited by parsimony assumptions, BEAM reliably reconstructs complex metastatic histories and reveals distinct modes of migration across clones, including primary tumor reseeding. Applications to lung and prostate cancer datasets demonstrate BEAM’s power for uncovering the timing, directionality, and mechanisms underlying metastatic spread.