Defining Metastatic Migration Patterns

The patterns by which primary tumors spread to metastatic sites remain poorly understood. To address this challenge, our laboratory has developed innovative experimental and computational approaches that integrate CRISPR/Cas9-based barcoding technology with computational analysis. Using our EvoCaP (Evolution in Cancer of the Prostate) mouse model, an lentiviral injection-based platform which induces  aggressive metastatic cancer to bone, liver, lungs, and lymph nodes, we define clonal patterns and routes of tumor cell migration (Serio et al., 2024). We detected widespread intratumoral heterogeneity seeding from the primary tumor to metastatic sites, with few clonal populations instigating most migration events. Metastasis-to-metastasis seeding was uncommon, as most cells remained confined within their tissue of origin. Notably, migration patterns in our model were congruent with human prostate cancer seeding topologies, validating our platform for translational discovery.

Adaptive Rewiring of Signaling Pathways & Metabolism in Cancer Evolution

Understanding the functional dependencies that enable metastatic success requires investigating how evolving tumors reprogram their cellular machinery. Prostate cancer is driven by genetic mutations that activate the undruggable transcription factor MYC, which dictates metabolic reprogramming in cancer cells. Using our EvoCaP platform, we aim to identify new therapeutic targets by systematically dissecting the metabolic dependencies that MYC-driven tumors acquire. Our work focuses on nucleotide metabolism, which becomes essential for supporting rapid proliferation and survival in metastatic disease. By combining genetic inhibition approaches with pharmacological screening, we are uncovering how disrupting these metabolic nodes can reprogram the cellular landscape, trigger metabolic stress responses, and ultimately block progression toward metastasis. This strategy leverages the vulnerability of MYC-driven cancers to specific metabolic constraints, opening new avenues for therapeutic intervention.