Metastasis and drug resistance cause the vast majority of cancer deaths, yet remain inherently difficult to study. Our lab develops high-resolution genetic and computational methods to probe these processes and build a comprehensive understanding of cancer disease progression. Specific research directions are highlighted below.
Which cells metastasize or survive therapy – and why?
Cancers are composed of diverse subpopulations with distinct histories and behaviors, yet only a small subset of these cells drives aggression. We use evolving barcode-based lineage recording to identify these rare but consequential populations with increasing precision. By integrating lineage information with single-cell sequencing, we link cell states to aggressive behaviors and uncover mechanisms and vulnerabilities underlying disease progression.
How do cells metastasize?
Beyond identifying which cancer cells seed metastases, we seek to understand how those cells disseminate to distant sites. Metastatic spread can follow various seeding topologies, including organotropism, metastatic cascading, and polyclonal seeding, yet relative contributions remain unclear. By reconstructing tumor phylogenies, we quantify dissemination networks – revealing how sites of cancer outgrowth are related – and ultimately, identify clinically important seeding mechanisms.
How do intercellular interactions impact cancer evolution?
Cancer cells do not emerge in isolation but are continually shaped by their microenvironment and by interactions with coevolving clones. We study how clonal competition and cooperation, as well as interactions with immune and stromal cells, influence metastatic success or failure. Our work aims to identify the signals that mediate these interactions and whether they promote or inhibit cancer aggression.
Expanding our toolkit
In addition to applying our existing lineage-recording approach, we are interested in developing new experimental and computational tools. This includes extending lineage recording through complementary readouts, such as spatial and epigenetic profiling, as well as developing new paradigms – such as signal recording – that generate annotated lineage trees linking historical exposures to present cancer behaviors and evolutionary outcomes.