We have made signifianct progress in both major areas of the our proposed program.
In one set of projects, we are advancing the set of analytical tools that are required for processing and making sense of our data, namely: phylogenies (family trees) of tumor cells, in which each “leaf” (cell) comes with its gene expression profile (transcriptome) and possibly also its location in the tissue (conduting our measurments of gene expression in an intact tissue and at a high spatial resolution). The work (three papers thus far; two published and one under review) includes new algorithms for (i) inferring lineage trees [Prillo et al REOMB 2025]; (ii) deciding whether an inferred lineage tree is accurate enough (without knowing the “correct” tree) [Zilber et al., bioRxiv], and (iii) estimating the divergence times between cells in a lineage tree [Prillo et al Systematic Biology 2025]. This work relies on the vast body of work in the field of phylogenetics (e.g. for studying divergence between species). However, it is geared toward the special case of cell lineages trees and accounts for the nuances of the respective experimental protocols. As such, the work in the three papers relies on solid mathematical foundation and its evaluation includes both theoretical and empirical results. In addition, we developed an algorithm that leverages lineage trees to approximate the transcriptional changes that the observed tumor cells will go through after a set time interval. This procedure helps shed new light on the ways by which tumors develop over time and enables predictions about their future state [this project was the subject of an MS thesis, and the manuscript is in preparation; early version of this idea appears in our work at Yang et al., Cell 2022].
In the second set of projects, we focus on empirical studies of tumor evolution, using mouse models and cell lines that are endowed with the lineage tracing system. Our first product, in collaboration with Jonatan Weissman (MIT) and Dian Yang (Columbia), is the first proof of concept study of high-resolution transcriptional phylogeography in tumors [Jones et al., bioRxiv]. By coupling spatial transcriptomics with Cas9-based lineage tracing in a mouse model of lung adenocarcinoma, we were able to study how the tissue environment of tumor cells is associated with critical sub-clonal properties, namely: expansion, plasticity, and metastases seeding. We found that rapid tumor expansion contributes to a hypoxic, immunosuppressive, and fibrotic microenvironment that is associated with certain types of macrophasges and fibroblasts. We also found that metastases arise from spatially-confined subclones of primary tumors and remodel the distant metastatic niche into a fibrotic, collagen-rich microenvironment. Together, this first study presented a comprehensive dataset integrating spatial assays and lineage tracing to elucidate how sequential changes in cancer cell state and microenvironmental structures cooperate to promote tumor progression.
