Cellular and Molecular Mechanisms of Cancer Progression and Metastasis at the Single-Cell Level

Friday, January 26, 2018 -
2:00pm to 3:00pm
The FUNG Auditorium
Devon Lawson

Assistant Professor, Physiology and Biophysics

Chao Family Comprehensive Cancer Center

University of California, Irvine

Cellular and Molecular Mechanisms of Cancer Progression and Metastasis at the Single-Cell Level


Individual tumors are remarkably heterogeneous, comprised of subpopulations of tumor cells with different genotypes, gene expression programs, and proteomic and epigenetic landscapes. This vast heterogeneity impacts the basic biology of the tumor’s function, as well as the clinical management of the disease. Numerous experimental and clinical data indicate that only a rare subpopulation of cancer cells within most tumors can metastasize, but it is not clear which cells these are or why. Recent work in our lab has focused on developing and applying new single-cell technologies to investigate the genetic and phenotypic properties of metastasis-initiating cells (MICs) in human Patient-derived xenograft (PDX) models of breast cancer. We have developed a highly sensitive, species-specific FACS-based assay for isolation of single human metastatic tumor cells from peripheral tissues of PDX mice. This allows us to isolate rare, early-stage metastatic cells from mice with low metastatic burden without introducing a reporter, to preserve the heterogeneity of the patient sample. Using this assay, we have found metastatic cells in each of the most common clinical sites of breast cancer in PDX mice, including lung, lymph nodes, liver, bone marrow, brain, and peripheral blood. Using a microfluidics-based platform (Fluidigm) for multiplex qPCR in single cells, we have found that low burden metastatic cells possess a distinct, stem cell-like gene expression signature. In contrast, high burden cells are more heterogeneous, similar to primary tumor cells, and a higher percentage of them express proliferative and luminal cell genes, suggesting they are more differentiated. We are currently using new single-cell sequencing technologies to investigate global genomic, transcriptomic, and epigenomic differences in metastasizing cells relative to primary tumor cells. The goal is to generate an integrated signature that defines cells capable of producing metastasis, and use this information to develop new strategies for preventing progression to lethal metastatic disease.


Dr. Lawson is an expert in epithelial cancer biology, where her most recently work focuses on utilizing single-cell omics tools to study the cellular and molecular mechanisms of breast cancer metastasis. She is an Assistant Professor in the the leader of the Department of Physiology and Biophysics, affiliated with the Chao Family Comprehensive Cancer Center where she is leader of the UCI Breast Disease Oriented Team (DOT). Her graduate training was under the mentorship of Dr. Owen Witte at UCLA, where she pioneered methods for prostate stem cell identification and investigated their role in prostate cancer initiation. As a postdoctoral fellow in Dr. Zena Werb’s lab at UCSF, Dr. Lawson built a FACS-based approach for single-cell transcriptome profiling of metastatic cells in human Patient-derived xenograft (PDX) models of breast cancer. This approach facilitates the identification, isolation, and characterization of rare human cells in peripheral tissues, and showed for the first time that early disseminating tumor cells expressed a gene expression profile akin to normal breast basal epithelial stem cells which differentiate as they progress to macrometastatic disease (Lawson DA et al., Nature, 2015). At UCI, her lab now focuses on using single-cell level technologies to study the cellular, molecular, and genetic mechanisms driving cancer metastasis by single-cell whole-exome and mRNA sequencing of patient and PDX tissues. Her lab utilizes an interdisciplinary approach to analyze and interpret datasets, combining recent advances in bioinformatics and computational biology, systems biology, and statistical and mathematical modeling. The ultimate goal of her research is to identify new biomarkers predictive for metastatic progression and drug targets to inhibit progression to advanced metastatic disease.