Brock Lab
Our lab investigates the role of heterogeneity in cell state transitions, cancer progression, and therapeutic responses.
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Our Focus
Drug resistance of tumor cells is the cause of treatment failure for most human cancers. Acquired resistance was observed in the very first trial of a chemotherapeutic in 1942. Since that time, vast improvements in chemotherapy- including novel drugs, combination therapies, reduced toxicity, and highly targeted treatments- have extended the lives of many cancer patients. Yet drug resistance remains a major scientific and clinical challenge in the treatment of human cancer. Tumor cell populations are characterized by enormous heterogeneity and plasticity. This variation contributes to disease progression and response to therapy, although the mechanisms are not yet well understood. To address these challenges, we are developing novel technologies and experimental systems rooted in the tools of systems and synthetic biology and bioengineering. |
Lab News
08/2022 Congratulations to both Sarah Meng, Michael Cotner and Kennedy Howland on passing their BME qualifying exams! 07/2022 Huge congrats to Didi for scoring a percentile score of 8% on her F31 fellowship application! 12/2021 We welcome three new PhD students to the lab! We are happy to have Michael Cotner, Sarah Meng, and Kennedy Howland join us! |
Research
COLBERT: Control of Lineages by Barcode Enabled Recombinant Transcription
Conventional lineage tracing by DNA barcoding enables quantitation of clonal
dynamics across high diversity, heterogeneous cell populations. However,
quantitation of barcode frequency by sequencing is a destructive measurement
and precludes isolation of a specific lineage of interest for downstream study. It
has not been possible to measure lineage dynamics of a population and also
perform molecular and functional live-cell studies of a selected lineage
subpopulation.
We have developed a new synthetic biology tool called Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT) to address this challenge.
Control of lineage-specific gene expression by a gRNA-barcode system enables activation of a reporter and isolation of a specific lineage of interest from within a high diversity population. The platform can be generalized to control expression of any gene in a lineage-specific way.
Conventional lineage tracing by DNA barcoding enables quantitation of clonal
dynamics across high diversity, heterogeneous cell populations. However,
quantitation of barcode frequency by sequencing is a destructive measurement
and precludes isolation of a specific lineage of interest for downstream study. It
has not been possible to measure lineage dynamics of a population and also
perform molecular and functional live-cell studies of a selected lineage
subpopulation.
We have developed a new synthetic biology tool called Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT) to address this challenge.
Control of lineage-specific gene expression by a gRNA-barcode system enables activation of a reporter and isolation of a specific lineage of interest from within a high diversity population. The platform can be generalized to control expression of any gene in a lineage-specific way.
Growth dynamics of initiating cell populations
While many models of tumor growth have focused on the late stages of tumor expansion, the earliest stages of tumor initiation and growth have not been well characterized. Growth dynamics at very low cell densities are of utmost clinical importance, as the ability of these cells to enter the exponential growth phase leads to new tumors and metastases. We hypothesize that the key principles that govern early stage tumor growth are analogous to ecological principles that describe the expansion of a species in its environment and the role of subpopulation interactions on population growth. These principles are explored through in vitro study and mathematical modeling. We anticipate this work will reveal insights into the requirements for interactions among cancer cells at the early stages of tumor initiation. Understanding these early stage dynamics may enable therapeutic rationales to consider manipulating population dynamics to disrupt interactions and ultimately prevent tumors from establishing.
Chemoresistance of heterogeneous tumor cells
Acquired resistance to cancer chemotherapy is a major cause of treatment failure and a longstanding clinical challenge. While many experimental models of resistance have relied on long-term selection of resistant clones, here we wanted to examine the onset of resistance following exposure to a short pulse of chemotherapeutic agent. We hypothesized that on this time scale, cells may evade treatment by switching into different heterogeneous gene expression states.
There are no specific markers to identify all of the cells in resistant and sensitive states. To address this challenge, we use a linked experiment and mathematical modeling approach to estimate the relative frequencies of cells with different drug sensitivity states over time.
Several hundred measurements were collected to assemble a dataset on the long-term dynamics of doxorubicin resistance in breast tumor cells. Using model selection, we demonstrate that a multi-state model of heterogeneous cell subpopulations best captures our changes in chemosensitivity over time. The approach we describe here uncovers chemoresistant subpopulations in breast cancer cells and is generalizable to any system in which subpopulations may play a role in a dynamic measurable outcome.