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 clinical administration of a chemotherapeutic agent 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
04/20/2023 Amy is a keynote speaker at the CPRIT Symposium on "Expanding Texas Leadership in Computational Oncology throughout the Cancer Continuum" today at Dell Medical School, UT Austin. 04/13/2023 Amy will be giving a talk and Carolina, Daylin, Didi, and Tyler will present posters at AACR 2023. Come visit with us! 02/2023 Congratulations to Didi on receiving the NOA for her F31 fellowship! 09/2023 The lab welcomes new graduate student Audrey Freischel! 08/2022 Congratulations to Sarah Meng, Michael Cotner and Kennedy Howland on passing their BME qualifying exams! |
Research
ClonMapper: A multifunctional cell barcoding platform for tracking heterogeneous cancer cells
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 cancer bioengineering platform to address this challenge. Expressed barcodes are encoded in gRNA sequences and enable tracking of clonal dynamics by high-resolution genomic sequencing, as well as clonally-resolved single cell transcriptomic analysis.
Beyond their utility as clonal cell labels, here barcodes can also serve as actuators. Control of clone-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. By pairing expressed barcodes with synthetic gene circuits, this tool can be generalized to control expression of genes in a clone-specific manner.
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 cancer bioengineering platform to address this challenge. Expressed barcodes are encoded in gRNA sequences and enable tracking of clonal dynamics by high-resolution genomic sequencing, as well as clonally-resolved single cell transcriptomic analysis.
Beyond their utility as clonal cell labels, here barcodes can also serve as actuators. Control of clone-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. By pairing expressed barcodes with synthetic gene circuits, this tool can be generalized to control expression of genes in a clone-specific manner.
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.