Publications by authors named "Lisa Brenan"

Background: Many biological processes, such as cancer metastasis, organismal development, and acquisition of resistance to cytotoxic therapy, rely on the emergence of rare sub-clones from a larger population. Understanding how the genetic and epigenetic features of diverse clones affect clonal fitness provides insight into molecular mechanisms underlying selective processes. While large-scale barcoding with NGS readout has facilitated cellular fitness assessment at the population level, this approach does not support characterization of clones prior to selection. Single-cell genomics methods provide high biological resolution, but are challenging to scale across large populations to probe rare clones and are destructive, limiting further functional analysis of important clones.

Results: Here, we develop CloneSifter, a methodology for tracking and enriching rare clones throughout their response to selection. CloneSifter utilizes a CRISPR sgRNA-barcode library that facilitates the isolation of viable cells from specific clones within the barcoded population using a sequence-specific retrieval reporter. We demonstrate that CloneSifter can measure clonal fitness of cancer cell models in vitro and retrieve targeted clones at abundance as low as 1 in 1883 in a heterogeneous cell population.

Conclusions: CloneSifter provides a means to track and access specific and rare clones of interest across dynamic changes in population structure to comprehensively explore the basis of these changes.

Purpose: Rhabdoid tumors are devastating pediatric cancers in need of improved therapies. We sought to identify small molecules that exhibit and efficacy against preclinical models of rhabdoid tumor.

Experimental Design: We screened eight rhabdoid tumor cell lines with 481 small molecules and compared their sensitivity with that of 879 other cancer cell lines. Genome-scale CRISPR-Cas9 inactivation screens in rhabdoid tumors were analyzed to confirm target vulnerabilities. Gene expression and CRISPR-Cas9 data were queried across cell lines and primary rhabdoid tumors to discover biomarkers of small-molecule sensitivity. Molecular correlates were validated by manipulating gene expression. Subcutaneous rhabdoid tumor xenografts were treated with the most effective drug to confirm results.

Results: Small-molecule screening identified the protein-translation inhibitor homoharringtonine (HHT), an FDA-approved treatment for chronic myelogenous leukemia (CML), as the sole drug to which all rhabdoid tumor cell lines were selectively sensitive. Validation studies confirmed the sensitivity of rhabdoid tumor to HHT was comparable with that of CML cell lines. Low expression of the antiapoptotic gene , which encodes Bcl-XL, was the strongest predictor of HHT sensitivity, and HHT treatment consistently depleted Mcl-1, the synthetic-lethal antiapoptotic partner of Bcl-XL. Rhabdoid tumor cell lines and primary-tumor samples expressed low , and overexpression of induced resistance to HHT in rhabdoid tumor cells. Furthermore, HHT treatment inhibited rhabdoid tumor cell line and patient-derived xenograft growth .

Conclusions: Rhabdoid tumor cell lines and xenografts are highly sensitive to HHT, at least partially due to their low expression of . HHT may have therapeutic potential against rhabdoid tumors.

Cancer evolution poses a central obstacle to cure, as resistant clones expand under therapeutic selection pressures. Genome sequencing of relapsed disease can nominate genomic alterations conferring resistance but sample collection lags behind, limiting therapeutic innovation. Genome-wide screens offer a complementary approach to chart the compendium of escape genotypes, anticipating clinical resistance. We report genome-wide open reading frame (ORF) resistance screens for first- and third-generation epidermal growth factor receptor (EGFR) inhibitors and a MEK inhibitor. Using serial sampling, dose gradients, and mathematical modeling, we generate genotype-fitness maps across therapeutic contexts and identify alterations that escape therapy. Our data expose varying dose-fitness relationship across genotypes, ranging from complete dose invariance to paradoxical dose dependency where fitness increases in higher doses. We predict fitness with combination therapy and compare these estimates to genome-wide fitness maps of drug combinations, identifying genotypes where combination therapy results in unexpected inferior effectiveness. These data are applied to nominate combination optimization strategies to forestall resistant disease.

BET-bromodomain inhibition (BETi) has shown pre-clinical promise for MYC-amplified medulloblastoma. However, the mechanisms for its action, and ultimately for resistance, have not been fully defined. Here, using a combination of expression profiling, genome-scale CRISPR/Cas9-mediated loss of function and ORF/cDNA driven rescue screens, and cell-based models of spontaneous resistance, we identify bHLH/homeobox transcription factors and cell-cycle regulators as key genes mediating BETi's response and resistance. Cells that acquire drug tolerance exhibit a more neuronally differentiated cell-state and expression of lineage-specific bHLH/homeobox transcription factors. However, they do not terminally differentiate, maintain expression of CCND2, and continue to cycle through S-phase. Moreover, CDK4/CDK6 inhibition delays acquisition of resistance. Therefore, our data provide insights about the mechanisms underlying BETi effects and the appearance of resistance and support the therapeutic use of combined cell-cycle inhibitors with BETi in MYC-amplified medulloblastoma.

Tumor-specific genomic information has the potential to guide therapeutic strategies and revolutionize patient treatment. Currently, this approach is limited by an abundance of disease-associated mutants whose biological functions and impacts on therapeutic response are uncharacterized. To begin to address this limitation, we functionally characterized nearly all (99.84%) missense mutants of MAPK1/ERK2, an essential effector of oncogenic RAS and RAF. Using this approach, we discovered rare gain- and loss-of-function ERK2 mutants found in human tumors, revealing that, in the context of this assay, mutational frequency alone cannot identify all functionally impactful mutants. Gain-of-function ERK2 mutants induced variable responses to RAF-, MEK-, and ERK-directed therapies, providing a reference for future treatment decisions. Tumor-associated mutations spatially clustered in two ERK2 effector-recruitment domains yet produced mutants with opposite phenotypes. This approach articulates an allele-characterization framework that can be scaled to meet the goals of genome-guided oncology.