Systematic identification of biomarker-driven drug combinations to overcome resistance.

The ability to understand and predict variable responses to therapeutic agents may improve outcomes in patients with cancer. We hypothesized that the basal gene-transcription state of cancer cell lines, coupled with cell viability profiles of small molecules, might be leveraged to nominate specific mechanisms of intrinsic resistance and to predict drug combinations that overcome resistance. We analyzed 564,424 sensitivity profiles to identify candidate gene-compound pairs, and validated nine such relationships. We determined the mechanism of a novel relationship, in which expression of the serine hydrolase enzymes monoacylglycerol lipase (MGLL) or carboxylesterase 1 (CES1) confers resistance to the histone lysine demethylase inhibitor GSK-J4 by direct enzymatic modification. Insensitive cell lines could be sensitized to GSK-J4 by inhibition or gene knockout. These analytical and mechanistic studies highlight the potential of integrating gene-expression features with small-molecule response to identify patient populations that are likely to benefit from treatment, to nominate rational candidates for combinations and to provide insights into mechanisms of action.

CloneSifter: enrichment of rare clones from heterogeneous cell populations.

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.

Comprehensive mutational scanning of EGFR reveals TKI sensitivities of extracellular domain mutants.

The epidermal growth factor receptor, EGFR, is frequently activated in lung cancer and glioblastoma by genomic alterations including missense mutations. The different mutation spectra in these diseases are reflected in divergent responses to EGFR inhibition: significant patient benefit in lung cancer, but limited in glioblastoma. Here, we report a comprehensive mutational analysis of EGFR function. We perform saturation mutagenesis of EGFR and assess function of ~22,500 variants in a human EGFR-dependent lung cancer cell line. This approach reveals enrichment of erlotinib-insensitive variants of known and unknown significance in the dimerization, transmembrane, and kinase domains. Multiple EGFR extracellular domain variants, not associated with approved targeted therapies, are sensitive to afatinib and dacomitinib in vitro. Two glioblastoma patients with somatic EGFR G598V dimerization domain mutations show responses to dacomitinib treatment followed by within-pathway resistance mutation in one case. In summary, this comprehensive screen expands the landscape of functional EGFR variants and suggests broader clinical investigation of EGFR inhibition for cancers harboring extracellular domain mutations.

Genotype-Fitness Maps of EGFR-Mutant Lung Adenocarcinoma Chart the Evolutionary Landscape of Resistance for Combination Therapy Optimization.

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.

Rhabdoid Tumors Are Sensitive to the Protein-Translation Inhibitor Homoharringtonine.

PURPOSE: Rhabdoid tumors are devastating pediatric cancers in need of improved therapies. We sought to identify small molecules that exhibit in vitro and in vivo 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 in vitro 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 BCL2L1, 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 BCL2L1, and overexpression of BCL2L1 induced resistance to HHT in rhabdoid tumor cells. Furthermore, HHT treatment inhibited rhabdoid tumor cell line and patient-derived xenograft growth in vivo. CONCLUSIONS: Rhabdoid tumor cell lines and xenografts are highly sensitive to HHT, at least partially due to their low expression of BCL2L1. HHT may have therapeutic potential against rhabdoid tumors.

Neuronal differentiation and cell-cycle programs mediate response to BET-bromodomain inhibition in MYC-driven medulloblastoma.

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.

Phenotypic Characterization of a Comprehensive Set of MAPK1/ERK2 Missense Mutants.

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.