Matthew G. Rees

Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

UNLABELLED Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2). SIGNIFICANCE We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses.

Correlating chemical sensitivity and basal gene expression reveals mechanism of action

Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with ~19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters, and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.

Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway

Plasticity of the cell state has been proposed to drive resistance to multiple classes of cancer therapies, thereby limiting their effectiveness. A high-mesenchymal cell state observed in human tumours and cancer cell lines has been associated with resistance to multiple treatment modalities across diverse cancer lineages, but the mechanistic underpinning for this state has remained incompletely understood. Here we molecularly characterize this therapy-resistant high-mesenchymal cell state in human cancer cell lines and organoids and show that it depends on a druggable lipid-peroxidase pathway that protects against ferroptosis, a non-apoptotic form of cell death induced by the build-up of toxic lipid peroxides. We show that this cell state is characterized by activity of enzymes that promote the synthesis of polyunsaturated lipids. These lipids are the substrates for lipid peroxidation by lipoxygenase enzymes. This lipid metabolism creates a dependency on pathways converging on the phospholipid glutathione peroxidase (GPX4), a selenocysteine-containing enzyme that dissipates lipid peroxides and thereby prevents the iron-mediated reactions of peroxides that induce ferroptotic cell death. Dependency on GPX4 was found to exist across diverse therapy-resistant states characterized by high expression of ZEB1, including epithelial–mesenchymal transition in epithelial-derived carcinomas, TGFβ-mediated therapy-resistance in melanoma, treatment-induced neuroendocrine transdifferentiation in prostate cancer, and sarcomas, which are fixed in a mesenchymal state owing to their cells of origin. We identify vulnerability to ferroptic cell death induced by inhibition of a lipid peroxidase pathway as a feature of therapy-resistant cancer cells across diverse mesenchymal cell-state contexts.

NAMPT Is the Cellular Target of STF-31-Like Small-Molecule Probes

The small-molecule probes STF-31 and its analogue compound 146 were discovered while searching for compounds that kill VHL-deficient renal cell carcinoma cell lines selectively and have been reported to act via direct inhibition of the glucose transporter GLUT1. We profiled the sensitivity of 679 cancer cell lines to STF-31 and found that the pattern of response is tightly correlated with sensitivity to three different inhibitors of nicotinamide phosphoribosyltransferase (NAMPT). We also performed whole-exome next-generation sequencing of compound 146-resistant HCT116 clones and identified a recurrent NAMPT-H191R mutation. Ectopic expression of NAMPT-H191R conferred resistance to both STF-31 and compound 146 in cell lines. We further demonstrated that both STF-31 and compound 146 inhibit the enzymatic activity of NAMPT in a biochemical assay in vitro. Together, our cancer-cell profiling and genomic approaches identify NAMPT inhibition as a critical mechanism by which STF-31-like compounds inhibit cancer cells.

Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics

High cancer death rates indicate the need for new anti-cancer therapeutic agents. Approaches to discover new cancer drugs include target-based drug discovery and phenotypic screening. Here, we identified phosphodiesterase 3A modulators as cell-selective cancer cytotoxic compounds by phenotypic compound library screening and target deconvolution by predictive chemogenomics. We found that sensitivity to 6-(4-(diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one, or DNMDP, across 766 cancer cell lines correlates with expression of the phosphodiesterase 3A gene, PDE3A. Like DNMDP, a subset of known PDE3A inhibitors kill selected cancer cells while others do not. Furthermore, PDE3A depletion leads to DNMDP resistance. We demonstrated that DNMDP binding to PDE3A promotes an interaction between PDE3A and Schlafen 12 (SLFN12), suggesting a neomorphic activity. Co-expression of SLFN12 with PDE3A correlates with DNMDP sensitivity, while depletion of SLFN12 results in decreased DNMDP sensitivity. Our results implicate PDE3A modulators as candidate cancer therapeutic agents and demonstrate the power of predictive chemogenomics in small-molecule discovery.

DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets.

Purpose: We used human stem and progenitor cells to develop a genetically accurate novel model of MYC-driven Group 3 medulloblastoma. We also developed a new informatics method, Disease-model Signature versus Compound-Variety Enriched Response (“DiSCoVER”), to identify novel therapeutics that target this specific disease subtype. Experimental Design: Human neural stem and progenitor cells derived from the cerebellar anlage were transduced with oncogenic elements associated with aggressive medulloblastoma. An in silico analysis method for screening drug sensitivity databases (DiSCoVER) was used in multiple drug sensitivity datasets. We validated the top hits from this analysis in vitro and in vivo. Results: Human neural stem and progenitor cells transformed with c-MYC, dominant-negative p53, constitutively active AKT and hTERT formed tumors in mice that recapitulated Group 3 medulloblastoma in terms of pathology and expression profile. DiSCoVER analysis predicted that aggressive MYC-driven Group 3 medulloblastoma would be sensitive to cyclin-dependent kinase (CDK) inhibitors. The CDK 4/6 inhibitor palbociclib decreased proliferation, increased apoptosis, and significantly extended the survival of mice with orthotopic medulloblastoma xenografts. Conclusions: We present a new method to generate genetically accurate models of rare tumors, and a companion computational methodology to find therapeutic interventions that target them. We validated our human neural stem cell model of MYC-driven Group 3 medulloblastoma and showed that CDK 4/6 inhibitors are active against this subgroup. Our results suggest that palbociclib is a potential effective treatment for poor prognosis MYC-driven Group 3 medulloblastoma tumors in carefully selected patients. Clin Cancer Res; 22(15); 3903–14. ©2016 AACR.

Discovery of a Small-Molecule Probe for V-ATPase Function

Lysosomes perform a critical cellular function as a site of degradation for diverse cargoes including proteins, organelles, and pathogens delivered through distinct pathways, and defects in lysosomal function have been implicated in a number of diseases. Recent studies have elucidated roles for the lysosome in the regulation of protein synthesis, metabolism, membrane integrity, and other processes involved in homeostasis. Complex small-molecule natural products have greatly contributed to the investigation of lysosomal function in cellular physiology. Here we report the discovery of a novel, small-molecule modulator of lysosomal acidification derived from diversity-oriented synthesis through high-content screening.

Abstract A18: Predicting synergistic drug combinations from genomic features and single-agent response profiles

Drug combinations promise to improve clinical responses and/or forestall drug resistance. To capitalize on this promise, we need to know which drugs to combine, and whom to give them to based on the genetic or pathological features of their disease. However, accomplishing this goal has been precluded by the infeasibility of performing comprehensive drug-combination studies across thousands of cellular contexts. We hypothesized that the basal gene-transcription state of cancer cell lines, in concert with the response profiles of hundreds of single-agent small molecules, might be leveraged to nominate synergistic drug combinations, eliminating the need to test all possible drug/drug combinations across cellular models. Specifically, we predicted that inhibiting the protein product of transcripts associated with drug resistance to a given small molecule might induce drug synergy. To test this notion, we analyzed public cell-line drug-sensitivity data to identify candidate compound-gene pairs. We identified 7 examples in which outlier expression of a druggable candidate protein was associated with lack of single-agent response. Inhibition of 6/7 candidate co-targets resulted in cell-line-specific synergistic cell killing across multiple cell line models, validating the overall approach. For example, consistent with clinical findings, we found that high expression of the MGMT gene, encoding O-6-methylguanine-DNA methyltransferase, was uniquely associated with response to alkylating agents such as temozolomide, and that combination of the MGMT inhibitor O-6-benzylguanine with temozolomide resulted in synergistic killing. These initial studies highlight the potential of integrating basal gene expression features with small-molecule response to nominate rational candidates for drug combinations. As public repositories of single-agent response data from diverse cellular contexts continue to expand, so too will our repertoire of therapeutic combinations. Moreover, this approach permits the parallel identification of genomic features that indicate which patient populations are most likely to benefit from such combinations. Citation Format: Matthew G. Rees, Lisa Brenan, Amanda Walker, Cory M. Johannessen. Predicting synergistic drug combinations from genomic features and single-agent response profiles [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A18.

Abstract C136: Identification of selective cancer cytotoxic modulators of phosphodiesterase 3a by predictive chemogenomics

High cancer death rates indicate the need for new anti-cancer therapeutic agents. Approaches to discover new cancer drugs include target-based drug discovery and phenotypic screening. Here, we identify phosphodiesterase 3A modulators as cell-selective cancer cytotoxic compounds by phenotypic compound library screening and target deconvolution by predictive chemogenomics. We found that sensitivity to 6-(4-(diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one, or DNMDP, across 766 cancer cell lines correlates with expression of the phosphodiesterase 3A gene, PDE3A. Like DNMDP, a subset of known PDE3A inhibitors kill selected cancer cells while others do not. Furthermore, PDE3A depletion leads to DNMDP resistance. We demonstrate that DNMDP binding to PDE3A promotes an interaction between PDE3A and Schlafen 12 (SLFN12), suggesting a neomorphic activity. Co-expression of SLFN12 with PDE3A correlates with DNMDP sensitivity, while depletion of SLFN12 results in decreased sensitivity to DNMDP. Our results implicate PDE3A modulators as candidate cancer therapeutic agents and demonstrate the power of predictive chemogenomics in small-molecule discovery. Citation Format: Lucian de Waal, Timothy A. Lewis, Matthew G. Rees, Aviad Tsherniak, Xiaoyun Wu, Peter S. Choi, Lara Gechijian, Christina Hartigan, Patrick W. Faloon, Mark J. Hickey, Nicola Tolliday, Steven A. Carr, Paul A. Clemons, Benito Munoz, Bridget K. Wagner, Alykhan F. Shamji, Angela N. Koehler, Monica Schenone, Alex B. Burgin, Stuart L. Schreiber, Heidi Greulich, Matthew Meyerson. Identification of selective cancer cytotoxic modulators of phosphodiesterase 3a by predictive chemogenomics. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr C136.

Abstract 4596: An integrated genomic characterization of the target of a small molecule identifies a novel cancer dependency

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Recent large sequencing and cancer dependency studies have accelerated the identification of candidate targets for precision medicine. However, the current drug development paradigm starting with target identification and validation can be slow and has thus far yielded a limited variety of successful targets. We sought to return to an empirical approach to drug discovery and performed a high throughput screen to identify small molecules that were both potent and selective. In a primary screen of 2000 compounds in two cell-lines: A549 and H1734, three compounds only affected H1734 viability. One of which validated in a dose-response experiment with great potency and specificity, we called this small molecule ‘Compound 1B’. In an effort to identify the target of Compound 1B, we profiled 766 genomically-characterized cancer cell lines and found that approximately 4% were sensitive to our compound. Sensitivity was not restricted to a particular tissue of origin. Interestingly, expression of Phosphodiesterase 3A (PDE3A) correlated with cytotoxicity. We further showed that Compound 1B specifically inhibited the enzymatic activity of PDE3A and PDE3B in a panel of 11 different phosphodiesterase family members. However, only a subset of other PDE3 inhibitors shared the same cytotoxic phenotype of Compound 1B. In a rescue screen of 1600 bioactive compounds, we identified the non-lethal PDE3 inhibitors as compounds that were able to rescue cell death induced by Compound 1B. Biochemical assays showed that both Compound 1B, cytotoxic and non-cytotoxic PDE3 inhibitors compete for binding to PDE3A. Knockdown of PDE3A did not affect cell viability and inhibited response of sensitive cell lines to Compound 1B. Thus we have identified a potent and selective small molecule that likely acts through PDE3A to induce cancer cell-line cytotoxicity. Our data suggest a hyper- or neomorphic function of PDE3A induced upon binding of Compound 1B. By cross-referencing integrative datasets with compound-sensitivity data, we show that reversal of the current drug-development paradigm can elucidate novel cancer targets, which are not yet identifiable by analysis of large next-generation sequencing datasets. Citation Format: Luc M. de Waal, Tim A. Lewis, Lara Gechijian, Aviad Tsherniak, Willmen Youngsaye, Matthew Rees, Oliver Mikse, Mark Hickey, Patrick Faloon, Nicola Tolliday, Angela Koehler, Monica Schenone, Kwok Wong, Alykhan Shamji, Benito Munoz, Stuart L. Schreiber, Heidi Greulich, Matthew L. Meyerson. An integrated genomic characterization of the target of a small molecule identifies a novel cancer dependency. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4596. doi:10.1158/1538-7445.AM2014-4596