Patricia Greninger

Systematic identification of genomic markers of drug sensitivity in cancer cells

Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines—which represent much of the tissue-type and genetic diversity of human cancers—with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing’s sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

Genomic Alterations of Anaplastic Lymphoma Kinase May Sensitize Tumors to Anaplastic Lymphoma Kinase Inhibitors

Selective kinase inhibitors have had a substantial impact on the field of medical oncology. Whereas these agents can elicit dramatic clinical responses in some settings, their activity is generally limited to a subset of treated patients whose tumor cells harbor a specific genetic lesion. We have established an automated platform for examining the sensitivity to various molecularly targeted inhibitors across a large panel of human tumor-derived cell lines to identify additional genotype-correlated responses that may be clinically relevant. Among the inhibitors tested in a panel of 602 cell lines derived from a variety of human cancers, we found that a selective inhibitor of the anaplastic lymphoma kinase (ALK) potently suppressed growth of a small subset of tumor cells. This subset included lines derived from anaplastic large cell lymphomas, non-small-cell lung cancers, and neuroblastomas. ALK is a receptor tyrosine kinase that was first identified as part of a protein fusion derived from a chromosomal translocation detected in the majority of anaplastic large cell lymphoma patients, and has recently been implicated as an oncogene in a small fraction of non-small-cell lung cancers and neuroblastomas. Significantly, sensitivity in these cell lines was well correlated with specific ALK genomic rearrangements, including chromosomal translocations and gene amplification. Moreover, in such cell lines, ALK kinase inhibition can lead to potent suppression of downstream survival signaling and an apoptotic response. These findings suggest that a subset of lung cancers, lymphomas, and neuroblastomas that harbor genomic ALK alterations may be clinically responsive to pharmacologic ALK inhibition.

Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.

Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling

Kinase inhibitors constitute an important new class of cancer drugs, whose selective efficacy is largely determined by underlying tumor cell genetics. We established a high-throughput platform to profile 500 cell lines derived from diverse epithelial cancers for sensitivity to 14 kinase inhibitors. Most inhibitors were ineffective against unselected cell lines but exhibited dramatic cell killing of small nonoverlapping subsets. Cells with exquisite sensitivity to EGFR, HER2, MET, or BRAF kinase inhibitors were marked by activating mutations or amplification of the drug target. Although most cell lines recapitulated known tumor-associated genotypes, the screen revealed low-frequency drug-sensitizing genotypes in tumor types not previously associated with drug susceptibility. Furthermore, comparing drugs thought to target the same kinase revealed striking differences, predictive of clinical efficacy. Genetically defined cancer subsets, irrespective of tissue type, predict response to kinase inhibitors, and provide an important preclinical model to guide early clinical applications of novel targeted inhibitors.

Targeting MYCN in Neuroblastoma by BET Bromodomain Inhibition

Bromodomain inhibition comprises a promising therapeutic strategy in cancer, particularly for hematologic malignancies. To date, however, genomic biomarkers to direct clinical translation have been lacking. We conducted a cell-based screen of genetically defined cancer cell lines using a prototypical inhibitor of BET bromodomains. Integration of genetic features with chemosensitivity data revealed a robust correlation between MYCN amplification and sensitivity to bromodomain inhibition. We characterized the mechanistic and translational significance of this finding in neuroblastoma, a childhood cancer with frequent amplification of MYCN. Genome-wide expression analysis showed downregulation of the MYCN transcriptional program accompanied by suppression of MYCN transcription. Functionally, bromodomain-mediated inhibition of MYCN impaired growth and induced apoptosis in neuroblastoma. BRD4 knockdown phenocopied these effects, establishing BET bromodomains as transcriptional regulators of MYCN. BET inhibition conferred a significant survival advantage in 3 in vivo neuroblastoma models, providing a compelling rationale for developing BET bromodomain inhibitors in patients with neuroblastoma.

Patient-derived models of acquired resistance can identify effective drug combinations for cancer

Targeted cancer therapies have produced substantial clinical responses, but most tumors develop resistance to these drugs. Here, we describe a pharmacogenomic platform that facilitates rapid discovery of drug combinations that can overcome resistance. We established cell culture models derived from biopsy samples of lung cancer patients whose disease had progressed while on treatment with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors and then subjected these cells to genetic analyses and a pharmacological screen. Multiple effective drug combinations were identified. For example, the combination of ALK and MAPK kinase (MEK) inhibitors was active in an ALK-positive resistant tumor that had developed a MAP2K1 activating mutation, and the combination of EGFR and fibroblast growth factor receptor (FGFR) inhibitors was active in an EGFR mutant resistant cancer with a mutation in FGFR3. Combined ALK and SRC (pp60c-src) inhibition was effective in several ALK-driven patient-derived models, a result not predicted by genetic analysis alone. With further refinements, this strategy could help direct therapeutic choices for individual patients. Secondary chemotherapies can be developed by screening drug-resistant cells from individual cancer patients. Drug resistance, up close and personal Cancer therapies that target specific genetic mutations driving tumor growth have shown promising results in patients; however, the response is often short-lived because the tumors acquire new mutations that render them resistant to these therapies. Complicating matters, the mechanism of resistance can vary from patient to patient. To identify drugs most likely to be effective against resistant tumors, Crystal et al. established cell lines from the tumors of individual patients after resistance occurred and performed a drug screen and genetic analysis on the cultured cells. This strategy successfully identified drug combinations that halted the growth of resistant tumor cells both in culture and in mice. In the future, pharmacological profiling of patient-derived cells could be an efficient way to direct therapeutic choices for individual cancer patients. Science, this issue p. 1480

A Landscape of Pharmacogenomic Interactions in Cancer

Summary Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Synthetic Lethal Interaction of Combined BCL-XL and MEK Inhibition Promotes Tumor Regressions in KRAS Mutant Cancer Models

KRAS is the most commonly mutated oncogene, yet no effective targeted therapies exist for KRAS mutant cancers. We developed a pooled shRNA-drug screen strategy to identify genes that, when inhibited, cooperate with MEK inhibitors to effectively treat KRAS mutant cancer cells. The anti-apoptotic BH3 family gene BCL-XL emerged as a top hit through this approach. ABT-263 (navitoclax), a chemical inhibitor that blocks the ability of BCL-XL to bind and inhibit pro-apoptotic proteins, in combination with a MEK inhibitor led to dramatic apoptosis in many KRAS mutant cell lines from different tissue types. This combination caused marked in vivo tumor regressions in KRAS mutant xenografts and in a genetically engineered KRAS-driven lung cancer mouse model, supporting combined BCL-XL/MEK inhibition as a potential therapeutic approach for KRAS mutant cancers.

Ligand-Dependent Platelet-Derived Growth Factor Receptor (PDGFR)-α Activation Sensitizes Rare Lung Cancer and Sarcoma Cells to PDGFR Kinase Inhibitors

Platelet-derived growth factor (PDGF) receptors (PDGFR) and their ligands play critical roles in several human malignancies. Sunitinib is a clinically approved multitargeted tyrosine kinase inhibitor that inhibits vascular endothelial growth factor receptor, c-KIT, and PDGFR, and has shown clinical activity in various solid tumors. Activation of PDGFR signaling has been described in gastrointestinal stromal tumors (PDGFRA mutations) as well as in chronic myeloid leukemia (BCR-PDGFRA translocation), and sunitinib can yield clinical benefit in both settings. However, the discovery of PDGFR activating mutations or gene rearrangements in other tumor types could reveal additional patient populations who might benefit from treatment with anti-PDGFR therapies, such as sunitinib. Using a high-throughput cancer cell line screening platform, we found that only 2 of 637 tested human tumor-derived cell lines show significant sensitivity to single-agent sunitinib exposure. These two cell lines [a non-small-cell lung cancer (NSCLC) and a rhabdomyosarcoma] showed expression of highly phosphorylated PDGFRA. In the sunitinib-sensitive adenosquamous NSCLC cell line, PDGFRA expression was associated with focal PFGRA gene amplification, which was similarly detected in a small fraction of squamous cell NSCLC primary tumor specimens. Moreover, in this NSCLC cell line, focal amplification of the gene encoding the PDGFR ligand PDGFC was also detected, and silencing PDGFRA or PDGFC expression by RNA interference inhibited proliferation. A similar codependency on PDGFRA and PDGFC was observed in the sunitinib-sensitive rhabdomyosarcoma cell line. These findings suggest that, in addition to gastrointestinal stromal tumors, rare tumors that show PDGFC-mediated PDGFRA activation may also be clinically responsive to pharmacologic PDGFRA or PDGFC inhibition.

Targeting Aberrant Glutathione Metabolism to Eradicate Human Acute Myelogenous Leukemia Cells

Background: Eradication of primary human leukemia cells represents a major challenge. Therapies have not substantially changed in over 30 years. Results: Using normal versus leukemia specimens enriched for primitive cells, we document aberrant regulation of glutathione metabolism. Conclusion: Aberrant glutathione metabolism is an intrinsic property of human leukemia cells. Significance: Interventions based on modulation of glutathione metabolism represent a powerful means to improve therapy. The development of strategies to eradicate primary human acute myelogenous leukemia (AML) cells is a major challenge to the leukemia research field. In particular, primitive leukemia cells, often termed leukemia stem cells, are typically refractory to many forms of therapy. To investigate improved strategies for targeting of human AML cells we compared the molecular mechanisms regulating oxidative state in primitive (CD34+) leukemic versus normal specimens. Our data indicate that CD34+ AML cells have elevated expression of multiple glutathione pathway regulatory proteins, presumably as a mechanism to compensate for increased oxidative stress in leukemic cells. Consistent with this observation, CD34+ AML cells have lower levels of reduced glutathione and increased levels of oxidized glutathione compared with normal CD34+ cells. These findings led us to hypothesize that AML cells will be hypersensitive to inhibition of glutathione metabolism. To test this premise, we identified compounds such as parthenolide (PTL) or piperlongumine that induce almost complete glutathione depletion and severe cell death in CD34+ AML cells. Importantly, these compounds only induce limited and transient glutathione depletion as well as significantly less toxicity in normal CD34+ cells. We further determined that PTL perturbs glutathione homeostasis by a multifactorial mechanism, which includes inhibiting key glutathione metabolic enzymes (GCLC and GPX1), as well as direct depletion of glutathione. These findings demonstrate that primitive leukemia cells are uniquely sensitive to agents that target aberrant glutathione metabolism, an intrinsic property of primary human AML cells.