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Metabolite Profiling Stratifies Pancreatic Ductal Adenocarcinomas

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Metabolite Profiling Stratifies Pancreatic Ductal Adenocarcinomas Metabolite profiling stratifies pancreatic ductal PNAS PLUS adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors Anneleen Daemena, David Petersonb,1, Nisebita Sahub,1, Ron McCordb,1, Xiangnan Duc, Bonnie Liuc, Katarzyna Kowanetzb, Rebecca Hongc, John Moffatd, Min Gaoc, Aaron Boudreaub, Rana Mroueb, Laura Corsonc, Thomas O’Brienc, Jing Qingc, Deepak Sampathc, Mark Merchantc, Robert Yauchb, Gerard Manninga, Jeffrey Settlemanb, Georgia Hatzivassiliouc, and Marie Evangelistab,2 aBioinformatics and Computational Biology, Genentech, South San Francisco, CA 94080; bDiscovery Oncology, Genentech, South San Francisco, CA 94080; cTranslational Oncology, Genentech, South San Francisco, CA 94080; and dBiochemical Pharmacology, Genentech, South San Francisco, CA 94080 Edited by Ronald A. DePinho, University of Texas M.D. Anderson Cancer Center, Houston, TX, and approved June 22, 2015 (received for review January 29, 2015) Although targeting cancer metabolism is a promising therapeutic therapeutic targets (9, 10). Just as tumors vary greatly in genomic strategy, clinical success will depend on an accurate diagnostic alterations that impact signaling and regulatory pathways, meta- identification of tumor subtypes with specific metabolic require- bolic transformation is also heterogeneous and dependent on tissue ments. Through broad metabolite profiling, we successfully identified type, proliferation rate, and isoenzyme use (9, 11). In addition, the three highly distinct metabolic subtypes in pancreatic ductal adeno- observed differences in the dependence on and utilization of the carcinoma (PDAC). One subtype was defined by reduced proliferative major nutrients—glutamine and glucose—are linked to oncogenic capacity, whereas the other two subtypes (glycolytic and lipogenic) signaling and the genomic features of a cancer cell (12). showed distinct metabolite levels associated with glycolysis, lipo- Large-scale pharmacogenomic screening is a powerful method genesis, and redox pathways, confirmed at the transcriptional level. for identifying biomarkers of drug response and can accelerate The glycolytic and lipogenic subtypes showed striking differences in the search for new cancer therapies (13, 14). In this study, we glucose and glutamine utilization, as well as mitochondrial function, used broad baseline metabolite profiling in cell line models of CELL BIOLOGY and corresponded to differences in cell sensitivity to inhibitors of pancreatic ductal adenocarcinoma (PDAC), a disease context glycolysis, glutamine metabolism, lipid synthesis, and redox balance. previously associated with altered metabolism (15–18), to iden- In PDAC clinical samples, the lipogenic subtype associated with the tify metabolic subtypes within PDAC and predict their sensitivity epithelial (classical) subtype, whereas the glycolytic subtype to various metabolic inhibitors. strongly associated with the mesenchymal (QM-PDA) subtype, suggesting functional relevance in disease progression. Pharmaco- Results ∼ genomic screening of an additional 200 non-PDAC cell lines vali- Baseline Metabolite Profiling Identifies Three Metabolic Subtypes in dated the association between mesenchymal status and metabolic PDAC. We examined cell lines derived from naturally occurring drug response in other tumor indications. Our findings highlight the tumors because they recapitulate many aspects of the tissue type utility of broad metabolite profiling to predict sensitivity of tumors to a variety of metabolic inhibitors. Significance metabolite profiling | metabolic subtypes in PDAC | glycolysis | lipid synthesis | biomarkers for metabolic inhibitors Targeting cancer metabolism requires personalized diagnostics for clinical success. Pancreatic ductal adenocarcinoma (PDAC) is characterized by metabolism addiction. To identify metabolic de- etabolic reprogramming during tumorigenesis is an essential pendencies within PDAC, we conducted broad metabolite profiling Mprocess in nearly all cancer cells. Tumors share a common and identified three subtypes that showed distinct metabolite phenotype of uncontrolled cell proliferation and must efficiently profiles associated with glycolysis, lipogenesis, and redox path- generate the energy and macromolecules required for cellular ways. Importantly, these profiles significantly correlated with growth. The first example of metabolic reprogramming was dis- enriched sensitivity to a variety of metabolic inhibitors including coveredmorethan80yagobyOttoWarburg:tumorcellscanshift inhibitors targeting glycolysis, glutaminolysis, lipogenesis, and re- from oxidative to fermentative metabolism in the course of onco- dox balance. In primary PDAC tumor samples, the lipid subtype genesis (1). More recently, there has been a resurgence of interest was strongly associated with an epithelial phenotype, whereas the in targeting cancer metabolism (2–4) because it may not only be ef- glycolytic subtype was strongly associated with a mesenchymal fective in inhibiting tumor growth, but may also provide a therapeutic phenotype, suggesting functional relevance in disease progression. window (5, 6). For example, inactivation of lactate dehydrogenase-A Our findings will provide valuable predictive utility for a number of (LDHA), an enzyme that catalyzes the final step of aerobic gly- metabolic inhibitors currently undergoing phase I testing. colysis, thereby reducing pyruvate to lactate, decreases tumorigenesis and induces regression of established tumors in mouse models of Author contributions: A.D., G.H., and M.E. designed research; A.D., D.P., K.K., R.Y., G.M., J.S., KRAS G.H., and M.E. performed research; A.D., D.P., N.S., R. McCord, X.D., B.L., K.K., R.H., J.M., lung cancer driven by oncogenic or epidermal growth factor M.G., A.B., R. Mroue, L.C., T.O., J.Q., and M.E. contributed new reagents/analytic tools; receptor (EGFR) while minimally affecting normal cell function A.D., D.P., N.S., R. McCord, X.D., B.L., K.K., R.H., J.M., M.G., A.B., R. Mroue, L.C., T.O., J.Q., (7). The finding that cancers have altered metabolism has prompted D.S., M.M., G.H., and M.E. analyzed data; and A.D. and M.E. wrote the paper. substantial investigation, both preclinically and in clinical trials, of The authors declare no conflict of interest. several metabolically targeted agents, including those that elevate This article is a PNAS Direct Submission. reactive oxygen species (ROS) or block glycolysis, lipid synthesis, Freely available online through the PNAS open access option. mitochondrial function, and glutamine synthesis pathways (8). 1D.P., R. McCord, and N.S. contributed equally to this work. The identification of distinct metabolic reprogramming events 2To whom correspondence should be addressed. Email: [email protected]. or metabolic subtypes in cancer may inform patient selection for This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. investigational metabolic inhibitors and in the selection of new 1073/pnas.1501605112/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1501605112 PNAS Early Edition | 1of8 Downloaded by guest on October 2, 2021 and genomic context of cancer (13, 14, 19, 20). Levels of 256 with the lipogenic lines (P < 0.05; Fig. 1E and Fig. S1K). In con- metabolites were quantified in 38 pancreatic cancer cell lines (five trast, cell lines within the lipogenic subtype were enriched for ex- biological replicates per cell line) in logarithmic growth phase using pression of lipogenesis genes involved in cholesterol and de novo media with physiological glucose and glutamine concentrations lipid synthesis including 7-dehydrocholesterol reductase (DHCR7), (Datasets S1–S3). We applied nonnegative matrix factorization stearoyl-CoA desaturase (SCD), and fatty acid synthase (FASN) (NMF) (21), a recently established approach for consensus clus- (adjusted P < 0.1; Fig. 1 E and F, Fig. S1 H and L,andDataset tering (22–24), to 153 metabolites with reproducible variation, S6). Thus, PDAC-derived cell lines can be clustered by their allowing the capture of the strongest signal of metabolic de- metabolite profiles and these differences appear to be determined pendency (SI Materials and Methods). This analysis revealed three in part by differences in gene expression. stable and reproducible subtypes with adequate data coherence (Fig. S1 A and B). The metabolite profiles of the cell lines ordered Glycolytic and Lipogenic Subtypes Use Glucose and Glutamine in a by subtype are shown in Fig. 1A for metabolites with distinct in- Different Manner. The metabolic and transcriptional profiles sug- tensities in at least one subtype compared with the other two gested that these two subtypes may differ in their use of glucose subtypes (F test, P < 0.05). These three subtypes provided a useful and glutamine, the most abundant carbon sources available to and interpretable basis for further analysis. cancer cells. We predicted that the lipogenic subtype would preferentially use glucose for the tricarboxylic acid (TCA) cycle Metabolic Characterization Reveals a Slow Proliferating, Glycolytic, and lipid synthesis, whereas the glycolytic subtype would use glu- and Lipogenic Subtype. The metabolite intensities within each cose more for aerobic glycolysis, and consequently, use more glu- subtypewerethenmappedtoknown,previouslyestablished tamine for TCA anaplerosis. 13C metabolic mass isotopomer metabolic ontologies (Dataset S1 and SI Materials and Methods) distribution analysis (MIDA) using either [U-13C5]glutamine or (25). One
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