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Increased adiposity and diets high in glycemic load (GL) are associated with increased risk of many chronic diseases including cancer. Using plasma from 80 healthy individuals [40 men/40 women, 29 with DXA-derived low fat mass (FM) and 51 with high FM] in a randomized cross-over–controlled feeding trial and arrays populated with 3,504 antibodies, we measured plasma proteins collected at baseline and end of each of two 28-day controlled diets: a low GL diet high in whole grains, legumes, fruits, and vegetables (WG) and a high GL diet high in refined grains and added sugars (RG). Following univariate testing for proteins differing by diet, we evaluated pathway-level involvement. Among all 80 participants, 172 proteins were identified as differing between diets. Stratifying participants by high and low FM identified 221 and 266 proteins, respectively, as differing between diets (unadjusted P < 0.05). These candidate proteins were tested for overrepresentation in Reactome pathways, corresponding to 142 (of 291) pathways in the high-FM group and 72 (of 274) pathways in the low-FM group. We observed that the cancer-related pathways, DNA Repair, DNA Replication, and Cell Cycle, were overrepresented in the high-FM participants while pathways involved in post-translational protein modification were overrepresented in participants with either FM. Although high-GL diets are associated with increased risk of some cancers, our study further suggests that biology associated with consumption of GL diets is variable depending on an individual's adiposity and dietary recommendations related to cancer prevention be made with the additional consideration of an individual's FM.

Supplementary Figure S1. EGF signaling enhances aerobic glycolysis in TNBC cells. Supplementary Figure S2. PKM2 is a novel binding partner of EGFR and its activity is inhibited by EGFR via Tyr phosphorylation. Supplementary Figure S3. EGFR reprograms cancer cell metabolism by PKM2 phosphorylation in TNBC cells. Supplementary Figure S4. Upregulation of HK2 expression by EGF signaling also contributes to aerobic glycolysis in TNBC cells. Supplementary Figure S5. Glycolysis metabolite, F1,6BP, binds directly to and enhances the activity of EGFR. Supplementary Figure S6. The combination of EGFR and glycolysis inhibitors synergistically suppresses TNBC cell proliferation. Supplementary Figure S7. EGF signaling-induced lactate inhibits cytotoxic T cell activity in TNBC cells.

Supplemental Materials and Methods

Supplementary Methods, Figures 1-6, Table 1 from Early Detection of Recurrent Breast Cancer Using Metabolite Profiling

Oncogenic signaling reprograms cancer cell metabolism to augment the production of glycolytic metabolites in favor of tumor growth. The ability of cancer cells to evade immunosurveillance and the role of metabolic regulators in T-cell functions suggest that oncogene-induced metabolic reprogramming may be linked to immune escape. EGF signaling, frequently dysregulated in triple-negative breast cancer (TNBC), is also associated with increased glycolysis. Here, we demonstrated in TNBC cells that EGF signaling activates the first step in glycolysis, but impedes the last step, leading to an accumulation of metabolic intermediates in this pathway. Furthermore, we showed that one of these intermediates, fructose 1,6 bisphosphate (F1,6BP), directly binds to and enhances the activity of the EGFR, thereby increasing lactate excretion, which leads to inhibition of local cytotoxic T-cell activity. Notably, combining the glycolysis inhibitor 2-deoxy-d-glucose with the EGFR inhibitor gefitinib effectively suppressed TNBC cell proliferation and tumor growth. Our results illustrate how jointly targeting the EGFR/F1,6BP signaling axis may offer an immediately applicable therapeutic strategy to treat TNBC. Cancer Res; 76(5); 1284–96. ©2016 AACR.

Increased adiposity and diets high in glycemic load (GL) are associated with increased risk of many chronic diseases including cancer. Using plasma from 80 healthy individuals [40 men/40 women, 29 with DXA-derived low fat mass (FM) and 51 with high FM] in a randomized cross-over–controlled feeding trial and arrays populated with 3,504 antibodies, we measured plasma proteins collected at baseline and end of each of two 28-day controlled diets: a low GL diet high in whole grains, legumes, fruits, and vegetables (WG) and a high GL diet high in refined grains and added sugars (RG). Following univariate testing for proteins differing by diet, we evaluated pathway-level involvement. Among all 80 participants, 172 proteins were identified as differing between diets. Stratifying participants by high and low FM identified 221 and 266 proteins, respectively, as differing between diets (unadjusted P < 0.05). These candidate proteins were tested for overrepresentation in Reactome pathways, corresponding to 142 (of 291) pathways in the high-FM group and 72 (of 274) pathways in the low-FM group. We observed that the cancer-related pathways, DNA Repair, DNA Replication, and Cell Cycle, were overrepresented in the high-FM participants while pathways involved in post-translational protein modification were overrepresented in participants with either FM. Although high-GL diets are associated with increased risk of some cancers, our study further suggests that biology associated with consumption of GL diets is variable depending on an individual's adiposity and dietary recommendations related to cancer prevention be made with the additional consideration of an individual's FM.

Background: The growing field of metabolite profiling (or metabolomics) focuses on the quantitative detection of multiple small molecules in biological systems that result from pathophysiological stimuli. As is the case for early detection of primary breast cancer, it is anticipated that early diagnosis of breast cancer recurrence will not only improve survival but also help clinicians determine the best therapeutic strategies for patients by avoiding under or over treatment. We report on the development of an early test for recurrent breast cancer using metabolite profiling methods. Methods: We applied a combination of nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (2D GC-MS) to analyze the metabolite profiles of 257 serial serum samples from breast cancer patients consisting 116 samples from breast cancer recurrence and 141 samples from breast cancer patients with no evidence of disease (NED). NMR and GC-MS data were analyzed by combining advanced univariate and multivariate statistical methods and comparison of individual spectral features between patients with and without recurrent breast cancer. Results: From multivariate analysis of hundreds of spectral features, ten metabolite markers (7 from NMR and 3 from GC-MS) were targeted to build logistic regression model, which yielded a prediction model with a high accuracy (AUROC >0.89 using 5 fold cross validation) with a sensitivity of 86% and specificity of 89%. When the model was tested using leave one patient cross validation it yielded a sensitivity of 75% and a specificity of 83% (AUROC >0.88). In addition, strikingly, over 55% of the patients could be correctly predicted to have recurrence as early as 13 months before the recurrence was actually diagnosed clinically, representing a large improvement over the current diagnostic assay CA 27.29. A second statistical approach using independent training and testing sets yielded very similar results. To the best of our knowledge, this is the first study to develop and validate a prediction model for early detection of recurrent breast cancer based on the metabolic profiling. The combination of complementary NMR and MS analytical methodologies is particularly useful for building accurate profiles. Conclusion: Metabolite profiling methods provide a powerful approach for the development of diagnostic tests for monitoring breast cancer patients. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-10-18.

Background: The proto-oncogene MYC encodes a bHLHLZ transcription factor that binds to its obligate DNA-binding partner, MAX, leading to heterodimer occupancy of E-Box motifs within the genome. MYC is normally mitogen-dependent and tightly regulated where it plays a role in transcriptionally coordinating metabolic sufficiency for cell cycle progression, regulating both proliferation and viability. MYC is deregulated in the majority of human cancers. Importantly, MYC exists within an extended family of related bHLHLZ transcription factors including the MYC antagonizing MAX dimerization (MXD) proteins, MYC paralogs, such as MYCN, as well as MAX-like protein X (MLX), and its nutrient-sensing heterodimerization partners MondoA and carbohydrate response element binding protein (ChREBP). We recently defined a role for the extended MAX/MLX network in the regulation of metabolism and survival of cancer cells specifically with deregulated MYC or MYCN, in which MYC-MAX or MYCN-MAX heterodimers coordinately regulate transcription of important metabolic targets with MondoA-MLX to facilitate tumor cell survival. Targeting a number of these downstream effectors is sufficient to kill cancer cells with deregulated MYC. Methods: Expanding on our initial findings we have used molecular and biochemical approaches to demonstrate non-redundant roles for both MondoA-MLX and ChREBP-MLX in the transcriptional regulation of metabolism and survival of human cancer cells with deregulated MYC, including liver and colon cancer cells. We have also characterized the metabolic and biological phenotypes associated with targeted deletion of Mlx in the mouse, combining in vivo and ex vivo analysis with both whole-body deletion and tissue-specific ablation to unmask novel functions of Mlx and its binding partners. Results: Our results demonstrate that MLX and its binding partners regulate pathways associated with mitochondrial function as well as glucose and lipid metabolism in a non-redundant, cell-type-dependent manner. The presence of MLX stabilizes MondoA and ChREBP, and deletion of Mlx in the mouse leads to decreased levels of MondoA and Chrebp proteins in vivo. Whole-body deletion of Mlx in the mouse is well tolerated but leads to male infertility and teratozoospermia. This sterility phenotype is recapitulated by specific deletion of Mlx in the Sertoli cells of the testis, a major metabolic cell type within the tissue and the central regulator of multiple steps associated with germ cell fate, including production and/or transport of critical metabolites required by the germ cells. This indicates both cell-autonomous (germ cell) and non-cell-autonomous (Sertoli cell) regulation of survival and metabolism in spermatogenesis mediated by Mlx. Importantly, human patients with teratozoospermia exhibit diminished expression of MondoA, MLX and their metabolic targets. Moreover, overexpression of MYCN, MondoA, MLX and their metabolic targets occurs in patients with germ cell-derived tumors, such as seminomas. Conclusions: Transcriptional coordination between the mitogen- and nutrient-responsive arms of the MAX/MLX network exhibits context-dependent lethality. MondoA and Chrebp exhibit non-redundant functions in both cancer cells and normal mouse tissue, and inactivation of Mlx phenocopies loss of both factors, suggesting that targeting Mlx can inhibit both MondoA and Chrebp transcriptional activity and subsequent metabolic output. Metabolic regulation by Mlx impacts normal testicular tissue homeostasis and differentiation in the mouse, and its deregulation is associated with human diseases such as loss of function associated with infertility and gain of function associated with germ cell malignancy. While deletion of Myc, Mycn or Max is embryonic lethal in the mouse, deletion of MondoA, Chrebp or Mlx is well tolerated, indicating that targeting this arm of the extended MYC network, or their critical downstream targets, could be of therapeutic value in metabolic syndrome, fertility and cancer. Citation Format: Patrick A. Carroll, Daniel Diolaiti, Pei-Feng Cheng, Haiwei Gu, Danijel Djukovic, Daniel Raftery, Donald E. Ayer, Charles H. Muller, Robert N. Eisenman. Transcriptional regulation of metabolism by MLX and its binding partners is essential for tumor cell survival and spermatogenesis. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr PR12.

Background: Colorectal cancer (CRC) represents the third most prevalent cancer in the US, despite being one of the most preventable. Given the strong connection between CRC and metabolism, a number of these efforts have been made using metabolomics. Currently, the majority of metabolomics studies use either nuclear magnetic resonance (NMR) or mass spectrometry (MS) separately. However, thus far the potential of combining NMR and MS for biomarker discovery and statistical modeling is still poorly recognized and not well developed (such as the challenges of variable selection in the combined data sets). In this study we examine the potential of combining NMR and MS metabolomics for the detection of patients with either CRC or polyps. Methods: A total of 127 serum samples from three groups of age, gender, and BMI-matched subjects (CRC (N=28), polyp patients (N=44) and healthy controls (N=55)) were analyzed, and potential biomarkers were selected from backward variable elimination incorporated multi-block partial least squares-discriminant analysis (BVE-PLSDA). In each iteration, one variable was dropped out, and the remaining variables were used for PLS-DA. The variables with the highest prediction accuracy for the test samples in Monte Carlo cross validation (MCCV) were kept for the next iteration. Results: For all the three pairwise comparisons among CRC, polyps, and healthy controls, the highest classification accuracy of the NMR+MS data was obviously better than that from NMR or MS alone. For example, in the case of CRC Vs healthy controls, the NMR+MS data provided the highest classification accuracy of 0.95±0.05 after BVE, compared to 0.84±0.07 from NMR and 0.93±0.05 from MS. As expected, an excessive number of variables deteriorated statistical models, and there was an optimal number/range of variables which could produce the best statistical performance. Our approach was able to select an optimal set of metabolites for each pairwise comparison, and we achieved a comprehensive profile of altered metabolites from the combined (NMR+MS) data that were related to CRC and polyps, including those in glycolysis, the TCA cycle, amino acid metabolism, etc. Both NMR and MS detected metabolites contributed to the mixed panel of biomarker candidates, and thus both analytical platforms are very valuable methods to identify metabolic changes occurring in patients with CRC and polyps. The combined set of metabolite biomarkers should be helpful to comprehensively understand metabolite alterations of CRC and the mechanisms during disease progression. The majority of important NMR and MS metabolites were different from each other, evidencing that both NMR and MS can provide unique contributions to statistical modeling in metabolomics. Interestingly, CRC had the lowest adenosine levels while the controls had the highest levels. However, orotate (and some other metabolites) did not continuously increase or decrease from controls to polyps, and then to CRC, which indicates that disease progression could be a very complex process metabolically. Conclusions: The metabolic profiles of blood serum from CRC patients, polyps, and healthy controls were measured by NMR and LC-MS/MS and showed significant changes in metabolism with the onset of CRC. BVE-PLSDA identified optimal sets of metabolites that could be further validated for the diagnosis of CRC and polyps. The combination of NMR and MS showed significantly better statistical performance than NMR or MS alone. Therefore, we recommend the combined approach of NMR and MS through appropriate variable selection methods in metabolomics, especially for the purpose of discovering biomarker candidates. Notably, our approach is relatively universal and can be expanded to combine other analytical technologies. Citation Format: Lingli Deng, Haiwei Gu, Jiangjiang Zhu, Nagana Gowda, Danijel Djukovic, Daniel Raftery. Detecting colorectal cancer and polyps using nuclear magnetic resonance spectroscopy and mass spectrometry based metabolomics. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B50.

Folliculin interacting protein-1 (Fnip1) is a cytoplasmic protein originally discovered through its interaction with Folliculin (Flcn), a tumor suppressor mutated in the rare fibrofolliculoma disorder Birt-Hogg Dubé syndrome. Fnip1 and Flcn both interact with the master metabolic regulator AMP kinase (AMPK). Our previous studies have shown that Fnip1 is required for the development of B-cells past the late pre-B stage (Park et al Immunity 2012) and for the development invariant natural killer T (iNKT) cells in the thymus (Park et al PNAS 2014). Fnip1 deficiency also prevented the development of B cell lymphoma in Eµ-Myc transgenic mice. Fnip1 deficient immune cells exhibited hyperactivation of mTORC1 concurrent with increased activation of AMPK. In this study, we examined the potential efficacy of Fnip1 disruption in cancer by disrupting Fnip1 in primary mouse and human B cell lines overexpressing the Myc oncogene. We show that constitutive depletion of Fnip1 in primary pre-B cells, conditional knockdown of Fnip1 in primary mouse mature B cells using the Cre-loxP system, or siRNA-mediated depletion of endogenous FNIP1 from a human B cell line expressing a conditional Myc allele, increase oxidative phosphorylation and Myc-induced glycolysis, which support cell division. Disruption of Fnip1 increases death of primary murine pre-B Eµ-Myc cells and human B cell lines in response to nutrient deprivation and chemotherapeutic drugs in vitro. Mass spectrometric analysis of a human B cell line depleted of Fnip1 reveals significant alterations in metabolites involved in glutaminolysis, glycolysis, antioxidant and serine biosynthesis pathways. These results suggest that inhibition of Fnip1 may provide a novel strategy to increase death of lymphoma cells in response to cytotoxic and metabolic stress. Citation Format: Julita A. Ramirez, Mark Tsang, Heon Park, Daciana Margineantu, Haiwei Gu, Daniel Raftery, David M. Hockenbery, Brian M. Iritani. Disruption of Folliculin interacting protein-1 modulates Myc-driven metabolism, increasing cell death following metabolic stress. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B31.

Introduction: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, and a major cause of human morbidity and mortality. A number of current efforts are focused on earlier detection of colon cancer using a variety of technologies including genomics, proteomics and metabolomics. Research focused on CRC disease status surveillance using metabolomics or other approaches has not been reported; Close monitoring of disease progression (DP) in CRC can be critical for patients' prognosis management and treatment decisions. In this study we investigate a targeted LC-MS/MS approach for serum metabolic profiling to monitor and predict patient disease progression, using a panel of significantly altered metabolites as potential biomarkers. Methods: 59 serum samples from 21 CRC patients were analyzed, including 23 samples from DP patients and 36 from other CRC disease status (e.g., stable disease and complete remission). Chromatographic separations were performed via an Agilent HPLC system installed with two hydrophilic interaction chromatography (HILIC) columns, and then targeted data acquisition was performed in multiple-reaction-monitoring (MRM) mode using an AB Sciex QTrap 5500 mass spectrometer. We monitored 106 and 58 MRM transitions in negative and positive mode, respectively. Univariate and multivariate statistical analyses (such as the Mann- Whitney U-test and PLS-DA) were applied for metabolite biomarker discovery and model development on a selected set of promising biomarker candidates. Monte Carlo cross validation (MCCV) was performed to evaluate model robustness. Results and conclusion: LC-MS/MS targeted analysis provided a robust system for metabolic profiling of CRC patient disease status monitoring using serum samples. Targeted screening of 164 metabolites, representing more than 20 different classes (such as amino acids, carboxylic acids, pyridines, and etc.) and from 25 important metabolic pathways (e.g., TCA cycle, amino acid metabolism, purine and pyrimidine metabolism, and glycolysis, and etc.) was performed using both positive and negative ionization modes. 131 metabolites could be reproducibly detected in the serum samples, with an average CV of 7.1% measured in pooled serum quality control samples. After univariate analysis, 36 metabolites from different classes, such as monosaccharides, amino acids, carboxylic acids and nucleosides, showed a significant statistical difference (p Citation Format: Jiangjiang Zhu, Danijel Djukovic, Lingli Deng, Lingli Deng, Haiwei Gu, Farhan Himmati, Mohammad Abu Zaid, E. Gabriela Chiorean, E. Gabriela Chiorean, Daniel Raftery, Daniel Raftery. Targeted LC-MS/MS metabolic profiling for colon cancer progression monitoring. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B52.

Incidence of esophageal adenocarcinoma (EA), a rapidly fatal cancer, has increased sharply over recent decades. Although several risk factors have been defined for EA and its precursor, Barrett's esophagus (BE), including reflux, Caucasian race, male gender, obesity, and smoking, a key challenge remains the identification of individuals at highest risk, since most with GERD do not develop BE, and most with BE do not progress to cancer. Cellular energy metabolism is fundamentally altered in a broad spectrum of cancers, and cancer-associated metabolic changes are detectable in blood and urine. To determine whether serum metabolites can discriminate between intermediate disease states preceding development of EA, we used high performance liquid chromatography-mass spectrometry (LC-MS) to profile 106 metabolites in three case groups: GERD (n=50), BE (n=50), and high-grade dysplasia (HGD)/early-stage EA (n=50). Multiple metabolites differed significantly (P Citation Format: Matthew F. Buas, Haiwei Gu, Danijel Djukovic, Jiangjiang Zhu, Brian J. Reid, Daniel Raftery, Thomas L. Vaughan. Identification of novel candidate serum metabolite biomarkers for distinguishing between gastro-esophageal reflux disease, Barrett's esophagus, and high-grade dysplasia/esophageal adenocarcinoma. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr PR10.

Breast cancer is the second most common cancer among women in the US. Genetics, environment, as well as dietary factors are thought to have significant impacts on breast cancer risk. Epidemiological evidence supports a role for vitamin D in protection against breast cancer. 1,25 dihydroxyvitamin D (1,25(OH)2D), the active form of vitamin D, is proposed to regulate cellular processes that are involved in breast cancer progression. Metabolic reprogramming of glucose, termed the Warburg effect, as well as increased glutamine uptake and metabolism for energy use is a characteristic of many cancer cells. The current studies were designed to investigate the effect of 1,25(OH)2D on glutamine metabolism in mammary cells during an early stage of cancer progression. We employed untransformed (MCF10A) and ras oncogene transformed MCF10A human breast epithelial cells (MCF10A-ras). Intracellular glutamine and glutamate levels, determined by nuclear magnetic resonance, were both reduced with 1,25(OH)2D by 23% in MCF10A-ras cells. However, 1,25(OH)2D decreased intracellular glutamine level in MCF10A cells by only 9%, with no effect on intracellular glutamate level in MCF10A cells. Glutamine and glutamate flux into the TCA cycle were determined using [U-13C5] L-glutamine and gas chromatography-mass spectrometry. Treatment with 1,25(OH)2D decreased glutamine flux into the TCA cycle by 18% and 31% in MCF10A and MCF10A-ras cells, respectively, and decreased glutamate flux into the TCA cycle by 13% and 17% in MCF10A and MCF10A-ras cells, respectively. The mRNA and protein expression of the major glutamine transporter, solute linked carrier family 1 member A5 (SLC1A5), was significantly decreased by 1,25(OH)2D in MCF10A-ras cells. Consistent with these results, glutamine uptake was reduced by 1,25(OH)2D, and the SLC1A5 inhibitor L-γ-Glutamyl-p-nitroanilide (GPNA) inhibited the effect of 1,25(OH)2D on glutamine uptake in both MCF10A and MCF10A-ras cells. The mRNA level of the glutamine transporter, SLC1A5, was also decreased by 1,25(OH)2D in MCF10A which overexpress ErbB2 (HER-2/neu) and in malignant MCF10CA1a cells. These results suggest 1,25(OH)2D may alter glutamine metabolism in MCF10A-ras cells by inhibiting glutamine uptake and utilization, in part through down-regulation of SLC1A5 transcript abundance. The 1,25(OH)2D down-regulation of the glutamine transporter, SLC1A5, may play a role in vitamin D prevention of breast cancer. Note: This abstract was not presented at the meeting. Citation Format: Xuanzhu Zhou, Wei Zheng, Fariba Tayyari, G.A.Nagana Gowda, Daniel Raftery, Shawn S. Donkin, Brian Bequette, Dorothy Teegarden. 1,25-Dihydroxyvitamin D regulation of glutamine metabolism in Harvey-ras transformed MCF10A human breast epithelial cells. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1200. doi:10.1158/1538-7445.AM2015-1200

In many tumors, the increased biosynthesis and growth required for tumor progression is achieved by metabolic reprogramming associated with the activation of specific oncogenes. This can manifest itself as a shift to aerobic glycolysis coupled with increased anabolic metabolism or altered catabolism. This is especially relevant to tumors with activated Myc, in which Myc-Max heterodimers are transcriptional mediators of biosynthesis and growth. Work from many labs has implicated Myc in multiple aspects of the shift to increased glucose and glutamine uptake and stimulation of mitochondrial biogenesis and activity. Importantly, Myc-Max does not function alone, but within the context of an extended transcriptional network comprised of the Max-binding, Myc-antagonizing Mxd proteins as well as the Max-like protein, Mlx, and its heterodimerization partners MondoA and ChREBP (also known as MondoB) In order to understand how functional interactions and dependencies within the network influence Myc's role in normal and neoplastic cell behavior, we examined cell growth after siRNA mediated knockdown (KD) of different Max and Mlx network members in a panel of c-Myc and N-myc inducible cell lines. Results showed a synthetic lethal interaction between high level of Myc expression and loss of MondoA, or its heterodimeric binding partner Mlx. This synthetic effect was observed both in tissue culture and in mouse xenografts. By employing transcriptomic and metabolomic analyses we show that the MondoA-Mlx arm of the extended Max-Mlx network is required for deregulated Myc to drive metabolic reprograming and maintain survival and growth of these tumor cell. We further show that a subset of Myc-induced genes involved in metabolism require MondoA for full expression. Interestingly, knockdown of a number of these genes alone, which control a variety of metabolic pathways, can recapitulate the synthetic lethal interaction with deregulated Myc. Taken together, these data suggest that both Myc-Max and MondoA-Mlx complexes coordinately regulate the transcriptional activity of a group of genes critical for Myc's ability to reprogram tumor metabolism. The integration of Myc and MondoA functions may serve to link Myc to nutrient sensing and to augment metabolic flexibility within the evolving tumor. PC and DD contributed equally to this work. This research supported by NIH/NCI grant R37CA57138. Citation Format: Patrick A. Carroll, Daniel Diolaiti, Lisa McFerrin, Michelle Hulrich, Pei Feng Cheng, Haiwei Gu, Danijel Djukovic, Daniel Raftery, Robert N. Eisenman. Synthetic lethal interactions between members of the extended MYC network during metabolic reprograming in MYC-driven cancers. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr B30.

We report on our progress towards developing a test for early breast cancer detection using metabolite profiling methods. Using a combination of nuclear magnetic resonance (NMR) and multivariate statistical methods principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal signal correction-partial least squares-discriminant analysis (O-PLS-DA) to analyzed the metabolite profiles of 142 breast cancer patients and 272 healthy control from four different sites. PCA and PLS-DA analysis was able to distinguish cancer patients from controls. From the analysis of the corresponding loading plots, 17 metabolites that were altered in concentration between cancer and controls were identified and tested individually and collectively using all samples. From a variable selection analysis, nine metabolite markers were shortlisted from analysis of samples from two of the locations using 10-fold cross validation. A PLS-DA model built using these markers with leave one out cross validation provided a sensitivity of 84% and a specificity of 98% (AUROC >0.95). Strikingly, when this model was used to test samples from the other two locations we achieved a sensitivity of 82% and specificity of 93% (AUROC >90%). Analysis of the samples after randomly assigning them into testing and training sets also performed well (AUROC >0.90). While additional work needs to be carried out on a broader range of normals and cancer patients, to the best of our knowledge, this is the first multisite study to identify and prevalidate a prediction model for early detection of breast cancer based on a metabolic profile. In particular, the combination of NMR and advanced multivariate analysis is a powerful approach for the early detection of breast cancer. This talk is also presented as Poster B20. Citation Information: Cancer Prev Res 2010;3(12 Suppl):PR-06.

This abstact is being presented as a short talk in Concurrent Session 9. A full abstract is printed in the Proffered Abstracts section (PR-06) of the Conference Proceedings. Citation Information: Cancer Prev Res 2010;3(12 Suppl):B20.