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Supplementary Methods from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Supplementary Table 3 from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Supplementary Table 9 from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Supplementary Table 7 from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Supplementary Table 6 from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Supplementary Table 2 from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Supplementary Table 1 from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Supplementary Table 4 from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Supplementary Table 8 from Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Metabolomics is a systems approach to the biology of health and disease and an ‘-omics’ discipline that measures small metabolites representing end products of a variety of metabolic and cellular processes as reflected in available biological specimens (e.g. blood, urine, saliva, feces and tissue). As an increasingly more common approach used for epidemiologic and clinical studies, metabolomics has the potential to improve disease risk assessment, screening, diagnosis, prognosis and predictive response to therapy, as well as provide disease mechanistic insight and help to establish criteria for causation. It is timely to establish mechanisms for leveraging existing resources and data for novel biomarker discovery using metabolomics approaches. To this end, the National Institutes of Health COnsortium of METabolomics Studies (COMETS) was established in 2014 (http://epi.grants.cancer.gov/comets/), and currently includes 34 prospective cohorts and 2 consortia from the United States, Europe, Asia and South America. The COMETS mission is to promote collaborations among prospective cohort studies that follow participants for a range of outcomes and perform metabolomic profiling of individuals. COMETS aims to facilitate an open exchange of ideas, knowledge, and results to accelerate a shared goal of identifying metabolomic profiles associated with chronic disease phenotypes (e.g. heart disease, diabetes, cancer). The structure of COMETS includes a steering committee with one representative from each participating cohort/consortium and a number of working groups, including age analysis, data harmonization, statistics, and trainee. In November 2016, COMETS held their inaugural scientific meeting. As a result of this meeting, more working groups are currently being established to support additional interests/projects among the consortium members. Here we further describe the COMETS structure, including preliminary descriptive data, which aims to advance the use and impact of metabolite profiling in population-based research. Citation Format: Rachael Z. Stolzenberg-Solomon, Steven Moore, Crnelia Ulrich, Elizabeth Poole, Marinella Temprosa, Mukesh Verma, Demetrius Albanes, Clara Barrios Barrera, Eric Boerwinkle, Juan P. Casas, Clary Clish, Robert Gerszten, Christian Gieger, Marc Gunter, Sei Harada, Tamara Harris, David Herrington, Ann Hsing, Mattias Johannson, Claudia Langenberg, Jessica Lasky-Su, Loic Le Marchand, Charles Matthews, Cristina Menni, Matej Oresic, Eric Orwoll, Alexandre Pereira, Kathyrn Rexrode, Svati Shah, Xiao-ou Shu, Victoria Stevens, Bing Yu, Hua Zhao, Krista Zanetti. COnsortium for METabolomics Studies (COMETS): leveraging resources to accelerate scientific discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4238. doi:10.1158/1538-7445.AM2017-4238

Metabolic changes are the final answer of the cell to environmental or genetic changes. While gene expression in breast cancer has extensively been studied, studies on the breast cancer metabolome and its correlation to transcriptional changes are rare. Therefore, a combined metabolomics and transcriptomics study was carried out by the METAcancer consortium. In this project, 275 breast cancer tissues collected as fresh-frozen samples in the METAcancer tumor bank were analyzed using gas chromatography combined with time of flight mass spectrometry (GC-TOF-MS). Out of these, 156 tumor samples were transcriptionally profiled using whole genome DASL. Before, we identified 10 increased and 9 decreased metabolites in the more aggressive estrogen receptor negative (ER-) compared to the estrogen receptor positive (ER+) breast cancer (poster #5573, AACR Annual Meeting 2010). Among these changes, the increase of beta-alanine (fold change = 2.4, p = 1.7E-20) was the strongest and most significant alteration. Comparing breast cancer and normal tissues, beta-alanine turned out to be strongly increased in the cancer tissues (fold change = 2.6, p = 1.1E-34). Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) we identified 135 enzymes that catalyze reactions of the 19 regulated metabolites. These enzymes were subjected to correlation analysis with their substrates and products. As most significant result, a negative relationship between 4-aminobutyrate aminotransferase (ABAT) expression and beta-alanine concentration was detected (Pearson correlation = –0.62, p = 1.1E-17). ABAT was significantly down-regulated in the more aggressive ER- subtype (fold change = –1.9, p = 1.6E-08). The differential expression of ABAT was confirmed in three publicly available breast cancer microarray data sets. The effect of ABAT inhibition was investigated in estrogen receptor positive MCF-7 and triple-negative MDA-MB-231 breast cancer cells. In both cell lines, treatment with vigabatrin, a small molecule that binds irreversibly to ABAT and inhibits the enzymatic activity, increased proliferation. In summary, we detected an accumulation of beta-alanine in breast cancer and an increase of beta-alanine and a down-regulation of ABAT in the more aggressive ER- subtype. The regulation of beta-alanine metabolism that underlies these changes is currently investigated in cell culture models. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4806. doi:1538-7445.AM2012-4806

Therapeutic options for prostate cancer are limited and treatment responses to currently existing therapies are often unsatisfactory. Thus, there is an urgent need for novel agents to target advanced and metastatic prostate cancer cells. We have recently carried out a chemical-biological high-throughput screening of 4,910 known drugs and drug-like molecules in four prostate cancer cell models and two non-tumorigenic prostate epithelial cell lines to identify prostate cancer cell growth selective inhibitors. Only four compounds, antibiotic ionophore monensin, aldehyde dehydrogenase (ALDH) inhibitor disulfiram, histone deacetylase inhibitor trichostatin A and fungicide thiram inhibited selectively cancer cell growth at nanomolar concentrations. The mechanistic studies indicated that monensin inhibited prostate cancer cell growth by inducing oxidative stress and apoptosis. In addition, monensin reduced androgen receptor signaling, showed a synergistic anti-proliferative effect with anti-androgens as well as reduced the levels of MYC and ERG oncogenes and reduced the activity of ALDH in prostate cancer cells. Moreover, antioxidant vitamin C rescued the monensin induced growth inhibition, indicating that oxidative stress plays a key role in the antineoplastic effect of monensin in cultured prostate cancer cells. Our previous Connectivity Map results indicated that monensin has agonistic effects to NF-κB inactivator and oxidative stress inducer niclosamide. Here, we show that monensin indeed reduced the activity of NF-κB pathway. NF-κB maintains cellular antioxidant defence capacity and its inhibition induces oxidative stress as well as reduces tumorigenesis, metastasis and cancer stem cell potential. Cancer stem cells have a controlled redox balance system including high ALDH and CD44 expression which protect cancer stem cells from oxidative stress. Our results confirmed that monensin reduced the cancer stem cell markers in prostate cancer cells. Moreover, monensin induced epithelial cell differentiation shown as well as reduced motility in cultured prostate cancer cells, suggesting that monensin inhibits prostate tumorigenesis by multiple ways. Furthermore, the steroid profiling indicated that monensin increases the levels of oxidative stress inducing steroids and reduces androgen precursors in cultured prostate cancer cells. In conclusion, our results suggest that impairing the redox control, which has a crucial role in cancer cells enabling survival under high intracellular ROS, is a potent way to target prostate cancer cells and potentially also prostate cancer stem cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A62.

There is an urgent need for more efficient and more targeted methods of prostate cancer treatment. We have previously shown PLA2G7 (also known as lipoprotein-associated phospholipase A2, Lp-PLA2) to be a possible biomarker and drug target especially in ERG positive prostate cancers by combining gene expression data from prostate cancer tissues in vivo and functional RNAi studies in vitro. To further study the potential of PLA2G7 in prostate cancer management immunohistochemical staining of 119 clinical prostate cancer samples and 112 adjacent normal prostate samples were performed. In addition, a global lipidomic, eicosanoid and gene expression analysis was utilized to study the effect of PLA2G7 silencing in ERG oncogene positive prostate cancer cells. The results emphasized high expression of PLA2G7 in 66 % of the cancer samples, whereas less than 4 % of the adjacent normal tissues showed positive staining. Furthermore, PLA2G7 expression significantly correlated with high Gleason score. PLA2G7 silencing in vitro induced a reduction in the amount of lysophosphatidyl choline and leukotriene E4, as well as in the expression of multiple genes and signaling pathways involved in cell adhesion and motility. In accordance, knock-down of PLA2G7 in 3D prostate cancer cell culture model lead to decreased invasion to extracellular matrix. Interestingly, PLA2G7 silencing also reduced the expression of ALDH1A1, a marker for malignant prostate stem cells and predictor of poor prostate cancer outcome. In conclusion, these novel findings suggest an oncogenic role for PLA2G7 in aggressive prostate cancers and support the rationale for PLA2G7 targeted therapy in these cancers. A novel PLA2G7 inhibitor is currently under clinical evaluation for cardiovascular diseases, presenting thus an interesting opportunity for drug repositioning to other indications, such as prostate cancer. In addition, lipid-lowering statins are known to lower PLA2G7 mass and activity, therefore PLA2G7 may also represent a target of lipid-lowering therapy in reducing the risk of aggressive prostate cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2597. doi:10.1158/1538-7445.AM2011-2597

Breast cancer is a biologically inhomogeneous disease that has been extensively studied using molecular high-throughput platforms like microarrays. Molecular methods have been shown to be useful for therapy selection, prediction of disease outcome or complications such as metastases. Worldwide, immunhistological determination of estrogene receptor (ER) and HER2 status is part of the everyday routine diagnostics. A bunch of new biomarkers and biomarker signatures is currently under development and expected to further individualize and improve breast cancer treatment. Here, we present results of a GC-TOF mass spectrometry (GC-MS) metabolite study conducted by the METACancer consortium. In this project, 275 breast cancer tissues collected as fresh-frozen samples in the METACancer tumor bank were analyzed using GC-MS. Prior to metabolic profiling, tumors were divided in a training (187 tumors) and a validation cohort (88 tumors) with comparable clinicopathological characteristics. Both cohorts were profiled at the Fiehn lab (UC Davis, CA), the training cohort at the end of 2008, the validation cohort at the beginning of 2009. Analysis of the training cohort led to the identification of 468 metabolites that are abundant in breast cancer tissues. 161 out of these could be mapped to known chemical structures and metabolite names. Metabolite-by-metabolite analysis of the training cohort revealed 70 metabolites with significantly (p < 0.05, Welch's t-test) different concentrations between between ER+ and ER- tumors. Many of these differences (59%) could be affirmed by analysis of the validation cohort. Only are few changes (9 metabolites), possibly false positives, could be detected between HER2+ and HER2- tumors. None of these changes could be reproduced in the validation cohort. Next, we asked if the tumor cells exhibit metabolite patterns that are specific for ER status. To this end, a metabolic index (MI) was constructed as linear combination of 15 metabolites. The MI was constructed and optimized using only tumors of the training cohort. ROC analysis showed an excellent perfomance of the MI for prediction of the ER status in the training cohort (AUC = 0.91, leave-one-out cross-validation). Validation of the MI in the validation cohort affirmed the excellent performance (AUC = 0.97). A similar approach for prediction of the HER2 status failed (AUC not significantly better than 0.50). In this project, we have shown that metabolic profiling of fresh-frozen breast cancer samples with GC-MS is feasible. Interestingly, we detected a strong dependence of the metabolite patterns on ER status, but no dependence on HER2 status. Analysis of the changes in metabolic pathways between ER+ and ER- breast cancers may contribute to a better understanding of estrogen driven tumor growth. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5573.