Image-guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake

Qianlu Yang , Sisi Deng , Heike Preibsch , Tim-Colin Schade , André Koch , Georgy Berezhnoy , Laimdota Zizmare , Anna Fischer , Brigitte Gückel , Annette Staebler , Andreas D. Hartkopf , Bernd J. Pichler , Christian la Fougère , Markus Hahn , Irina Bonzheim , Konstantin Nikolaou , Christoph Trautwein

Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (2) : e1550

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Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (2) : e1550 DOI: 10.1002/ctm2.1550
RESEARCH ARTICLE

Image-guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake

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Abstract

Background: Breast cancer is a metabolically heterogeneous disease, and although the concept of heterogeneous cancer metabolism is known, its precise role in human breast cancer is yet to be fully elucidated.

Methods: We investigated in an explorative approach a cohort of 42 primary mamma carcinoma patients with positron emission tomography/magnetic resonance imaging (PET/MR) prior to surgery, followed by histopathology and molecular diagnosis. From a subset of patients, which showed high metabolic heterogeneity based on tracer uptake and pathology classification, tumour centre and periphery specimen tissue samples were further investigated by a targeted breast cancer gene expression panel and quantitative metabolomics by nuclear magnetic resonance (NMR) spectroscopy. All data were analysed in a combinatory approach.

Results: [18F]FDG (2-deoxy-2-[fluorine-18]fluoro-D-glucose) tracer uptake confirmed dominance of glucose metabolism in the breast tumour centre, with lower levels in the periphery. Additionally, we observed differences in lipid and proliferation related genes between luminal A and B subtypes in the centre and periphery. Tumour periphery showed elevated acetate levels and enrichment in lipid metabolic pathways genes especially in luminal B. Furthermore, serine was increased in the periphery and higher expression of thymidylate synthase (TYMS) indicated one-carbon metabolism increased in tumour periphery. The overall metabolic activity based on [18F]FDG uptake of luminal B subtype was higher than that of luminal A and the difference between the periphery and centre increased with tumour grade.

Conclusion: Our analysis indicates variations in metabolism among different breast cancer subtypes and sampling locations which details the heterogeneity of the breast tumours. Correlation analysis of [18F]FDG tracer uptake, transcriptome and tumour metabolites like acetate and serine facilitate the search for new candidates for metabolic tracers and permit distinguishing luminal A and B. This knowledge may help to differentiate subtypes preclinically or to provide patients guide for neoadjuvant therapy and optimised surgical protocols based on individual tumour metabolism.

Keywords

[ 18F]FDG / 1H-NMR / gene expression / PET/MR / tumour microenvironment / multi-omics

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Qianlu Yang, Sisi Deng, Heike Preibsch, Tim-Colin Schade, André Koch, Georgy Berezhnoy, Laimdota Zizmare, Anna Fischer, Brigitte Gückel, Annette Staebler, Andreas D. Hartkopf, Bernd J. Pichler, Christian la Fougère, Markus Hahn, Irina Bonzheim, Konstantin Nikolaou, Christoph Trautwein. Image-guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake. Clinical and Translational Medicine, 2024, 14(2): e1550 DOI:10.1002/ctm2.1550

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References

[1]

Joseph C, Papadaki A, Althobiti M, Alsaleem M, Aleskandarany MA, Rakha EA. Breast cancer intratumour heterogeneity: current status and clinical implications. Histopathology. 2018;73(5):717-731.

[2]

Sweeney C, Bernard PS, Factor RE, et al. Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics. Cancer Epidemiol Biomarkers Prev. 2014;23(5):714-724.

[3]

Prat A, Pineda E, Adamo B, et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast. 2015;24:S26-S35.

[4]

Parker JS, Mullins M, Cheang MCU, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160.

[5]

Folmes CDL, Dzeja PP, Nelson TJ, Terzic A. Metabolic plasticity in stem cell homeostasis and differentiation. Cell Stem Cell. 2012;11(5):596-606.

[6]

Kim YY, Um J-H, Yoon J-H, et al. p53 regulates mitochondrial dynamics by inhibiting Drp1 translocation into mitochondria during cellular senescence. FASEB J. 2020;34(2):2451-2464.

[7]

Takashima Y, Hayano A, Yamanaka R. Metabolome analysis reveals excessive glycolysis via PI3K/AKT/mTOR and RAS/MAPK signaling in methotrexate-resistant primary CNS lymphoma-derived cellsexcessive glycolysis in MTX-resistant primary CNS lymphoma. Clin Cancer Res. 2020;26(11):2754-2766.

[8]

Jiang X, Guo S, Wang S, et al. EIF4A3-induced circARHGAP29 promotes aerobic glycolysis in docetaxel-resistant prostate cancer through IGF2BP2/c-Myc/LDHA signaling. Cancer Res. 2022;82(5):831-845.

[9]

Locasale JW. Serine, glycine and one-carbon units: cancer metabolism in full circle. Nat Rev Cancer. 2013;13(8):572-583.

[10]

Tambay V, Raymond V-A, Bilodeau M. Myc rules: leading glutamine metabolism toward a distinct cancer cell phenotype. Cancers. 2021;13(17):4484.

[11]

Chen J-Q, Russo J. Dysregulation of glucose transport, glycolysis, TCA cycle and glutaminolysis by oncogenes and tumor suppressors in cancer cells. Biochim Biophys Acta. 2012;1826(2):370-384.

[12]

Blücher C, Stadler SC. Obesity and breast cancer: current insights on the role of fatty acids and lipid metabolism in promoting breast cancer growth and progression. Front Endocrinol (Lausanne). 2017;8:293.

[13]

Subramani R, Poudel S, Smith KD, Estrada A, Lakshmanaswamy R. Metabolomics of breast cancer: a review. Metabolites. 2022;12(7):643.

[14]

Collaborative Group on Hormonal Factors in Breast Cancer. Type and timing of menopausal hormone therapy and breast cancer risk: individual participant meta-analysis of the worldwide epidemiological evidence. Lancet North Am Ed. 2019;394(10204):1159-1168.

[15]

Picon-Ruiz M, Morata-Tarifa C, Valle-Goffin JJ, Friedman ER, Slingerland JM. Obesity and adverse breast cancer risk and outcome: mechanistic insights and strategies for intervention. CA Cancer J Clin. 2017;67(5):378-397.

[16]

Simeone P, Tacconi S, Longo S, et al. Expanding roles of de novo lipogenesis in breast cancer. Int J Environ Res Public Health. 2021;18(7):3575.

[17]

Lieu EL, Nguyen T, Rhyne S, Kim J. Amino acids in cancer. Exp Mol Med. 2020;52(1):15-30.

[18]

Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883-892.

[19]

Grove O, Berglund AE, Schabath MB, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One. 2015;10(3):e0118261.

[20]

Elia I, Haigis MC. Metabolites and the tumour microenvironment: from cellular mechanisms to systemic metabolism. Nat Metab. 2021;3(1):21-32.

[21]

Zhang D, Xu X, Ye Q. Metabolism and immunity in breast cancer. Front Med. 2021;15:178-207.

[22]

Monaco ME. Fatty acid metabolism in breast cancer subtypes. Oncotarget. 2017;8(17):29487.

[23]

Kulkoyluoglu-Cotul E, Arca A, Madak-Erdogan Z. Crosstalk between estrogen signaling and breast cancer metabolism. Trends Endocrinol Metab. 2019;30(1):25-38.

[24]

Onitilo AA, Engel JM, Greenlee RT, Mukesh BN. Breast cancer subtypes based on ER/PR and Her2 expression: comparison of clinicopathologic features and survival. Clin Med Res. 2009;7(1-2):4-13.

[25]

Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 1991;19(5):403-410.

[26]

Perez EA, Cortés J, Gonzalez-Angulo AM, Bartlett JMS. HER2 testing: current status and future directions. Cancer Treat Rev. 2014;40(2):276-284.

[27]

Wallden B, Storhoff J, Nielsen T, et al. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med Genet. 2015;8(1):1-14.

[28]

Delso G, Fürst S, Jakoby B, et al. Performance measurements of the Siemens mMR integrated whole-body PET/MR scanner. J Nucl Med. 2011;52(12):1914-1922.

[29]

Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545-15550.

[30]

Pang Z, Chong J, Zhou G, et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021;49(W1):W388-W396.

[31]

Pathmanathan N, Balleine RL. Ki67 and proliferation in breast cancer. J Clin Pathol. 2013;66(6):512-516.

[32]

Henríquez-Hernández. Gene polymorphisms in TYMS, MTHFR, p53 and MDR1 as risk factors for breast cancer: a case-control study. Oncol Rep. 2009;22(6):1425-1433.

[33]

Lehmann BD, Colaprico A, Silva TC, et al. Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes. Nat Commun. 2021;12(1):6276.

[34]

Ektefaie Y, Yuan W, Dillon DA, et al. Integrative multiomics-histopathology analysis for breast cancer classification. NPJ Breast Cancer. 2021;7(1):147.

[35]

Wu Qi, Huang Q-X, Zeng H-L, et al. Prediction of metabolic disorders using NMR-based metabolomics: the Shanghai Changfeng Study. Phenomics. 2021;1(4):186-198.

[36]

Kim J, Deberardinis RJ. Mechanisms and implications of metabolic heterogeneity in cancer. Cell Metab. 2019;30(3):434-446.

[37]

Xiao Y, Ma D, Yang Y-S, et al. Comprehensive metabolomics expands precision medicine for triple-negative breast cancer. Cell Res. 2022;32(5):477-490.

[38]

Sullivan MR, Danai LV, Lewis CA, et al. Quantification of microenvironmental metabolites in murine cancers reveals determinants of tumor nutrient availability. eLife. 2019;8:e44235.

[39]

Yang Q, Bae G, Nadiradze G, et al. Acidic ascites inhibits ovarian cancer cell proliferation and correlates with the metabolomic, lipidomic and inflammatory phenotype of human patients. J Transl Med. 2022;20(1):1-19.

[40]

Liberti MV, Locasale JW. The Warburg effect: how does it benefit cancer cells?Trends Biochem Sci. 2016;41(3):211-218.

[41]

Li X-F, Ma Y, Sun X, Humm JL, Ling CC, O'donoghue JA. High 18F-FDG uptake in microscopic peritoneal tumors requires physiologic hypoxia. J Nucl Med. 2010;51(4):632-638.

[42]

Pugachev A, Ruan S, Carlin S, et al. Dependence of FDG uptake on tumor microenvironment. Int J Radiat Oncol Biol Phys. 2005;62(2):545-553.

[43]

Qi S-A, Wu Q, Chen Z, et al. High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis. Sci Rep. 2021;11(1):11805.

[44]

Santos CR, Schulze A. Lipid metabolism in cancer. FEBS J. 2012;279(15):2610-2623.

[45]

Glunde K, Jie C, Bhujwalla ZM. Molecular causes of the aberrant choline phospholipid metabolism in breast cancer. Cancer Res. 2004;64(12):4270-4276.

[46]

Menendez JA, Lupu R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat Rev Cancer. 2007;7(10):763-777.

[47]

Shah T, Krishnamachary B, Wildes F, Wijnen JP, Glunde K, Bhujwalla ZM. Molecular causes of elevated phosphoethanolamine in breast and pancreatic cancer cells. NMR Biomed. 2018;31(8):e3936.

[48]

Bakovic M, Fullerton MD, Michel V. Metabolic and molecular aspects of ethanolamine phospholipid biosynthesis: the role of CTP: phosphoethanolamine cytidylyltransferase (Pcyt2). Biochem Cell Biol. 2007;85(3):283-300.

[49]

Zhu L, Johnson C, Bakovic M. Stimulation of the human CTP: phosphoethanolamine cytidylyltransferase gene by early growth response protein 1. J Lipid Res. 2008;49(10):2197-2211.

[50]

Sowers ML, Herring J, Zhang W, et al. Analysis of glucose-derived amino acids involved in one-carbon and cancer metabolism by stable-isotope tracing gas chromatography mass spectrometry. Anal Biochem. 2019;566:1-9.

[51]

Lan X, Field MS, Stover PJ. Cell cycle regulation of folate-mediated one-carbon metabolism. Wiley Interdiscip Rev Syst Biol Med. 2018;10(6):e1426.

[52]

Yang M, Vousden KH. Serine and one-carbon metabolism in cancer. Nat Rev Cancer. 2016;16(10):650-662.

[53]

Robinson AD, Eich M-L, Varambally S. Dysregulation of de novo nucleotide biosynthetic pathway enzymes in cancer and targeting opportunities. Cancer Lett. 2020′;470:134-140.

[54]

Newman AC, Maddocks ODK. Serine and functional metabolites in cancer. Trends Cell Biol. 2017;27(9):645-657.

[55]

Zhou X, He L, Wu C, Zhang Y, Wu X, Yin Y. Serine alleviates oxidative stress via supporting glutathione synthesis and methionine cycle in mice. Mol Nutr Food Res. 2017;61(11):1700262.

[56]

Barbi J, Pardoll D, Pan F. Treg functional stability and its responsiveness to the microenvironment. Immunol Rev. 2014;259(1):115-139.

[57]

Kurniawan H, Franchina DG, Guerra L, et al. Glutathione restricts serine metabolism to preserve regulatory T cell function. Cell Metab. 2020;31(5):920-936. e7.

[58]

Shokrgozar N, Amirian N, Ranjbaran R, Bazrafshan A, Sharifzadeh S. Evaluation of regulatory T cells frequency and FoxP3/GDF-15 gene expression in β-thalassemia major patients with and without alloantibody; correlation with serum ferritin and folate levels. Ann Hematol. 2020;99:421-429.

[59]

Vriens K, Christen S, Parik S, et al. Evidence for an alternative fatty acid desaturation pathway increasing cancer plasticity. Nature. 2019;566(7744):403-406.

[60]

Li Z, Kang Y. Lipid metabolism fuels cancer's spread. Cell Metab. 2017;25(2):228-230.

[61]

Janßen H, Steinbüchel A. Fatty acid synthesis in Escherichia coli and its applications towards the production of fatty acid based biofuels. Biotechnol Biofuels. 2014;7(1):1-26.

[62]

Bensaad K, Favaro E, Lewis CA, et al. Fatty acid uptake and lipid storage induced by HIF-1α contribute to cell growth and survival after hypoxia-reoxygenation. Cell Rep. 2014;9(1):349-365.

[63]

Havas KM, Milchevskaya V, Radic K, et al. Metabolic shifts in residual breast cancer drive tumor recurrence. J Clin Invest. 2017;127(6):2091-2105.

[64]

Incio J, Ligibel JA, Mcmanus DT, et al. Obesity promotes resistance to anti-VEGF therapy in breast cancer by up-regulating IL-6 and potentially FGF-2. Sci Transl Med. 2018;10(432):eaag0945.

[65]

Pepino MY, Kuda O, Samovski D, Abumrad NA. Structure-function of CD36 and importance of fatty acid signal transduction in fat metabolism. Annu Rev Nutr. 2014;34:281-303.

[66]

Mishra P, Ambs SJM. Metabolic signatures of human breast cancer. Mol Cell Oncol. 2015;2(3):e992217.

[67]

Sørlie T. Molecular portraits of breast cancer: tumour subtypes as distinct disease entities. Eur J Cancer. 2004;40(18):2667-2675.

[68]

Saleem M, Qadir MI, Perveen N, et al. Inhibitors of apoptotic proteins: new targets for anticancer therapy. Chem Biol Drug Des. 2013;82(3):243-251.

[69]

Shukla K, Sharma AK, Ward A, et al. MicroRNA-30c-2-3p negatively regulates NF-κB signaling and cell cycle progression through downregulation of TRADD and CCNE1 in breast cancer. Mol Oncol. 2015;9(6):1106-1119.

[70]

Freeman-Cook K, Hoffman RL, Miller N, et al. Expanding control of the tumor cell cycle with a CDK2/4/6 inhibitor. Cancer Cell. 2021;39(10):1404-1421. e11.

[71]

Fu Z, Jiao Y, Li Y, Ji B, Jia B, Liu B. TYMS presents a novel biomarker for diagnosis and prognosis in patients with pancreatic cancer. Medicine (Baltimore). 2019;98(51):e18487.

[72]

Ntzeros K, Stanier P, Mazis D, et al. MKI67 (marker of proliferation Ki-67). Atlas Genet Cytogenet Oncol Haematol. 2015;19(2):105-116.

[73]

Fu H, Liu N, Dong Q, et al. SENP6-mediated M18BP1 deSUMOylation regulates CENP-A centromeric localization. Cell Res. 2019;29(3):254-257.

[74]

Ai L, Tao Q, Zhong S, et al. Inactivation of Wnt inhibitory factor-1 (WIF1) expression by epigenetic silencing is a common event in breast cancer. Carcinogenesis. 2006;27(7):1341-1348.

[75]

Apostolou P, Papasotiriou I. Current perspectives on CHEK2 mutations in breast cancer. Breast Cancer (Dove Med Press). 2017;9:331-335.

[76]

Hu Z, Cano I, Saez-Torres KL, et al. Elements of the endomucin extracellular domain essential for VEGF-induced VEGFR2 activity. Cells. 2020;9(6):1413.

[77]

Pernas S, Tolaney SM, Winer EP, Goel S. CDK4/6 inhibition in breast cancer: current practice and future directions. Ther Adv Med Oncol. 2018;10:1758835918786451.

[78]

Bazer FW, Seo H, Johnson GA, Wu G. One-carbon metabolism and development of the conceptus during pregnancy: lessons from studies with sheep and pigs. Adv Exp Med Biol. 2021;1285:1-15.

[79]

Mehla K, Singh PK. MUC1: a novel metabolic master regulator. Biochim Biophys Acta. 2014;1845(2):126-135.

[80]

Wu S, Le H. Dual roles of PKM2 in cancer metabolism. Acta Biochim Biophys Sin. 2013;45(1):27-35.

[81]

Kosugi M, Ahmad R, Alam M, Uchida Y, Kufe D. MUC1-C oncoprotein regulates glycolysis and pyruvate kinase M2 activity in cancer cells. PLoS One. 2011;6(11):e28234.

[82]

Mayanil CS, Siddiqui MR, Tomita T. Novel functions of folate receptor alpha in CNS development and diseases. Neurosci Discov. 2014;2(5).

[83]

Tan-Shalaby JL, Carrick J, Edinger K, et al. Modified Atkins diet in advanced malignancies-final results of a safety and feasibility trial within the Veterans Affairs Pittsburgh Healthcare System. Nutr Metab. 2016;13(1):1-12.

[84]

Schwalb M, Taubmann M, Hines S, Reinwald H, Ruggiero M. Clinical observation of a novel, complementary, immunotherapeutic approach based on ketogenic diet, chondroitin sulfate, vitamin D3, oleic acid and a fermented milk and colostrum product. Am J Immunol. 2016;12(4):91-98.

[85]

Bartmann C, Janaki Raman SR, Flöter J, et al. Beta-hydroxybutyrate (3-OHB) can influence the energetic phenotype of breast cancer cells, but does not impact their proliferation and the response to chemotherapy or radiation. Cancer Metab. 2018;6:1-19.

[86]

Cappelletti V, Iorio E, Miodini P, Silvestri M, Dugo M, Daidone MG. Metabolic footprints and molecular subtypes in breast cancer. Dis Markers. 2017;2017:7687851.

[87]

Cheng C, Geng F, Cheng X, Guo D. Lipid metabolism reprogramming and its potential targets in cancer. Cancer Commun. 2018;38:1-14.

[88]

Zhang H, Xiong Z, He Q, Fan F. ACSS2-related autophagy has a dual impact on memory. Chin Neurosurg J. 2019;5(1):1-7.

[89]

Murad JM, Place CS, Ran C, et al. Inhibitor of DNA binding 4 (ID4) regulation of adipocyte differentiation and adipose tissue formation in mice. J Biol Chem. 2010;285(31):24164-24173.

[90]

Junankar S, Baker LA, Roden DL, et al. ID4 controls mammary stem cells and marks breast cancers with a stem cell-like phenotype. Nat Commun. 2015;6(1):6548.

[91]

Baker LA, Holliday H, Roden D, et al. Proteogenomic analysis of Inhibitor of Differentiation 4 (ID4) in basal-like breast cancer. Breast Cancer Res. 2020;22(1):1-18.

[92]

Branham MT, Campoy E, Laurito S, et al. Epigenetic regulation of ID4 in the determination of the BRCAness phenotype in breast cancer. Breast Cancer Res Treat. 2016;155:13-23.

[93]

Mori N, Wildes F, Kakkad S, et al. Choline kinase-α protein and phosphatidylcholine but not phosphocholine are required for breast cancer cell survival. NMR Biomed. 2015;28(12):1697-1706.

[94]

Rossi C, Cicalini I, Cufaro MC, et al. Breast cancer in the era of integrating “Omics” approaches. Oncogenesis. 2022;11(1):17.

[95]

Brighi C, Waddington D, Keall P, et al. NIMG-70. magnetic resonance hypoxia imaging for radiation treatment guidance in glioblastoma multiforme—a diagnostic/prognostic clinical imaging study. Neuro-oncol. 2022;24(7):vii180-vii181. Supplement_.

[96]

Yoshimoto M, Waki A, Yonekura Y, et al. Characterization of acetate metabolism in tumor cells in relation to cell proliferation: acetate metabolism in tumor cells. Nucl Med Biol. 2001;28(2):117-122.

[97]

Wachter S, Tomek S, Kurtaran A, et al. 11C-acetate positron emission tomography imaging and image fusion with computed tomography and magnetic resonance imaging in patients with recurrent prostate cancer. J Clin Oncol. 2006;24(16):2513-2519.

[98]

Ho C-L, Yu SHY, Yeung DWC. 11C-acetate PET imaging in hepatocellular carcinoma and other liver masses. J Nucl Med. 2003;44(2):213-221.

[99]

Trautwein C, Zizmare L, Mäurer I, et al. Tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology, and treatment. JCI insight. 2022;7(3):e153526.

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