Metabolomic profiling of breast tumors using ductal fluid

Matos Do Canto,Luisa; Marian,Catalin; Varghese,Rency S.; Ahn,Jaeil; Da Cunha,Patricia A.; Willey,Shawna; Sidawy,Mary; Rone,Janice D.; Cheema,Amrita K.; Luta,George; Nezami Ranjbar,Mohammad R.; Ressom,Habtom W.; Haddad,Bassem R.

doi:10.3892/ijo.2016.3732

Metabolomic profiling of breast tumors using ductal fluid

Authors:
- Luisa Matos Do Canto
- Catalin Marian
- Rency S. Varghese
- Jaeil Ahn
- Patricia A. Da Cunha
- Shawna Willey
- Mary Sidawy
- Janice D. Rone
- Amrita K. Cheema
- George Luta
- Mohammad R. Nezami Ranjbar
- Habtom W. Ressom
- Bassem R. Haddad
View Affiliations

Affiliations: Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington DC, USA, Biochemistry Department, ‘Victor Babes’ University of Medicine and Pharmacy, Timisoara, Romania
Published online on: October 13, 2016 https://doi.org/10.3892/ijo.2016.3732
Pages: 2245-2254
Copyright: © Matos Do Canto et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Identification of new biomarkers for breast cancer remains critical in order to enhance early detection of the disease and improve its prognosis. Towards this end, we performed an untargeted metabolomic analysis of breast ductal fluid using an ultra-performance liquid chromatography coupled with a quadrupole time-of-light (UPLC-QTOF) mass spectrometer. We investigated the metabolomic profiles of breast tumors using ductal fluid samples collected by ductal lavage (DL). We studied fluid from both the affected breasts and the unaffected contralateral breasts (as controls) from 43 women with confirmed unilateral breast cancer. Using this approach, we identified 1560 ions in the positive mode and 538 ions in the negative mode after preprocessing of the UPLC‑QTOF data. Paired t-tests applied on these data matrices identified 209 ions (positive and negative modes combined) with significant change in intensity level between affected and unaffected control breasts (adjusted p-values <0.05). Among these, 83 ions (39.7%) showed a fold change (FC) >1.2 and 66 ions (31.6%) were identified with putative compound names. The metabolites that we identified included endogenous metabolites such as amino acid derivatives (N-Acetyl-DL-tryptophan) or products of lipid metabolism such as N-linoleoyl taurine, trans-2-dodecenoylcarnitine, lysophosphatidylcholine LysoPC(18:2(9Z,12Z)), glycerophospholipids PG(18:0/0:0), and phosphatidylserine PS(20:4(5Z,8Z,11Z,14Z). Generalized LASSO regression further selected 21 metabolites when race, menopausal status, smoking, grade and TNM stage were adjusted for. A predictive conditional logistic regression model, using the LASSO selected 21 ions, provided diagnostic accuracy with the area under the curve of 0.956 (sensitivity/specificity of 0.907/0.884). This is the first study that shows the feasibility of conducting a comprehensive metabolomic profiling of breast tumors using breast ductal fluid to detect changes in the cellular microenvironment of the tumors and shows the potential for this approach to be used to improve detection of breast cancer.

View Figures

View References

1	American Cancer Society. Global Cancer Facts and Figures. 3rd edition. American Cancer Society; Atlanta, GA: 2015
2	Warburg O: On the origin of cancer cells. Science. 123:309–314. 1956. View Article : Google Scholar : PubMed/NCBI
3	Denkert C, Bucher E, Hilvo M, Salek R, Orešič M, Griffin J, Brockmöller S, Klauschen F, Loibl S, Barupal DK, et al: Metabolomics of human breast cancer: New approaches for tumor typing and biomarker discovery. Genome Med. 4:372012.PubMed/NCBI
4	Sengupta D and Pratx G: Imaging metabolic heterogeneity in cancer. Mol Cancer. 15:42016. View Article : Google Scholar : PubMed/NCBI
5	Long JP, Li XN and Zhang F: Targeting metabolism in breast cancer: How far we can go? World J Clin Oncol. 7:122–130. 2016. View Article : Google Scholar : PubMed/NCBI
6	Mannello F and Ligi D: Resolving breast cancer heterogeneity by searching reliable protein cancer biomarkers in the breast fluid secretome. BMC Cancer. 13:3442013. View Article : Google Scholar : PubMed/NCBI
7	Shajahan-Haq AN, Cheema MS and Clarke R: Application of metabolomics in drug resistant breast cancer research. Metabolites. 5:100–118. 2015. View Article : Google Scholar : PubMed/NCBI
8	Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I and Di Leo A: Metabolomics: Available results, current research projects in breast cancer, and future applications. J Clin Oncol. 25:2840–2846. 2007. View Article : Google Scholar : PubMed/NCBI
9	Wang J, Zuo Y, Man YG, Avital I, Stojadinovic A, Liu M, Yang X, Varghese RS, Tadesse MG and Ressom HW: Pathway and network approaches for identification of cancer signature markers from omics data. J Cancer. 6:54–65. 2015. View Article : Google Scholar : PubMed/NCBI
10	Sitter B, Lundgren S, Bathen TF, Halgunset J, Fjosne HE and Gribbestad IS: Comparison of HR MAS MR spectroscopic profiles of breast cancer tissue with clinical parameters. NMR Biomed. 19:30–40. 2006. View Article : Google Scholar
11	Mountford CE, Somorjai RL, Malycha P, Gluch L, Lean C, Russell P, Barraclough B, Gillett D, Himmelreich U, Dolenko B, et al: Diagnosis and prognosis of breast cancer by magnetic resonance spectroscopy of fine-needle aspirates analysed using a statistical classification strategy. Br J Surg. 88:1234–1240. 2001. View Article : Google Scholar : PubMed/NCBI
12	Budczies J, Denkert C, Müller BM, Brockmöller SF, Klauschen F, Györffy B, Dietel M, Richter-Ehrenstein C, Marten U, Salek RM, et al: Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue - a GC-TOFMS based metabolomics study. BMC Genomics. 13:3342012. View Article : Google Scholar : PubMed/NCBI
13	Budczies J, Brockmöller SF, Müller BM, Barupal DK, Richter-Ehrenstein C, Kleine-Tebbe A, Griffin JL, Orešič M, Dietel M, Denkert C, et al: Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast cancer: Alterations in glutamine and beta-alanine metabolism. J Proteomics. 94:279–288. 2013. View Article : Google Scholar : PubMed/NCBI
14	Jobard E, Pontoizeau C, Blaise BJ, Bachelot T, Elena-Herrmann B and Trédan O: A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer. Cancer Lett. 343:33–41. 2014. View Article : Google Scholar
15	Asiago VM, Alvarado LZ, Shanaiah N, Gowda GA, Owusu-Sarfo K, Ballas RA and Raftery D: Early detection of recurrent breast cancer using metabolite profiling. Cancer Res. 70:8309–8318. 2010. View Article : Google Scholar : PubMed/NCBI
16	Budhu A, Terunuma A, Zhang G, Hussain SP, Ambs S and Wang XW: Metabolic profiles are principally different between cancers of the liver, pancreas and breast. Int J Biol Sci. 10:966–972. 2014. View Article : Google Scholar : PubMed/NCBI
17	Armitage EG and Barbas C: Metabolomics in cancer biomarker discovery: Current trends and future perspectives. J Pharm Biomed Anal. 87:1–11. 2014. View Article : Google Scholar
18	Tredwell GD, Miller JA, Chow HH, Thompson PA and Keun HC: Metabolomic characterization of nipple aspirate fluid by ¹H NMR spectroscopy and GC-MS. J Proteome Res. 13:883–889. 2014. View Article : Google Scholar
19	Do Canto LM, Marian C, Willey S, Sidawy M, Da Cunha PA, Rone JD, Li X, Gusev Y and Haddad BR: MicroRNA analysis of breast ductal fluid in breast cancer patients. Int J Oncol. 48:2071–2078. 2016.PubMed/NCBI
20	Isaacs C, Cavalli LR, Cohen Y, Pennanen M, Shankar LK, Freedman M, Singh B, Liu M, Gallagher A, Rone JD, et al: Detection of LOH and mitochondrial DNA alterations in ductal lavage and nipple aspirate fluids from high-risk patients. Breast Cancer Res Treat. 84:99–105. 2004. View Article : Google Scholar : PubMed/NCBI
21	Masood S: Development of a novel approach for breast cancer prediction and early detection using minimally invasive procedures and molecular analysis: How cytomorphology became a breast cancer risk predictor. Breast J. 21:82–96. 2015. View Article : Google Scholar : PubMed/NCBI
22	Sheikh KD, Khanna S, Byers SW, Fornace A Jr and Cheema AK: Small molecule metabolite extraction strategy for improving LC/MS detection of cancer cell metabolome. J Biomol Tech. 22:1–4. 2011.PubMed/NCBI
23	Kaur P, Rizk N, Ibrahim S, Luo Y, Younes N, Perry B, Dennis K, Zirie M, Luta G and Cheema AK: Quantitative metabolomic and lipidomic profiling reveals aberrant amino acid metabolism in type 2 diabetes. Mol Biosyst. 9:307–317. 2013. View Article : Google Scholar
24	Patterson AD, Li H, Eichler GS, Krausz KW, Weinstein JN, Fornace AJ Jr, Gonzalez FJ and Idle JR: UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation. Anal Chem. 80:665–674. 2008. View Article : Google Scholar : PubMed/NCBI
25	Kuhl C, Tautenhahn R, Böttcher C, Larson TR and Neumann S: CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 84:283–289. 2012. View Article : Google Scholar
26	Storey J: False discovery rates. International Encyclopedia of Statistical Science. 1st edition. Lovric M: Springer; pp. 16732011
27	Westerhuis JA, van Velzen EJ, Hoefsloot HC and Smilde AK: Multivariate paired data analysis: Multilevel PLSDA versus OPLSDA. Metabolomics. 6:119–128. 2010. View Article : Google Scholar : PubMed/NCBI
28	Friedman J, Hastie T and Tibshirani R: Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 33:1–22. 2010. View Article : Google Scholar : PubMed/NCBI
29	Reid S and Tibshirani R: Regularization paths for conditional logistic regression: The clogitL1 package. J Stat Softw. 58:122014. View Article : Google Scholar
30	Zhou B, Wang J and Ressom HW: MetaboSearch: Tool for mass-based metabolite identification using multiple databases. PLoS One. 7:e400962012. View Article : Google Scholar : PubMed/NCBI
31	Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N, Dong E, Bouatra S, et al: HMDB: A knowledgebase for the human metabolome. Nucleic Acids Res. 37(Database): D603–D610. 2009. View Article : Google Scholar :
32	Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R and Siuzdak G: METLIN: A metabolite mass spectral database. Ther Drug Monit. 27:747–751. 2005. View Article : Google Scholar
33	Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF, Westler WM, Eghbalnia HR, Sussman MR and Markley JL: Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol. 26:162–164. 2008. View Article : Google Scholar : PubMed/NCBI
34	Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, et al: LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35(Database): D527–D532. 2007. View Article : Google Scholar
35	Fan Y, Zhou X, Xia TS, Chen Z, Li J, Liu Q, Alolga RN, Chen Y, Lai MD, Li P, et al: Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer. Oncotarget. 7:9925–9938. 2016.PubMed/NCBI
36	Mishra P and Ambs S: Metabolic signatures of human breast cancer. Mol Cell Oncol. 2:22015.
37	Qiu Y, Zhou B, Su M, Baxter S, Zheng X, Zhao X, Yen Y and Jia W: Mass spectrometry-based quantitative metabolomics revealed a distinct lipid profile in breast cancer patients. Int J Mol Sci. 14:8047–8061. 2013. View Article : Google Scholar : PubMed/NCBI
38	Shen J, Yan L, Liu S, Ambrosone CB and Zhao H: Plasma metabolomic profiles in breast cancer patients and healthy controls: By race and tumor receptor subtypes. Transl Oncol. 6:757–765. 2013. View Article : Google Scholar
39	Willmann L, Schlimpert M, Halbach S, Erbes T, Stickeler E and Kammerer B: Metabolic profiling of breast cancer: Differences in central metabolism between subtypes of breast cancer cell lines. J Chromatogr B Analyt Technol Biomed Life Sci. 1000:95–104. 2015. View Article : Google Scholar : PubMed/NCBI
40	Yamashita J, Ogawa M and Sakai K: Prognostic significance of three novel biologic factors in a clinical trial of adjuvant therapy for node-negative breast cancer. Surgery. 117:601–608. 1995. View Article : Google Scholar : PubMed/NCBI
41	Yamashita S, Yamashita J, Sakamoto K, Inada K, Nakashima Y, Murata K, Saishoji T, Nomura K and Ogawa M: Increased expression of membrane-associated phospholipase A2 shows malignant potential of human breast cancer cells. Cancer. 71:3058–3064. 1993. View Article : Google Scholar : PubMed/NCBI
42	Takayama T, Tsutsui H, Shimizu I, Toyama T, Yoshimoto N, Endo Y, Inoue K, Todoroki K, Min JZ, Mizuno H, et al: Diagnostic approach to breast cancer patients based on target metabolomics in saliva by liquid chromatography with tandem mass spectrometry. Clin Chim Acta. 452:18–26. 2016. View Article : Google Scholar
43	Hadi NI and Jamal Q: ‘OMIC’ tumor markers for breast cancer: A review. Pak J Med Sci. 31:1256–1262. 2015. View Article : Google Scholar : PubMed/NCBI