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Luchinat, C. Precision Oncology via NMR-Based Metabolomics. Encyclopedia. Available online: https://encyclopedia.pub/entry/11022 (accessed on 19 April 2024).
Luchinat C. Precision Oncology via NMR-Based Metabolomics. Encyclopedia. Available at: https://encyclopedia.pub/entry/11022. Accessed April 19, 2024.
Luchinat, Claudio. "Precision Oncology via NMR-Based Metabolomics" Encyclopedia, https://encyclopedia.pub/entry/11022 (accessed April 19, 2024).
Luchinat, C. (2021, June 18). Precision Oncology via NMR-Based Metabolomics. In Encyclopedia. https://encyclopedia.pub/entry/11022
Luchinat, Claudio. "Precision Oncology via NMR-Based Metabolomics." Encyclopedia. Web. 18 June, 2021.
Precision Oncology via NMR-Based Metabolomics
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This entry is adapted from the peer-reviewed paper 10.3390/ijms22094687

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status.

metabolomics NMR breast cancer precision medicine

1. Breast Cancer: Why Precision Oncology?

Precision medicine, also called personalized medicine, is an emerging approach for disease treatment and prevention that takes into account genetics, epigenetics, metabolism, environment, and lifestyle of each individual person with the goal to select the optimal therapy for the right patient. In oncology, tumor molecular profiling leads to the identification of patient specific alterations that could inform about the optimal treatments and maximize patient’s survival.
For several years breast cancer (BC) has been seen as a single clinical entity and treated with one general approach. However, now it has become extremely clear that BC has to be considered a highly heterogeneous disease with different subclasses. The discovery of endocrine receptors, and the understanding that endocrine therapy significantly improves outcomes in patients with hormone receptor-positive disease, marks the beginning of the target therapy for patients with BC [1][2][3]. By the late 1990s, it was discovered that a subgroup of breast tumors (15–20%) overexpresses the HER2 receptor or have HER2 gene amplification. HER2-positive disease had a dismal outcome until the development of targeted agents, which has significantly improved outcomes in both the (neo)adjuvant [4][5][6][7][8] and the metastatic setting [9][10]. The more recent gene-expression assays allow clinicians to assess the risk of recurrence in early breast cancer (EBC) [11][12][13], as well as to predict potential benefit from adjuvant chemotherapy [14][15][16][17]. In many patients found to have a disease with favorable gene-expression profile, chemotherapy could be avoided; however, a significant population of EBC patients may still be overtreated. Precision oncology aims at identifying the optimal treatment for each patient, specifically tailored to each unique cancer profile and to each individual health status in order to maximize survival and quality of life. Omics sciences are instrumental for this aim (Figure 1).
Ijms 22 04687 g001 550
Figure 1. Precision oncology in a nutshell.

2. Metabolomics and NMR

Metabolomics, one of the latest -Omic sciences, entails the comprehensive characterization of the ensemble of endogenous and exogenous metabolites presents in a biological specimen. Metabolites simultaneously represent the downstream output of the genome, the transcriptome, and the proteome, as well as the upstream input from various external factors such as environment, lifestyle, diet, and drug exposure [18]. As a consequence, in the last few years, metabolomic phenotyping has been extensively applied in biomedical research.

Nuclear Magnetic Resonance spectroscopy (NMR) and mass spectrometry are the two most widely used analytical platforms for metabolomics. These two techniques can be considered complementary, since the weaknesses of one platform are compensated by the strengths of the other [19]. In contrast to the approach typically adopted in mass spectrometry, which is focused on target metabolites of interest, NMR metabolomics is usually performed using a high-throughput, untargeted approach, which provides a complete picture of all metabolites present or quantifiable in the sample above the NMR detection limit (concentrations >1μM) [19][20]. To date, NMR metabolomics are increasingly used for successful patient stratification in various diseases, and it provided unique insights into the fundamental causes of several physiological and pathophysiological conditions [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].

3. Translation of NMR-Based Metabolomics in Clinics

BC is the most common type of cancer and the second most common cause of death in women worldwide [33]. Early detection and prompt treatment has been associated with a significantly improved prognosis observed over time in patients with BC.
The serum tumor markers, CEA and CA 15.3, are routinely used in therapy monitoring and follow up of patients with BC; conversely, their sensitivity and specificity for early diagnosis are poor [34]. Mammography is considered the gold standard in BC screening, however it has a sensitivity of 86.9% with relevant variability depending on tissue density and age [35].
Malignant tumors are characterized by increased gluconeogenesis, glycolysis, and fat mobilization, and decreased protein synthesis. The results described in the original article show that these metabolic changes peculiar to malignant neoplastic change can be detected by metabolomics. Metabolomics is able to discriminate between cancer and normal breast tissue from the same patient with accuracy, sensitivity, and specificity around 90% [36]. Moreover, the metabolite analysis of blood and urine samples from BC patients differs significantly from healthy controls [37][38][39][40][41][42][43]. This evidence offers potential for the use of metabolomics, a minimally invasive technique, for early diagnosis of BC in the general population [44].
BC is a heterogeneous disease with high variability in prognosis and response to treatment driven by genetic, epigenetic, and phenotypic differences. The identification of the mechanisms underpinning this heterogeneity support the development of new drugs targeted to specific subgroup of patients, with the final aim to improve patient outcome. Transcriptomics and proteomics have attempted to classify breast tumors according to gene expression (intrinsic molecular subtypes—[45]) and protein expression (RPPA subtypes—[46]). Metabolomics can provide additional information to these -omics, leading to a deeper tumor characterization. ER and HER2 status are well estimated by metabolite analysis [47]. In addition, metabolomics can identify metabolic clusters within breast tumors, not reflecting the intrinsic molecular subtypes, but presenting significant differences in gene expression and protein expression profiles, and unique susceptibility to metabolically targeted drugs [48].
Neoadjuvant chemotherapy is commonly used to treat BC, not only for downsizing tumors, but also for the potential to monitor individual drug response. Moreover, in selected molecular subtypes, the achievement of a pCR after neoadjuvant treatment correlates with excellent long-term outcomes and a lower risk of disease recurrence [49]. Currently HER2 positivity, triple negative subtype, high Ki67, and the presence of tumor infiltrating lymphocytes (TILs) are the biomarkers most frequently used in recommending neoadjuvant chemotherapy. Predicting response to chemotherapy can spare patients with unresponsive disease from unnecessary side effects. Metabolomics was shown to play a role in predicting response to NAC.
Metabolomic profiling of serum samples collected before neoadjuvant chemotherapy was able to predict response in two small cohorts of patients. The first cohort was unselected for molecular subtype [50], while the second included only HER2-positive breast tumors [51]. The potential role of metabolomics in predicting response to treatment was also evaluated on breast tumor tissue. This analysis demonstrated that tumor metabolism changed significantly in response to neoadjuvant treatment. Metabolomic analysis on post-treatment tissue samples was able to discriminate between patients who experienced disease response to treatment and those who had non-responsive cancer. However, metabolomic analysis of pre-treatment tumor biopsies was not predictive probability of response to chemotherapy [52][53][54].
Developing prognostic biomarkers is one of the focuses of metabolomics in BC. Clinicopathological features are used to predict the risk of recurrence or development of metastatic disease. More recently, gene-expression assays such as Oncotype DX and Mammaprint have been introduced in clinical practice to refine risk estimation and prediction from adjuvant chemotherapy. However, these assays are time consuming, expensive, and can overestimate the risk of recurrence [55]. In addition, they are estimated on the primary tumor tissue and cannot identify the presence or absence of occult micro-metastases. Metabolomics can contribute to overcoming these limitations. As already detailed in the above paragraphs, our group developed a metabolomic score that classified patients as high or low risk of recurrent disease on the basis of the degree of metabolomic similarities with MBC fingerprints [56][57]. A high metabolomic score correlates with increased risk of recurrence and worse disease-free survival. Moreover, this metabolomic risk score can be used to sub-stratify the three Oncotype DX risk categories [58].
However, how far are we now from adopting NMR-based metabolomics as a population-wide screening method? The conceptual distance from the present situation to this ambitious goal is still wide, but it can be bridged by working in two directions: first it is necessary to standardize both the pre-analytical and the analytical procedures. Indeed, the biochemical composition of biospecimens is affected by how samples are collected, stored, prepared, and analyzed, and consequently differences in these steps can be particularly detrimental in multi-center studies [59]. Specifications for pre-examination processes for metabolomics in urine, venous blood serum and plasma have been already published by CEN (CEN/TS 16945:2016) [60]; however, these recommendations are still not universally employed. Secondly, to increase the robustness and the reliability of the results already provided, well-planned, large-scale, multicenter, population-based studies in which all heterogeneous BC patient groups are well represented are needed. NMR-based metabolomics is a fast, high-throughput, robust, and reproducible technique, thus moving from the analysis of hundreds to thousands of samples is realistically an approachable target [61][62].

References

  1. Lerner, H.J.; Band, P.R.; Israel, L.; Leung, B.S. Phase II Study of Tamoxifen: Report of 74 Patients with Stage IV Breast Cancer. Cancer Treat. Rep. 1976, 60, 1431–1435.
  2. Wiggans, R.G.; Woolley, P.V.; Smythe, T.; Hoth, D.; Macdonald, J.S.; Green, L.; Schein, P.S. Phase-II Trial of Tamoxifen in Advanced Breat Cancer. Cancer Chemother. Pharm. 1979, 3, 45–48.
  3. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Effects of Chemotherapy and Hormonal Therapy for Early Breast Cancer on Recurrence and 15-Year Survival: An Overview of the Randomised Trials. Lancet 2005, 365, 1687–1717.
  4. Slamon, D.; Eiermann, W.; Robert, N.; Pienkowski, T.; Martin, M.; Press, M.; Mackey, J.; Glaspy, J.; Chan, A.; Pawlicki, M.; et al. Adjuvant Trastuzumab in HER2-Positive Breast Cancer. N. Engl. J. Med. 2011, 365, 1273–1283.
  5. Von Minckwitz, G.; Procter, M.; de Azambuja, E.; Zardavas, D.; Benyunes, M.; Viale, G.; Suter, T.; Arahmani, A.; Rouchet, N.; Clark, E.; et al. Adjuvant Pertuzumab and Trastuzumab in Early HER2-Positive Breast Cancer. N. Engl. J. Med. 2017, 377, 122–131.
  6. Von Minckwitz, G.; Huang, C.-S.; Mano, M.S.; Loibl, S.; Mamounas, E.P.; Untch, M.; Wolmark, N.; Rastogi, P.; Schneeweiss, A.; Redondo, A.; et al. Trastuzumab Emtansine for Residual Invasive HER2-Positive Breast Cancer. N. Engl. J. Med. 2019, 380, 617–628.
  7. Gianni, L.; Pienkowski, T.; Im, Y.-H.; Roman, L.; Tseng, L.-M.; Liu, M.-C.; Lluch, A.; Staroslawska, E.; de la Haba-Rodriguez, J.; Im, S.-A.; et al. Efficacy and Safety of Neoadjuvant Pertuzumab and Trastuzumab in Women with Locally Advanced, Inflammatory, or Early HER2-Positive Breast Cancer (NeoSphere): A Randomised Multicentre, Open-Label, Phase 2 Trial. Lancet Oncol. 2012, 13, 25–32.
  8. Martin, M.; Holmes, F.A.; Ejlertsen, B.; Delaloge, S.; Moy, B.; Iwata, H.; von Minckwitz, G.; Chia, S.K.L.; Mansi, J.; Barrios, C.H.; et al. Neratinib after Trastuzumab-Based Adjuvant Therapy in HER2-Positive Breast Cancer (ExteNET): 5-Year Analysis of a Randomised, Double-Blind, Placebo-Controlled, Phase 3 Trial. Lancet Oncol. 2017, 18, 1688–1700.
  9. Swain, S.M.; Kim, S.-B.; Cortés, J.; Ro, J.; Semiglazov, V.; Campone, M.; Ciruelos, E.; Ferrero, J.-M.; Schneeweiss, A.; Knott, A.; et al. Pertuzumab, Trastuzumab, and Docetaxel for HER2-Positive Metastatic Breast Cancer (CLEOPATRA Study): Overall Survival Results from a Randomised, Double-Blind, Placebo-Controlled, Phase 3 Study. Lancet Oncol. 2013, 14, 461–471.
  10. Verma, S.; Miles, D.; Gianni, L.; Krop, I.E.; Welslau, M.; Baselga, J.; Pegram, M.; Oh, D.-Y.; Diéras, V.; Guardino, E.; et al. Trastuzumab Emtansine for HER2-Positive Advanced Breast Cancer. N. Engl. J. Med. 2012, 367, 1783–1791.
  11. Audeh, W.; Blumencranz, L.; Kling, H.; Trivedi, H.; Srkalovic, G. Prospective Validation of a Genomic Assay in Breast Cancer: The 70-Gene MammaPrint Assay and the MINDACT Trial. Acta Med. Acad. 2019, 48, 18–34.
  12. Sestak, I.; Martín, M.; Dubsky, P.; Kronenwett, R.; Rojo, F.; Cuzick, J.; Filipits, M.; Ruiz, A.; Gradishar, W.; Soliman, H.; et al. Prediction of Chemotherapy Benefit by EndoPredict in Patients with Breast Cancer Who Received Adjuvant Endocrine Therapy plus Chemotherapy or Endocrine Therapy Alone. Breast Cancer Res. Treat. 2019, 176, 377–386.
  13. Wallden, B.; Storhoff, J.; Nielsen, T.; Dowidar, N.; Schaper, C.; Ferree, S.; Liu, S.; Leung, S.; Geiss, G.; Snider, J.; et al. Development and Verification of the PAM50-Based Prosigna Breast Cancer Gene Signature Assay. BMC Med. Genom. 2015, 8, 54.
  14. Paik, S.; Tang, G.; Shak, S.; Kim, C.; Baker, J.; Kim, W.; Cronin, M.; Baehner, F.L.; Watson, D.; Bryant, J.; et al. Gene Expression and Benefit of Chemotherapy in Women with Node-Negative, Estrogen Receptor-Positive Breast Cancer. J. Clin. Oncol. 2006, 24, 3726–3734.
  15. Albain, K.S.; Barlow, W.E.; Shak, S.; Hortobagyi, G.N.; Livingston, R.B.; Yeh, I.-T.; Ravdin, P.; Bugarini, R.; Baehner, F.L.; Davidson, N.E.; et al. Prognostic and Predictive Value of the 21-Gene Recurrence Score Assay in Postmenopausal Women with Node-Positive, Oestrogen-Receptor-Positive Breast Cancer on Chemotherapy: A Retrospective Analysis of a Randomised Trial. Lancet Oncol. 2010, 11, 55–65.
  16. Sparano, J.A.; Gray, R.J.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E.; Dees, E.C.; Goetz, M.P.; Olson, J.A.; et al. Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer. N. Engl. J. Med. 2018, 379, 111–121.
  17. Kalinsky, K.; Barlow, W.E.; Meric-Bernstam, F.; Gralow, J.R.; Albain, K.S.; Hayes, D.; Lin, N.; Perez, E.A.; Goldstein, L.J.; Chia, S.; et al. Abstract GS3-00: First Results from a Phase III Randomized Clinical Trial of Standard Adjuvant Endocrine Therapy (ET) +/- Chemotherapy (CT) in Patients (Pts) with 1-3 Positive Nodes, Hormone Receptor-Positive (HR+) and HER2-Negative (HER2-) Breast Cancer (BC) with Recurrence Score (RS) <25: SWOG S1007 (RxPonder). Cancer Res. 2021, 81.
  18. Vignoli, A.; Tenori, L.; Giusti, B.; Takis, P.G.; Valente, S.; Carrabba, N.; Balzi, D.; Barchielli, A.; Marchionni, N.; Gensini, G.F.; et al. NMR-Based Metabolomics Identifies Patients at High Risk of Death within Two Years after Acute Myocardial Infarction in the AMI-Florence II Cohort. BMC Med. 2019, 17, 3.
  19. Zhang, L.; Zhu, B.; Zeng, Y.; Shen, H.; Zhang, J.; Wang, X. Clinical Lipidomics in Understanding of Lung Cancer: Opportunity and Challenge. Cancer Lett. 2020, 470, 75–83.
  20. Bertini, I.; Cacciatore, S.; Jensen, B.V.; Schou, J.V.; Johansen, J.S.; Kruhøffer, M.; Luchinat, C.; Nielsen, D.L.; Turano, P. Metabolomic NMR Fingerprinting to Identify and Predict Survival of Patients with Metastatic Colorectal Cancer. Cancer Res. 2012, 72, 356–364.
  21. Brindle, J.T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J.K.; Bethell, H.W.L.; Clarke, S.; Schofield, P.M.; McKilligin, E.; Mosedale, D.E.; et al. Rapid and Noninvasive Diagnosis of the Presence and Severity of Coronary Heart Disease Using 1H-NMR-Based Metabonomics. Nat. Med. 2002, 8, 1439–1444.
  22. Wishart, D.S. Emerging Applications of Metabolomics in Drug Discovery and Precision Medicine. Nat. Rev. Drug Discov. 2016, 15, 473–484.
  23. Vignoli, A.; Orlandini, B.; Tenori, L.; Biagini, M.R.; Milani, S.; Renzi, D.; Luchinat, C.; Calabrò, A.S. Metabolic Signature of Primary Biliary Cholangitis and Its Comparison with Celiac Disease. J. Proteome. Res. 2019, 18, 1228–1236.
  24. Albenberg, L.G.; Wu, G.D. Diet and the Intestinal Microbiome: Associations, Functions, and Implications for Health and Disease. Gastroenterology 2014, 146, 1564–1572.
  25. Vignoli, A.; Tenori, L.; Giusti, B.; Valente, S.; Carrabba, N.; Baizi, D.; Barchielli, A.; Marchionni, N.; Gensini, G.F.; Marcucci, R.; et al. Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used to Predict Death. J. Proteome Res. 2020, 19, 949–961.
  26. Shah, S.H.; Kraus, W.E.; Newgard, C.B. Metabolomic Profiling for Identification of Novel Biomarkers and Mechanisms Related to Common Cardiovascular Diseases: Form and Function. Circulation 2012, 126, 1110–1120.
  27. Basoglu, A.; Baspinar, N.; Tenori, L.; Vignoli, A.; Yildiz, R. Plasma Metabolomics in Calves with Acute Bronchopneumonia. Metabolomics 2016, 12, 128.
  28. Rittweger, J.; Albracht, K.; Fluck, M.; Ruoss, S.; Brocca, L.; Longa, E.; Moriggi, M.; Seynnes, O.; Di Giulio, I.; Tenori, L.; et al. Sarcolab Pilot Study into Skeletal Muscle’s Adaptation to Longterm Spaceflight. NPJ Microgravity 2018, 4, 18.
  29. Basoglu, A.; Baspinar, N.; Tenori, L.; Vignoli, A.; Gulersoy, E. Effects of Boron Supplementation on Peripartum Dairy Cows’ Health. Biol. Trace Elem. Res. 2017, 179, 218–225.
  30. Calvani, R.; Brasili, E.; Praticò, G.; Sciubba, F.; Roselli, M.; Finamore, A.; Marini, F.; Marzetti, E.; Miccheli, A. Application of NMR-Based Metabolomics to the Study of Gut Microbiota in Obesity. J. Clin. Gastroenterol. 2014, 48 (Suppl. S1), S5–S7.
  31. Vignoli, A.; Tenori, L.; Luchinat, C.; Saccenti, E. Age and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects. J. Proteome Res. 2018, 17, 97–107.
  32. Vignoli, A.; Rodio, D.M.; Bellizzi, A.; Sobolev, A.P.; Anzivino, E.; Mischitelli, M.; Tenori, L.; Marini, F.; Priori, R.; Scrivo, R.; et al. NMR-Based Metabolomic Approach to Study Urine Samples of Chronic Inflammatory Rheumatic Disease Patients. Anal. Bioanal. Chem. 2017, 409, 1405–1413.
  33. Ferlay, J.; Colombet, M.; Soerjomataram, I.; Mathers, C.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Estimating the Global Cancer Incidence and Mortality in 2018: GLOBOCAN Sources and Methods. Int. J. Cancer 2019, 144, 1941–1953.
  34. Duffy, M.J. Serum Tumor Markers in Breast Cancer: Are They of Clinical Value? Clin. Chem. 2006, 52, 345–351.
  35. Lehman, C.D.; Arao, R.F.; Sprague, B.L.; Lee, J.M.; Buist, D.S.M.; Kerlikowske, K.; Henderson, L.M.; Onega, T.; Tosteson, A.N.A.; Rauscher, G.H.; et al. National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. Radiology 2017, 283, 49–58.
  36. Bathen, T.F.; Geurts, B.; Sitter, B.; Fjøsne, H.E.; Lundgren, S.; Buydens, L.M.; Gribbestad, I.S.; Postma, G.; Giskeødegård, G.F. Feasibility of MR Metabolomics for Immediate Analysis of Resection Margins during Breast Cancer Surgery. PLoS ONE 2013, 8, e61578.
  37. Cala, M.P.; Aldana, J.; Medina, J.; Sánchez, J.; Guio, J.; Wist, J.; Meesters, R.J.W. Multiplatform Plasma Metabolic and Lipid Fingerprinting of Breast Cancer: A Pilot Control-Case Study in Colombian Hispanic Women. PLoS ONE 2018, 13, e0190958.
  38. Lécuyer, L.; Victor Bala, A.; Deschasaux, M.; Bouchemal, N.; Nawfal Triba, M.; Vasson, M.-P.; Rossary, A.; Demidem, A.; Galan, P.; Hercberg, S.; et al. NMR Metabolomic Signatures Reveal Predictive Plasma Metabolites Associated with Long-Term Risk of Developing Breast Cancer. Int. J. Epidemiol. 2018, 484–494.
  39. Suman, S.; Sharma, R.K.; Kumar, V.; Sinha, N.; Shukla, Y. Metabolic Fingerprinting in Breast Cancer Stages through 1H NMR Spectroscopy-Based Metabolomic Analysis of Plasma. J. Pharm. Biomed. Anal. 2018, 160, 38–45.
  40. Singh, A.; Sharma, R.K.; Chagtoo, M.; Agarwal, G.; George, N.; Sinha, N.; Godbole, M.M. 1H NMR Metabolomics Reveals Association of High Expression of Inositol 1, 4, 5 Trisphosphate Receptor and Metabolites in Breast Cancer Patients. PLoS ONE 2017, 12, e169330.
  41. Wojtowicz, W.; Wróbel, A.; Pyziak, K.; Tarkowski, R.; Balcerzak, A.; Bębenek, M.; Młynarz, P. Evaluation of MDA-MB-468 Cell Culture Media Analysis in Predicting Triple-Negative Breast Cancer Patient Sera Metabolic Profiles. Metabolites 2020, 10, 173.
  42. Jové, M.; Collado, R.; Quiles, J.L.; Ramírez-Tortosa, M.-C.; Sol, J.; Ruiz-Sanjuan, M.; Fernandez, M.; de la Torre Cabrera, C.; Ramírez-Tortosa, C.; Granados-Principal, S.; et al. A Plasma Metabolomic Signature Discloses Human Breast Cancer. Oncotarget 2017, 8, 19522–19533.
  43. Wang, Q.; Sun, T.; Cao, Y.; Gao, P.; Dong, J.; Fang, Y.; Fang, Z.; Sun, X.; Zhu, Z. A Dried Blood Spot Mass Spectrometry Metabolomic Approach for Rapid Breast Cancer Detection. Onco Targets 2016, 9, 1389–1398.
  44. Yang, L.; Wang, Y.; Cai, H.; Wang, S.; Shen, Y.; Ke, C. Application of Metabolomics in the Diagnosis of Breast Cancer: A Systematic Review. J. Cancer 2020, 11, 2540–2551.
  45. Perou, C.M.; Sørlie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; Rees, C.A.; Pollack, J.R.; Ross, D.T.; Johnsen, H.; Akslen, L.A.; et al. Molecular Portraits of Human Breast Tumours. Nature 2000, 406, 747–752.
  46. Cancer Genome Atlas Network. Comprehensive Molecular Portraits of Human Breast Tumours. Nature 2012, 490, 61–70.
  47. Cao, M.D.; Lamichhane, S.; Lundgren, S.; Bofin, A.; Fjøsne, H.; Giskeødegård, G.F.; Bathen, T.F. Metabolic Characterization of Triple Negative Breast Cancer. BMC Cancer 2014, 14, 941.
  48. Haukaas, T.H.; Euceda, L.R.; Giskeødegård, G.F.; Lamichhane, S.; Krohn, M.; Jernström, S.; Aure, M.R.; Lingjærde, O.C.; Schlichting, E.; Garred, Ø.; et al. Metabolic Clusters of Breast Cancer in Relation to Gene- and Protein Expression Subtypes. Cancer Metab. 2016, 4, 12.
  49. Cortazar, P.; Zhang, L.; Untch, M.; Mehta, K.; Costantino, J.P.; Wolmark, N.; Bonnefoi, H.; Cameron, D.; Gianni, L.; Valagussa, P.; et al. Pathological Complete Response and Long-Term Clinical Benefit in Breast Cancer: The CTNeoBC Pooled Analysis. Lancet 2014, 384, 164–172.
  50. Wei, S.; Liu, L.; Zhang, J.; Bowers, J.; Gowda, G.A.N.; Seeger, H.; Fehm, T.; Neubauer, H.J.; Vogel, U.; Clare, S.E.; et al. Metabolomics Approach for Predicting Response to Neoadjuvant Chemotherapy for Breast Cancer. Mol. Oncol. 2013, 7, 297–307.
  51. Miolo, G.; Muraro, E.; Caruso, D.; Crivellari, D.; Ash, A.; Scalone, S.; Lombardi, D.; Rizzolio, F.; Giordano, A.; Corona, G. Pharmacometabolomics Study Identifies Circulating Spermidine and Tryptophan as Potential Biomarkers Associated with the Complete Pathological Response to Trastuzumab-Paclitaxel Neoadjuvant Therapy in HER-2 Positive Breast Cancer. Oncotarget 2016, 7, 39809–39822.
  52. Choi, J.S.; Baek, H.-M.; Kim, S.; Kim, M.J.; Youk, J.H.; Moon, H.J.; Kim, E.-K.; Nam, Y.K. Magnetic Resonance Metabolic Profiling of Breast Cancer Tissue Obtained with Core Needle Biopsy for Predicting Pathologic Response to Neoadjuvant Chemotherapy. PLoS ONE 2013, 8.
  53. Euceda, L.R.; Haukaas, T.H.; Giskeødegård, G.F.; Vettukattil, M.R.; Engel, J.; Silwal-Pandit, L.; Lundgren, S.; Borgen, E.; Garred, Ø.; Postma, G.; et al. Evaluation of Metabolomic Changes during Neoadjuvant Chemotherapy Combined with Bevacizumab in Breast Cancer Using MR Spectroscopy. Metabolomics 2017, 13, 1–14.
  54. Cao, M.D.; Sitter, B.; Bathen, T.F.; Bofin, A.; Lønning, P.E.; Lundgren, S.; Gribbestad, I.S. Predicting Long-Term Survival and Treatment Response in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy by MR Metabolic Profiling. NMR Biomed. 2012, 25, 369–378.
  55. Blok, E.J.; Bastiaannet, E.; van den Hout, W.B.; Liefers, G.J.; Smit, V.T.H.B.M.; Kroep, J.R.; van de Velde, C.J.H. Systematic Review of the Clinical and Economic Value of Gene Expression Profiles for Invasive Early Breast Cancer Available in Europe. Cancer Treat. Rev. 2018, 62, 74–90.
  56. Hart, C.D.; Vignoli, A.; Tenori, L.; Uy, G.L.; To, T.V.; Adebamowo, C.; Hossain, S.M.; Biganzoli, L.; Risi, E.; Love, R.R.; et al. Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population. Clin. Cancer Res. 2017, 23, 1422–1431.
  57. Tenori, L.; Oakman, C.; Morris, P.G.; Gralka, E.; Turner, N.; Cappadona, S.; Fornier, M.; Hudis, C.; Norton, L.; Luchinat, C.; et al. Serum Metabolomic Profiles Evaluated after Surgery May Identify Patients with Oestrogen Receptor Negative Early Breast Cancer at Increased Risk of Disease Recurrence. Results from a Retrospective Study. Mol. Oncol. 2015, 9, 128–139.
  58. McCartney, A.; Vignoli, A.; Tenori, L.; Fornier, M.; Rossi, L.; Risi, E.; Luchinat, C.; Biganzoli, L.; Di Leo, A. Metabolomic Analysis of Serum May Refine 21-Gene Expression Assay Risk Recurrence Stratification. NPJ Breast Cancer 2019, 5, 26.
  59. Nannini, G.; Meoni, G.; Amedei, A.; Tenori, L. Metabolomics Profile in Gastrointestinal Cancers: Update and Future Perspectives. World J. Gastroenterol. 2020, 26, 2514–2532.
  60. PD CEN/TS 16945:2016. PD CEN/TS 16945:201. Molecular in Vitro Diagnostic Examinations. Specifications for Pre-Examination Processes for Metabolomics in Urine, Venous Blood Serum and Plasma; ISO: Geneva, Switzerland, 2016.
  61. Vignoli, A.; Ghini, V.; Meoni, G.; Licari, C.; Takis, P.G.; Tenori, L.; Turano, P.; Luchinat, C. High-Throughput Metabolomics by 1D NMR. Angew. Chem. Int. Ed. Engl. 2019, 58, 968–994.
  62. Trivedi, D.K.; Hollywood, K.A.; Goodacre, R. Metabolomics for the Masses: The Future of Metabolomics in a Personalized World. New Horiz. Transl. Med. 2017, 3, 294–305.
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Subjects: Oncology
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Claudio Luchinat
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Revisions: 2 times (View History)
Update Date: 21 Jun 2021
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