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Deng X, Niu H, Zhang Q, Wen J, Zhao Y, Naren G, Liu H, Guo X, Zhang F, Wu C. Plasma metabolites and inflammatory proteins profiling predict outcome of Fufang Duzhong Jiangu granules treating Kashin-Beck disease. Biomed Chromatogr 2024; 38:e5945. [PMID: 38973475 DOI: 10.1002/bmc.5945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/07/2024] [Accepted: 03/20/2024] [Indexed: 07/09/2024]
Abstract
To investigate predictive biomarkers that could be used to identify patients' response to treatment, plasma metabolomics and proteomics analyses were performed in Kashin-Beck disease (KBD) patients treated with Fufang Duzhong Jiangu Granules (FDJG). Plasma was collected from 12 KBD patients before treatment and 1 month after FDJG treatment. LC-MS and olink proteomics were employed for obtaining plasma metabolomics profiling and inflammatory protein profiles. Patients were classified into responders and non-responders based on drug efficacy. Enrichment analyses of differential metabolites and proteins of the responders at baseline and after treatment were conducted to study the mechanism of drug action. Differential metabolites and proteins between the two groups were screened as biomarkers to predict the drug efficacy. The receiver operating characteristic curve was used to evaluate the prediction accuracy of biomarkers. The changes in metabolites and inflammatory proteins in responders after treatment reflected the mechanism of FDJG treatment for KBD, which may act on glycerophospholipid metabolism, d-glutamine and d-glutamate metabolism, nitrogen metabolism and NF-kappa B signaling pathway. Three metabolites were identified as potential predictors: N-undecanoylglycine, β-aminopropionitrile and PC [18:3(6Z,9Z,12Z)/20:4(8Z,11Z,14Z,17Z)]. For inflammatory protein, interleukin-8 was identified as a predictive biomarker to detect responders. Combined use of these four biomarkers had high predictive ability (area under the curve = 0.972).
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Affiliation(s)
- Xingxing Deng
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Hui Niu
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Qian Zhang
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jinfeng Wen
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yijun Zhao
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Gaowa Naren
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Huan Liu
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiong Guo
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Clinical Research Center for Endemic Disease of Shaanxi Province, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China
| | - Feng Zhang
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Cuiyan Wu
- Key Laboratory of Environmental and Endemic Diseases of National Health Commission of the People's Republic of China, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Clinical Research Center for Endemic Disease of Shaanxi Province, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China
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Yamada M, Jinno H, Naruse S, Isono Y, Maeda Y, Sato A, Matsumoto A, Ikeda T, Sugimoto M. Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics. Breast Cancer Res Treat 2024; 207:393-404. [PMID: 38740665 DOI: 10.1007/s10549-024-07370-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/25/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach. METHODS Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles. RESULTS Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points. CONCLUSION The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.
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Affiliation(s)
- Miki Yamada
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan
| | - Hiromitsu Jinno
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan.
| | - Saki Naruse
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan
| | - Yuka Isono
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan
| | - Yuka Maeda
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan
| | - Ayana Sato
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan
| | - Akiko Matsumoto
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan
| | - Tatsuhiko Ikeda
- Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan
- Institute of Medical Science, Tokyo Medical University, Shinjuku, Shinjuku-ku, Tokyo, 160-8402, Japan
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Talarico MCR, Derchain S, da Silva LF, Sforça ML, Rocco SA, Cardoso MR, Sarian LO. Metabolomic Profiling of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy for Predicting Disease-Free and Overall Survival. Int J Mol Sci 2024; 25:8639. [PMID: 39201325 DOI: 10.3390/ijms25168639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 08/04/2024] [Accepted: 08/06/2024] [Indexed: 09/02/2024] Open
Abstract
Breast cancer (BC) remains a significant global health concern, with neoadjuvant chemotherapy (NACT) offering preoperative benefits like tumor downstaging and treatment response assessment. However, identifying factors influencing post-NACT treatment response and survival outcomes is challenging. Metabolomic approaches offer promising insights into understanding these outcomes. This study analyzed the serum of 80 BC patients before and after NACT, followed for up to five years, correlating with disease-free survival (DFS) and overall survival (OS). Using untargeted nuclear magnetic resonance (NMR) spectroscopy and a novel statistical model that avoids collinearity issues, we identified metabolic changes associated with survival outcomes. Four metabolites (histidine, lactate, serine, and taurine) were significantly associated with DFS. We developed a metabolite-related survival score (MRSS) from these metabolites, stratifying patients into low- and high-risk relapse groups, independent of classical prognostic factors. High-risk patients had a hazard ratio (HR) for DFS of 3.42 (95% CI 1.51-7.74; p = 0.003) after adjustment for disease stage and age. A similar trend was observed for OS (HR of 3.34, 95% CI 1.64-6.80; p < 0.001). Multivariate Cox proportional hazards analysis confirmed the independent prognostic value of the MRSS. Our findings suggest the potential of metabolomic data, alongside traditional markers, in guiding personalized treatment decisions and risk stratification in BC patients undergoing NACT. This study provides a methodological framework for leveraging metabolomics in survival analyses.
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Affiliation(s)
- Maria Cecília Ramiro Talarico
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, School of Medical Sciences, University of Campinas (UNICAMP-Universidade Estadual de Campinas), Campinas 13083-881, SP, Brazil
| | - Sophie Derchain
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, School of Medical Sciences, University of Campinas (UNICAMP-Universidade Estadual de Campinas), Campinas 13083-881, SP, Brazil
| | | | - Maurício L Sforça
- Brazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-100, SP, Brazil
| | - Silvana A Rocco
- Brazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-100, SP, Brazil
| | - Marcella R Cardoso
- Division of Gynecologic Oncology-MGH Global Disaster Response, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Center for Global Health, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Luís Otávio Sarian
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, School of Medical Sciences, University of Campinas (UNICAMP-Universidade Estadual de Campinas), Campinas 13083-881, SP, Brazil
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Bauer BA, Schmidt CM, Ruddy KJ, Olson JE, Meydan C, Schmidt JC, Smith SY, Couch FJ, Earls JC, Price ND, Dudley JT, Mason CE, Zhang B, Phipps SM, Schmidt MA. A Multiomics, Molecular Atlas of Breast Cancer Survivors. Metabolites 2024; 14:396. [PMID: 39057719 PMCID: PMC11279123 DOI: 10.3390/metabo14070396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Revised: 07/09/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.
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Affiliation(s)
| | - Caleb M. Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis and Human Performance Group, Boulder, CO 80302, USA
| | | | | | - Cem Meydan
- Thorne Research, Inc., Summerville, SC 29483, USA
| | - Julian C. Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis and Human Performance Group, Boulder, CO 80302, USA
| | | | | | | | - Nathan D. Price
- Thorne Research, Inc., Summerville, SC 29483, USA
- Buck Institute for Research on Aging, Novato, CA 94945, USA
| | | | | | - Bodi Zhang
- Thorne Research, Inc., Summerville, SC 29483, USA
| | | | - Michael A. Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis and Human Performance Group, Boulder, CO 80302, USA
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Silva AAR, Cardoso MR, de Oliveira DC, Godoy P, Talarico MCR, Gutiérrez JM, Rodrigues Peres RM, de Carvalho LM, Miyaguti NADS, Sarian LO, Tata A, Derchain SFM, Porcari AM. Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2024; 16:2473. [PMID: 39001535 PMCID: PMC11240312 DOI: 10.3390/cancers16132473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 06/27/2024] [Accepted: 07/04/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data. METHODS Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively. RESULTS The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance. CONCLUSION We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.
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Affiliation(s)
- Alex Ap. Rosini Silva
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Marcella R. Cardoso
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP—Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Danilo Cardoso de Oliveira
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Pedro Godoy
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Maria Cecília R. Talarico
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP—Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil
| | - Junier Marrero Gutiérrez
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Raquel M. Rodrigues Peres
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Lucas M. de Carvalho
- Post Graduate Program in Health Sciences, São Francisco University, Bragança Paulista 12916900, São Paulo, Brazil
| | - Natália Angelo da Silva Miyaguti
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Luis O. Sarian
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP—Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil
| | - Alessandra Tata
- Laboratory of Experimental Chemistry, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Viale Fiume 78, 36100 Vicenza, Italy;
| | - Sophie F. M. Derchain
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP—Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil
| | - Andreia M. Porcari
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
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Alvarez-Frutos L, Barriuso D, Duran M, Infante M, Kroemer G, Palacios-Ramirez R, Senovilla L. Multiomics insights on the onset, progression, and metastatic evolution of breast cancer. Front Oncol 2023; 13:1292046. [PMID: 38169859 PMCID: PMC10758476 DOI: 10.3389/fonc.2023.1292046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/23/2023] [Indexed: 01/05/2024] Open
Abstract
Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.
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Affiliation(s)
- Lucia Alvarez-Frutos
- Laboratory of Cell Stress and Immunosurveillance, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Daniel Barriuso
- Laboratory of Cell Stress and Immunosurveillance, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Mercedes Duran
- Laboratory of Molecular Genetics of Hereditary Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Mar Infante
- Laboratory of Molecular Genetics of Hereditary Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France
- Department of Biology, Institut du Cancer Paris CARPEM, Hôpital Européen Georges Pompidou, Paris, France
| | - Roberto Palacios-Ramirez
- Laboratory of Cell Stress and Immunosurveillance, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Laura Senovilla
- Laboratory of Cell Stress and Immunosurveillance, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France
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Chen Y, Qi Y, Wang K. Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress. Front Oncol 2023; 13:1169010. [PMID: 37854685 PMCID: PMC10579937 DOI: 10.3389/fonc.2023.1169010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 09/14/2023] [Indexed: 10/20/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.
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Affiliation(s)
- Yushi Chen
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Yu Qi
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Kuansong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
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Tan J, Shu M, Liao J, Liang R, Liu S, Kuang M, Peng S, Xiao H, Zhou Q. Identification and validation of a plasma metabolomics-based model for risk stratification of intrahepatic cholangiocarcinoma. J Cancer Res Clin Oncol 2023; 149:12365-12377. [PMID: 37436513 DOI: 10.1007/s00432-023-05119-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/04/2023] [Indexed: 07/13/2023]
Abstract
BACKGROUND Liver resection is the mainstay of curative treatment for intrahepatic cholangiocarcinoma (ICC) while the postoperative prognosis varies greatly, with no recognized biomarker. We aimed to identify the plasma metabolomic biomarkers that could be used for preoperative risk stratification of ICC patients. METHODS 108 eligible ICC patients who underwent radical surgical resection between August 2012 and October 2020 were enrolled. Patients were randomly divided into a discovery cohort (n = 76) and a validation cohort (n = 32) by 7:3. Metabolomics profiling of preoperative plasma was performed and clinical data were collected. The least absolute shrinkage and selection operator (LASSO) regression, Cox regression, and receiver operating characteristic (ROC) analyses were used to screen and validate the survival-related metabolic biomarker panel and construct a LASSO-Cox prediction model. RESULTS 10 survival-related metabolic biomarkers were used for construction of a LASSO-Cox prediction model. In the discovery and validation cohorts, the LASSO-Cox prediction model achieved an AUC of 0.876 (95%CI: 0.777-0.974) and 0.860 (95%CI: 0.711-1.000) in evaluating 1-year OS of ICC patients, respectively. The OS of ICC patients in the high-risk group was significantly worse than that in the low-risk group (discovery cohort, p < 0.0001; validation cohort: p = 0.041). Also, the LASSO-Cox risk score (HR 2.43, 95%CI: 1.81-3.26, p < 0.0001) was a significant independent risk factor associated with OS. CONCLUSIONS The LASSO-Cox prediction model has potential as an important tool in evaluating the OS of ICC patients after surgical resection and can be used as prediction tools to implement the best treatment options that could result in better outcomes.
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Affiliation(s)
- Jiehui Tan
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Man Shu
- Department of Pathology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Junbin Liao
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Ruiming Liang
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Shiyi Liu
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Ming Kuang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Zhongshan School of Medicine, Sun Yat-Sen University, No. 58, Zhongshan Road 2, 510080, Guangzhou, People's Republic of China
| | - Sui Peng
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China
| | - Han Xiao
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Road 2, 510080, Guangzhou, People's Republic of China.
| | - Qian Zhou
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, Guangdong Province, China.
- Zhongshan School of Medicine, Sun Yat-Sen University, No. 58, Zhongshan Road 2, 510080, Guangzhou, People's Republic of China.
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Macioszek S, Dudzik D, Biesemans M, Wozniak A, Schöffski P, Markuszewski MJ. A multiplatform metabolomics approach for comprehensive analysis of GIST xenografts with various KIT mutations. Analyst 2023; 148:3883-3891. [PMID: 37458061 DOI: 10.1039/d3an00599b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Metabolites in biological matrices belong to diverse chemical groups, ranging from non-polar long-chain fatty acids to small polar molecules. The goal of untargeted metabolomic analysis is to measure the highest number of metabolites in the sample. Nevertheless, from an analytical point of view, no single technique can measure such a broad spectrum of analytes. Therefore, we selected a method based on GC-MS and LC-MS with two types of stationary phases for the untargeted profiling of gastrointestinal stromal tumours. The procedure was applied to GIST xenograft samples (n = 71) representing four different mutation models, half of which were treated with imatinib. We aimed to verify the method coverage and advantages of applying each technique. RP-LC-MS measured most metabolites due to a significant fraction of lipid components of the tumour tissue. What is unique and worth noting is that all applied techniques were able to distinguish between different mutation models. However, for detecting imatinib-induced alterations in the GIST metabolome, RP-LC-MS and GC-MS proved to be more relevant than HILIC-LC-MS, resulting in a higher number of significantly changed metabolites in four treated models. Undoubtedly, the inclusion of all mentioned techniques makes the method more comprehensive. Nonetheless, for green chemistry and time and labour saving, we assume that RP-LC-MS and GC-MS analyses are sufficient to cover the global GIST metabolome.
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Affiliation(s)
- Szymon Macioszek
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Hallera 107, 80-416 Gdańsk, Poland.
| | - Danuta Dudzik
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Hallera 107, 80-416 Gdańsk, Poland.
| | - Margot Biesemans
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Hallera 107, 80-416 Gdańsk, Poland.
| | - Agnieszka Wozniak
- Laboratory of Experimental Oncology, Department of Oncology, KU Leuven, and Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium
| | - Patrick Schöffski
- Laboratory of Experimental Oncology, Department of Oncology, KU Leuven, and Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium
| | - Michal J Markuszewski
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Hallera 107, 80-416 Gdańsk, Poland.
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10
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Penet MF, Sharma RK, Bharti S, Mori N, Artemov D, Bhujwalla ZM. Cancer insights from magnetic resonance spectroscopy of cells and excised tumors. NMR IN BIOMEDICINE 2023; 36:e4724. [PMID: 35262263 PMCID: PMC9458776 DOI: 10.1002/nbm.4724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/03/2022] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
Multinuclear ex vivo magnetic resonance spectroscopy (MRS) of cancer cells, xenografts, human cancer tissue, and biofluids is a rapidly expanding field that is providing unique insights into cancer. Starting from the 1970s, the field has continued to evolve as a stand-alone technology or as a complement to in vivo MRS to characterize the metabolome of cancer cells, cancer-associated stromal cells, immune cells, tumors, biofluids and, more recently, changes in the metabolome of organs induced by cancers. Here, we review some of the insights into cancer obtained with ex vivo MRS and provide a perspective of future directions. Ex vivo MRS of cells and tumors provides opportunities to understand the role of metabolism in cancer immune surveillance and immunotherapy. With advances in computational capabilities, the integration of artificial intelligence to identify differences in multinuclear spectral patterns, especially in easily accessible biofluids, is providing exciting advances in detection and monitoring response to treatment. Metabolotheranostics to target cancers and to normalize metabolic changes in organs induced by cancers to prevent cancer-induced morbidity are other areas of future development.
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Affiliation(s)
- Marie-France Penet
- Division of Cancer Imaging Research, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland, USA
- Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Raj Kumar Sharma
- Division of Cancer Imaging Research, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland, USA
| | - Santosh Bharti
- Division of Cancer Imaging Research, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland, USA
| | - Noriko Mori
- Division of Cancer Imaging Research, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland, USA
| | - Dmitri Artemov
- Division of Cancer Imaging Research, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland, USA
- Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Zaver M. Bhujwalla
- Division of Cancer Imaging Research, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland, USA
- Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
- Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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11
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Primary Treatment Effects for High-Grade Serous Ovarian Carcinoma Evaluated by Changes in Serum Metabolites and Lipoproteins. Metabolites 2023; 13:metabo13030417. [PMID: 36984856 PMCID: PMC10053757 DOI: 10.3390/metabo13030417] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
High-grade serous ovarian carcinoma (HGSOC) is the most common and deadliest ovarian cancer subtype. Despite advances in treatment, the overall prognosis remains poor. Regardless of efforts to develop biomarkers to predict surgical outcome and recurrence risk and resistance, reproducible indicators are scarce. Exploring the complex tumor heterogeneity, serum profiling of metabolites and lipoprotein subfractions that reflect both systemic and local biological processes were utilized. Furthermore, the overall impact on the patient from the tumor and the treatment was investigated. The aim was to characterize the systemic metabolic effects of primary treatment in patients with advanced HGSOC. In total 28 metabolites and 112 lipoproteins were analyzed by nuclear magnetic resonance (NMR) spectroscopy in longitudinal serum samples (n = 112) from patients with advanced HGSOC (n = 24) from the IMPACT trial with linear mixed effect models and repeated measures ANOVA simultaneous component analysis. The serum profiling revealed treatment-induced changes in both lipoprotein subfractions and circulating metabolites. The development of a more atherogenic lipid profile throughout the treatment, which was more evident in patients with short time to recurrence, indicates an enhanced systemic inflammation and increased risk of cardiovascular disease after treatment. The findings suggest that treatment-induced changes in the metabolome reflect mechanisms behind the diversity in disease-related outcomes.
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12
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Understanding the Functional Role of the Microbiome and Metabolome in Asthma. Curr Allergy Asthma Rep 2023; 23:67-76. [PMID: 36525159 DOI: 10.1007/s11882-022-01056-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE OF REVIEW Asthma is a heterogenous respiratory disease characterized by airway inflammation and obstruction. However, the causes of asthma are unknown. Several studies have reported microbial and metabolomic dysbiosis in asthmatic patients; but, little is known about the functional role of the microbiota or the host-microbe metabolome in asthma pathophysiology. Current multi-omic studies are linking both the metabolome and microbiome in different organ systems to help identify the interactions involved in asthma, with the goal of better identifying endotypes/phenotypes, causal links, and potential targets of treatment. This review thus endeavors to explore the benefits of and current advances in studying microbiome-metabolome interactions in asthma. RECENT FINDINGS This is a narrative review of the current state of research surrounding the interaction between the microbiome and metabolome and their role in asthma. Associations with asthma onset, severity, and phenotype have been identified in both the microbiome and the metabolome, most frequently in the gut. More recently, studies have begun to investigate the role of the respiratory microbiome in airway disease and its association with the systemic metabolome, which has provided further insights into its role in asthma phenotypes. This review also identifies gaps in the field in understanding the direct link between respiratory microbiome and metabolome, hypothesizes the benefits for conducting such studies in the future for asthma treatment and prevention, and identifies current analytical limitations that need to be addressed to advance the field. This is a comprehensive review of the current state of research on the interaction between the microbiome and metabolome and their role in asthma.
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13
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Vignoli A, Meoni G, Ghini V, Di Cesare F, Tenori L, Luchinat C, Turano P. NMR-Based Metabolomics to Evaluate Individual Response to Treatments. Handb Exp Pharmacol 2023; 277:209-245. [PMID: 36318327 DOI: 10.1007/164_2022_618] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using 1H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the 1H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.
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Affiliation(s)
- Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Gaia Meoni
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Veronica Ghini
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy.,Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), Sesto Fiorentino, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy.,Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), Sesto Fiorentino, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy. .,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy. .,Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), Sesto Fiorentino, Italy.
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14
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Wang H, Jia H, Gao Y, Zhang H, Fan J, Zhang L, Ren F, Yin Y, Cai Y, Zhu J, Zhu ZJ. Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer. Nat Commun 2022; 13:7802. [PMID: 36528604 PMCID: PMC9759530 DOI: 10.1038/s41467-022-35511-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.
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Affiliation(s)
- Hongmiao Wang
- grid.9227.e0000000119573309Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Huixun Jia
- grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.452404.30000 0004 1808 0942Department of Biostatistics, Fudan University Shanghai Cancer Center, Shanghai, 200032 China
| | - Yang Gao
- grid.9227.e0000000119573309Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Haosong Zhang
- grid.9227.e0000000119573309Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Jin Fan
- grid.452404.30000 0004 1808 0942Department of Biostatistics, Fudan University Shanghai Cancer Center, Shanghai, 200032 China
| | - Lijie Zhang
- grid.452404.30000 0004 1808 0942Department of Biostatistics, Fudan University Shanghai Cancer Center, Shanghai, 200032 China
| | - Fandong Ren
- grid.9227.e0000000119573309Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032 China
| | - Yandong Yin
- grid.9227.e0000000119573309Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032 China
| | - Yuping Cai
- grid.9227.e0000000119573309Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032 China
| | - Ji Zhu
- grid.417397.f0000 0004 1808 0985Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310005 China ,grid.9227.e0000000119573309Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, Hangzhou, 310000 China ,Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, 310000 China ,grid.452404.30000 0004 1808 0942Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200031 China ,grid.513063.2Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032 China
| | - Zheng-Jiang Zhu
- grid.9227.e0000000119573309Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032 China ,Shanghai Key Laboratory of Aging Studies, Shanghai, 201210 China
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15
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Song Y, Zhang Y, Xie S, Song X. Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning. Front Cell Dev Biol 2022; 10:1075810. [PMID: 36589750 PMCID: PMC9798417 DOI: 10.3389/fcell.2022.1075810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 11/22/2022] [Indexed: 12/23/2022] Open
Abstract
We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice.
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Affiliation(s)
- Yaoyao Song
- Department of General Surgery, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China,Department of Burn and Plastic Surgery, The Fourth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yan Zhang
- Department of General Surgery, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Songhai Xie
- Department of Chemistry, Fudan University, Shanghai, China
| | - Xiaowei Song
- Department of Chemistry, Fudan University, Shanghai, China,*Correspondence: Xiaowei Song,
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16
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Kozioł A, Pupek M. Application of Metabolomics in Childhood Leukemia Diagnostics. Arch Immunol Ther Exp (Warsz) 2022; 70:28. [DOI: 10.1007/s00005-022-00665-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 10/04/2022] [Indexed: 11/09/2022]
Abstract
AbstractMetabolomics is a new field of science dealing with the study and analysis of metabolites formed in living cells. The biological fluids used in this test method are: blood, blood plasma, serum, cerebrospinal fluid, saliva and urine. The most popular methods of assessing the composition of metabolites include nuclear magnetic resonance spectroscopy and mass spectrometry (MS) in combination with gas chromatography–MS or liquid chromatography–MS. Metabolomics is used in many areas of medicine. The variability of biochemical processes in neoplastic cells in relation to healthy cells is the starting point for this type of research. The aim of the research currently being carried out is primarily to find biomarkers for quick diagnosis of the disease, assessment of its advancement and treatment effectiveness. The development of metabolomics may also contribute to the individualization of treatment of patients, adjusting drugs depending on the metabolic profile, and thus may improve the effectiveness of therapy, reduce side effects and help to improve the quality of life of patients. Here, we review the current and potential applications of metabolomics, focusing on its use as a biomarker method for childhood leukemia.
Graphic abstract
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17
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Lv J, Jia H, Mo M, Yuan J, Wu Z, Zhang S, Zhe F, Gu B, Fan B, Li C, Zhang T, Zhu J. Changes of serum metabolites levels during neoadjuvant chemoradiation and prediction of the pathological response in locally advanced rectal cancer. Metabolomics 2022; 18:99. [PMID: 36441416 DOI: 10.1007/s11306-022-01959-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 11/09/2022] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT. OBJECTIVES This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients. METHODS Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models. RESULTS Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively. CONCLUSION This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.
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Affiliation(s)
- Jiali Lv
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Huixun Jia
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Clinical Statistics Center, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Miao Mo
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Clinical Statistics Center, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing Yuan
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Clinical Statistics Center, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Zhenyu Wu
- Department of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
| | - Shuai Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Fan Zhe
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bingbing Gu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bingbing Fan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chunxia Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Tao Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Ji Zhu
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
- Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China.
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer. Cancers (Basel) 2022; 14:cancers14205055. [PMID: 36291837 PMCID: PMC9600495 DOI: 10.3390/cancers14205055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
Simple Summary Neoadjuvant chemotherapy (NACT) is offered to breast cancer (BC) patients to downstage the disease. However, some patients may not respond to NACT, being resistant. We used the serum metabolic profile by Nuclear Magnetic Resonance (NMR) combined with disease characteristics to differentiate between sensitive and resistant BC patients. We obtained accuracy above 80% for the response prediction and showcased how NMR can substantially enhance the prediction of response to NACT. Abstract Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.
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Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research. Metabolites 2022; 12:metabo12100963. [PMID: 36295865 PMCID: PMC9609461 DOI: 10.3390/metabo12100963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 12/02/2022] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.
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Bai J, Withycombe J, Eldridge RC. Metabolic Pathways Associated With Psychoneurological Symptoms in Children With Cancer Receiving Chemotherapy. Biol Res Nurs 2022; 24:281-293. [PMID: 35285272 PMCID: PMC9343884 DOI: 10.1177/10998004211069619] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2024]
Abstract
CONTEXT Children with cancer undergoing chemotherapy experience a cluster of psychoneurological symptoms (PNS), including pain, fatigue, anxiety, and depressive symptoms. Metabolomics is promising to differentiate metabolic pathways associated with the PNS cluster. OBJECTIVES Identify metabolic pathways associated with the PNS cluster in children with cancer before and after chemotherapy. METHODS Pain, fatigue, anxiety, and depressive symptoms were assessed using the Pediatric PROMIS scales. T-scores were computed and divided dichotomously by a cutoff point of 50; the PNS cluster was a sum of the four symptoms ranging from 0 (all T-scores <50) to 4 (all T-scores ≥50). Serum metabolites were processed using liquid chromatography mass-spectrometry untargeted metabolomics approach. Linear regression models examined metabolites associated with the PNS cluster. Metabolic pathway enrichment analysis was performed. RESULTS Participant demographics (n = 40) were 55% female, 60% white, 62.5% aged 13-19 years, and 62.5% diagnoses of Hodgkin's lymphoma and B-cell acute lymphocytic leukemia. Among 9276 unique metabolic features, 454 were associated with pain, 281 with fatigue, 596 with anxiety, 551 with depressive symptoms, and 300 with the PNS cluster across one chemotherapy cycle. Fatty acids pathways were associated with pain: de novo fatty acid biosynthesis (p < .001), fatty acid metabolism (p = .001), fatty acid activation (p = .004), and omega-3 fatty acid metabolism (p = .009). Tryptophan amino acid pathway was associated with fatigue (p < .001), anxiety (p = .015), and the PNS cluster (p = .037). Carnitine shuttle was associated with the PNS cluster (p = .015). CONCLUSION Fatty acids and amino acids pathways were associated with PNS in children undergoing chemotherapy. These findings require further investigation in a larger sample.
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Affiliation(s)
- Jinbing Bai
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | | | - Ronald C. Eldridge
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA
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21
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Krstic J, Schindlmaier K, Prokesch A. Combination strategies to target metabolic flexibility in cancer. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2022; 373:159-197. [PMID: 36283766 DOI: 10.1016/bs.ircmb.2022.03.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Therapeutically interfering with metabolic pathways has great merit to curtail tumor growth because the demand for copious amounts of energy for growth-supporting biomass production is common to all cancer entities. A major impediment to a straight implementation of metabolic cancer therapy is the metabolic flexibility and plasticity of cancer cells (and their microenvironment) resulting in therapy resistance and evasion. Metabolic combination therapies, therefore, are promising as they are designed to target several energetic routes simultaneously and thereby diminish the availability of alternative substrates. Thus, dietary restrictions, specific nutrient limitations, and/or pharmacological interventions impinging on metabolic pathways can be combined to improve cancer treatment efficacy, to overcome therapy resistance, or even act as a preventive measure. Here, we review the most recent developments in metabolic combination therapies particularly highlighting in vivo reports of synergistic effects and available clinical data. We close with identifying the challenges of the field (metabolic tumor heterogeneity, immune cell interactions, inter-patient variabilities) and suggest a "metabo-typing" strategy to tailor evidence-based metabolic combination therapies to the energetic requirements of the tumors and the patient's nutritional habits and status.
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Affiliation(s)
- Jelena Krstic
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signaling, Metabolism & Aging, Medical University of Graz, Graz, Austria
| | - Katharina Schindlmaier
- Division of Pulmonology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Andreas Prokesch
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signaling, Metabolism & Aging, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria.
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22
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Kaur P, Singh D, Singh G, Attri S, Singh D, Sharma M, Buttar HS, Bedi N, Singh B, Arora S. Pharmacokinetics and toxicity profiling of 4-(methylthio)butyl isothiocyanate with special reference to pre-clinical safety assessment studies. Toxicon 2022; 212:19-33. [PMID: 35395273 DOI: 10.1016/j.toxicon.2022.03.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/25/2022] [Accepted: 03/26/2022] [Indexed: 11/30/2022]
Abstract
4-(methylthio)butyl isothiocyanate (4-MTBITC) also called erucin is abundantly present in the seeds of Eruca sativa plant closely related to cruciferous vegetables rich in isothiocyanates. We have previously reported the molecular targets of 4-MTBITC, but no acute, subacute and subchronic toxicity studies have been carried out to evaluate its safety. The non-everted gut sac method was used to study intestinal absorption and it revealed the highest absorption of 4-MTBITC in the jejunum. Dose-dependent pharmacokinetic parameters were observed in rats given 10, 20, and 40 mg/kg oral doses of 4-MTBITC. At the highest dose of 40 mg/kg, Cmax was 437.33 μg/ml and Tmax was 30 min, suggesting quick absorption and delayed elimination with elimination constant, 0.0036 ± 0.0002min-1. In a 14 days toxicity study, the mean LD50 of 4-MTBITC was 500 mg/kg body weight. After 28 and 90 days of treatment with 4-MTBITC (2.5, 10, 40 mg/kg/day), significant increases were observed in SGOT, cholesterol, and antioxidant enzymes. The levels of glycine, alanine and lysine were markedly increased in the liver tissue, thereby indicating that the liver was the target organ of 4-MTBITC induced toxicity in female animals. The histopathological examination of liver, kidney, and lung tissues revealed little focal necrosis, apoptosis, and reduction in the levels of amino acids involved in cellular metabolic pathways, indicating the anti-proliferative potential of 4-MTBITC against rapidly growing cells.
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Affiliation(s)
- Prabhjot Kaur
- Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, India
| | - Davinder Singh
- Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, India
| | - Gurdeep Singh
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, India
| | - Shivani Attri
- Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, India
| | - Dilpreet Singh
- Department of Pharmaceutics, ISF College of Pharmacy, Moga, India
| | - Manisha Sharma
- Consultant Pathologist, Smt. Paarvati Devi Hospital, Amritsar, India
| | - Harpal Singh Buttar
- Department of Pathology and Laboratory Medicine, University of Ottawa, Faculty of Medicine, Ottawa, Ontario, Canada
| | - Neena Bedi
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, India
| | - Balbir Singh
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, India
| | - Saroj Arora
- Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, India.
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23
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Díaz C, González-Olmedo C, Díaz-Beltrán L, Camacho J, Mena García P, Martín-Blázquez A, Fernández-Navarro M, Ortega-Granados AL, Gálvez-Montosa F, Marchal JA, Vicente F, Pérez Del Palacio J, Sánchez-Rovira P. Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach. Mol Oncol 2022; 16:2658-2671. [PMID: 35338693 PMCID: PMC9297806 DOI: 10.1002/1878-0261.13216] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/17/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.
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Affiliation(s)
- Caridad Díaz
- Fundación MEDINA; Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Granada, Andalucía, Spain
| | | | | | - José Camacho
- Department of Signal Theory, Networking and Communications, University of Granada, 18071, Granada, Spain
| | - Patricia Mena García
- Fundación MEDINA; Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Granada, Andalucía, Spain
| | - Ariadna Martín-Blázquez
- Fundación MEDINA; Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Granada, Andalucía, Spain
| | | | | | | | - Juan Antonio Marchal
- Biopathology and Regenerative Medicine Institute (IBIMER), Centre for Biomedical Research, University of Granada, Granada, E-18100, Spain.,Instituto de Investigación Biosanitaria ibs.GRANADA, University of Granada, 18100, Granada, Spain.,Department of Human Anatomy and Embryology, Faculty of Medicine, University of Granada, Granada, E-18012, Spain.,Excellence Research Unit "Modeling Nature" (MNat), University of Granada, Spain
| | - Francisca Vicente
- Fundación MEDINA; Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Granada, Andalucía, Spain
| | - José Pérez Del Palacio
- Fundación MEDINA; Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Granada, Andalucía, Spain
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24
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Debik J, Sangermani M, Wang F, Madssen TS, Giskeødegård GF. Multivariate analysis of NMR-based metabolomic data. NMR IN BIOMEDICINE 2022; 35:e4638. [PMID: 34738674 DOI: 10.1002/nbm.4638] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/08/2021] [Accepted: 09/29/2021] [Indexed: 06/13/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.
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Affiliation(s)
- Julia Debik
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Matteo Sangermani
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Feng Wang
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
- Clinic of Surgery, St. Olavs Hospital HF, Trondheim, Norway
| | - Torfinn S Madssen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Guro F Giskeødegård
- Clinic of Surgery, St. Olavs Hospital HF, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
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25
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Morozumi K, Kawasaki Y, Maekawa M, Takasaki S, Sato T, Shimada S, Kawamorita N, Yamashita S, Mitsuzuka K, Mano N, Ito A. Predictive model for recurrence of renal cell carcinoma by comparing pre- and postoperative urinary metabolite concentrations. Cancer Sci 2021; 113:182-194. [PMID: 34710258 PMCID: PMC8748223 DOI: 10.1111/cas.15180] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022] Open
Abstract
To improve treatment outcomes in real practice, useful biomarkers are desired when predicting postoperative recurrence for renal cell carcinoma (RCC). We collected data from patients who underwent definitive surgery for RCC and for benign urological tumor at our department between November 2016 and December 2019. We evaluated the differences in pre‐ and postoperative urinary metabolites with our precise quantitative method and identified predictive factors for RCC recurrence. Additionally, to clarify the significance of metabolites, we measured the intracellular metabolite concentration of three RCC cell lines. Among the 56 patients with RCC, nine had a recurrence (16.0%). When comparing 27 patients with T1a RCC and 10 with benign tumor, a significant difference was observed between pre‐ and postoperative concentrations among 10 urinary metabolites. In these 10 metabolites, multiple logistic regression analysis identified five metabolites (lactic acid, glycine, 2‐hydroxyglutarate, succinic acid, and kynurenic acid) as factors to build our recurrence prediction model. The values of area under the receiver operating characteristic curve, sensitivity, and specificity in this predictive model were 0.894%, 88.9%, and 88.0%, respectively. When stratified into low and high risk groups of recurrence based on this model, we found a significant drop of recurrence‐free survival rates among the high risk group. In in vitro studies, intracellular metabolite concentrations of metastatic tumor cell lines were much higher than those of primary tumor cell lines. By using our quantitative evaluation of urinary metabolites, we could predict postoperative recurrence with high sensitivity and specificity. Urinary metabolites could be noninvasive biomarkers to improve patient outcome.
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Affiliation(s)
- Kento Morozumi
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Yoshihide Kawasaki
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Masamitsu Maekawa
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Shinya Takasaki
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Tomonori Sato
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Shuichi Shimada
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Naoki Kawamorita
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Shinichi Yamashita
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Koji Mitsuzuka
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Nariyasu Mano
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Akihiro Ito
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
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26
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Nannini G, Meoni G, Tenori L, Ringressi MN, Taddei A, Niccolai E, Baldi S, Russo E, Luchinat C, Amedei A. Fecal metabolomic profiles: A comparative study of patients with colorectal cancer vs adenomatous polyps. World J Gastroenterol 2021; 27:6430-6441. [PMID: 34720532 PMCID: PMC8517777 DOI: 10.3748/wjg.v27.i38.6430] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/17/2021] [Accepted: 08/25/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC), the third most common cause of death in both males and females worldwide, shows a positive response to therapy and usually a better prognosis when detected at an early stage. However, the survival rate declines when the diagnosis is late and the tumor spreads to other organs. Currently, the measures widely used in the clinic are fecal occult blood test and evaluation of serum tumor markers, but the lack of sensitivity and specificity of these markers restricts their use for CRC diagnosis. Due to its high sensitivity and precision, colonoscopy is currently the gold-standard screening technique for CRC, but it is a costly and invasive procedure. Therefore, the implementation of custom-made methodologies including those with minimal invasiveness, protection, and reproducibility is highly desirable. With regard to other screening methods, the screening of fecal samples has several benefits, and metabolomics is a successful method to classify the metabolite shift in living systems as a reaction to pathophysiological influences, genetic modifications, and environmental factors.
AIM To characterize the variation groups and potentially recognize some diagnostic markers, we compared with healthy controls (HCs) the fecal nuclear magnetic resonance (NMR) metabolomic profiles of patients with CRC or adenomatous polyposis (AP).
METHODS Proton nuclear magnetic resonance spectroscopy was used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profiles of 32 CRC patients, 16 AP patients, and 38 HCs well matched in age, sex, and body mass index.
RESULTS NMR metabolomic analyses revealed that fecal sample profiles differed among CRC patients, AP patients, and HCs, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine and leucine were identified.
CONCLUSION In conclusion, we are confident that our data can be a forerunner for future studies on CRC management, especially the diagnosis and evaluation of the effectiveness of treatments.
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Affiliation(s)
- Giulia Nannini
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Gaia Meoni
- Department of Chemistry “Ugo Schiff”, University of Florence, Florence 50134, Italy
| | - Leonardo Tenori
- Department of Chemistry “Ugo Schiff”, University of Florence, Florence 50134, Italy
| | - Maria Novella Ringressi
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Antonio Taddei
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Elena Niccolai
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Simone Baldi
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Edda Russo
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Claudio Luchinat
- Department of Chemistry & Magnetic Resonance Center (CERM), University of Florence, Florence 50134, Italy
| | - Amedeo Amedei
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
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Letertre MPM, Giraudeau P, de Tullio P. Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives. Front Mol Biosci 2021; 8:698337. [PMID: 34616770 PMCID: PMC8488110 DOI: 10.3389/fmolb.2021.698337] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.
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Affiliation(s)
| | | | - Pascal de Tullio
- Metabolomics Group, Center for Interdisciplinary Research of Medicine (CIRM), Department of Pharmacy, Université de Liège, Liège, Belgique
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Impact of the Pd 2Spm (Spermine) Complex on the Metabolism of Triple-Negative Breast Cancer Tumors of a Xenograft Mouse Model. Int J Mol Sci 2021; 22:ijms221910775. [PMID: 34639114 PMCID: PMC8509401 DOI: 10.3390/ijms221910775] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 09/29/2021] [Accepted: 09/29/2021] [Indexed: 12/22/2022] Open
Abstract
The interest in palladium(II) compounds as potential new anticancer drugs has increased in recent years, due to their high toxicity and acquired resistance to platinum(II)-derived agents, namely cisplatin. In fact, palladium complexes with biogenic polyamines (e.g., spermine, Pd2Spm) have been known to display favorable antineoplastic properties against distinct human breast cancer cell lines. This study describes the in vivo response of triple-negative breast cancer (TNBC) tumors to the Pd2Spm complex or to cisplatin (reference drug), compared to tumors in vehicle-treated mice. Both polar and lipophilic extracts of tumors, excised from a MDA-MB-231 cell-derived xenograft mouse model, were characterized through nuclear magnetic resonance (NMR) metabolomics. Interestingly, the results show that polar and lipophilic metabolomes clearly exhibit distinct responses for each drug, with polar metabolites showing a stronger impact of the Pd(II)-complex compared to cisplatin, whereas neither drug was observed to significantly affect tumor lipophilic metabolism. Compared to cisplatin, exposure to Pd2Spm triggered a higher number of, and more marked, variations in some amino acids, nucleotides and derivatives, membrane precursors (choline and phosphoethanolamine), dimethylamine, fumarate and guanidine acetate, a signature that may be relatable to the cytotoxicity and/or mechanism of action of the palladium complex. Putative explanatory biochemical hypotheses are advanced on the role of the new Pd2Spm complex in TNBC metabolism.
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29
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Comparison of the blood, bone marrow, and cerebrospinal fluid metabolomes in children with b-cell acute lymphoblastic leukemia. Sci Rep 2021; 11:19613. [PMID: 34608220 PMCID: PMC8490393 DOI: 10.1038/s41598-021-99147-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/20/2021] [Indexed: 12/30/2022] Open
Abstract
Metabolomics may shed light on treatment response in childhood acute lymphoblastic leukemia (ALL), however, most assessments have analyzed bone marrow or cerebrospinal fluid (CSF), which are not collected during all phases of therapy. Blood is collected frequently and with fewer risks, but it is unclear whether findings from marrow or CSF biomarker studies may translate. We profiled end-induction plasma, marrow, and CSF from N = 10 children with B-ALL using liquid chromatography-mass spectrometry. We estimated correlations between plasma and marrow/CSF metabolite abundances detected in ≥ 3 patients using Spearman rank correlation coefficients (rs). Most marrow metabolites were detected in plasma (N = 661; 81%), and we observed moderate-to-strong correlations (median rs 0.62, interquartile range [IQR] 0.29–0.83). We detected 328 CSF metabolites in plasma (90%); plasma-CSF correlations were weaker (median rs 0.37, IQR 0.07–0.70). We observed plasma-marrow correlations for metabolites in pathways associated with end-induction residual disease (pyruvate, asparagine) and plasma-CSF correlations for a biomarker of fatigue (gamma-glutamylglutamine). There is considerable overlap between the plasma, marrow, and CSF metabolomes, and we observed strong correlations for biomarkers of clinically relevant phenotypes. Plasma may be suitable for biomarker studies in B-ALL.
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30
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Gómez-Cebrián N, Vázquez Ferreiro P, Carrera Hueso FJ, Poveda Andrés JL, Puchades-Carrasco L, Pineda-Lucena A. Pharmacometabolomics by NMR in Oncology: A Systematic Review. Pharmaceuticals (Basel) 2021; 14:ph14101015. [PMID: 34681239 PMCID: PMC8539252 DOI: 10.3390/ph14101015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 12/14/2022] Open
Abstract
Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.
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Affiliation(s)
- Nuria Gómez-Cebrián
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain;
| | | | | | | | - Leonor Puchades-Carrasco
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain;
- Correspondence: (L.P.-C.); (A.P.-L.); Tel.: +34-963246713 (L.P.-C.)
| | - Antonio Pineda-Lucena
- Molecular Therapeutics Program, Centro de Investigación Médica Aplicada, 31008 Navarra, Spain
- Correspondence: (L.P.-C.); (A.P.-L.); Tel.: +34-963246713 (L.P.-C.)
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Vignoli A, Risi E, McCartney A, Migliaccio I, Moretti E, Malorni L, Luchinat C, Biganzoli L, Tenori L. Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer. Int J Mol Sci 2021; 22:ijms22094687. [PMID: 33925233 PMCID: PMC8124948 DOI: 10.3390/ijms22094687] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 12/22/2022] Open
Abstract
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. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).
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Affiliation(s)
- Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
| | - Emanuela Risi
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Amelia McCartney
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
- School of Clinical Sciences, Monash University, Melbourne 3800, Australia
| | - Ilenia Migliaccio
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Erica Moretti
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Luca Malorni
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
- Correspondence: ; Tel.: +39-055-457-4296
| | - Laura Biganzoli
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
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Serum metabolomic profiling correlated with ISS and clinical outcome for multiple myeloma patients treated with high-dose melphalan and autologous stem cell transplantation. Exp Hematol 2021; 97:79-88.e8. [PMID: 33609593 DOI: 10.1016/j.exphem.2021.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/27/2021] [Accepted: 02/13/2021] [Indexed: 11/20/2022]
Abstract
The metabolome, which is the final down-stream global product of metabolic processes in organisms, is not sufficiently described in multiple myeloma (MM) patients. The aim of this study was, therefore, to study the serum metabolomic profile using proton nuclear magnetic resonance (1H-NMR) spectroscopy, and its relationship to clinical characteristics and patient outcome. Serum samples, which were taken at diagnosis, from 201 MM patients who underwent high-dose melphalan followed by autologous stem cell transplantation as the first-line therapy, were analyzed. We found that the metabolomic profile differed between patients with different MM International Staging System (ISS) stages. The profile revealed increased levels of cholesterol, phospholipids, high-density lipoprotein, low-density lipoprotein, apolipoproteins A1 and A2, valine, and leucine in ISS I patients compared with ISS III patients. The metabolomic profile also differed between patients with IgA and IgG paraproteins, predominantly because of higher levels of high- and low-density lipoprotein subfractions in IgA patients. The exact pathway of metabolism leading to accumulation of these metabolites is still elusive, but this study indicates an area of interest for further investigation in the search for new therapy targets and prognostic markers for this disease.
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High-coverage quantitative liver metabolomics using perfused and non-perfused liver tissues. Anal Chim Acta 2021; 1153:338300. [PMID: 33714446 DOI: 10.1016/j.aca.2021.338300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 11/21/2022]
Abstract
Comprehensive analysis of the liver metabolome can be very useful for discovering disease biomarkers and studying diseases, especially liver-related diseases. However, the presence of a relatively large amount of blood in liver tissue may have a profound effect on liver tissue metabolome analysis. We designed a study to address this issue in order to develop a liver metabolomics workflow based on high-coverage quantitative metabolome analysis using differential chemical isotope labeling (CIL) LC-MS. In the first set of experiments, we compared the metabolomes of mouse serum, non-perfused liver, and perfused liver without and with varying amounts of blood added. We found that there was a significant metabolome difference between the perfused liver and non-perfused liver. To illustrate the effects of perfusion conditions on tissue metabolome analysis, we analyzed the mouse livers that were subjected to perfusion under two different conditions. We found that ice-cold temperature perfusion led to less change of the liver metabolome, compared to room temperature perfusion; however, there was still a significant metabolome difference between the ice-cold-perfused liver and the non-perfused liver. Finally, we applied the method to a chemical (carbon tetrachloride) exposure liver injury model to examine the effects of blood in liver on the detection of significantly changed metabolites in two comparative groups of mice. Using multivariate and univariate analyses of the serum and liver metabolomes of control and diseased mice, we detected many unique significant metabolites in serum as well as in liver. This work demonstrates that perfusion can alter the liver metabolome significantly. Therefore, we recommend the use of non-perfused liver for high-coverage liver metabolomics.
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Di Gregorio E, Miolo G, Saorin A, Muraro E, Cangemi M, Revelant A, Minatel E, Trovò M, Steffan A, Corona G. Radical Hemithoracic Radiotherapy Induces Systemic Metabolomics Changes That Are Associated with the Clinical Outcome of Malignant Pleural Mesothelioma Patients. Cancers (Basel) 2021; 13:cancers13030508. [PMID: 33572739 PMCID: PMC7866164 DOI: 10.3390/cancers13030508] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/19/2021] [Accepted: 01/25/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Radical hemithoracic radiotherapy represents a promising new advance in the field of radiation oncology and encouraging results have been achieved in the treatment of malignant pleural mesothelioma patients. This study showed that this radiotherapy modality produces significant changes in serum metabolomics profile mainly affecting arginine and polyamine biosynthesis pathways. Interestingly, individual metabolomics alterations were found associated with the clinical overall survival outcome of the radiotherapy treatment. These results highlight metabolomics profile analysis as a powerful prognostic tool useful to better understand the mechanisms underlying the interpatients variability and to identify patients who may receive the best benefit from this specific radiotherapy treatment. Abstract Radical hemithoracic radiotherapy (RHRT) represents an advanced therapeutic option able to improve overall survival of malignant pleural mesothelioma patients. This study aims to investigate the systemic effects of this radiotherapy modality on the serum metabolome and their potential implications in determining the individual clinical outcome. Nineteen patients undergoing RHRT at the dose of 50 Gy in 25 fractions were enrolled. Serum targeted metabolomics profiles were investigated at baseline and the end of radiotherapy by liquid chromatography and tandem mass spectrometry. Univariate and multivariate OPLS-DA analyses were applied to study the serum metabolomics changes induced by RHRT while PLS regression analysis to evaluate the association between such changes and overall survival. RHRT was found to affect almost all investigated metabolites classes, in particular, the amino acids citrulline and taurine, the C14, C18:1 and C18:2 acyl-carnitines as well as the unsaturated long chain phosphatidylcholines PC ae 42:5, PC ae 44:5 and PC ae 44:6 were significantly decreased. The enrichment analysis showed arginine metabolism and the polyamine biosynthesis as the most perturbed pathways. Moreover, specific metabolic changes encompassing the amino acids and acyl-carnitines resulted in association with the clinical outcome accounting for about 60% of the interpatients overall survival variability. This study highlighted that RHRT can induce profound systemic metabolic effects some of which may have a significant prognostic value. The integration of metabolomics in the clinical assessment of the malignant pleural mesothelioma could be useful to better identify the patients who can achieve the best benefit from the RHRT treatment.
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Affiliation(s)
- Emanuela Di Gregorio
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy; (E.D.G.); (A.S.); (E.M.); (M.C.); (A.S.)
| | - Gianmaria Miolo
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy;
| | - Asia Saorin
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy; (E.D.G.); (A.S.); (E.M.); (M.C.); (A.S.)
| | - Elena Muraro
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy; (E.D.G.); (A.S.); (E.M.); (M.C.); (A.S.)
| | - Michela Cangemi
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy; (E.D.G.); (A.S.); (E.M.); (M.C.); (A.S.)
| | - Alberto Revelant
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy; (A.R.); (E.M.)
| | - Emilio Minatel
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy; (A.R.); (E.M.)
| | - Marco Trovò
- Radiation Oncology Department, Azienda Sanitaria Integrata, 33100 Udine, Italy;
| | - Agostino Steffan
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy; (E.D.G.); (A.S.); (E.M.); (M.C.); (A.S.)
| | - Giuseppe Corona
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy; (E.D.G.); (A.S.); (E.M.); (M.C.); (A.S.)
- Correspondence: ; Tel.: +39-0434-659-666
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Lai X, Zhang W, Ye M, Liu X, Luo X. Development and validation of a predictive model for the prognosis in aneurysmal subarachnoid hemorrhage. J Clin Lab Anal 2020; 34:e23542. [PMID: 32860455 PMCID: PMC7755773 DOI: 10.1002/jcla.23542] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/22/2020] [Accepted: 07/29/2020] [Indexed: 12/12/2022] Open
Abstract
Background This study was to conduct a predictive model for the prognosis of aneurysmal subarachnoid hemorrhage (aSAH) and validate the clinical data. Methods A total of 235 aSAH patients were enrolled in this study, dividing into the favorable or poor prognosis groups based on Modified Rankin Scale (mRS) at 3 months postoperatively. Multivariate analysis was assessed using binary Logistic regression and Fisher discriminant analysis. The receiver operating characteristic (ROC) curve was used to determine the cut‐off value. Results Our findings showed that the high Glasgow Coma Scale (GCS) score 24‐hour after surgery reduced the risk of poor prognosis, and the surgical clipping and elevated neutrophil‐lymphocyte ratio (NLR) increased the risk of poor prognosis. The discriminant function was V = 0.881 × GCS score − 0.523 × NLR − 0.422 × therapeutic approach, and V = −0.689 served as a cut‐off value. When V ≥ −0.689, the good prognosis was considered among these patients with aSAH. The correctness for predicting the prognostic outcomes by self‐validation was 85.11%. Conclusion This predictive model established by a discriminant analysis is a useful tool for predicting the prognostic outcomes of aSAH patients, which may help clinicians identify patients at high risk for poor prognosis and optimize treatment after surgery.
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Affiliation(s)
- Xiang Lai
- Department of Neurosurgery, Meizhou People's Hospital (Huangtang Hospital), Meizhou Hospital Affiliated to Sun Yat-Sen University, Meizhou, China
| | - Wenbo Zhang
- Department of Neurosurgery, Meizhou People's Hospital (Huangtang Hospital), Meizhou Hospital Affiliated to Sun Yat-Sen University, Meizhou, China
| | - Min Ye
- Department of Neurosurgery, Meizhou People's Hospital (Huangtang Hospital), Meizhou Hospital Affiliated to Sun Yat-Sen University, Meizhou, China
| | - Xiaoping Liu
- Department of Neurosurgery, Meizhou People's Hospital (Huangtang Hospital), Meizhou Hospital Affiliated to Sun Yat-Sen University, Meizhou, China
| | - Xingda Luo
- Department of Neurosurgery, Meizhou People's Hospital (Huangtang Hospital), Meizhou Hospital Affiliated to Sun Yat-Sen University, Meizhou, China
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