1
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Dudka I, Lundquist K, Wikström P, Bergh A, Gröbner G. Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes. J Transl Med 2023; 21:860. [PMID: 38012666 PMCID: PMC10683247 DOI: 10.1186/s12967-023-04747-7] [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: 05/22/2023] [Accepted: 11/21/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis. METHODS A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity). RESULTS Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B. CONCLUSIONS The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.
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Affiliation(s)
- Ilona Dudka
- Department of Chemistry, Umeå University, Umeå, Sweden
| | | | - Pernilla Wikström
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden.
| | - Anders Bergh
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
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2
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Ye Y, Liu Z, Zhu J, Wu J, Sun K, Peng Y, Qiu J, Gong L. Development trends and knowledge framework in the application of magnetic resonance imaging in prostate cancer: a bibliometric analysis from 1984 to 2022. Quant Imaging Med Surg 2023; 13:6761-6777. [PMID: 37869318 PMCID: PMC10585509 DOI: 10.21037/qims-23-446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/07/2023] [Indexed: 10/24/2023]
Abstract
Background Prostate cancer (PCa) is the most common tumor of the male genitourinary system. With the development of imaging technology, the role of magnetic resonance imaging (MRI) in the management of PCa is increasing. The present study summarizes research on the application of MRI in the field of PCa using bibliometric analysis and predicts future research hotspots. Methods Articles regarding the application of MRI in PCa between January 1, 1984 and June 30, 2022 were selected from the Web of Science Core Collection (WoSCC) on November 6, 2022. Microsoft Excel 2016 and the Bibliometrix Biblioshiny R-package software were used for data analysis and bibliometric indicator extraction. CiteSpace (version 6.1.R3) was used to visualize literature feature clustering, including co-occurrence analysis of countries, institutions, authors, references, and burst keywords analysis. Results A total of 10,230 articles were included in the study. Turkbey was the most prolific author. The USA was the most productive country and had strong partnerships with other countries. The most productive institution was Memorial Sloan Kettering Cancer Center. Journal of Magnetic Resonance Imaging and Radiology were the most productive and highest impact factor (IF) journals in the field, respectively. Timeline views showed that "#1 multiparametric magnetic resonance imaging", "#4 pi-rads", and "#8 psma" were currently the latest research hotspots. Keywords burst analysis showed that "machine learning", "psa density", "multi parametric mri", "deep learning", and "artificial intelligence" were the most frequently used keywords in the past 3 years. Conclusions MRI has a wide range of applications in PCa. The USA is the leading country in this field, with a concentration of highly productive and high-level institutions. Meanwhile, it can be projected that "deep learning", "radiomics", and "artificial intelligence" will be research hotspots in the future.
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Affiliation(s)
- Yinquan Ye
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhixuan Liu
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jianghua Zhu
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jialong Wu
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ke Sun
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yun Peng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jia Qiu
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lianggeng Gong
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
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3
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Bansal N, Kumar M, Sankhwar SN, Gupta A. Evaluation of prostate cancer tissue metabolomics: would clinics utilise it for diagnosis? Expert Rev Mol Med 2023; 25:e26. [PMID: 37548191 DOI: 10.1017/erm.2023.22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The difficulty of diagnosing prostate cancer (PC) with the available biomarkers frequently leads to over-diagnosis and overtreatment of PC, underscoring the need for novel molecular signatures. The purpose of this review is to provide a summary of the currently available cellular metabolomics for PC molecular signatures. A comprehensive search on PubMed was conducted to find studies published between January 2004 and August 2022 that reported biomarkers for PC detection, development, aggressiveness, recurrence and treatment response. Although potential studies have reported the presence of distinguishing molecules that can distinguish between benign and cancerous prostate tissue. However, there are few studies looking into signature molecules linked to disease development, therapy response or tumour recurrence. The majority of these studies use high-dimensional datasets, and the number of potential metabolites investigated frequently exceeds the size of the available samples. In light of this, pre-analytical, statistical, methodological and confounding factors such as antiandrogen therapy (NAT) may also be linked to the identified chemometric multivariate differences between PC and relevant control samples in the datasets. Despite the methodological and procedural challenges, a range of methodological groups and processes have consistently identified a number of signature metabolites and pathways that appear to imply a substantial involvement in the cellular metabolomics of PC for tumour formation and recurrence.
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Affiliation(s)
- Navneeta Bansal
- Department of Urology, King George's Medical University, Lucknow, India
| | - Manoj Kumar
- Department of Urology, King George's Medical University, Lucknow, India
| | - Satya N Sankhwar
- Department of Urology, King George's Medical University, Lucknow, India
| | - Ashish Gupta
- Centre of Biomedical Research, SGPGIMS Campus, Lucknow, India
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4
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Deal M, Bardet F, Walker PM, de la Vega MF, Cochet A, Cormier L, Bentellis I, Loffroy R. Three-dimensional nuclear magnetic resonance spectroscopy: a complementary tool to multiparametric magnetic resonance imaging in the identification of aggressive prostate cancer at 3.0T. Quant Imaging Med Surg 2021; 11:3749-3766. [PMID: 34341747 DOI: 10.21037/qims-21-331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/13/2021] [Indexed: 12/12/2022]
Abstract
Background The limitations of the assessment of tumor aggressiveness by Prostate Imaging Reporting and Data System (PI-RADS) and biopsies suggest that the diagnostic algorithm could be improved by quantitative measurements in some chosen indications. We assessed the tumor high-risk predictive performance of 3.0 Tesla (3.0T) multiparametric magnetic resonance imaging (mp-MRI) combined with nuclear magnetic resonance spectroscopic sequences (NMR-S) in order to show that the metabolic analysis could bring out an evocative result for the aggressive form of prostate cancer. Methods We conducted a retrospective study of 26 patients (mean age, 62.4 years) who had surgery for prostate cancer between 2009 and 2016 after pre-therapeutic assessment with 3.0T mp-MRI and NMR-S. Groups within the intermediate range of the D'Amico risk classification were divided into two categories, low risk (n=20) and high risk (n=6), according to the International Society of Urological Pathology (ISUP) 2-3 limit. Histoprognostic discordances within various risk groups were compared with the corresponding predictive MRI values. The performance of predictive models was assessed based on sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves. Results After prostatectomy, histological analysis reclassified 18 patients as high-risk, including 16 who were T3 MRI grade, of whom 13 (81.3%) were found to be pT3. Among the patients who had cT1 or cT2 digital rectal examinations, the T3 MRI factor multiplied by 8.7 [odds ratio (OR), 8.7; 95% confidence interval (CI), 1.3-56.2; P=0.024] the relative risk of being pT3 and by 5.8 (OR, 5.8; 95% CI, 0.95-35.7; P=0.05) the relative risk of being pGleason (pGS) > GS-prostate biopsy. Spectroscopic data showed that the choline concentration was significantly higher (P=0.001) in aggressive disease. Conclusions The predictive model of tumor aggressiveness combining mp-MRI plus NMR-S was better than the mp-MRI model alone (AUC, 0.95 vs. 0.86). Information obtained by mp-MRI coupled with spectroscopy may improve the detection of occult aggressive disease, helping in the discrimination of intermediate risks.
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Affiliation(s)
- Michael Deal
- Department of Urology and Andrology, Arnault Tzanck Private Institute, Mougins Sophia-Antipolis, Mougins Cedex, France.,Department of Urology and Andrology, François-Mitterrand University Hospital, Dijon, France
| | - Florian Bardet
- Department of Urology and Andrology, François-Mitterrand University Hospital, Dijon, France
| | - Paul-Michael Walker
- Department of Spectroscopy and Nuclear Magnetic Resonance, François-Mitterrand University Hospital, Dijon, France.,ImViA Laboratory, EA-7535, Training and Research Unit in Health Sciences, University of Bourgogne/Franche-Comté, Dijon, France
| | | | - Alexandre Cochet
- Department of Spectroscopy and Nuclear Magnetic Resonance, François-Mitterrand University Hospital, Dijon, France.,ImViA Laboratory, EA-7535, Training and Research Unit in Health Sciences, University of Bourgogne/Franche-Comté, Dijon, France
| | - Luc Cormier
- Department of Urology and Andrology, François-Mitterrand University Hospital, Dijon, France
| | - Imad Bentellis
- Department of Urology and Andrology, Sophia Antipolis University Hospital, Nice, France
| | - Romaric Loffroy
- ImViA Laboratory, EA-7535, Training and Research Unit in Health Sciences, University of Bourgogne/Franche-Comté, Dijon, France.,Department of Radiology and Medical Imaging, François-Mitterrand University Hospital, Dijon, France
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McGee KP, Hwang KP, Sullivan DC, Kurhanewicz J, Hu Y, Wang J, Li W, Debbins J, Paulson E, Olsen JR, Hua CH, Warner L, Ma D, Moros E, Tyagi N, Chung C. Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294. Med Phys 2021; 48:e697-e732. [PMID: 33864283 PMCID: PMC8361924 DOI: 10.1002/mp.14884] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/24/2021] [Accepted: 03/28/2021] [Indexed: 12/16/2022] Open
Abstract
A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.
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Affiliation(s)
- Kiaran P McGee
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, Division of Diagnostic Imaging, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Daniel C Sullivan
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - John Kurhanewicz
- Department of Radiology, University of California, San Francisco, California, USA
| | - Yanle Hu
- Department of Radiation Oncology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Jihong Wang
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Wen Li
- Department of Radiation Oncology, University of Arizona, Tucson, Arizona, USA
| | - Josef Debbins
- Department of Radiology, Barrow Neurologic Institute, Phoenix, Arizona, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jeffrey R Olsen
- Department of Radiation Oncology, University of Colorado Denver - Anschutz Medical Campus, Denver, Colorado, USA
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | | | - Daniel Ma
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Eduardo Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
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6
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Qin H, Zhang V, Bok RA, Santos RD, Cunha JA, Hsu IC, Santos Bs JD, Lee JE, Sukumar S, Larson PEZ, Vigneron DB, Wilson DM, Sriram R, Kurhanewicz J. Simultaneous Metabolic and Perfusion Imaging Using Hyperpolarized 13C MRI Can Evaluate Early and Dose-Dependent Response to Radiation Therapy in a Prostate Cancer Mouse Model. Int J Radiat Oncol Biol Phys 2020; 107:887-896. [PMID: 32339646 DOI: 10.1016/j.ijrobp.2020.04.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate use of a novel imaging approach, hyperpolarized (HP) 13C magnetic resonance imaging (MRI) for simultaneous metabolism and perfusion assessment, to evaluate early and dose-dependent response to radiation therapy (RT) in a prostate cancer mouse model. METHODS AND MATERIALS Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) mice (n = 18) underwent single-fraction RT (4-14 Gy steep dose across the tumor) and were imaged serially at pre-RT baseline and 1, 4, and 7 days after RT using HP 13C MRI with combined [1-13C]pyruvate (metabolic active agent) and [13C]urea (perfusion agent), coupled with conventional multiparametric 1H MRI including T2-weighted, dynamic contrast-enhanced, and diffusion-weighted imaging. Tumor tissues were collected 4 and 7 days after RT for biological correlative studies. RESULTS We found a significant decrease in HP pyruvate-to-lactate conversion in tumors responding to RT, with concomitant significant increases in HP pyruvate-to-alanine conversion and HP urea signal; the opposite changes were observed in tumors resistant to RT. Moreover, HP lactate change was dependent on radiation dose; tumor regions treated with higher radiation doses (10-14 Gy) exhibited a greater decrease in HP lactate signal than low-dose regions (4-7 Gy) as early as 1 day post-RT, consistent with lactate dehydrogenase enzyme activity and expression data. We also found that HP [13C]urea MRI provided assessments of tumor perfusion similar to those provided by 1H dynamic contrast-enhanced MRI in this animal model. However, apparent diffusion coefficien , a conventional 1H MRI functional biomarker, did not exhibit statistically significant changes within 7 days after RT. CONCLUSION These results demonstrate the ability of HP 13C MRI to monitor radiation-induced physiologic changes in a timely and dose-dependent manner, providing the basic science premise for further clinical investigation and translation.
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Affiliation(s)
- Hecong Qin
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California; Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California
| | - Vickie Zhang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Robert A Bok
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Romelyn Delos Santos
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - J Adam Cunha
- Department of Radiation Oncology, University of California, San Francisco, California
| | - I-Chow Hsu
- Department of Radiation Oncology, University of California, San Francisco, California
| | - Justin Delos Santos Bs
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Jessie E Lee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Subramaniam Sukumar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California; Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California
| | - Daniel B Vigneron
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California; Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California
| | - David M Wilson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Renuka Sriram
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - John Kurhanewicz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California; Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California.
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Dinges SS, Vandergrift LA, Wu S, Berker Y, Habbel P, Taupitz M, Wu CL, Cheng LL. Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer. NMR IN BIOMEDICINE 2019; 32:e4038. [PMID: 30609175 PMCID: PMC7366614 DOI: 10.1002/nbm.4038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 09/05/2018] [Accepted: 10/13/2018] [Indexed: 05/05/2023]
Abstract
In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.
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Affiliation(s)
- Sarah S. Dinges
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114 USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114 USA
- Department of Haematology and Oncology, CCM, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Radiology, Charité Medical University of Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Lindsey A. Vandergrift
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114 USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114 USA
| | - Shulin Wu
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114 USA
| | - Yannick Berker
- Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Piet Habbel
- Department of Haematology and Oncology, CCM, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Matthias Taupitz
- Department of Radiology, Charité Medical University of Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Chin-Lee Wu
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114 USA
| | - Leo L. Cheng
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114 USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114 USA
- Corresponding author: Leo L. Cheng, PhD, 149 13 St, CNY 6, Charlestown, MA 02129, Ph. 617-724-6593,
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Kdadra M, Höckner S, Leung H, Kremer W, Schiffer E. Metabolomics Biomarkers of Prostate Cancer: A Systematic Review. Diagnostics (Basel) 2019; 9:E21. [PMID: 30791464 PMCID: PMC6468767 DOI: 10.3390/diagnostics9010021] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 02/13/2019] [Accepted: 02/14/2019] [Indexed: 12/27/2022] Open
Abstract
Prostate cancer (PCa) diagnosis with current biomarkers is difficult and often results in unnecessary invasive procedures as well as over-diagnosis and over-treatment, highlighting the need for novel biomarkers. The aim of this review is to provide a summary of available metabolomics PCa biomarkers, particularly for clinically significant disease. A systematic search was conducted on PubMed for publications from July 2008 to July 2018 in accordance with PRISMA guidelines to report biomarkers with respect to their application in PCa diagnosis, progression, aggressiveness, recurrence, and treatment response. The vast majority of studies report biomarkers with the ability to distinguish malignant from benign prostate tissue with a few studies investigating biomarkers associated with disease progression, treatment response or tumour recurrence. In general, these studies report high dimensional datasets and the number of analysed metabolites often significantly exceeded the number of available samples. Hence, observed multivariate differences between case and control samples in the datasets might potentially also be associated with pre-analytical, technical, statistical and confounding factors. Giving the technical and methodological hurdles, there are nevertheless a number of metabolites and pathways repeatedly reported across various technical approaches, cohorts and sample types that appear to play a predominant role in PCa tumour biology, progression and recurrence.
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Affiliation(s)
| | | | - Hing Leung
- Institute of Cancer Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G61 1QH, UK.
- CRUK Beatson Institute, Bearsden, Glasgow G61 1BD, UK.
| | - Werner Kremer
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, 93053 Regensburg, Germany.
| | - Eric Schiffer
- Numares AG, Am BioPark 9, 93053 Regensburg, Germany.
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9
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Tayari N, Obels J, Kobus T, Scheenen TWJ, Heerschap A. Simple and broadly applicable automatic quality control for 3D 1 H MR spectroscopic imaging data of the prostate. Magn Reson Med 2018; 81:2887-2895. [PMID: 30506721 DOI: 10.1002/mrm.27616] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 10/13/2018] [Accepted: 10/31/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE Quality control (QC) is a prerequisite for clinical MR spectroscopic imaging (MRSI) to avoid that bad spectra hamper data interpretation. The aim of this work was to present a simple automatic QC for prostate 1 H MRSI that can handle data obtained with different commonly used pulse sequences, echo times, field strengths, and MR platforms. METHODS A QC method was developed with a ratio (Qratio) where the numerator and the denominator are functions of several signal heights, logically combined for their positive or negative contribution to spectral quality. This Qratio was tested on 4 data sets obtained at 1.5, 3, and 7T, with and without endorectal coil and different localization sequences and echo times. Spectra of 25,248 voxels in 26 prostates were labeled as acceptable or unacceptable by MRS experts as gold standard. A threshold value was determined for Qratio from a subset of voxels, labeled in consensus by 4 experts, for an optimal accuracy to separate spectra. RESULTS Applying this Qratio threshold to the remaining test voxels, an automatic separation of good and bad spectra was possible with an accuracy of 0.88, similar to manual separation between the 2 classes. Qratio values were used to generate maps representing spectral quality on a binary or continuous scale. CONCLUSION Automated QC of prostate 1 H MRSI by Qratio is fast, simple, easily transferable and more practical than supervised feature extraction methods and therefore easy to integrate into different clinical MR systems. Moreover, quality maps can be generated to read the reliability of spectra in each voxel.
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Affiliation(s)
- Nassim Tayari
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jiri Obels
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thiele Kobus
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tom W J Scheenen
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Arend Heerschap
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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10
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Press RH, Shu HKG, Shim H, Mountz JM, Kurland BF, Wahl RL, Jones EF, Hylton NM, Gerstner ER, Nordstrom RJ, Henderson L, Kurdziel KA, Vikram B, Jacobs MA, Holdhoff M, Taylor E, Jaffray DA, Schwartz LH, Mankoff DA, Kinahan PE, Linden HM, Lambin P, Dilling TJ, Rubin DL, Hadjiiski L, Buatti JM. The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective. Int J Radiat Oncol Biol Phys 2018; 102:1219-1235. [PMID: 29966725 PMCID: PMC6348006 DOI: 10.1016/j.ijrobp.2018.06.023] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 05/25/2018] [Accepted: 06/14/2018] [Indexed: 02/07/2023]
Abstract
Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.
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Affiliation(s)
- Robert H. Press
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - Hui-Kuo G. Shu
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - Hyunsuk Shim
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - James M. Mountz
- Dept. of Radiology, University of Pittsburgh, Pittsburgh, PA
| | | | | | - Ella F. Jones
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA
| | - Nola M. Hylton
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA
| | - Elizabeth R. Gerstner
- Dept. of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | | | - Lori Henderson
- Cancer Imaging Program, National Cancer Institute, Bethesda, MD
| | | | - Bhadrasain Vikram
- Radiation Research Program/Division of Cancer Treatment & Diagnosis, National Cancer Institute, Bethesda, MD
| | - Michael A. Jacobs
- Dept. of Radiology and Radiological Science, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore MD
| | - Matthias Holdhoff
- Brain Cancer Program, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore MD
| | - Edward Taylor
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - David A. Jaffray
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | | | - David A. Mankoff
- Dept. of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | | | - Philippe Lambin
- Dept. of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Thomas J. Dilling
- Dept. of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | | | | | - John M. Buatti
- Dept. of Radiation Oncology, University of Iowa, Iowa City, IA
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11
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Biomarkers for prostate cancer: present challenges and future opportunities. Future Sci OA 2015; 2:FSO72. [PMID: 28031932 PMCID: PMC5137959 DOI: 10.4155/fso.15.72] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 08/10/2015] [Indexed: 01/30/2023] Open
Abstract
Prostate cancer (PCa) has variable biological potential with multiple treatment options. A more personalized approach, therefore, is needed to better define men at higher risk of developing PCa, discriminate indolent from aggressive disease and improve risk stratification after treatment by predicting the likelihood of progression. This may improve clinical decision-making regarding management, improve selection for active surveillance protocols and minimize morbidity from treatment. Discovery of new biomarkers associated with prostate carcinogenesis present an opportunity to provide patients with novel genetic signatures to better understand their risk of developing PCa and help forecast their clinical course. In this review, we examine the current literature evaluating biomarkers in PCa. We also address current limitations and present several ideas for future studies.
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