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Cui F, Tang X, Man C, Fan Y. Prognostic value of 17-Gene genomic prostate score in patients with clinically localized prostate cancer: a meta-analysis. BMC Cancer 2024; 24:628. [PMID: 38783246 PMCID: PMC11112896 DOI: 10.1186/s12885-024-12389-1] [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: 03/15/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND The 17-gene Genomic Prostate Score (GPS) test has been clinically employed to predict adverse prognosis in prostate cancer. In this meta-analysis, we aimed to evaluate the prognostic value of the 17-gene GPS in patients with prostate cancer. METHODS Potentially relevant studies were obtained by searching PubMed, Web of Science, Embase databases from their inception to December 1, 2023. Studies were considered eligible if they evaluated the association of the 17-gene GPS with distant metastases, biochemical recurrence, or prostate cancer-specific mortality (PCSM) in prostate cancer patients. To estimate the prognostic value, we pooled the adjusted hazard ratio (HR) with 95% confidence intervals (CI) for the high versus low GPS group or per 20-unit increase in GPS. RESULTS Seven cohort studies that reported on 8 articles comprising 1,962 patients satisfied the eligibility criteria. Meta-analysis showed that per 20-unit increase in GPS was significantly associated with distant metastases (HR 2.99; 95% CI 1.97-4.53), biochemical recurrence (HR 2.18; 95% CI 1.64-2.89), and PCSM (HR 3.14; 95% CI 1.86-5.30). Moreover, patients with high GPS (> 40 points) had an increased risk of distant metastases (HR 5.22; 95% CI 3.72-7.31), biochemical recurrence (HR 4.41; 95% CI 2.29-8.49), and PCSM (HR 3.81; 95% CI 1.74-8.33) than those with low GPS (≤ 40 points). CONCLUSIONS A higher 17-gene GPS significantly predicts distant metastases, biochemical recurrence, and PCSM in men with clinically localized prostate cancer. However, large-scale multicenter prospective studies are necessary to further validate these findings.
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Affiliation(s)
- Feilun Cui
- Department of Urology, Affiliated Taizhou Second People's Hospital of Yangzhou University, Taizhou, 225500, China
- Department of Urology, The Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, China
| | - Xuan Tang
- Cancer Institute, The Affiliated People's Hospital, Jiangsu University, No. 8 Dianli Road, Zhenjiang, Zhenjiang, 212002, China
| | - Changfeng Man
- Cancer Institute, The Affiliated People's Hospital, Jiangsu University, No. 8 Dianli Road, Zhenjiang, Zhenjiang, 212002, China.
| | - Yu Fan
- Cancer Institute, The Affiliated People's Hospital, Jiangsu University, No. 8 Dianli Road, Zhenjiang, Zhenjiang, 212002, China.
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2
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Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
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Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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3
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Lorenzo G, Heiselman JS, Liss MA, Miga MI, Gomez H, Yankeelov TE, Reali A, Hughes TJ. A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model. CANCER RESEARCH COMMUNICATIONS 2024; 4:617-633. [PMID: 38426815 PMCID: PMC10906139 DOI: 10.1158/2767-9764.crc-23-0449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/15/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. SIGNIFICANCE Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
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Affiliation(s)
- Guillermo Lorenzo
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| | - Jon S. Heiselman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Michael A. Liss
- Department of Urology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Michael I. Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery, Radiology, and Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Gomez
- School of Mechanical Engineering, Weldon School of Biomedical Engineering, and Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
- Livestrong Cancer Institutes and Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, The University of Texas at Austin, Austin, Texas
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Thomas J.R. Hughes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
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4
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Rade M, Kreuz M, Borkowetz A, Sommer U, Blumert C, Füssel S, Bertram C, Löffler D, Otto DJ, Wöller LA, Schimmelpfennig C, Köhl U, Gottschling AC, Hönscheid P, Baretton GB, Wirth M, Thomas C, Horn F, Reiche K. A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer. Mol Med 2024; 30:19. [PMID: 38302875 PMCID: PMC10835874 DOI: 10.1186/s10020-024-00789-9] [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: 07/11/2023] [Accepted: 01/22/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Clinical manifestation of prostate cancer (PCa) is highly variable. Aggressive tumors require radical treatment while clinically non-significant ones may be suitable for active surveillance. We previously developed the prognostic ProstaTrend RNA signature based on transcriptome-wide microarray and RNA-sequencing (RNA-Seq) analyses, primarily of prostatectomy specimens. An RNA-Seq study of formalin-fixed paraffin-embedded (FFPE) tumor biopsies has now allowed us to use this test as a basis for the development of a novel test that is applicable to FFPE biopsies as a tool for early routine PCa diagnostics. METHODS All patients of the FFPE biopsy cohort were treated by radical prostatectomy and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of 176 FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves and Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. In addition, we developed a prostate single-cell atlas of 41 PCa patients from 5 publicly available studies to analyze gene expression of ProstaTrend genes in different cell compartments. RESULTS Validation of the TRS using the original ProstaTrend signature in the cohort of FFPE biopsies revealed a relevant impact of FFPE-associated degradation on gene expression and consequently no significant association with prognosis (Cox-regression, p-value > 0.05) in FFPE tissue. However, the TRS based on the new version of the ProstaTrend-ffpe signature, which included 204 genes (of originally 1396 genes), was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value < 0.001) and retained prognostic relevance when adjusted for Gleason Grade Groups. We confirmed a significant association with BCR in 9 independent cohorts including 1109 patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that ProstaTrend-ffpe was among the best-ranked panels. We generated a PCa cell atlas to associate ProstaTrend genes with cell lineages or cell types. Tumor-specific luminal cells have a significantly higher TRS than normal luminal cells in all analyzed datasets. In addition, TRS of epithelial and luminal cells was correlated with increased Gleason score in 3 studies. CONCLUSIONS We developed a prognostic gene-expression signature for PCa that can be applied to FFPE biopsies and may be suitable to support clinical decision-making.
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Affiliation(s)
- Michael Rade
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Markus Kreuz
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Angelika Borkowetz
- Department of Urology, Faculty of Medicine, University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Ulrich Sommer
- Institute of Pathology, Faculty of Medicine, University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Conny Blumert
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Susanne Füssel
- Department of Urology, Faculty of Medicine, University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Catharina Bertram
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Dennis Löffler
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Dominik J Otto
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
- Basic Science Division, Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Livia A Wöller
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Carolin Schimmelpfennig
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Ulrike Köhl
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
- Institute of Clinical Immunology, University of Leipzig, Leipzig, Germany
| | - Ann-Cathrin Gottschling
- Department of Urology, Faculty of Medicine, University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Pia Hönscheid
- Institute of Pathology, Faculty of Medicine, University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Gustavo B Baretton
- Institute of Pathology, Faculty of Medicine, University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Manfred Wirth
- Department of Urology, Faculty of Medicine, University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Christian Thomas
- Department of Urology, Faculty of Medicine, University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Friedemann Horn
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.
- Institute of Clinical Immunology, University of Leipzig, Leipzig, Germany.
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), University of Leipzig, 04105, Leipzig, Germany.
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5
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Boyer MJ, Carpenter DJ, Gingrich JR, Raman SR, Sirohi D, Tabriz AA, Rompre-Broduer A, Lunyera J, Basher F, Bitting RL, Kosinski A, Cantrell S, Gordon AM, Ear B, Gierisch JM, Jacobs M, Goldstein KM. Genomic classifiers and prognosis of localized prostate cancer: a systematic review. Prostate Cancer Prostatic Dis 2024:10.1038/s41391-023-00766-z. [PMID: 38200096 DOI: 10.1038/s41391-023-00766-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/26/2023] [Accepted: 11/20/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND Refinement of the risk classification for localized prostate cancer is warranted to aid in clinical decision making. A systematic analysis was undertaken to evaluate the prognostic ability of three genomic classifiers, Decipher, GPS, and Prolaris, for biochemical recurrence, development of metastases and prostate cancer-specific mortality in patients with localized prostate cancer. METHODS Data sources: MEDLINE, Embase, and Web of Science were queried for reports published from January 2010 to April 2022. STUDY SELECTION prospective or retrospective studies reporting prognosis for patients with localized prostate cancer. DATA EXTRACTION relevant data were extracted into a customized database by one researcher with a second overreading. Risk of bias was assessed using a validated tool for prognostic studies, Quality in Prognosis Studies (QUIPS). Disagreements were resolved by consensus or by input from a third reviewer. We assessed the certainty of evidence by GRADE incorporating adaptation for prognostic studies. RESULTS Data synthesis: a total of 39 studies (37 retrospective) involving over 10,000 patients were identified. Twenty-two assessed Decipher, 5 GPS, and 14 Prolaris. Thirty-four studies included patients who underwent prostatectomy. Based on very low to low certainty of evidence, each of the three genomic classifiers modestly improved upon the prognostic ability for biochemical recurrence, development of metastases, and prostate cancer-specific mortality compared to standard clinical risk-classification schemes. LIMITATIONS downgrading of confidence in the evidence stemmed largely from bias due to the retrospective nature of the studies, heterogeneity in treatment received, and era in which patients were treated (i.e., prior to the 2000s). CONCLUSIONS Genomic classifiers provide a small but consistent improvement upon the prognostic ability of clinical classification schemes, which may be helpful when treatment decisions are uncertain. However, evidence from current management-era data and of the predictive ability of these tests is needed.
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Affiliation(s)
- Matthew J Boyer
- Durham VA Health Care System, Durham, NC, USA.
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.
| | | | - Jeffrey R Gingrich
- Durham VA Health Care System, Durham, NC, USA
- Department of Urology, Duke University School of Medicine, Durham, NC, USA
| | - Sudha R Raman
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Deepika Sirohi
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Amir Alishahi Tabriz
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Joseph Lunyera
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Fahmin Basher
- Division of Medical Oncology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Rhonda L Bitting
- Durham VA Health Care System, Durham, NC, USA
- Division of Medical Oncology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Andrzej Kosinski
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Sarah Cantrell
- Duke University Medical Center Library & Archives, Duke University School of Medicine, Durham, NC, USA
| | | | - Belinda Ear
- Durham VA Health Care System, Durham, NC, USA
| | - Jennifer M Gierisch
- Durham VA Health Care System, Durham, NC, USA
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Department of Population Health, Duke University School of Medicine, Durham, NC, USA
| | | | - Karen M Goldstein
- Durham VA Health Care System, Durham, NC, USA
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
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Dinis Fernandes C, Schaap A, Kant J, van Houdt P, Wijkstra H, Bekers E, Linder S, Bergman AM, van der Heide U, Mischi M, Zwart W, Eduati F, Turco S. Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer. Cancers (Basel) 2023; 15:3074. [PMID: 37370685 DOI: 10.3390/cancers15123074] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.
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Affiliation(s)
- Catarina Dinis Fernandes
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Annekoos Schaap
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Joan Kant
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Petra van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Hessel Wijkstra
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Department of Urology, Amsterdam University Medical Centers, 1100 DD Amsterdam, The Netherlands
| | - Elise Bekers
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Simon Linder
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Andries M Bergman
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Division of Medical Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Uulke van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Massimo Mischi
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Wilbert Zwart
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Federica Eduati
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Simona Turco
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
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7
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Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains. Sci Rep 2022; 12:9329. [PMID: 35665770 PMCID: PMC9167293 DOI: 10.1038/s41598-022-13332-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 05/23/2022] [Indexed: 12/20/2022] Open
Abstract
Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or “optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel “optical stains” with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.
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8
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Lehto TPK, Stürenberg C, Malén A, Erickson AM, Koistinen H, Mills IG, Rannikko A, Mirtti T. Transcript analysis of commercial prostate cancer risk stratification panels in hard-to-predict grade group 2-4 prostate cancers. Prostate 2021; 81:368-376. [PMID: 33734461 DOI: 10.1002/pros.24108] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 01/22/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND Improved prognostication is needed to minimize overtreatment in grade group (GG) 2-4 prostate cancer. Our aim was to determine, at messenger RNA (mRNA) level, the performance of the genes in the commercial panels Decipher, Oncotype DX, Prolaris, and mutational panel MSK-IMPACT to predict metastasis-free and prostate cancer-specific death (PCSD) in patients with GG 2-4 prostate cancer at radical prostatectomy. METHODS The retrospective cohort consisted of GG 2-4 patients treated with radical prostatectomy (median follow-up 10.4 years). Seventy-six cases with postoperative metastasis or PCSD and 84 controls with similar clinical baseline risk, but without progression, were analyzed. Index lesion mRNA transcripts were analyzed using NanoString technology. Random forest models were trained using panel gene sets to predict clinical endpoints and area under the curve (AUC), sensitivity, specificity, Youden index, and number needed to diagnose (NND) was measured. Survival probability was assessed with Kaplan-Meier estimator. RESULTS All gene sets outperformed clinical parameters and predicted metastasis-free and prostate cancer-specific survival. However, there were significant differences between the panels. In metastasis prediction, the genes in Oncotype DX had inferior performance (area under the curve [AUC] = 0.65) compared to other panels (AUC = 0.73-0.74). Decipher, MSK-IMPACT and Prolaris showed similar NND (2.83-3.12) with Oncotype DX having highest NND (4.79). In PCSD prediction, the Prolaris gene set performed worse (AUC = 0.66) than MSK-IMPACT or Decipher (AUC = 0.72). Oncotype DX performed similarly to other panels (AUC = 0.69, p > .05). Oncotype DX demonstrated lowest NND (2.79) compared to other panels (4.22-5.66). CONCLUSION Transcript analysis of genes included in commercial panels is feasible in survival prediction of GG 2-4 patients after radical prostatectomy and may aid in clinical decision making. There were significant differences between the panels, and overall stronger predictive gene sets are needed. Prospective investigation is warranted in biopsy materials.
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Affiliation(s)
- Timo-Pekka K Lehto
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Carolin Stürenberg
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Adrian Malén
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Andrew M Erickson
- Nuffield Department of Surgical Sciences, University of Oxford, Oxfordshire, UK
| | - Hannu Koistinen
- Department of Clinical Chemistry and Haematology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Ian G Mills
- Nuffield Department of Surgical Sciences, University of Oxford, Oxfordshire, UK
- School of Medicine, Dentistry and Biomedical Sciences, Patrick G Johnston Center for Cancer Research, Queen's University of Belfast, UK
| | - Antti Rannikko
- Department of Urology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas Mirtti
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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9
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Berends J, Dupati A, Dibianco J, George AK. Focal Therapy is a viable treatment for low risk Prostate cancer. J Endourol 2021; 35:1281-1283. [PMID: 33849341 DOI: 10.1089/end.2021.0235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Focal therapy (FT) is a promising alternative to definitive therapy for localized prostate cancer (PCa). Rather than simply monitoring cancer through active surveillance (AS) or risking the side effects of robotic radical prostatectomy (RP) it offers an intermediate solution. Azzouzi et al. examined vascular-targeted photodynamic therapy (PDT) for PCa. In this randomized, controlled clinical trial, patients with low-risk and localized PCa with no prior treatment were either assigned to a group undergoing vascular-targeted photodynamic therapy or to another group undergoing AS. Participants in the PDT group had a longer median disease progression time, lower proportion of disease progression at 24 months after treatment, and a higher proportion of negative biopsies at 24 months after treatment than the patients in the AS group. Use of PDT was associated with almost a five-fold decrease in reduction of use of definitive therapy. Cryoablation and high intensity focused ultrasound (HIFU) are additional forms of FT for localized PCa. They have been associated with a 5-year metastasis-free survival rate of up to 98% in low or intermediate risk patients. Patients undergoing salvage RP after FT may also see benefits compared to those who undergo RP after radiation therapy (RT). Ribiero et al. compared toxicity and oncological outcomes of patients who underwent salvage RP (sRP) after FT with those who underwent sRP after RT. Patients who underwent sRP after FT were found to have better continence at 12 months after surgery, almost 3 times less the rate of positive surgical margins, and lower postoperative complication rates within the first 30 days of surgery than those who had sRP after undergoing RT. Methods of FT including PDT, HIFU, and cryoablation provide an intermediate treatment option between AS and RP. Further research should be conducted to look into the potential benefits of FT for localized PCa.
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Affiliation(s)
- Joel Berends
- University of Michigan, Department of Urology, Ann Arbor, Michigan, United States;
| | - Ajith Dupati
- University of Michigan, 1259, Department of Urology, Ann Arbor, Michigan, United States;
| | - John Dibianco
- University of Michigan, Department of Urology, Ann Arbor, United States;
| | - Arvin K George
- University of Michigan, 1259, Department of Urology, Ann Arbor, Michigan, United States;
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10
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Degeling K, Pereira-Salgado A, Corcoran NM, Boutros PC, Kuhn P, IJzerman MJ. Health Economic Evidence for Liquid- and Tissue-based Molecular Tests that Inform Decisions on Prostate Biopsies and Treatment of Localised Prostate Cancer: A Systematic Review. EUR UROL SUPPL 2021; 27:77-87. [PMID: 34337517 PMCID: PMC8317795 DOI: 10.1016/j.euros.2021.03.002] [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] [Accepted: 03/06/2021] [Indexed: 11/16/2022] Open
Abstract
Context Several liquid- and tissue-based biomarker tests (LTBTs) are available to inform the need for prostate biopsies and treatment of localised prostate cancer (PCa) through risk stratification, but translation into routine practice requires evidence of their clinical utility and economic impact. Objective To review and summarise the health economic evidence on the ability of LTBTs to inform decisions on prostate biopsies and treatment of localised PCa through risk stratification. Evidence acquisition A systematic search was performed in the EMBASE, MEDLINE, Health Technology Assessment, and National Health Service Health Economic Evaluation databases. Eligible publications were those presenting health economic evaluations of an LTBT to select individuals for biopsy or risk-stratify PCa patients for treatment. Data on the study objectives, context, methodology, clinical utility, and outcomes were extracted and summarised. Evidence synthesis Of the 22 studies included, 14 were focused on test-informed biopsies and eight on treatment selection. Most studies performed cost-effectiveness analyses (n = 7), followed by costing (n = 4) or budget impact analyses (n = 3). Most (18 of 22) studies concluded that biomarker tests could decrease health care costs or would be cost-effective. However, downstream consequences and long-term outcomes were typically not included in studies that evaluated LTBT to inform biopsies. Long-term effectiveness was modelled by linking evidence from different sources instead of using data from prospective studies. Conclusions Although studies concluded that LTBTs would probably be cost-saving or -effective, the strength of this evidence is disputable because of concerns around the validity and transparency of the assumptions made. This warrants prospective interventional trials to inform health economic analyses to ensure collection of direct evidence of clinical outcomes based on LTBT use. Patient summary We reviewed studies that evaluated whether blood, urine, and tissue tests can reduce the health and economic burden of prostate cancer. Results indicate that these tests could be cost-effective, but clinical studies of long-term outcomes are needed to confirm the findings.
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Affiliation(s)
- Koen Degeling
- Cancer Health Services Research, Centre for Cancer Research, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia.,Cancer Health Services Research, Centre for Health Policy, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia
| | - Amanda Pereira-Salgado
- Cancer Health Services Research, Centre for Cancer Research, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia.,Cancer Health Services Research, Centre for Health Policy, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia
| | - Niall M Corcoran
- Department of Urology, Frankston Hospital, Frankston, Australia.,Department of Surgery, The University of Melbourne, Melbourne, Australia.,Division of Urology, Royal Melbourne Hospital, Melbourne, Australia
| | - Paul C Boutros
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA, USA.,Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA.,Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA, USA.,Departments of Human Genetics and Urology, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Peter Kuhn
- USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.,Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, CA, USA.,Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Maarten J IJzerman
- Cancer Health Services Research, Centre for Cancer Research, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia.,Cancer Health Services Research, Centre for Health Policy, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia.,Department of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Australia.,Health Technology and Services Research, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, The Netherlands
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11
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Wu X, Lv D, Eftekhar M, Khan A, Cai C, Zhao Z, Gu D, Liu Y. A new risk stratification system of prostate cancer to identify high-risk biochemical recurrence patients. Transl Androl Urol 2020; 9:2572-2586. [PMID: 33457230 PMCID: PMC7807327 DOI: 10.21037/tau-20-1019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Background Biochemical recurrence (BCR) is considered a decisive risk factor for clinical recurrence and the metastasis of prostate cancer (PCa). Therefore, we developed and validated a signature which could be used to accurately predict BCR risk and aid in the selection of PCa treatments. Methods A comprehensive genome-wide analysis of data concerning PCa from previous datasets of the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) was performed. Lasso and Cox regression analyses were performed to develop and validate a novel signature to help predict BCR risk. Moreover, a nomogram was constructed by combining the signature and clinical variables. Results A total of 977 patients were involved in the study. This consisted of patients from the TCGA (n=405), GSE21034 (n=131), GSE70770 (n=193) and GSE116918 (n=248) datasets. A 9-mRNA signature was identified in the TCGA dataset (composed of C9orf152, EPHX2, ASPM, MMP11, CENPF, KIF4A, COL1A1, ASPN, and FANCI) which was significantly associated with BCR (HR =3.72, 95% CI: 2.30-6.00, P<0.0001). This signature was validated in the GSE21034 (HR =7.54, 95% CI: 3.15-18.06, P=0.019), GSE70770 (HR =2.52, 95% CI: 1.50-4.22, P=0.0025) and GSE116918 datasets (HR =4.75, 95% CI: 2.51-9.02, P=0.0035). Multivariate Cox regression and stratified analysis showed that the 9-mRNA signature was a clinical factor independent of prostate-specific antigen (PSA), Gleason score (GS), or AJCC T staging. The mean AUC for 5-year BCR-free survival predictions of the 9-mRNA signature (0.81) was higher than the AUC for PSA, GS, or AJCC T staging (0.52-0.73). Furthermore, we combined the 9-mRNA signature with PSA, GS, or AJCC T staging and demonstrated that this could enhance prognostic accuracy. Conclusions The proposed 9-mRNA signature is a promising biomarker for predicting BCR-free survival in PCa. However, further controlled trials are needed to validate our results and explore a role in individualized management of PCa.
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Affiliation(s)
- Xiangkun Wu
- Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China
| | - Daojun Lv
- Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China
| | - Md Eftekhar
- Department of Family Medicine, CanAm International Medical Center, Shenzhen, China
| | - Aisha Khan
- Department of Family Medicine, Yunshan Medical Hospital, Shenzhen, China
| | - Chao Cai
- Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China
| | - Zhijian Zhao
- Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China
| | - Di Gu
- Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China
| | - Yongda Liu
- Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China
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12
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Carrion A, Ingelmo-Torres M, Lozano JJ, Montalbo R, D'Anna M, Mercader C, Velez E, Ribal MJ, Alcaraz A, Mengual L. Prognostic classifier for predicting biochemical recurrence in localized prostate cancer patients after radical prostatectomy. Urol Oncol 2020; 39:493.e17-493.e25. [PMID: 33189527 DOI: 10.1016/j.urolonc.2020.10.075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/23/2020] [Accepted: 10/29/2020] [Indexed: 11/28/2022]
Abstract
OBJECTIVE The purpose of the study was to develop an improved classifier for predicting biochemical recurrence (BCR) in clinically localized PCa patients after radical prostatectomy. METHODS AND MATERIALS Retrospective study including 122 PCa patients who attended our department between 2000 and 2007. Gene expression patterns were analyzed in 21 samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in 101 independent PCa patients who underwent radical prostatectomy. Logistic regression analysis was used to identify individual predictors of BCR. A risk score for predicting BCR including clinicopathological and gene expression variables was developed. Interaction networks were built by GeneMANIA Cytoscape plugin. RESULTS A total of 37 patients developed BCR (36.6%) in a median follow-up of 120 months. Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR < 0.1). We found that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion, and positive margins were independent prognostic factors of BCR. A risk score generated using these variables was able to discriminate between 2 groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, P< 0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the 2 genes of the model were not found. CONCLUSION Combination of gene expression patterns and clinicopathological variables in a robust, easy-to-use, and reliable classifier may contribute to improve PCa risk stratification.
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Affiliation(s)
- Albert Carrion
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Mercedes Ingelmo-Torres
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Juan José Lozano
- CIBERehd. Plataforma de Bioinformática, Centro de Investigación Biomédica en red de Enfermedades Hepáticas y Digestivas, Spain
| | - Ruth Montalbo
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Maurizio D'Anna
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Clàudia Mercader
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Elena Velez
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Maria José Ribal
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Antonio Alcaraz
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Lourdes Mengual
- Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.
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13
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Rochow H, Jung M, Weickmann S, Ralla B, Stephan C, Elezkurtaj S, Kilic E, Zhao Z, Jung K, Fendler A, Franz A. Circular RNAs and Their Linear Transcripts as Diagnostic and Prognostic Tissue Biomarkers in Prostate Cancer after Prostatectomy in Combination with Clinicopathological Factors. Int J Mol Sci 2020; 21:ijms21217812. [PMID: 33105568 PMCID: PMC7672590 DOI: 10.3390/ijms21217812] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/13/2020] [Accepted: 10/20/2020] [Indexed: 12/13/2022] Open
Abstract
As new biomarkers, circular RNAs (circRNAs) have been largely unexplored in prostate cancer (PCa). Using an integrative approach, we aimed to evaluate the potential of circRNAs and their linear transcripts (linRNAs) to act as (i) diagnostic biomarkers for differentiation between normal and tumor tissue and (ii) prognostic biomarkers for the prediction of biochemical recurrence (BCR) after radical prostatectomy. In a first step, eight circRNAs (circATXN10, circCRIM1, circCSNK1G3, circGUCY1A2, circLPP, circNEAT1, circRHOBTB3, and circSTIL) were identified as differentially expressed via a genome-wide circRNA-based microarray analysis of six PCa samples. Additional bioinformatics and literature data were applied for this selection process. In total, 115 malignant PCa and 79 adjacent normal tissue samples were examined using robust RT-qPCR assays specifically established for the circRNAs and their linear counterparts. Their diagnostic and prognostic potential was evaluated using receiver operating characteristic curves, Cox regressions, decision curve analyses, and C-statistic calculations of prognostic indices. The combination of circATXN10 and linSTIL showed a high discriminative ability between malignant and adjacent normal tissue PCa. The combination of linGUCY1A2, linNEAT1, and linSTIL proved to be the best predictive RNA-signature for BCR. The combination of this RNA signature with five established reference models based on only clinicopathological factors resulted in an improved predictive accuracy for BCR in these models. This is an encouraging study for PCa to evaluate circRNAs and their linRNAs in an integrative approach, and the results showed their clinical potential in combination with standard clinicopathological variables.
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Affiliation(s)
- Hannah Rochow
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
- Berlin Institute for Urologic Research, 10115 Berlin, Germany
| | - Monika Jung
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
| | - Sabine Weickmann
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
| | - Bernhard Ralla
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
| | - Carsten Stephan
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
- Berlin Institute for Urologic Research, 10115 Berlin, Germany
| | - Sefer Elezkurtaj
- Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (S.E.); (E.K.)
| | - Ergin Kilic
- Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (S.E.); (E.K.)
- Institute of Pathology, Hospital Leverkusen, 51375 Leverkusen, Germany
| | - Zhongwei Zhao
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
- Department of Urology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Klaus Jung
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
- Berlin Institute for Urologic Research, 10115 Berlin, Germany
- Correspondence: ; Tel.: +49-450-515041
| | - Annika Fendler
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
- Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Cancer Research Program, 13125 Berlin, Germany
- Cancer Dynamics Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Antonia Franz
- Department of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (H.R.); (M.J.); (S.W.); (B.R.); (C.S.); (Z.Z.); (A.F.); (A.F.)
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14
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Kreuz M, Otto DJ, Fuessel S, Blumert C, Bertram C, Bartsch S, Loeffler D, Puppel SH, Rade M, Buschmann T, Christ S, Erdmann K, Friedrich M, Froehner M, Muders MH, Schreiber S, Specht M, Toma MI, Benigni F, Freschi M, Gandaglia G, Briganti A, Baretton GB, Loeffler M, Hackermüller J, Reiche K, Wirth M, Horn F. ProstaTrend-A Multivariable Prognostic RNA Expression Score for Aggressive Prostate Cancer. Eur Urol 2020; 78:452-459. [PMID: 32631745 DOI: 10.1016/j.eururo.2020.06.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 06/02/2020] [Indexed: 01/02/2023]
Abstract
BACKGROUND Prostate cancer (PCa) is the most prevalent solid cancer among men in Western Countries. The clinical behavior of localized PCa is highly variable. Some cancers are aggressive leading to death, while others can even be monitored safely. Hence, there is a high clinical need for precise biomarkers for identification of aggressive disease in addition to established clinical parameters. OBJECTIVE To develop an RNA expression-based score for the prediction of PCa prognosis that facilitates clinical decision making. DESIGN, SETTING, AND PARTICIPANTS We assessed 233 tissue specimens of PCa patients with long-term follow-up data from fresh-frozen radical prostatectomies (RPs), from formalin-fixed and paraffin-embedded RP specimens and biopsies by transcriptome-wide next-generation sequencing and customized expression microarrays. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS We applied Cox proportional hazard models to the cohorts from different platforms and specimen types. Evidence from these models was combined by fixed-effect meta-analysis to identify genes predictive of the time to death of disease (DoD). Genes were combined by a weighted median approach into a prognostic score called ProstaTrend and transferred for the prediction of biochemical recurrence (BCR) after RP in an independent cohort of The Cancer Genome Atlas (TCGA). RESULTS AND LIMITATIONS ProstaTrend comprising ∼1400 genes was significantly associated with DoD in the training cohort of PCa patients treated by RP (leave-one-out cross-validation, Cox regression: p=2e-09) and with BCR in the TCGA validation cohort (Cox regression: p=3e-06). The prognostic impact persisted after multivariable Cox regression analysis adjusting for Gleason grading group (GG) ≥3 and resection status (p=0.001; DoD, training cohort) and for GG≥3, pathological stage ≥T3, and resection state (p=0.037; BCR, validation cohort). CONCLUSIONS ProstaTrend is a transcriptome-based score that predicts DoD and BCR in cohorts of PCa patients treated with RP. PATIENT SUMMARY ProstaTrend provides molecular patient risk stratification after radical prostatectomy.
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Affiliation(s)
- Markus Kreuz
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany; Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Dominik J Otto
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany; Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany
| | - Susanne Fuessel
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Conny Blumert
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany; Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany
| | - Catharina Bertram
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Sophie Bartsch
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Dennis Loeffler
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany; Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany
| | - Sven-Holger Puppel
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Michael Rade
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Tilo Buschmann
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Sabina Christ
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Kati Erdmann
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT), Dresden, Germany
| | - Maik Friedrich
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany; Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany
| | - Michael Froehner
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; Department of Urology, Zeisigwaldkliniken Bethanien, Chemnitz, Germany
| | - Michael H Muders
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; Rudolf-Becker-Laboratory for Prostate Cancer Research, Institute of Pathology, University of Bonn Medical Center, Bonn, Germany
| | | | - Michael Specht
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Marieta I Toma
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Fabio Benigni
- Department of Urology and Division of Experimental Oncology, URI, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Freschi
- Pathology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giorgio Gandaglia
- Department of Urology and Division of Experimental Oncology, URI, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alberto Briganti
- Department of Urology and Division of Experimental Oncology, URI, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Gustavo B Baretton
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | | | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany; Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany.
| | - Manfred Wirth
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Friedemann Horn
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany; Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany
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15
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A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature. Cancers (Basel) 2019; 11:cancers11101603. [PMID: 31640261 PMCID: PMC6826532 DOI: 10.3390/cancers11101603] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 10/17/2019] [Indexed: 12/24/2022] Open
Abstract
Within five to ten years after radical prostatectomy (RP), approximately 15–34% of prostate cancer (PCa) patients experience biochemical recurrence (BCR), which is defined as recurrence of serum levels of prostate-specific antigen >0.2 µg/L, indicating probable cancer recurrence. Models using clinicopathological variables for predicting this risk for patients lack accuracy. There is hope that new molecular biomarkers, like microRNAs (miRNAs), could be potential candidates to improve risk prediction. Therefore, we evaluated the BCR prognostic capability of 20 miRNAs, which were selected by a systematic literature review. MiRNA expressions were measured in formalin-fixed, paraffin-embedded (FFPE) tissue RP samples of 206 PCa patients by RT-qPCR. Univariate and multivariate Cox regression analyses were performed, to assess the independent prognostic potential of miRNAs. Internal validation was performed, using bootstrapping and the split-sample method. Five miRNAs (miR-30c-5p/31-5p/141-3p/148a-3p/miR-221-3p) were finally validated as independent prognostic biomarkers. Their prognostic ability and accuracy were evaluated using C-statistics of the obtained prognostic indices in the Cox regression, time-dependent receiver-operating characteristics, and decision curve analyses. Models of miRNAs, combined with relevant clinicopathological factors, were built. The five-miRNA-panel outperformed clinically established BCR scoring systems, while their combination significantly improved predictive power, based on clinicopathological factors alone. We conclude that this miRNA-based-predictor panel will be worth to be including in future studies.
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