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Gil JF, Moura CS, Silverio V, Gonçalves G, Santos HA. Cancer Models on Chip: Paving the Way to Large-Scale Trial Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300692. [PMID: 37103886 DOI: 10.1002/adma.202300692] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 04/05/2023] [Indexed: 06/19/2023]
Abstract
Cancer kills millions of individuals every year all over the world (Global Cancer Observatory). The physiological and biomechanical processes underlying the tumor are still poorly understood, hindering researchers from creating new, effective therapies. Inconsistent results of preclinical research, in vivo testing, and clinical trials decrease drug approval rates. 3D tumor-on-a-chip (ToC) models integrate biomaterials, tissue engineering, fabrication of microarchitectures, and sensory and actuation systems in a single device, enabling reliable studies in fundamental oncology and pharmacology. This review includes a critical discussion about their ability to reproduce the tumor microenvironment (TME), the advantages and drawbacks of existing tumor models and architectures, major components and fabrication techniques. The focus is on current materials and micro/nanofabrication techniques used to manufacture reliable and reproducible microfluidic ToC models for large-scale trial applications.
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Affiliation(s)
- João Ferreira Gil
- Centre for Rapid and Sustainable Product Development, Polytechnic of Leiria, Marinha Grande, 2430-028, Portugal
- INESC Microsistemas e Nanotecnologias (INESC MN), Rua Alves Redol 9, Lisbon, 1000-029, Portugal
- TEMA, Mechanical Engineering Department, University of Aveiro, Aveiro, 3810-193, Portugal
| | - Carla Sofia Moura
- Centre for Rapid and Sustainable Product Development, Polytechnic of Leiria, Marinha Grande, 2430-028, Portugal
- Polytechnic Institute of Coimbra, Applied Research Institute, Coimbra, 3045-093, Portugal
| | - Vania Silverio
- INESC Microsistemas e Nanotecnologias (INESC MN), Rua Alves Redol 9, Lisbon, 1000-029, Portugal
- Department of Physics, Instituto Superior Técnico, Lisbon, 1049-001, Portugal
- Associate Laboratory Institute for Health and Bioeconomy - i4HB, Lisbon, Portugal
| | - Gil Gonçalves
- TEMA, Mechanical Engineering Department, University of Aveiro, Aveiro, 3810-193, Portugal
- Intelligent Systems Associate Laboratory (LASI), Aveiro, 3810-193, Portugal
| | - Hélder A Santos
- Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, Groningen, 9713 AV, The Netherlands
- W.J. Korf Institute for Biomedical Engineering and Materials Science, University Medical Center Groningen, University of Groningen, Groningen, 9713 AV, The Netherlands
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, 00014, Finland
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Wardale L, Cardenas R, Gnanapragasam VJ, Cooper CS, Clark J, Brewer DS. Combining Molecular Subtypes with Multivariable Clinical Models Has the Potential to Improve Prediction of Treatment Outcomes in Prostate Cancer at Diagnosis. Curr Oncol 2022; 30:157-170. [PMID: 36661662 PMCID: PMC9857957 DOI: 10.3390/curroncol30010013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/16/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Clinical management of prostate cancer is challenging because of its highly variable natural history and so there is a need for improved predictors of outcome in non-metastatic men at the time of diagnosis. In this study we calculated the model score from the leading clinical multivariable model, PREDICT prostate, and the poor prognosis DESNT molecular subtype, in a combined expression and clinical dataset that were taken from malignant tissue at prostatectomy (n = 359). Both PREDICT score (p < 0.0001, IQR HR = 1.59) and DESNT score (p < 0.0001, IQR HR = 2.08) were significant predictors for time to biochemical recurrence. A joint model combining the continuous PREDICT and DESNT score (p < 0.0001, IQR HR = 1.53 and 1.79, respectively) produced a significantly improved predictor than either model alone (p < 0.001). An increased probability of mortality after diagnosis, as estimated by PREDICT, was characterised by upregulation of cell-cycle related pathways and the downregulation of metabolism and cholesterol biosynthesis. The DESNT molecular subtype has distinct biological characteristics to those associated with the PREDICT model. We conclude that the inclusion of biological information alongside current clinical prognostic tools has the potential to improve the ability to choose the optimal treatment pathway for a patient.
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Affiliation(s)
- Lewis Wardale
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Ryan Cardenas
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Vincent J. Gnanapragasam
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
- Division of Urology, Department of Surgery, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Colin S. Cooper
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Jeremy Clark
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Daniel S. Brewer
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
- The Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK
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Frantzi M, Heidegger I, Roesch MC, Gomez-Gomez E, Steiner E, Vlahou A, Mullen W, Guler I, Merseburger AS, Mischak H, Culig Z. Validation of diagnostic nomograms based on CE-MS urinary biomarkers to detect clinically significant prostate cancer. World J Urol 2022; 40:2195-2203. [PMID: 35841414 PMCID: PMC9427869 DOI: 10.1007/s00345-022-04077-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 06/09/2022] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Prostate cancer (PCa) is one of the most common cancers and one of the leading causes of death worldwide. Thus, one major issue in PCa research is to accurately distinguish between indolent and clinically significant (csPCa) to reduce overdiagnosis and overtreatment. In this study, we aim to validate the usefulness of diagnostic nomograms (DN) to detect csPCa, based on previously published urinary biomarkers. METHODS Capillary electrophoresis/mass spectrometry was employed to validate a previously published biomarker model based on 19 urinary peptides specific for csPCa. Added value of the 19-biomarker (BM) model was assessed in diagnostic nomograms including prostate-specific antigen (PSA), PSA density and the risk calculator from the European Randomized Study of Screening. For this purpose, urine samples from 147 PCa patients were collected prior to prostate biopsy and before performing digital rectal examination (DRE). The 19-BM score was estimated via a support vector machine-based software based on the pre-defined cutoff criterion of - 0.07. DNs were subsequently developed to assess added value of integrative diagnostics. RESULTS Independent validation of the 19-BM resulted in an 87% sensitivity and 65% specificity, with an AUC of 0.81, outperforming PSA (AUC PSA: 0.64), PSA density (AUC PSAD: 0.64) and ERSPC-3/4 risk calculator (0.67). Integration of 19-BM with the rest clinical variables into distinct DN, resulted in improved (AUC range: 0.82-0.88) but not significantly better performances over 19-BM alone. CONCLUSION 19-BM alone or upon integration with clinical variables into DN, might be useful for detecting csPCa by decreasing the number of biopsies.
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Affiliation(s)
- Maria Frantzi
- Department of Biomarker Research, Mosaiques Diagnostics, Hannover, Germany
| | - Isabel Heidegger
- Experimental Urology Department of Urology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Marie C. Roesch
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Enrique Gomez-Gomez
- Urology Department, Reina Sofía University Hospital, Maimonides Institute of Biomedical Research of Cordoba (IMIBIC), University of Cordoba (UCO), Cordoba, Spain
| | - Eberhard Steiner
- Experimental Urology Department of Urology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Antonia Vlahou
- Systems Biology Center, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - William Mullen
- Institute of Cardiovascular and Medical Science, University of Glasgow, Glasgow, UK
| | - Ipek Guler
- Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), Katholiek Universiteit (KU) Leuven, University of Leuven, Leuven, Belgium
| | - Axel S. Merseburger
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Harald Mischak
- Department of Biomarker Research, Mosaiques Diagnostics, Hannover, Germany ,Institute of Cardiovascular and Medical Science, University of Glasgow, Glasgow, UK
| | - Zoran Culig
- Experimental Urology Department of Urology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
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Boldrini L, Faviana P, Galli L, Paolieri F, Erba PA, Bardi M. Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database. Genes (Basel) 2021; 12:1350. [PMID: 34573332 PMCID: PMC8468120 DOI: 10.3390/genes12091350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/20/2021] [Accepted: 08/26/2021] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PC) is a polygenic disease with multiple gene interactions. Therefore, a detailed analysis of its epidemiology and evaluation of risk factors can help to identify more accurate predictors of aggressive disease. We used the transcriptome data from a cohort of 243 patients from the Cancer Genome Atlas (TCGA) database. Key regulatory genes involved in proliferation activity, in the regulation of stress, and in the regulation of inflammation processes of the tumor microenvironment were selected to test a priori multi-dimensional scaling (MDS) models and create a combined score to better predict the patients' survival and disease-free intervals. Survival was positively correlated with cortisol expression and negatively with Mini-Chromosome Maintenance 7 (MCM7) and Breast-Related Cancer Antigen2 (BRCA2) expression. The disease-free interval was negatively related to the expression of enhancer of zeste homolog 2 (EZH2), MCM7, BRCA2, and programmed cell death 1 ligand 1 (PD-L1). MDS suggested two separate pathways of activation in PC. Within these two dimensions three separate clusters emerged: (1) cortisol and brain-derived neurotrophic factor BDNF, (2) PD-L1 and cytotoxic-T-lymphocyte-associated protein 4 (CTL4); (3) and finally EZH2, MCM7, BRCA2, and c-Myc. We entered the three clusters of association shown in the MDS in several Kaplan-Meier analyses. It was found that only Cluster 3 was significantly related to the interval-disease free, indicating that patients with an overall higher activity of regulatory genes of proliferation and DNA repair had a lower probability to have a longer disease-free time. In conclusion, our data study provided initial evidence that selecting patients with a high grade of proliferation and DNA repair activity could lead to an early identification of an aggressive PC with a potentials for metastatic development.
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Affiliation(s)
- Laura Boldrini
- Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy;
| | - Pinuccia Faviana
- Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy;
| | - Luca Galli
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy; (L.G.); (F.P.); (P.A.E.)
| | - Federico Paolieri
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy; (L.G.); (F.P.); (P.A.E.)
| | - Paola Anna Erba
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy; (L.G.); (F.P.); (P.A.E.)
| | - Massimo Bardi
- Department of Psychology & Behavioral Neuroscience, Randolph-Macon College, Ashland, VA 23005, USA;
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Connell SP, Mills R, Pandha H, Morgan R, Cooper CS, Clark J, Brewer DS. Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy. Cancers (Basel) 2021; 13:cancers13092102. [PMID: 33925381 PMCID: PMC8123800 DOI: 10.3390/cancers13092102] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/13/2021] [Accepted: 04/14/2021] [Indexed: 11/21/2022] Open
Abstract
Simple Summary Prostate cancer is a disease responsible for a large proportion of all male cancer deaths but there is a high chance that a patient will die with the disease rather than from. Therefore, there is a desperate need for improvements in diagnosing and predicting outcomes for prostate cancer patients to minimise overdiagnosis and overtreatment whilst appropriately treating men with aggressive disease, especially if this can be done without taking an invasive biopsy. In this work we develop a test that predicts whether a patient has prostate cancer and how aggressive the disease is from a urine sample. This model combines the measurement of a protein-marker called EN2 and the levels of 10 genes measured in urine and proves that integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy. Abstract The objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (n = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0–1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85–0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC = 0.84 (95% CI: 0.78–0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.
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Affiliation(s)
- Shea P. Connell
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (S.P.C.); (C.S.C.); (J.C.)
| | - Robert Mills
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk NR4 7UY, UK;
| | - Hardev Pandha
- Faculty of Health and Medical Sciences, The University of Surrey, Guildford GU2 7XH, UK;
| | - Richard Morgan
- School of Pharmacy and Medical Sciences, University of Bradford, Bradford BD7 1DP, UK;
| | - Colin S. Cooper
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (S.P.C.); (C.S.C.); (J.C.)
| | - Jeremy Clark
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (S.P.C.); (C.S.C.); (J.C.)
| | - Daniel S. Brewer
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (S.P.C.); (C.S.C.); (J.C.)
- The Earlham Institute, Norwich Research Park, Norwich, Norfolk NR4 7UZ, UK
- Correspondence: ; Tel.: +44-(0)-1603-593761
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Doultsinos D, Mills IG. Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges. Cancers (Basel) 2021; 13:495. [PMID: 33525365 PMCID: PMC7865812 DOI: 10.3390/cancers13030495] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 01/21/2021] [Accepted: 01/22/2021] [Indexed: 12/15/2022] Open
Abstract
Prostate cancer is a high-incidence cancer that requires improved patient stratification to ensure accurate predictions of risk and treatment response. Due to the significant contributions of transcription factors and epigenetic regulators to prostate cancer progression, there has been considerable progress made in developing gene signatures that may achieve this. Some of these are aligned to activities of key drivers such as the androgen receptor, whilst others are more agnostic. In this review, we present an overview of these signatures, the strategies for their derivation, and future perspectives on their continued development and evolution.
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Affiliation(s)
- Dimitrios Doultsinos
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK;
| | - Ian G. Mills
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK;
- Patrick G Johnston Centre for Cancer Research, Queen’s University of Belfast, Belfast BT9 7AE, UK
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Luca BA, Moulton V, Ellis C, Connell SP, Brewer DS, Cooper CS. Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression. Genes (Basel) 2020; 11:genes11070802. [PMID: 32708551 PMCID: PMC7397325 DOI: 10.3390/genes11070802] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 06/27/2020] [Accepted: 07/10/2020] [Indexed: 12/25/2022] Open
Abstract
The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using entirely different approaches indicate genes fundamental for controlling cancer progression. For prostate cancer, we found two sets of signatures that had significant overlaps suggesting important genes (p < 10−34 for paired overlaps, hypergeometrical test). These overlapping signatures defined a core set of genes linking hormone signalling (HES6-AR), cell cycle progression (Prolaris) and a molecular subgroup of patients (PCS1) derived by Non Negative Matrix Factorization (NNMF) of control pathways, together designated as SIG-HES6. The second set (designated SIG-DESNT) consisted of the DESNT diagnostic signature and a second NNMF signature PCS3. Stratifications using SIG-HES6 (HES6, PCS1, Prolaris) and SIG-DESNT (DESNT) classifiers frequently detected the same individual high-risk cancers, indicating that the underlying mechanisms associated with SIG-HES6 and SIG-DESNT may act together to promote aggressive cancer development. We show that the use of combinations of a SIG-HES6 signature together with DESNT substantially increases the ability to predict poor outcome, and we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways that has implication for therapeutic design.
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Affiliation(s)
- Bogdan-Alexandru Luca
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (B.-A.L.); (V.M.); (C.E.)
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (S.P.C.); (D.S.B.)
| | - Vincent Moulton
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (B.-A.L.); (V.M.); (C.E.)
| | - Christopher Ellis
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (B.-A.L.); (V.M.); (C.E.)
| | - Shea P. Connell
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (S.P.C.); (D.S.B.)
| | - Daniel S. Brewer
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (S.P.C.); (D.S.B.)
- The Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Colin S. Cooper
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK; (S.P.C.); (D.S.B.)
- Correspondence:
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