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Ren S, Li J, Dorado J, Sierra A, González-Díaz H, Duardo A, Shen B. From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine. Health Inf Sci Syst 2024; 12:6. [PMID: 38125666 PMCID: PMC10728428 DOI: 10.1007/s13755-023-00264-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.
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Affiliation(s)
- Shumin Ren
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Jiakun Li
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
| | - Julián Dorado
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Alejandro Sierra
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Humbert González-Díaz
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Aliuska Duardo
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Bairong Shen
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
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Neupane S, Nevalainen J, Raitanen J, Talala K, Kujala P, Taari K, Tammela TLJ, Steyerberg EW, Auvinen A. Prognostic Index for Predicting Prostate Cancer Survival in a Randomized Screening Trial: Development and Validation. Cancers (Basel) 2021; 13:435. [PMID: 33498854 PMCID: PMC7865328 DOI: 10.3390/cancers13030435] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/08/2021] [Accepted: 01/20/2021] [Indexed: 11/17/2022] Open
Abstract
We developed and validated a prognostic index to predict survival from prostate cancer (PCa) based on the Finnish randomized screening trial (FinRSPC). Men diagnosed with localized PCa (N = 7042) were included. European Association of Urology risk groups were defined. The follow-up was divided into three periods (0-3, 3-9 and 9-20 years) for development and two corresponding validation periods (3-6 and 9-15 years). A multivariable complementary log-log regression model was used to calculate the full prognostic index. Predicted cause-specific survival at 10 years from diagnosis was calculated for the control arm using a simplified risk score at diagnosis. The full prognostic index discriminates well men with PCa with different survival. The area under the curve (AUC) was 0.83 for both the 3-6 year and 9-15 year validation periods. In the simplified risk score, patients with a low risk score at diagnosis had the most favorable survival, while the outcome was poorest for the patients with high risk scores. The prognostic index was able to distinguish well between men with higher and lower survival, and the simplified risk score can be used as a basis for decision making.
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Affiliation(s)
- Subas Neupane
- Unit of Health Sciences, Faculty of Social Sciences, Tampere University, FI-33014 Tampere, Finland; (J.N.); (J.R.); (A.A.)
| | - Jaakko Nevalainen
- Unit of Health Sciences, Faculty of Social Sciences, Tampere University, FI-33014 Tampere, Finland; (J.N.); (J.R.); (A.A.)
| | - Jani Raitanen
- Unit of Health Sciences, Faculty of Social Sciences, Tampere University, FI-33014 Tampere, Finland; (J.N.); (J.R.); (A.A.)
- UKK Institute for Health Promotion Research, FI-33014 Tampere, Finland
| | - Kirsi Talala
- Finnish Cancer Registry, FI-00130 Helsinki, Finland;
| | - Paula Kujala
- Department of Pathology, FIMLAB laboratory services, FI-33014 Tampere, Finland;
| | - Kimmo Taari
- Department of Urology, Helsinki University Hospital, University of Helsinki, FI-00014 Helsinki, Finland;
| | - Teuvo L. J. Tammela
- Department of Urology, Tampere University Hospital, University of Tampere, FI-33521 Tampere, Finland;
| | - Ewout W. Steyerberg
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands;
- Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | - Anssi Auvinen
- Unit of Health Sciences, Faculty of Social Sciences, Tampere University, FI-33014 Tampere, Finland; (J.N.); (J.R.); (A.A.)
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Abstract
The challenge of decoding information about complex diseases hidden in huge number of single nucleotide polymorphism (SNP) genotypes is undertaken based on five dbGaP studies. Current genome-wide association studies have successfully identified many high-risk SNPs associated with diseases, but precise diagnostic models for complex diseases by these or more other SNP genotypes are still unavailable in the literature. We report that lung cancer, breast cancer and prostate cancer as the first three top cancers worldwide can be predicted precisely via 240–370 SNPs with accuracy up to 99% according to leave-one-out and 10-fold cross-validation. Our findings (1) confirm an early guess of Dr. Mitchell H. Gail that about 300 SNPs are needed to improve risk forecasts for breast cancer, (2) reveal an incredible fact that SNP genotypes may contain almost all information that one wants to know, and (3) show a hopeful possibility that complex diseases can be precisely diagnosed by means of SNP genotypes without using phenotypical features. In short words, information hidden in SNP genotypes can be extracted in efficient ways to make precise diagnoses for complex diseases.
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Veen KM, de Angst IB, Mokhles MM, Westgeest HM, Kuppen M, Groot CAUD, Gerritsen WR, Kil PJM, Takkenberg JJM. A clinician's guide for developing a prediction model: a case study using real-world data of patients with castration-resistant prostate cancer. J Cancer Res Clin Oncol 2020; 146:2067-2075. [PMID: 32556680 PMCID: PMC7324416 DOI: 10.1007/s00432-020-03286-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 05/12/2020] [Indexed: 01/26/2023]
Abstract
PURPOSE With the increasing interest in treatment decision-making based on risk prediction models, it is essential for clinicians to understand the steps in developing and interpreting such models. METHODS A retrospective registry of 20 Dutch hospitals with data on patients treated for castration-resistant prostate cancer was used to guide clinicians through the steps of developing a prediction model. The model of choice was the Cox proportional hazard model. RESULTS Using the exemplary dataset several essential steps in prediction modelling are discussed including: coding of predictors, missing values, interaction, model specification and performance. An advanced method for appropriate selection of main effects, e.g. Least Absolute Shrinkage and Selection Operator (LASSO) regression, is described. Furthermore, the assumptions of Cox proportional hazard model are discussed, and how to handle violations of the proportional hazard assumption using time-varying coefficients. CONCLUSION This study provides a comprehensive detailed guide to bridge the gap between the statistician and clinician, based on a large dataset of real-world patients treated for castration-resistant prostate cancer.
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Affiliation(s)
- Kevin M Veen
- Department of Cardio-Thoracic Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Isabel B de Angst
- Department of Urology, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands.
| | - Mostafa M Mokhles
- Department of Cardio-Thoracic Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Hans M Westgeest
- Department of Internal Medicine, Amphia Hospital, Breda, The Netherlands
| | - Malou Kuppen
- Institute for Medical Technology Assessment, Erasmus School of Health Policy and Management, Erasmus University, Rotterdam, The Netherlands
| | - Carin A Uyl-de Groot
- Institute for Medical Technology Assessment, Erasmus School of Health Policy and Management, Erasmus University, Rotterdam, The Netherlands
| | - Winald R Gerritsen
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Paul J M Kil
- Department of Urology, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Johanna J M Takkenberg
- Department of Cardio-Thoracic Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
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