Artificial intelligence in molecular and genomic prostate cancer diagnostics

Introduction. Many molecular genetic analyses have been proposed to predict the course of prostate cancer (PCa). They have the potential to develop artificial intelligence (AI) algorithms by processing large amounts of data and define connections between them.

Objective. To evaluate the possibilities of using artificial intelligence in early diagnosis and prognosis of prostate cancer.

Materials & methods. We conducted a systematic review of the literature on the Medline citation database. We have selected papers that provide data on the use of AI in vitro, in vivo and in silico systems to determine biological and genetic markers and/or their relationship to clinical data of PCa-patients from 2020 to 2023. The quantitative synthesis includes 16 articles.

Results. AI can identify metabolic and genetic «signature» of PCa, the key elements of signal pathways, thus fulfilling complex tasks in the field of bioinformatics. AI analyses various biomaterials: prostate tissue, blood, and urine. When evaluating prostate tissue for aberrations, AI can help a pathologist. For example, AI can predict the histological status of genes, eliminating the need for IHC or tissue sequencing, significantly reducing the economic cost of predicting the severity of the disease. In most cases, prostate tissue sequencing provides information to the attending physician, allowing the start of optimal treatment, considering the molecular or genetic «signature» of PCa. AI can be used as an alternative to existing population screening tools and a predictive castration-resistant PCa. The use of AI capabilities is more appropriate for blood and urine analysis, procedures that do not require additional economic costs for biomaterial sampling. In theory, this may be more affordable for the patient and the medical institution. It is worth noting that a few studies were conducted in silico (based on the analysis of molecular genetic databases without validation on cell lines or on real patients) and are useful as background information. However, the results can serve as a robust basis for further research in molecular diagnostics and genomics.

Conclusion. It is possible to use AI in the search for key metabolites and genes of the elements of signalling pathways, as well as the determination of metastasis potential, because molecular or genetic «signature» of PCa allows the physician to start optimal treatment.

1. Barros-Silva D, Costa-Pinheiro P, Duarte H, Sousa EJ, Evangelista AF, Graça I, Carneiro I, Martins AT, Oliveira J, Carvalho AL, Marques MM, Henrique R, Jerónimo C. MicroRNA-27a-5p regulation by promoter methylation and MYC signaling in prostate carcinogenesis. Cell Death Dis. 2018;9(2):167. DOI: 10.1038/s41419-017-0241-y

2. Zhou K, Arslanturk S, Craig DB, Heath E, Draghici S. Discovery of primary prostate cancer biomarkers using cross cancer learning. Sci Rep. 2021;11(1):10433. DOI: 10.1038/s41598-021-89789-x

3. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36-S40. DOI: 10.1016/j.metabol.2017.01.011

4. Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform. 2018;9:38. DOI: 10.4103/jpi.jpi_53_18

5. Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86(5):334-8. DOI: 10.1308/147870804290

6. Akkus Z, Cai J, Boonrod A, Zeinoddini A, Weston AD, Philbrick KA, Erickson BJ. A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow. J Am Coll Radiol. 2019;16(9 Pt B):1318-1328. DOI: 10.1016/j.jacr.2019.06.004

7. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81. DOI: 10.1080/13645706.2019.1575882

8. Niel O, Bastard P. Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives. Am J Kidney Dis. 2019;74(6):803-810. DOI: 10.1053/j.ajkd.2019.05.020

9. Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, ElBaz A. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors (Basel). 2021;21(8):2586. DOI: 10.3390/s21082586

10. Timofeeva E.Yu., Azilgareeva K.R., Morozov A.O., Taratkin M.S., Enikeev D.V. Use of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancer. Urology Herald. 2023;11(3):142-148. (In Russian).

11. Rajwa P, Schuettfort VM, Quhal F, Mori K, Katayama S, Laukhtina E, Pradere B, Motlagh RS, Mostafaei H, Grossmann NC, Aulitzky A, Paradysz A, Karakiewicz PI, Fajkovic H, Zimmermann K, Heidenreich A, Gontero P, Shariat SF. Role of systemic immune-inflammation index in patients treated with salvage radical prostatectomy. World J Urol. 2021;39(10):3771-3779. DOI: 10.1007/s00345-021-03715-4

12. Yanagisawa T, Kawada T, Rajwa P, Mostafaei H, Motlagh RS, Quhal F, Laukhtina E, König F, Pallauf M, Pradere B, Karakiewicz PI, Nyirady P, Kimura T, Egawa S, Shariat SF. Sequencing impact and prognostic factors in metastatic castration-resistant prostate cancer patients treated with cabazitaxel: A systematic review and meta-analysis. Urol Oncol. 2023;41(4):177-191. DOI: 10.1016/j.urolonc.2022.06.018

13. Enikeev D, Morozov A, Babaevskaya D, Bazarkin A, Malavaud B. A Systematic Review of Circulating Tumor Cells Clinical Application in Prostate Cancer Diagnosis. Cancers (Basel). 2022;14(15):3802. DOI: 10.3390/cancers14153802

14. Reichl F, Muhr D, Rebhan K, Kramer G, Shariat SF, Singer CF, Tan YY. Cancer Spectrum, Family History of Cancer and Overall Survival in Men with Germline BRCA1 or BRCA2 Mutations. J Pers Med. 2021;11(9):917. DOI: 10.3390/jpm11090917

15. Perera M, Mirchandani R, Papa N, Breemer G, Effeindzourou A, Smith L, Swindle P, Smith E. PSA-based machine learning model improves prostate cancer risk stratification in a screening population. World J Urol. 2021;39(6):1897-1902. DOI: 10.1007/s00345-020-03392-9

16. Rodrigues VC, Soares JC, Soares AC, Braz DC, Melendez ME, Ribas LC, Scabini LFS, Bruno OM, Carvalho AL, Reis RM, Sanfelice RC, Oliveira ON Jr. Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3. Talanta. 2021;222:121444. DOI: 10.1016/j.talanta.2020.121444

17. Cario CL, Chen E, Leong L, Emami NC, Lopez K, Tenggara I, Simko JP, Friedlander TW, Li PS, Paris PL, Carroll PR, Witte JS. A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer. BMC Cancer. 2020;20(1):820. DOI: 10.1186/s12885-020-07318-x

18. Gumaei A, Sammouda R, Al-Rakhami M, AlSalman H, ElZaart A. Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression. Health Informatics J. 2021;27(1):1460458221989402. DOI: 10.1177/1460458221989402

19. Alshareef AM, Alsini R, Alsieni M, Alrowais F, Marzouk R, Abunadi I, Nemri N. Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression. J Healthc Eng. 2022;2022:7364704. DOI: 10.1155/2022/7364704

20. Penney KL, Tyekucheva S, Rosenthal J, El Fandy H, Carelli R, Borgstein S, Zadra G, Fanelli GN, Stefanizzi L, Giunchi F, Pomerantz M, Peisch S, Coulson H, Lis R, Kibel AS, Fiorentino M, Umeton R, Loda M. Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum. Mol Cancer Res. 2021;19(3):475-484. DOI: 10.1158/1541-7786.MCR-20-0548

21. Pachynski RK, Kim EH, Miheecheva N, Kotlov N, Ramachandran A, Postovalova E, Galkin I, Svekolkin V, Lyu Y, Zou Q, Cao D, Gaut J, Ippolito JE, Bagaev A, Bruttan M, Gancharova O, Nomie K, Tsiper M, Andriole GL, Ataullakhanov R, Hsieh JJ. Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer. Clin Cancer Res. 2021;27(12):3478-3490. DOI: 10.1158/1078-0432.CCR-20-4217

22. Cosma G, McArdle SE, Foulds GA, Hood SP, Reeder S, Johnson C, Khan MA, Pockley AG. Prostate Cancer: Early Detection and Assessing Clinical Risk Using Deep Machine Learning of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data. Front Immunol. 2021;12:786828. DOI: 10.3389/fimmu.2021.786828

23. Dadhania V, Gonzalez D, Yousif M, Cheng J, Morgan TM, Spratt DE, Reichert ZR, Mannan R, Wang X, Chinnaiyan A, Cao X, Dhanasekaran SM, Chinnaiyan AM, Pantanowitz L, Mehra R. Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer. BMC Cancer. 2022;22(1):494. DOI: 10.1186/s12885-022-09559-4

24. Li R, Zhu J, Zhong WD, Jia Z. Comprehensive Evaluation of Machine Learning Models and Gene Expression Signatures for Prostate Cancer Prognosis Using Large Population Cohorts. Cancer Res. 2022;82(9):1832-1843. DOI: 10.1158/0008-5472.CAN-21-3074

25. Williams C, Khondakar NR, Daneshvar MA, O'Connor LP, Gomella PT, Mehralivand S, Yerram NK, Egan J, Gurram S, Rompré-Brodeur A, Webster BR, Owens-Walton J, Parnes H, Merino MJ, Wood BJ, Choyke P, Turkbey B, Pinto PA. The Risk of Prostate Cancer Progression in Active Surveillance Patients with Bilateral Disease Detected by Combined Magnetic Resonance Imaging-Fusion and Systematic Biopsy. J Urol. 2021;206(5):1157-1165. DOI: 10.1097/JU.0000000000001941

26. Hamzeh O, Alkhateeb A, Zheng J, Kandalam S, Rueda L. Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data. BMC Bioinformatics. 2020;21(Suppl 2):78. DOI: 10.1186/s12859-020-3345-9

27. Belinky F, Nativ N, Stelzer G, Zimmerman S, Iny Stein T, Safran M, Lancet D. PathCards: multi-source consolidation of human biological pathways. Database (Oxford). 2015;2015:bav006. DOI: 10.1093/database/bav006

28. Guo H, Zhang Z, Wang Y, Xue S. Identification of crucial genes and pathways associated with prostate cancer in multiple databases. J Int Med Res. 2021;49(6):3000605211016624. DOI: 10.1177/03000605211016624

29. Shamsara E, Shamsara J. Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2. Genomics. 2020;112(6):3871-3882. DOI: 10.1016/j.ygeno.2020.06.035

30. Xue J, Pu Y, Smith J, Gao X, Wang C, Wu B. Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods. Sci Rep. 2021;11(1):2282. DOI: 10.1038/s41598-021-81945-7

31. Mansinho A, Macedo D, Fernandes I, Costa L. CastrationResistant Prostate Cancer: Mechanisms, Targets and Treatment. Adv Exp Med Biol. 2018;1096:117-133. DOI: 10.1007/978-3-319-99286-0_7

32. Lin E, Hahn AW, Nussenzveig RH, Wesolowski S, Sayegh N, Maughan BL, McFarland T, Rathi N, Sirohi D, Sonpavde G, Swami U, Kohli M, Rich T, Sartor O, Yandell M, Agarwal N. Identification of Somatic Gene Signatures in Circulating Cell-Free DNA Associated with Disease Progression in Metastatic Prostate Cancer by a Novel Machine Learning Platform. Oncologist. 2021;26(9):751-760. DOI: 10.1002/onco.13869

33. Paul N, Carabet LA, Lallous N, Yamazaki T, Gleave ME, Rennie PS, Cherkasov A. Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor. J Chem Inf Model. 2016;56(12):2507-2516. DOI: 10.1021/acs.jcim.6b00400

34. Bruce CL, Melville JL, Pickett SD, Hirst JD. Contemporary QSAR classifiers compared. J Chem Inf Model. 2007;47(1):219-27. DOI: 10.1021/ci600332j

35. Wan Q, Pal R. An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge. PLoS One. 2014;9(6):e101183. DOI: 10.1371/journal.pone.0101183

36. Gawehn E, Hiss JA, Schneider G. Deep Learning in Drug Discovery. Mol Inform. 2016;35(1):3-14. DOI: 10.1002/minf.201501008

37. Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model. 2015;55(2):263-74. DOI: 10.1021/ci500747n

38. Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform. 2019;20(5):1878-1912. DOI: 10.1093/bib/bby061

39. Snow O, Lallous N, Ester M, Cherkasov A. Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies. Int J Mol Sci. 2020;21(16):5847. DOI: 10.3390/ijms21165847

40. Morozov A, Taratkin M, Bazarkin A, Rivas JG, Puliatti S, Checcucci E, Belenchon IR, Kowalewski KF, Shpikina A, Singla N, Teoh JYC, Kozlov V, Rodler S, Piazza P, Fajkovic H, Yakimov M, Abreu AL, Cacciamani GE, Enikeev D; Young Academic Urologists (YAU) Working Group in Uro-technology of the European Association of Urology. A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading. Prostate Cancer Prostatic Dis. 2023;26(4):681-692. DOI: 10.1038/s41391-023-00673-3

41. Kowalewski KF, Egen L, Fischetti CE, Puliatti S, Juan GR, Taratkin M, Ines RB, Sidoti Abate MA, Mühlbauer J, Wessels F, Checcucci E, Cacciamani G; Young Academic Urologists (YAU)-Urotechnology-Group. Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian J Urol. 2022;9(3):243-252. DOI: 10.1016/j.ajur.2022.05.003

42. Zeng J, Cheng Q, Zhang D, Fan M, Shi C, Luo L. Diagnostic Ability of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Prostate Cancer and Clinically Significant Prostate Cancer in Equivocal Lesions: A Systematic Review and Meta-Analysis. Front Oncol. 2021;11:620628. DOI: 10.3389/fonc.2021.620628