Predicting time to castration resistance in hormone sensitive prostate cancer by a personalization algorithm based on a mechanistic model integrating patient data

BACKGROUND Prostate cancer (PCa) is a leading cause of cancer death of men worldwide. In hormone‐sensitive prostate cancer (HSPC), androgen deprivation therapy (ADT) is widely used, but an eventual failure on ADT heralds the passage to the castration‐resistant prostate cancer (CRPC) stage. Because p...

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Bibliographic Details
Published in:	The Prostate Vol. 76; no. 1; pp. 48 - 57
Main Authors:	Elishmereni, Moran, Kheifetz, Yuri, Shukrun, Ilan, Bevan, Graham H., Nandy, Debashis, McKenzie, Kyle M., Kohli, Manish, Agur, Zvia
Format:	Journal Article
Language:	English
Published:	United States Blackwell Publishing Ltd 01-01-2016 Wiley Subscription Services, Inc
Subjects:	Aged Aged, 80 and over Algorithms Androgen Antagonists - therapeutic use androgen deprivation therapy (ADT) Antineoplastic Agents, Hormonal - therapeutic use Bayesian estimation biochemical failure (BF) Disease Progression Humans Male mathematical model Middle Aged Models, Theoretical Neoplasm Grading Neoplasm Staging non-linear mixed-effect modeling (NLMEM) Prognosis Prostate-Specific Antigen Prostatic Neoplasms, Castration-Resistant - blood Prostatic Neoplasms, Castration-Resistant - diagnosis Prostatic Neoplasms, Castration-Resistant - pathology Prostatic Neoplasms, Castration-Resistant - therapy Retrospective Studies Time Factors mathematical model Bayesian estimation androgen deprivation therapy (ADT) biochemical failure (BF) non-linear mixed-effect modeling (NLMEM)
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Summary:	BACKGROUND Prostate cancer (PCa) is a leading cause of cancer death of men worldwide. In hormone‐sensitive prostate cancer (HSPC), androgen deprivation therapy (ADT) is widely used, but an eventual failure on ADT heralds the passage to the castration‐resistant prostate cancer (CRPC) stage. Because predicting time to failure on ADT would allow improved planning of personal treatment strategy, we aimed to develop a predictive personalization algorithm for ADT efficacy in HSPC patients. METHODS A mathematical mechanistic model for HSPC progression and treatment was developed based on the underlying disease dynamics (represented by prostate‐specific antigen; PSA) as affected by ADT. Following fine‐tuning by a dataset of ADT‐treated HSPC patients, the model was embedded in an algorithm, which predicts the patient's time to biochemical failure (BF) based on clinical metrics obtained before or early in‐treatment. RESULTS The mechanistic model, including a tumor growth law with a dynamic power and an elaborate ADT‐resistance mechanism, successfully retrieved individual time‐courses of PSA (R2 = 0.783). Using the personal Gleason score (GS) and PSA at diagnosis, as well as PSA dynamics from 6 months after ADT onset, and given the full ADT regimen, the personalization algorithm accurately predicted the individual time to BF of ADT in 90% of patients in the retrospective cohort (R2 = 0.98). CONCLUSIONS The algorithm we have developed, predicting biochemical failure based on routine clinical tests, could be especially useful for patients destined for short‐lived ADT responses and quick progression to CRPC. Prospective studies must validate the utility of the algorithm for clinical decision‐making. Prostate 76:48–57, 2016. © 2015 Wiley Periodicals, Inc.
Bibliography:	ArticleID:PROS23099 istex:2E25B7DFFF585735C76615D5F2F4AD4BCA1556EF ark:/67375/WNG-8087BG63-4 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0270-4137 1097-0045
DOI:	10.1002/pros.23099