title: Predicting time to castration resistance in hormone sensitive prostate cancer by a personalization algorithm based on a mechanistic model integrating patient data.
abstract: 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 (R(2) = 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 (R(2) = 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.
authors.affiliation: Institute for Medical Biomathematics (IMBM), Bene Ataroth, Israel.|Optimata Ltd., Bene Ataroth, Israel.
authors.forename: Moran
authors.initials: M
authors.lastname: Elishmereni
authors.affiliation: Optimata Ltd., Bene Ataroth, Israel.
authors.forename: Yuri
authors.initials: Y
authors.lastname: Kheifetz
authors.affiliation: Optimata Ltd., Bene Ataroth, Israel.
authors.forename: Ilan
authors.initials: I
authors.lastname: Shukrun
authors.affiliation: Mayo Clinic, Rochester, Minnesota.
authors.forename: Graham H
authors.initials: GH
authors.lastname: Bevan
authors.affiliation: Mayo Clinic, Rochester, Minnesota.
authors.forename: Debashis
authors.initials: D
authors.lastname: Nandy
authors.affiliation: Mayo Clinic, Rochester, Minnesota.
authors.forename: Kyle M
authors.initials: KM
authors.lastname: McKenzie
authors.affiliation: Mayo Clinic, Rochester, Minnesota.
authors.forename: Manish
authors.initials: M
authors.lastname: Kohli
authors.affiliation: Institute for Medical Biomathematics (IMBM), Bene Ataroth, Israel.|Optimata Ltd., Bene Ataroth, Israel.
authors.forename: Zvia
authors.initials: Z
authors.lastname: Agur
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000726:Androgen Antagonists / Q000627:therapeutic use; D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use; D018450:Disease Progression; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D011379:Prognosis; D017430:Prostate-Specific Antigen; D064129:Prostatic Neoplasms, Castration-Resistant / Q000097:blood / Q000175:diagnosis / Q000473:pathology / Q000628:therapy; D012189:Retrospective Studies; D013997:Time Factors
chemical_list: D000726:Androgen Antagonists; D018931:Antineoplastic Agents, Hormonal; D017430:Prostate-Specific Antigen
keywords: Bayesian estimation; androgen deprivation therapy (ADT); biochemical failure (BF); mathematical model; non-linear mixed-effect modeling (NLMEM)