Personalized prediction of tumor response and cancer progression on prostate needle biopsy.
OBJECTIVE
To our knowledge in patients with prostate cancer there are no available tests except clinical variables to determine the likelihood of disease progression. We developed a patient specific, biology driven tool to predict outcome at diagnosis. We also investigated whether biopsy androgen receptor levels predict a durable response to therapy after secondary treatment.


METHODS
We evaluated paraffin embedded prostate needle biopsy tissue from 1,027 patients with cT1c-T3 prostate cancer treated with surgery and followed a median of 8 years. Machine learning was done to integrate clinical data with biopsy quantitative biometric features. Multivariate models were constructed to predict disease progression with the C index to estimate performance.


RESULTS
In a training set of 686 patients (total of 87 progression events) 3 clinical and 3 biopsy tissue characteristics were identified to predict clinical progression within 8 years after prostatectomy with 78% sensitivity, 69% specificity, a C index of 0.74 and a HR of 5.12. Validation in an independent cohort of 341 patients (total of 44 progression events) yielded 76% sensitivity, 64% specificity, a C index of 0.73 and a HR of 3.47. Increased androgen receptor in tumor cells in the biopsy highly significantly predicted resistance to therapy, ie androgen ablation with or without salvage radiotherapy, and clinical failure (p <0.0001).


CONCLUSIONS
Morphometry reliably classifies Gleason pattern 3 tumors. When combined with biomarker data, it adds to the hematoxylin and eosin analysis, and prostate specific antigen values currently used to assess outcome at diagnosis. Biopsy androgen receptor levels predict the likelihood of a response to therapy after recurrence and may guide future treatment decisions.
Donovan
Michael J
MJ
Aureon Laboratories, Inc., New York, New York 10701, USA. Michael.Donovan@aureon.com
Khan
Faisal M
FM
Fernandez
Gerardo
G
Mesa-Tejada
Ricardo
R
Sapir
Marina
M
Zubek
Valentina Bayer
VB
Powell
Douglas
D
Fogarasi
Stephen
S
Vengrenyuk
Yevgen
Y
Teverovskiy
Mikhail
M
Segal
Mark R
MR
Karnes
R Jeffrey
RJ
Gaffey
Thomas A
TA
Busch
Christer
C
Haggman
Michael
M
Hlavcak
Peter
P
Freedland
Stephen J
SJ
Vollmer
Robin T
RT
Albertsen
Peter
P
Costa
Jose
J
Cordon-Cardo
Carlos
C
D000368:Aged; D000704:Analysis of Variance; D001707:Biopsy, Needle / Q000379:methods*; D015331:Cohort Studies; D018450:Disease Progression; D005500:Follow-Up Studies; D006801:Humans; D007150:Immunohistochemistry; D008297:Male; D008875:Middle Aged; D009361:Neoplasm Invasiveness / Q000473:pathology; D009364:Neoplasm Recurrence, Local / Q000401:mortality / Q000473:pathology*; D009367:Neoplasm Staging; D016612:Paraffin Embedding / Q000379:methods; D011237:Predictive Value of Tests; D011336:Probability; D017430:Prostate-Specific Antigen / Q000097:blood*; D011468:Prostatectomy / Q000379:methods; D011471:Prostatic Neoplasms / Q000401:mortality / Q000473:pathology* / Q000601:surgery*; D012189:Retrospective Studies; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome
D017430:Prostate-Specific Antigen