Improved prediction of prostate cancer recurrence through systems pathology.
We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication.
Cordon-Cardo
Carlos
C
Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
Kotsianti
Angeliki
A
Verbel
David A
DA
Teverovskiy
Mikhail
M
Capodieci
Paola
P
Hamann
Stefan
S
Jeffers
Yusuf
Y
Clayton
Mark
M
Elkhettabi
Faysal
F
Khan
Faisal M
FM
Sapir
Marina
M
Bayer-Zubek
Valentina
V
Vengrenyuk
Yevgen
Y
Fogarsi
Stephen
S
Saidi
Olivier
O
Reuter
Victor E
VE
Scher
Howard I
HI
Kattan
Michael W
MW
Bianco
Fernando J
FJ
Wheeler
Thomas M
TM
Ayala
Gustavo E
GE
Scardino
Peter T
PT
Donovan
Michael J
MJ
D002467:Cell Nucleus / Q000378:metabolism; D018572:Disease-Free Survival; D006801:Humans; D008297:Male; D008954:Models, Biological; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology*; D010336:Pathology / Q000379:methods*; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000378:metabolism / Q000473:pathology*; D011944:Receptors, Androgen / Q000378:metabolism; D012680:Sensitivity and Specificity; D015996:Survival Rate; D049490:Systems Biology / Q000379:methods*; D046888:Tissue Array Analysis
D011944:Receptors, Androgen; D017430:Prostate-Specific Antigen