Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer.
OBJECTIVE
Prognostic models for overall survival (OS) for patients with metastatic castration-resistant prostate cancer (mCRPC) are dated and do not reflect significant advances in treatment options available for these patients. This work developed and validated an updated prognostic model to predict OS in patients receiving first-line chemotherapy.


METHODS
Data from a phase III trial of 1,050 patients with mCRPC were used (Cancer and Leukemia Group B CALGB-90401 [Alliance]). The data were randomly split into training and testing sets. A separate phase III trial served as an independent validation set. Adaptive least absolute shrinkage and selection operator selected eight factors prognostic for OS. A predictive score was computed from the regression coefficients and used to classify patients into low- and high-risk groups. The model was assessed for its predictive accuracy using the time-dependent area under the curve (tAUC).


RESULTS
The model included Eastern Cooperative Oncology Group performance status, disease site, lactate dehydrogenase, opioid analgesic use, albumin, hemoglobin, prostate-specific antigen, and alkaline phosphatase. Median OS values in the high- and low-risk groups, respectively, in the testing set were 17 and 30 months (hazard ratio [HR], 2.2; P < .001); in the validation set they were 14 and 26 months (HR, 2.9; P < .001). The tAUCs were 0.73 (95% CI, 0.70 to 0.73) and 0.76 (95% CI, 0.72 to 0.76) in the testing and validation sets, respectively.


CONCLUSIONS
An updated prognostic model for OS in patients with mCRPC receiving first-line chemotherapy was developed and validated on an external set. This model can be used to predict OS, as well as to better select patients to participate in trials on the basis of their prognosis.
Susan Halabi, Chen-Yen Lin, and Ellen B. Kaplan, Duke University; Judd W. Moul, Duke Cancer Institute, Durham, NC; W. Kevin Kelly, Thomas Jefferson University, Philadelphia, PA; Karim S. Fizazi, Institut Gustave Roussy, University of Paris Sud, Villejuif, France; Michael J. Morris, Memorial Sloan-Kettering Cancer Center, New York, NY; and Eric J. Small, University of California, San Francisco, San Francisco, CA.
Susan
S
Halabi
Chen-Yen
CY
Lin
W Kevin
WK
Kelly
Karim S
KS
Fizazi
Judd W
JW
Moul
Ellen B
EB
Kaplan
Michael J
MJ
Morris
Eric J
EJ
Small
D000368:Aged; D061067:Antibodies, Monoclonal, Humanized / Q000008:administration & dosage; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D000068258:Bevacizumab; D014408:Biomarkers, Tumor / Q000097:blood; D017326:Clinical Trials, Phase III as Topic; D000077143:Docetaxel; D005858:Germany; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D049451:Nomograms; D009919:Orchiectomy; D011237:Predictive Value of Tests; D011241:Prednisone / Q000008:administration & dosage; D011379:Prognosis; D016016:Proportional Hazards Models; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000097:blood / Q000188:drug therapy / Q000401:mortality* / Q000473:pathology*; D011897:Random Allocation; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D043823:Taxoids / Q000008:administration & dosage
D061067:Antibodies, Monoclonal, Humanized; D014408:Biomarkers, Tumor; D043823:Taxoids; D000077143:Docetaxel; D000068258:Bevacizumab; D017430:Prostate-Specific Antigen; D011241:Prednisone