Prognostic Utility of a New mRNA Expression Signature of Gleason Score.
OBJECTIVE
Gleason score strongly predicts prostate cancer mortality; however, scoring varies among pathologists, and many men are diagnosed with intermediate-risk Gleason score 7. We previously developed a 157-gene signature for Gleason score using a limited gene panel. Using a new whole-transcriptome expression dataset, we verified the previous signature's performance and developed a new Gleason signature to improve lethal outcome prediction among men with Gleason score 7.


METHODS
We generated mRNA expression data from prostate tumor tissue from men in the Physicians' Health Study and Health Professionals Follow-Up Study (N = 404) using the Affymetrix Human Gene 1.0 ST microarray. The Prediction Analysis for Microarrays method was used to develop a signature to distinguish high (≥8) versus low (≤6) Gleason score. We evaluated the signature's ability to improve prediction of lethality among men with Gleason score 7, adjusting for 3 + 4/4 + 3 status, by quantifying the area under the receiver operating characteristic (ROC) curve (AUC).


RESULTS
We identified a 30-gene signature that best distinguished Gleason score ≤6 from ≥8. The AUC to predict lethal disease among Gleason score 7 men was 0.76 [95% confidence interval (CI), 0.67-0.84] compared with 0.68 (95% CI, 0.59-0.76) using 3 + 4/4 + 3 status alone (P = 0.0001). This signature was a nonsignificant (P = 0.09) improvement over our previous signature (AUC = 0.72).


CONCLUSIONS
Our new 30-gene signature improved prediction of lethality among men with Gleason score 7. This signature can potentially become a useful prognostic tool for physicians to improve treatment decision making. Clin Cancer Res; 23(1); 81-87. ©2016 AACRSee related commentary by Yin et al., p. 6.
Department of Statistics, Ohio State University, Columbus, Ohio.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Jennifer A
JA
Sinnott
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Sam F
SF
Peisch
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Departments of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Svitlana
S
Tyekucheva
Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida.
Travis
T
Gerke
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.
Rosina
R
Lis
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
Jennifer R
JR
Rider
Pathology Unit, Addarii Institute, S. Orsola-Malpighi Hospital, Bologna, Italy.
Michelangelo
M
Fiorentino
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.|Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Meir J
MJ
Stampfer
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Lorelei A
LA
Mucci
Pathology Unit, Addarii Institute, S. Orsola-Malpighi Hospital, Bologna, Italy.|Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Massimo
M
Loda
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. kpenney@hsph.harvard.edu.|Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Kathryn L
KL
Penney
D000368:Aged; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000401:mortality; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000401:mortality; D012372:ROC Curve; D015203:Reproducibility of Results; D059467:Transcriptome
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