The ABC model of prostate cancer: A conceptual framework for the design and interpretation of prognostic studies.
There has been limited success in identifying prognostic biomarkers in prostate cancer. A partial explanation may be that insufficient emphasis has been put on clearly defining what type of marker or patient category a biomarker study aims to identify and how different cohort characteristics affect the ability to identify such a marker. In this article, the authors put forth the ABC model of prostate cancer, which defines 3 groups of patients with localized disease that an investigator may seek to identify: patients who, within a given time frame, will not develop metastases even if untreated (category A), will not develop metastases because of radical treatment (category B), or will develop metastases despite radical treatment (category C). The authors demonstrate that follow-up time and prostate-specific antigen screening intensity influence the prevalence of patients in categories A, B, and C in a study cohort, and that prognostic markers must be tested in both treated and untreated cohorts to accurately distinguish the 3 groups. The authors suggest that more emphasis should be put on considering these factors when planning, conducting, and interpreting the results from prostate cancer biomarker studies, and propose the ABC model as a framework to aid in that process. Cancer 2017;123:1490-1496. © 2017 American Cancer Society.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Clinical Epidemiology Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden.
Andreas
A
Pettersson
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, Tampa, Florida.
Travis
T
Gerke
Clinical Epidemiology and Biostatistics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Katja
K
Fall
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Yudi
Y
Pawitan
Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
Lars
L
Holmberg
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Edward L
EL
Giovannucci
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.|Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.
Philip W
PW
Kantoff
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.|Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Hans-Olov
HO
Adami
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
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, Harvard Medical School, Boston, Massachusetts.
Lorelei A
LA
Mucci
D015415:Biomarkers / Q000378:metabolism; D018572:Disease-Free Survival; D006801:Humans; D008297:Male; D009362:Neoplasm Metastasis; D011379:Prognosis; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000378:metabolism / Q000628:therapy*; D057832:Watchful Waiting
D015415:Biomarkers
biomarkers; epidemiology; predictive markers; prognostic markers; prostate cancer; survival