Optimized outcome prediction in breast cancer by combining the 70-gene signature with clinical risk prediction algorithms.
Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether adding the 70-gene signature to clinical risk prediction algorithms can optimize outcome prediction and consequently treatment decisions in early stage, node-negative breast cancer patients. A 70-gene signature was available for 427 patients participating in the RASTER study (cT1-3N0M0). Median follow-up was 61.6 months. Based on 5-year distant-recurrence free interval (DRFI) probabilities survival areas under the curve (AUC) were calculated and compared for risk estimations based on the six clinical risk prediction algorithms: Adjuvant! Online (AOL), Nottingham Prognostic Index (NPI), St. Gallen (2003), the Dutch National guidelines (CBO 2004 and NABON 2012), and PREDICT plus. Also, survival AUC were calculated after adding the 70-gene signature to these clinical risk estimations. Systemically untreated patients with a high clinical risk estimation but a low risk 70-gene signature had an excellent 5-year DRFI varying between 97.1 and 100 %, depending on the clinical risk prediction algorithms used in the comparison. The best risk estimation was obtained in this cohort by adding the 70-gene signature to CBO 2012 (AUC: 0.644) and PREDICT (AUC: 0.662). Clinical risk estimations by all clinical algorithms improved by adding the 70-gene signature. Patients with a low risk 70-gene signature have an excellent survival, independent of their clinical risk estimation. Adding the 70-gene signature to clinical risk prediction algorithms improves risk estimations and therefore might improve the identification of early stage node-negative breast cancer patients for whom AST has limited value. In this cohort, the PREDICT plus tool in combination with the 70-gene signature provided the best risk prediction.
Department of Surgical Oncology, Netherlands Cancer Institute, Postbus 90203, 1006 BE, Amsterdam, The Netherlands.
C A
CA
Drukker
M V
MV
Nijenhuis
J M
JM
Bueno-de-Mesquita
V P
VP
Retèl
W H
WH
van Harten
H
H
van Tinteren
J
J
Wesseling
M K
MK
Schmidt
L J
LJ
Van't Veer
G S
GS
Sonke
E J T
EJ
Rutgers
M J
MJ
van de Vijver
S C
SC
Linn
D000230:Adenocarcinoma / Q000235:genetics / Q000401:mortality / Q000628:therapy; D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000401:mortality* / Q000628:therapy; D017024:Chemotherapy, Adjuvant; D003661:Decision Support Techniques; D005260:Female; D005544:Forecasting / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D053208:Kaplan-Meier Estimate; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local; D012044:Regression Analysis; D012306:Risk; D018570:Risk Assessment; D016896:Treatment Outcome; D055815:Young Adult