Improved breast cancer prognosis through the combination of clinical and genetic markers.
BACKGROUND
Accurate prognosis of breast cancer can spare a significant number of breast cancer patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more practical strategy would be to utilize both clinical and genetic marker information that may be complementary.


METHODS
A computational study is performed on publicly available microarray data, which has spawned a 70-gene prognostic signature. The recently proposed I-RELIEF algorithm is used to identify a hybrid signature through the combination of both genetic and clinical markers. A rigorous experimental protocol is used to estimate the prognostic performance of the hybrid signature and other prognostic approaches. Survival data analyses is performed to compare different prognostic approaches.


RESULTS
The hybrid signature performs significantly better than other methods, including the 70-gene signature, clinical makers alone and the St. Gallen consensus criterion. At the 90% sensitivity level, the hybrid signature achieves 67% specificity, as compared to 47% for the 70-gene signature and 48% for the clinical makers. The odds ratio of the hybrid signature for developing distant metastases within five years between the patients with a good prognosis signature and the patients with a bad prognosis is 21.0 (95% CI:6.5-68.3), far higher than either genetic or clinical markers alone.


BACKGROUND
The breast cancer dataset is available at www.nature.com and Matlab codes are available upon request.
Sun
Yijun
Y
Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL 32611, USA.
Goodison
Steve
S
Li
Jian
J
Liu
Li
L
Farmerie
William
W
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis* / Q000235:genetics / Q000628:therapy; D019295:Computational Biology; D005260:Female; D005819:Genetic Markers; D006801:Humans; D008954:Models, Biological; D009362:Neoplasm Metastasis / Q000175:diagnosis; D009364:Neoplasm Recurrence, Local / Q000145:classification / Q000175:diagnosis; D016017:Odds Ratio; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor; D005819:Genetic Markers