Genome-wide association studies utilizing human tissue samples have enhanced the prognostic capacity of cancer outcomes. Four breast cancer signatures, including intrinsic subtypes (14
), poor prognosis signature (MammaPrint®) (15
), recurrence score (Oncotype DX®) (16
), and wound response (17
), represent largely the same prognostic space (18
). Our identified 28-gene breast cancer prognostic signature predicted disease-free survival and overall survival in a large population of more than 2,000 breast cancer patient with heterogeneous disease stage, including both early stage and advanced breast cancers (3
). In the evaluation, the 28-gene prognostic signature is comparable as Oncotype DX® and could potentially be more accurate than the other above mentioned signatures in terms of predicting disease-free survival and overall survival in van de Vijver’s cohort (15
). More importantly, the 28-gene breast cancer signature showed prognostic ability beyond early-stage breast cancer. The 28-gene prognostic signature quantified disease-free survival and overall survival in a broad patient population including those with advanced stage (T3/T4), tumor grade III, lymph node metastasis, or negative estrogen receptor status (ER−) (4
). These results indicate that the 28-gene signature might extend the prognostic space defined by MammaPrint® and Oncotype DX® that primarily target early stage breast cancer. To confirm this conjecture, this study investigated whether the 28-gene prognostic signature could predict clinical outcomes in other tumor types of epithelial origin, including ovarian cancer (n
= 124), colon cancer (n
= 74), and lung adenocarcinoma (n
In each studied cancer type, a patient stratification scheme was developed based on the expression of the 28-gene prognostic signature, and was validated on independent patient cohorts. Based on the clinical outcome provided in two colon cancer cohorts, a machine learning algorithm Linear Discriminant Analysis was used in the model construction on the training set (n = 50) with stage II colon carcinoma to predict patients’ recurrence after surgery. The model accuracy was 94% on the training cohort in a 10-fold cross validation. This prognostic model was applied to a test set (n = 24) and achieved an overall accuracy of 75% in the independent validation. These results are more accurate (P < 0.04) compared with random predictions. In the prognostic validation of lung adenocarcinoma, a prognostic model was built with Cox model using the gene expression profiles as covariates. The cutoff point for prognostic categorization was defined based on histogram of gene expression defined-risk scores on the training cohort (n = 256). This stratification scheme was applied to an independent validate set (n = 186). The gene signature separated patients into different prognostic groups with different (log-rank P = 0.07) clinical outcomes in Kaplan-Meier analysis. Similarly, the Cox model was used in the prognostic validation on ovarian cancer. In both training and test cohorts (n = 124), the gene expression defined-model provided significant (log-rank P < 0.0075) post-operative prognostic stratification in Kaplan-Meier analyses.
Epidemiological studies strongly indicate that an association exists between breast cancer and the risk of subsequent ovarian cancer (1
). Begfeldt’s group found that a primary breast cancer patient has a two-fold increased risk of a primary ovarian cancer. Several genes have been identified to be associated with susceptibility to breast cancer and ovarian cancer, including BRCA1, BRCA2, TP53, PTEN
, and STK11/LKB1
. However, mutations in these genes only account for very limited portions of breast cancer and ovarian cancer (2
). Identification of other susceptibility genes could provide essential information to guide clinicians to assess the risk of subsequent ovarian cancer in breast cancer patients. The 28-gene signature was shown to be predictive of clinical outcomes in both breast cancer and ovarian cancer. Furthermore, the signature genes were shown to interact with TP53
in the biological association network (). Together, this signature might reveal essential genomic information for estimating the risk of consequent ovarian cancer in breast cancer patients.
This study confirmed that the identified 28-gene prognostic signature could predict clinical outcomes in multiple cancer types with epithelial origins. Thus, this 28-gene signature could extend breast cancer prognostic space defined by MammaPrint® and Oncotype DX®, among other breast cancer signatures with potential clinical utility (5
). The functional pathway analysis with curated IPA database delineated a biological network with tight connections between the signature genes and numerous well established cancer hallmarks, indicating important roles of this prognostic gene signature in tumor genesis and progression.