Abstract
We recently developed a malignancy-risk gene signature that was shown to identify histologically-normal tissues with a cancer-like profile. Because the signature was rich with proliferative genes, we postulated it might also be prognostic for existing breast cancers. We evaluated the malignancy risk gene signature to see its clinical association with cancer relapse/progression, and cancer prognosis using six independent external datasets. Six independent external breast cancer datasets were collected and analyzed using the malignancy risk gene signature designed to assess normal breast tissues. Evaluation of the signature in external datasets suggested a strong clinical association with cancer relapse/progression, and prognosis with minimal overlap of signature gene sets. These results suggest a prognostic role for the malignancy risk gene signature in the assessment of existing cancer. Proliferative biology dominates not only the earliest stages of tumor development but also later stages of tumor progression and metastasis.
Introduction
Predicting breast cancer risk in histologically-benign breast tissue has always been a challenge that has been relegated to the pathologist who must judge the risk of breast cancer based on the presence of histological abnormalities such as atypical ductal hyperplasia (ADH) and lobular carcinoma in situ (LCIS). Unfortunately, many breast cancers do not seem to be preceded by these characteristic lesions, and even when present, these are not uniformly predictive of cancer risk. For this reason, we developed a malignancy risk signature that identified histologically-normal, but molecularly-abnormal breast tissues with a invasive ductal cancer-like gene expression profile. The signature was found to show increased prevalence of expression from histologically-normal tissue to ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC). Moreover, the signature was composed of genes markedly enriched for cell cycle and proliferative gene functions. For this reason, we postulated that the signature might be prognostic for existing breast cancers. In this study, we evaluated the malignancy risk gene signature to see its clinical association with cancer relapse/progression, and cancer prognosis using seven independent external datasets. To accomplish this goal, we had to identify microarray datasets with curated longitudinal clinical endpoints. We were able to then test the proficiency of the malignancy risk gene signature in predicting outcomes such as recurrence and survival using these datasets. We were also able to determine the degree of overlap of the malignancy risk gene set with any published gene sets prognostic for similar clinical endpoints, finding surprisingly little overlap amongst genes.
There is a need to better stratify breast cancer patients in order to better apply potentially toxic therapies for best
therapeutic outcome. It is rational to predict that the genes linked to identifying patients at risk for harboring breast
cancer may be the same as those predicting the progression of cancer.