In the United States about 2 million women are diagnosed annually with benign breast disease (BBD). Although epidemiological studies indicate that women with BBD are at increased risk for breast cancer, this group of patients has been largely understudied and no molecular biomarkers for risk assessment have been identified. Our colleagues at Mayo Clinic have recently determined that risk of progression to breast cancer is not uniformly distributed among women with BBD, but is entirely associated with a subset of patients who have failed to undergo normal, age-related lobular involution. Lobular involution (LI) occurs normally following menopause and is histologically associated with morphological changes in the epithelial cells that comprise the terminal duct lobules of the human breast. Most breast cancer arises from such structures, and women over 55 years of age who have failed to undergo LI are at >3-fold increased risk for breast cancer, compared to age-matched women who exhibit complete lobular involution. Thus, assessment of LI status is potentially a very important tool in risk assessment associated with BBD. Unfortunately, histological assessment of LI is subject and requires a very experienced breast pathologist. There is a pressing need to identify molecular markers to augment histological evaluation. Our goal is to identify microRNAs (miRNAs) that are differentially expressed in human mammary epithelial cells (HMECs) from involuted and non- involuted patient samples and use these miRNAs as biomarkers for assessment of LI status and ultimately breast cancer risk. To this end we will use next generation small RNA sequence analysis to identify all miRNA species (mature miRNAs, polymorphisms, and isomiRs) that are differentially expressed in HMECs and in clinical samples from involuted and non-involuted breast. In parallel, we will initiate a pilot study to identify miRNAs that are functionally linked to the mesenchymal phenotype that is characteristic of HMECs from non-involuted breast. Our immediate goal is to identify potential biomarkers, defined by both overexpression and functional significance, that can then be evaluated for predictive power in a larger retrospective study that will define the link between individual miRNAs (or miRNA signatures) and breast cancer risk in BBD patients.
About 2 million women are diagnosed with benign breast disease (BBD) each year in the United States. Although these women are at significantly increased risk for developing breast cancer, the connections between BBD and breast cancer progression have been largely understudied. Our colleagues have recently determined that failure to undergo normal, age-related breast involution is a significant factor in progression from BBD to breast cancer. In fact, all of the increased risk among women with BBD is associated with individuals who have failed to undergo a process known as lobular involution (LI). Evaluation of LI status is therefore a very important aspect of risk assessment among BBD patients. However, it is very difficult to quantify LI status using conventional pathological techniques, which involve counting the number of terminal duct lobules and scoring them for the extent of involution. This procedure is very subjective and requires a highly skilled pathologist with a lot of experience in assessing breast tissue structure. What are needed at this time are good molecular markers that can be used to quantify the LI status in women with BBD. Our objective is to identify small RNAs (microRNAs) that can be used to this end. We will initially use state of the art high throughput small RNA sequencing technology to identify and count all of the microRNAs in cells from involuted and non-involuted breasts. We will determine if there are microRNAs that are differentially expressed in such cells and tissues. We will determine if any of these microRNAs are functionally linked to the more cancer-like properties of cells from non-involuted breast tissues. In this manner we will identify microRNAs that can be used as quantitative markers of LI status and which may be mechanistically linked to breast cancer risk in BBD patients.
| Kalari, Krishna R; Necela, Brian M; Tang, Xiaojia et al. (2013) An integrated model of the transcriptome of HER2-positive breast cancer. PLoS One 8:e79298 |
| Asmann, Yan W; Necela, Brian M; Kalari, Krishna R et al. (2012) Detection of redundant fusion transcripts as biomarkers or disease-specific therapeutic targets in breast cancer. Cancer Res 72:1921-8 |
