Ductal carcinoma in situ (DCIS) includes a heterogeneous group of noninvasive breast malignancies with a variable potential for evolution. It is characterized by a proliferation of malignant epithelial cells that are confined to the duct without invasion through the basement membrane into the surrounding stroma [1]. When it was initially recognized as a distinct clinical entity during the first half of the twentieth century, DCIS accounted for only 1–2 % of newly diagnosed breast cancers. Since it generally presented as a large palpable mass, mastectomy was accepted as the standard of care and was routinely curative [2]. Due to improvements in both quality and consistency of mammographic screening, the incidence of DCIS has continued to increase and it now accounts for approximately 20 % of all breast cancer diagnoses [3]. DCIS is also able to be detected at an earlier stage with overall smaller tumor burden. These advances have led to the widespread use of breast conservation therapy for women with DCIS who are appropriate candidates. Early studies conducted following the institution of breast-conserving surgery for DCIS showed a local recurrence rate of approximately 25 % in women who did not undergo adjuvant radiation therapy with half of these recurrences presenting as invasive disease [4–8]. Although the use of adjuvant radiation and endocrine therapy have substantially decreased the local recurrence rate in these women, our understanding of the pathophysiology of DCIS and the factors involved in its progression to invasive disease is still unclear. Developing a mechanism that can better classify DCIS subtypes that are more likely to progress to invasive disease would greatly enhance individualization of management strategies by assisting in both reducing overtreatment of less concerning lesions and identifying those patients who should be treated more aggressively. The study of the biologic features and molecular markers of DCIS that could offer accurate prognostic information and ultimately provide treatment guidelines is of paramount importance.

The natural history of breast cancer involves progression of normal breast tissue to invasive carcinoma due to accumulation of somatic mutations involving key growth, differentiation, and cell communication pathways. The initiation of breast cancer is due to transforming events in a single cell with point mutations, copy number alterations, epigenetic modifications, and general genome instability being frequently associated with disease progression [9, 10]. DCIS is thought to be the immediate precursor of invasive disease based on molecular and pathological studies [11, 12]. Information on the specific functional events that drive progression of DCIS to invasive carcinoma is limited. Numerical grading systems have been developed to reflect tumor differentiation and growth by assigning points for specific cellular features of the tumor. Although no accepted standard method of grading is available for DCIS, there are three overall grades used that describe the tumor cells according to the degree to which they resemble normal breast cells. The grades commonly used are well differentiated (grade 1), moderately differentiated (grade 2), and poorly differentiated (grade 3) DCIS.

Progressive growth of DCIS results from alterations in normal growth-regulating mechanisms [13]. A molecular biological marker , or biomarker , is an objective measurement of a normal biological response. Biomarkers most commonly associated with breast cancer include the hormones estrogen and progesterone and their respective receptors, the oncogene erbB2 (HER2/neu), the tumor suppressor proteins p16 and p53, and the tumor proliferation marker Ki-67. Although numerous histopathological characteristics have been identified as predictors of recurrence of DCIS, including lesion size, nuclear grade, architectural pattern, margin status, and presence of comedonecrosis, finding molecular markers to further target the malignancy in question and guide risk stratification at the time of diagnosis is paramount [14]. Understanding these biomarkers and their predictive value for both treatment response and potential disease recurrence could help individualize treatment plans to optimize patient outcomes.