Abstract
IntroductionScreening mammography has contributed to a significant increase in the diagnosis of ductal carcinoma in situ (DCIS), raising concerns about overdiagnosis and overtreatment. Building on prior observations from lineage evolution analysis, we examined whether measuring genomic features of DCIS would predict association with invasive breast carcinoma (IBC). The long-term goal is to enhance standard clinicopathologic measures of low- versus high-risk DCIS and to enable risk-appropriate treatment.MethodsWe studied three common chromosomal copy number alterations (CNA) in IBC and designed fluorescence in situ hybridization-based assay to measure copy number at these loci in DCIS samples. Clinicopathologic data were extracted from the electronic medical records of Stanford Cancer Institute and linked to demographic data from the population-based California Cancer Registry; results were integrated with data from tissue microarrays of specimens containing DCIS that did not develop IBC versus DCIS with concurrent IBC. Multivariable logistic regression analysis was performed to describe associations of CNAs with these two groups of DCIS.ResultsWe examined 271 patients with DCIS (120 that did not develop IBC and 151 with concurrent IBC) for the presence of 1q, 8q24 and 11q13 copy number gains. Compared to DCIS-only patients, patients with concurrent IBC had higher frequencies of CNAs in their DCIS samples. On multivariable analysis with conventional clinicopathologic features, the copy number gains were significantly associated with concurrent IBC. The state of two of the three copy number gains in DCIS was associated with a risk of IBC that was 9.07 times that of no copy number gains, and the presence of gains at all three genomic loci in DCIS was associated with a more than 17-fold risk (P = 0.0013).ConclusionsCNAs have the potential to improve the identification of high-risk DCIS, defined by presence of concurrent IBC. Expanding and validating this approach in both additional cross-sectional and longitudinal cohorts may enable improved risk stratification and risk-appropriate treatment in DCIS.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-015-0623-y) contains supplementary material, which is available to authorized users.
Highlights
Screening mammography has contributed to a significant increase in the diagnosis of ductal carcinoma in situ (DCIS), raising concerns about overdiagnosis and overtreatment
Since genomic change appears to correlate with progression [9], we aimed to study these changes in a large cohort at the level of the preinvasive DCIS lesion, and to characterize its association with clinical and demographic data [17, 18]
Simulation analysis of single nucleotide variation (SNV) and copy number alterations (CNA) frequencies as predictors of invasive carcinoma in DCIS We conducted a simulation experiment to determine the types of genomic characteristics most likely to be useful features in a predictive model of DCIS risk in invasive breast carcinoma (IBC)
Summary
Screening mammography has contributed to a significant increase in the diagnosis of ductal carcinoma in situ (DCIS), raising concerns about overdiagnosis and overtreatment. Our studies have examined hyperplasia, DCIS and IBC from cross-sectional samples, by both targeted sequencing [12] and whole genome sequencing [10], to identify genomic changes that occur in progression from these pathologically defined neoplasias These data have identified specific genomic changes to pathologic lesions defined by morphology whose risks have previously been studied at an epidemiologic level [13], including common CNAs and SNVs that have been identified in IBC [14, 15].
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