Abstract Background Women with benign breast disease (BBD) have an increased risk for breast cancer (BC). Almost 30% of all BCs develop in women with prior BBD. Information about the features of the expected BCs after BBD would enable individualized surveillance and prevention strategies for these women. We sought to characterize BCs developing in a large cohort of women with BBD. Methods Our cohort includes 13,485 women who underwent breast biopsy for mammographic or palpable concern between 1967-2001. Biopsy slides were reviewed and classified as nonproliferative (NP), proliferative disease without atypia (PDWA), or atypical hyperplasia (AH). BCs were identified by follow-up questionnaires, medical records, and Tumor Registry data. BC tissues were obtained and reviewed. Results With a median follow-up of 15.8 years, 1273 women developed BC. Most BCs were invasive (81%), among which 61% were ductal, 13% mixed ductal/lobular, and 14% lobular. Two thirds were intermediate or high-grade, and 29% were node positive. Cancer characteristics were similar across the three histologic categories of BBD, with similar frequency of DCIS and invasive disease, tumor size, time to invasive BC, histologic type of BC, nodal positivity and HER2 positivity. Women with AH had a higher frequency of ER+ BC (91%) vs women with PD (80%) or NP (85%), p=0.02. Conclusion A substantial proportion of all BCs develop in women with prior BBD. We show that the majority of BCs after BBD are invasive tumors of ductal type, with a substantial proportion node positive. 84% of cancers were ER-positive. Chemoprevention should be strongly encouraged in higher-risk women with BBD. Citation Format: Visscher DW, Frost MH, Hartmanan LC, Frank RD, Vierkant RA, McCullough AE, Winham SJ, Vachon C, Ghosh K, Brandt KR, Farrell AM, Tarabishy Y, Hieken TJ, Haddad TC, Kraft RA, Radisky DC, Degnim AC. Clinicopathologic features of breast cancers that develop in women with previous benign breast disease. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-10-19.
Clinicopathologic features of breast cancers that develop in women with previous benign breast disease
Purpose Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD–to–breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.
Understanding the Premalignant Potential of Atypical Hyperplasia through Its Natural History: A Longitudinal Cohort Study
Abstract High throughput sequencing technologies open up a new dimension in cancer genomics by enabling the characterization of cancer genomes at base-pair resolution. To date, the majority of large-scale genomics projects rely on fresh-frozen biospecimens in their studies, which are difficult to obtain and often lack long-term clinical data. Unlike fresh-frozen samples, archived formalin-fixed paraffin-embedded (FFPE) tissues are more readily accessible, and are often associated with known clinical outcomes and more complete clinical annotations. However, sequencing library preparation methods need to be further optimized with regard to applicability to FFPE samples. To test whether next-generation sequencing technologies could overcome previously reported artifacts associated with formalin fixation and report accurate sequencing results, we compared whole-genome and targeted enrichment DNA sequencing data obtained from five FFPE tumor samples for which matching frozen tissues were available. We successfully generated sequencing libraries using 1 µg of genomic DNA as input material and a modified TruSeq™ sample preparation protocol. We designed a custom enrichment pool targeting exons from 215 cancer-related genes and utilized this pool to pull-down and sequence approximately 1.3 Mb region of the genome with an average 400x coverage depth to test if deep sequencing would improve validation of tumor-specific somatic mutations. We used the Illumina sequence analysis pipeline and the Windows-based second generation DNA sequencing software NextGENe® to analyze the data and identify sequence variations that are different from the human reference genome. CNV detection was overall higher among FFPE samples compared with fresh-frozen samples, demonstrating that tissue processing impacts sequencing data quality. We obtained good concordance in variant calls between matched FFPE and fresh-frozen samples. Discordant variant calls were mainly due to low depth of coverage in the regions where variant calls were made. Improvements to DNA sequencing methods for archived samples will significantly enhance cancer research and will result in more reliable prediction of individual cancer therapies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3179. doi:1538-7445.AM2012-3179