Vachon, Celine M.

6531 Background: Annual mammography is recommended to screen residual breast tissue for new cancers and recurrent disease after treatment for early stage breast cancer. This study aimed to assess mammography rates over time in breast cancer survivors. Methods: We used administrative claims data from a large U.S. commercial insurance database, OptumLabs, to retrospectively identify privately- and Medicare Advantage-insured women with operable breast cancer who had residual breast tissue after definitive breast surgery between 2006 and 2015. We required coverage for at least 13 months following surgery. For each subsequent 13-month time period, we only included women without a loss of coverage, bilateral mastectomy, metastatic breast cancer diagnosis, or non-breast cancer diagnosis. We calculated the proportion of patients who had a mammogram during each 13-month period following breast surgery. We used multivariable logistic regression to test for factors associated with mammography in the first 13 months. Results: The cohort included 26,011 women followed for a median of 2.9 years (IQR 1.9-4.6) after surgery; 63.1% were less than 65 years of age, and 74.4% were white. In their first year of follow-up, 86% underwent mammography, but by year 7, this decreased to 73%. Fewer than 1% underwent MRI instead of mammography. In multivariable analysis, mammograms were less likely during the first year after surgery among women aged < 50 years (odds ratio [OR], 0.7; 95% confidence interval [CI], 0.6 to 0.8), African Americans (OR, 0.7; 95% CI, 0.7 to 0.8), patients who underwent mastectomy (OR, 0.7; 95% CI, 0.6 to 0.7), and patients residing in the Western part of the country (OR, 0.9; 95% CI, 0.7 to 0.9). Those with 1-2 comorbidities were more likely (OR, 1.1; 95% CI 1.1-1.2) than those with none to have a mammogram during that period. Mammography use did not differ significantly by year of diagnosis (2006-2015). Conclusions: Even in an insured cohort, a substantial proportion of breast cancer survivors do not undergo annual surveillance mammography. Mammography use falls as the time from the early stage breast cancer diagnosis increases. Understanding factors associated with lack of mammographic screening may help improve survivorship care.

Abstract Purpose: Volumetric breast density and body mass index (BMI) are increasingly used for breast cancer risk stratification. We examine if the effect of volumetric breast density on breast cancer risk increases with increasing BMI. Methods: Participants were from two case-control studies within mammography practices, the San Francisco Mammography Registry and the Mayo Clinic Rochester, Breast Screening Practice. Breast cancers were included if diagnosed between January 2007 and 2015 and with a screening full-field digital mammogram at least 6 months prior to diagnosis; the earliest mammogram within 5 years of diagnosis was selected. Up to three controls were matched to each case on age, race, date, mammography machine, and state of residence. Volumetric percent density (VPD), dense volume (DV) and non-dense volume (NDV) were measured using VolparaTM software. Breast cancer risk was assessed using logistic regression stratified by menopause status and adjusted for matching factors, family history of breast cancer, parity/age at first birth, and postmenopausal hormone therapy. Models for DV were additionally adjusted for NDV, and NDV models for DV. Multiplicative interactions were fit between BMI categories [&lt;25 (underweight), 25-30 (normal weight) and &gt;30 (overweight/obese) kg/m2] and VPD, DV, and NDV, and trend tests assessed for increasing odds ratios (OR) with increasing BMI. Results: A total of 781 premenopausal and 1850 postmenopausal breast cancers and 3535 controls were included in the analysis. Median age of premenopausal women was 45 years (IQR: 6.1) and 63.3 years (IQR: 14) for postmenopausal women. Cases vs. controls had greater VPD and DV for both premenopausal (VPD: 14.9% vs. 12.0%, DV: 74.1 cm3 vs. 64.4 cm3) and postmenopausal women (VPD: 6.8% vs. 6.1%, DV: 53.4 cm3 vs. 48.0 cm3)(all p's&lt;0.001). Trends between increasing BMI and VPD were evident for both pre (ptrend=0.0007) and postmenopausal (ptrend=0.0005) women. Among premenopausal women, the odds ratio (OR) for breast cancer associated with a 10% increase in VPD was of 1.39, 2.19 and 2.88 for BMI &lt;25, 25-30 and &gt;30 kg/m2 (p-trend=0.0007), respectively. For DV, OR's were 1.39, 1.33 and 1.51 for a 1 SD increase in DV, respectively, though the interaction with DV was not significant (ptrend=0.68). Among postmenopausal women, a 10% increase in VPD was associated with OR's of 1.35, 2.03, 3.6 for BMI &lt;25, 25-29, &gt;30-kg/m2 (ptrend =0.0001), respectively, and 1.31, 1.34 and 1.65 for a 1 SD increase in DV (ptrend =0.01), respectively. Associations between NDV and breast cancer risk did not differ by BMI category for premenopausal (ptrend =0.52) or postmenopausal (ptrend =0.07) women. Conclusions: The effect of VPD on breast cancer risk is strongest in overweight/obese women. As volumetric breast density and BMI are commonly used in clinical risk stratification, these differences in risk have high clinical relevance for informing prevention decisions. Citation Format: Natalie J. Engmann, Christopher G. Scott, Matthew R. Jensen, Stacey J. Winham, Lin Ma, Kathleen R. Brandt, Amir Mahmoudzadeh, Serghei Malkov, Dana Whaley, Carrie Hruska, Fang Fang Wu, Diana L. Miglioretti, Aaron D. Norman, John Heine, John Shepherd, Vernon S. Pankratz, Celine M. Vachon, Karla Kerlikowske. Overweight and obese women with high volumetric breast density at high breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3226.

Abstract CLL is a highly heritable cancer with first degree relatives of CLL cases having a 7.5-fold increased CLL risk. Genome-wide association studies (GWAS) and linkage studies have been performed to study inherited predisposition; however a larger proportion of heritability to CLL remains unexplained. Rare coding variants might account for the missing heritability information. Inherited loss of function variants in shelterin complex genes (POT1, ACD, TERF1, TINF2, TERF2, TERF2IP- involved in telomere regulation), CDK1 (critical for cell division) and ATM (tumor suppressor gene) have been found to co-segregate in CLL families and be enriched in CLL cases using exome-wide sequencing data. Our study evaluates rare germline variants from these suspect genes segregating in CLL families who are followed at the Mayo Clinic. Using whole exome sequencing (WES), we sequenced 93 CLL families with at least 2 reported CLL cases consisting of 443 individuals: 160 with CLL, 73 with monoclonal B-cell lymphocytosis (MBL), and 210 relatives. DNA was extracted from buccal cells, coding exons were selectively captured using Agilent 50Mb and SureSelect Human All Exon V4 capture kits; sequencing was performed using Illumina HiSeq 2000. Mayo Clinic's DNASeq pipeline uses Novoalign (initial read alignment), Picard (marking duplicate reads), and the Genome Analysis Toolkit (GATK) for local realignment, recalibration, and variant calling. The variant discovery step leverages GATK's HaplotypeCaller in per sample mode and all of the samples across the cohort are jointly genotyped together. All called variants are evaluated with GATK's Variant Quality Score Recalibration tool and annotated for biological relevance (BIOR). Quality control included removing variants that had &lt;75% call rate across the two capture kits, &lt;8x coverage, or phred score&lt;10, resulting in 317,666 remaining variants. Of these, over 80% of the coding sequence had a median read depth of 23 reads. In our pedigrees, we searched for rare variants within the genes described above. We identified suspect variants with the following criteria: 1) enriched in CLL and MBL samples compared to unaffected samples; 2) multiple affected members with the variant within a family; 3) variants present in all sequenced affecteds within the family; 4) rarely seen in an in-house database of non-cancer controls or 1K Genomes; and 5) predicted to have a functional damaging effect (using SIFT). We identified three novel rare missense variants, defined as functionally deleterious, which each co-segregated within a CLL family. Specifically, these variants from shelterin complex genes; POT1 (rs116916706), TERF2IP (rs138458227), and TERF2 (rs749171225), met the criteria. This study further highlights telomere dysregulation as a key process in CLL development. Investigating rare variants within CLL pedigrees with WES can help identify germline variants impacting predisposition to familial CLL. Citation Format: Alyssa I. Clay-Gilmour, Daniel R. O'Brien, Sara J. Achenbach, Celine M. Vachon, Kari G. Chaffee, Timothy G. Call, Jose F. Leis, Aaron D. Norman, Brian F. Kabat, Sameer A. Parikh, Neil E. Kay, Esteban Braggio, James R. Cerhan, Susan L. Slager. Rare germline variants segregating in chronic lymphocytic leukemia (CLL) families [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1226.

Abstract Cohort studies have been central to the establishment of the known causes of cancer. To dissect cancer etiology in more detail—for instance, for personalized risk prediction and prevention, assessment of risks of subtypes of cancer, and assessment of small elevations in risk—there is a need for analyses of far larger cohort datasets than available in individual existing studies. To address these challenges, the NCI Cohort Consortium was founded in 2001. It brings together 58 cancer epidemiology cohorts from 20 countries to undertake large-scale pooling research. The cohorts in aggregate include over nine million study participants, with biospecimens available for about two million of these. Research in the Consortium is undertaken by &gt;40 working groups focused on specific cancer sites, exposures, or other research areas. More than 180 publications have resulted from the Consortium, mainly on genetic and other cancer epidemiology, with high citation rates. This article describes the foundation of the Consortium; its structure, governance, and methods of working; the participating cohorts; publications; and opportunities. The Consortium welcomes new members with cancer-oriented cohorts of 10,000 or more participants and an interest in collaborative research. Cancer Epidemiol Biomarkers Prev; 27(11); 1307–19. ©2018 AACR.

Background More than 1.5 million women per year have a benign breast biopsy resulting in concern about their future breast cancer (BC) risk. This study examined the performance of 2 BC risk models that integrate clinical and histologic findings in this population. Methods The BC risk at 5 and 10 years was estimated with the Breast Cancer Surveillance Consortium (BCSC) and Benign Breast Disease to Breast Cancer (BBD‐BC) models for women diagnosed with benign breast disease (BBD) at the Mayo Clinic from 1997 to 2001. Women with BBD were eligible for the BBD‐BC model, but the BCSC model also required a screening mammogram. Calibration and discrimination were assessed. Results Fifty‐six cases of BC were diagnosed among the 2142 women with BBD (median age, 50 years) within 5 years (118 were diagnosed within 10 years). The BBD‐BC model had slightly better calibration at 5 years (0.89; 95% confidence interval [CI], 0.71‐1.21) versus 10 years (0.81; 95% CI, 0.70‐1.00) but similar discrimination in the 2 time periods: 0.68 (95% CI, 0.60‐0.75) and 0.66 (95% CI, 0.60‐0.71), respectively. In contrast, among the 1089 women with screening mammograms (98 cases of BC within 10 years), the BCSC model had better calibration (0.94; 95% CI, 0.85‐1.43) and discrimination (0.63; 95% CI, 0.56‐0.71) at 10 years versus 5 years (calibration, 1.31; 95% CI, 0.94‐2.25; discrimination, 0.59; 95% CI, 0.46‐0.71) where discrimination was not different from chance. Conclusions The BCSC and BBD‐BC models were validated in the Mayo BBD cohort, although their performance differed by 5‐year risk versus 10‐year risk. Further enhancement of these models is needed to provide accurate BC risk estimates for women with BBD.

Purpose Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but existing risk prediction models do not provide accurate individualized estimates of risk in this subset of high-risk women. Here, we used the Mayo benign breast disease cohort to develop and validate a model of BC risk prediction that is specifically for women with AH, which we have designated as AH-BC. Patients and Methods Retrospective cohorts of women age 18 to 85 years with pathologically confirmed benign AH from Rochester, MN, and Nashville, TN, were used for model development and external validation, respectively. Clinical risk factors and histologic features of the tissue biopsy were selected using L1-penalized Cox proportional hazards regression. Identified features were included in a Fine and Gray regression model to estimate BC risk, with death as a competing risk. Model discrimination and calibration were assessed in the model-building set and an external validation set. Results The model-building set consisted of 699 women with AH, 142 of whom developed BC (median follow-up, 8.1 years), and the external validation set consisted of 461 women with 114 later BC events (median follow-up, 11.4 years). The final AH-BC model included three covariates: age at biopsy, age at biopsy squared, and number of foci of AH. At 10 years, the AH-BC model demonstrated good discrimination (0.63 [95% CI, 0.57 to 0.70]) and calibration (0.87 [95% CI, 0.66 to 1.24]). In the external validation set, the model showed acceptable discrimination (0.59 [95% CI, 0.51 to 0.67]) and calibration (0.91 [95% CI, 0.65 to 1.42]). Conclusion We have created a new model with which to refine BC risk prediction for women with AH. The AH-BC model demonstrates good discrimination and calibration, and it validates in an external data set.

Key Points PRS, based on the known CLL loci, predicts CLL risk with high discrimination. This PRS predicts risk of monoclonal B-cell lymphocytosis, a precursor to CLL and a condition that has clinical impact beyond risk for CLL.