Citations

Abstract Background: Women with benign breast disease (BBD) experience an increased risk of breast cancer (BC). Histologic classification of BBD, as non-proliferative disease (NP), proliferative disease without atypia (PDWA) or atypical hyperplasia (AH), stratifies groups of patients into progressively higher categories of BC risk. However, this classification does not comprehensively assess the proliferative state of the epithelium throughout the biopsy. In addition, while AH is considered the most high-risk class of BBD, it is not always a highly proliferative lesion; atypical ductal lesions may reflect focal cytologic and architectural changes. We evaluated the association of an alternative classification of BBD severity and BC risk based on subjective grading of: 1) the maximal degree of epithelial proliferation and 2) multifocality of epithelial proliferation. Methods: Pathologists reviewed biopsies from participants aged 18 to 85 years in the Mayo BBD cohort (2002-2013), masked to BC outcomes, ascertained via questionnaires, tumor registry data and medical record review. Biopsies were classified as NP, PDWA or AH and semi-quantitatively scored for: 1) maximal degree of epithelial proliferation within a focus (DP) (0-3; none to severe) and 2) multifocality of proliferation (MP) (0-3; none to multiple foci). DP and MP scores were also summed to give a DP+MP score (0-6). Associations of DP and MP with BC risk were examined using Cox proportional hazards regression analyses, adjusting for age at BBD biopsy. Women were followed from date of initial biopsy to date of BC, death or last follow-up. Results: Of the 1529 assessable biopsies, 544 (35.6%) were classified as NP, 708 (46.3%) as PDWA and 277 (18.1%) as AH. Both DP and MP scores had significant positive correlation with increasing BBD severity (DP: r=0.51, p< 0.001; MP: r=0.52, p< 0.001). Mean (SD) DP scores were 0.6 (0.6) for NP, 1.6 (0.9) for PDWA, and 1.8 (0.7) for AH (ANOVA p<0.001). Mean (SD) for MP scores were 0.6 (0.6) for NP, 1.4 (0.8) for PDWA, and 1.8 (0.8) for AH (ANOVA p<0.001). Mean (SD) for DP+MP scores were 1.2 (1.2) for NP, 2.9 (1.5) for PDWA, and 3.6 (1.2) for AH (ANOVA p<0.001). With median follow-up of 8.8 years for controls and 5.3 years for cases, 10.6% of the women in the cohort developed BC. Compared to those with DP scores of 0, women with DP scores of 3 had significantly increased BC risk (HR 1.42, 95% CI: 1.16, 1.74, p=0.003). MP was associated with a non-significant increase in BC risk for scores of 3 versus 0 (HR: 1.20, 95% CI: 0.97,1.49, p=0.11). DP+MP scores of 6 conferred the highest BC risk (HR (score 6 vs. 0): 1.62, 95% CI 1.18,2.21, p=0.02). Results did not substantively differ after adjusting for BBD severity as NP, PDWA or AH. Conclusions: In this preliminary analysis within the Mayo BBD cohort, both proliferative degree (DP) and multifocality (MP) scores were correlated with histologic severity of BBD. DP and DP+MP scores were each associated with increased BC risk. We conclude that improved characterization of epithelial proliferation in BBD biopsies may enable refined prediction of individual BC risk. Citation Format: Jodi M Carter, Matthew R Jensen, Robert A Vierkant, Stacey J Winham, Tanya L Hoskin, Marlene Frost, Karthik Ghosh, Derek C Radisky, Amy C Degnim, Mark E Sherman. Epithelial proliferation score as an independent breast cancer risk predictor in benign breast disease [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD10-10.

Abstract High alcohol intake and breast density increase breast cancer (BC) risk, but their interrelationship is unknown. We examined whether volumetric density modifies and/or mediates the alcohol-BC association. BC cases (n = 2233) diagnosed from 2006 to 2013 in the San Francisco Bay area had screening mammograms 6 or more months before diagnosis; controls (n = 4562) were matched on age, mammogram date, race or ethnicity, facility, and mammography machine. Logistic regression was used to estimate alcohol-BC associations adjusted for age, body mass index, and menopause; interaction terms assessed modification. Percent mediation was quantified as the ratio of log (odds ratios [ORs]) from models with and without density measures. Alcohol consumption was associated with increased BC risk (2-sided Ptrend = .004), as were volumetric percent density (OR = 1.45 per SD, 95% confidence interval [CI] = 1.36 to 1.56) and dense volume (OR = 1.30, 95% CI = 1.24 to 1.37). Breast density did not modify the alcohol-BC association (2-sided P > .10 for all). Dense volume mediated 25.0% (95% CI = 5.5% to 44.4%) of the alcohol-BC association (2-sided P = .01), suggesting alcohol may partially increase BC risk by increasing fibroglandular tissue.

Abstract Background: Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner–Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45–74. We validate using the Mayo Mammography Health Study (MMHS). Methods: We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990–2000. MD (using Cumulus software) and PRS were assessed in a nested case–control study. We assess model performance using this case–control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up. Results: In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: ORper SD = 1.48 [95% confidence interval (CI): 1.31–1.68], followed by PRS, ORper SD = 1.37 (95% CI: 1.21–1.55) and QS, ORper SD = 1.25 (95% CI: 1.11–1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls. Conclusion: A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk. Impact: This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention. See related commentary by Tehranifar et al., p. 587

Abstract Background: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers. Methods: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data. Results: Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10−7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation. Conclusions: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis. Impact: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.

Abstract Statistically significant findings are more likely to be published than non-significant or null findings, leaving scientists and healthcare personnel to make decisions based on distorted scientific evidence. Continuously expanding ´file drawers’ of unpublished data from well-designed experiments waste resources creates problems for researchers, the scientific community and the public. There is limited awareness of the negative impact that publication bias and selective reporting have on the scientific literature. Alternative publication formats have recently been introduced that make it easier to publish research that is difficult to publish in traditional peer reviewed journals. These include micropublications, data repositories, data journals, preprints, publishing platforms, and journals focusing on null or neutral results. While these alternative formats have the potential to reduce publication bias, many scientists are unaware that these formats exist and don’t know how to use them. Our open source file drawer data liberation effort (fiddle) tool (RRID:SCR_017327 available at: http://s-quest.bihealth.org/fiddle/) is a match-making Shiny app designed to help biomedical researchers to identify the most appropriate publication format for their data. Users can search for a publication format that meets their needs, compare and contrast different publication formats, and find links to publishing platforms. This tool will assist scientists in getting otherwise inaccessible, hidden data out of the file drawer into the scientific community and literature. We briefly highlight essential details that should be included to ensure reporting quality, which will allow others to use and benefit from research published in these new formats.

AbstractProblemPrevious studies identified circulating CD14+HLA‐DRlo/− monocytic cells as an immune suppressive subset in solid malignancies, such as prostate, renal cell carcinoma, and pancreatic cancer. Such monocytic cells have been implicated not only in tumour progression but also as a potential barrier for immunotherapy. This study examined the relationship between the frequency of circulating monocytic cells and epithelial ovarian cancer (EOC) progression pre‐ and post‐frontline chemotherapy, defined by disease stage, which is a leading prognostic factor for this malignancy.Method of studyIncident cases of 236 women with EOC were recruited and comprehensive flow cytometry was utilized to assess the frequency of peripheral blood CD33+CD11b+HLA‐DR−/lowCD14+CD15− monocytic cells, henceforth termed CD14+HLA‐DRlo/− monocytic cells, prior to and after completion of frontline chemotherapy. Multivariable odds ratios (OR) were used to estimate the association between CD14+HLA‐DRlo/− monocytic cell percentages and disease stage. Wilcoxon signed‐rank tests evaluated changes in these monocytic cell levels pre‐ and post‐chemotherapy in a patient subset (n = 70).ResultsPatients with elevated frequencies of circulating CD14+HLA‐DRlo/− monocytic cells at diagnosis were at 3.33‐fold greater odds of having advanced stage (III/IV) EOC (CI: 1.04‐10.64), with a significant trend in increasing CD14+HLA‐DRlo/−monocytic cell levels (P = .04). There was a 2.02% median decrease of these monocytic cells post‐chemotherapy among a subset of patients with advanced stage disease (P < .0001).ConclusionThese findings support the potential clinical relevance of CD14+HLA‐DRlo/−monocytic cells in EOC for prognosis and may indicate a non‐invasive biomarker to measure disease progression.

Abstract Copy number alterations and loss of heterozygosity (LOH) are common in ovarian tumors, including those on the X chromosome. In females, one of the two copies of the X chromosome is transcriptionally silenced during embryonic development in the epigenetic process of X chromosome inactivation. Specifically, the long non-coding RNA XIST is expressed from the inactive X (Xi) and triggers chromosome-wide DNA methylation. In breast and ovarian cancer cell line studies, Xi deletion with concomitant duplication of the active X (Xa) has been observed. We sought to examine which copy of the X chromosome, Xi or Xa, was gained or lost in a set of primary ovarian tumors, using levels of XIST expression as a marker of X chromosome inactivation. To determine which copy of the X chromosome harbors the alterations, we compared local copy number and LOH in Xq13.2 to the levels of XIST expression in a set of N=134 primary tumors from Mayo Clinic ovarian cancer patients. Associations with XIST expression were examined using ANOVA, with copy number of two and absence of LOH as the reference level. Associations between expression of XIST and expression of other genes were examined with a mixed effects model, to account for the repeated observations across patients, after adjusting for local copy number and LOH. Associations with XIST expression and overall survival were performed with Cox regression. Additional analyses were performed in a subset of 24 patient tumors with X chromosome-wide alterations to simplify interpretation, including a visual examination of chromosome-wide DNA methylation patterns. Analyses were replicated in N=106 ovarian tumors from TCGA with X chromosome-wide alterations. Across N=134 ovarian tumors from Mayo Clinic, abnormal copy number (not equal to two) was associated with reduced XIST expression (p=0.0016). Levels of XIST expression were similar for tumors with one copy and tumors with three or more copies. Similarly, levels of XIST expression were also lower for tumors with LOH (p=5.2E-5). Reduced XIST expression was also associated with poorer survival, after adjusting for age (p=0.02). Results were consistent in the subset of tumors with whole chromosome alterations (pCN=0.0013, pLOH=0.0019). For these tumors, when LOH was present, XIST expression was low and the DNA methylation distribution is bi-modal, suggesting that the inactive X was lost and the active X was gained. Furthermore, after adjusting for copy number and LOH, XIST expression was weakly positively correlated with the median expression of other genes (R2=0.04, p=0.026). In N=106 TCGA tumors with whole X chromosome alterations, XIST expression was also reduced in tumors with abnormal copy number (p=0.006) and with LOH (p=1.5E-10). Taken together, because XIST is expressed from Xi and not Xa, associations of reduced XIST expression with abnormal copy number and presence of LOH suggest that Xi is frequently lost and Xa is gained in ovarian tumors, consistent with cell line studies. These results should be further validated in larger sample sets and examined for association with clinical outcome. Citation Format: Yilin Song, Bryan M. McCauley, Melissa C. Larson, Sebastian M. Armasu, Kate Lawrenson, Ellen L. Goode, Nicholas B. Larson, Stacey J. Winham. Evidence of decreased X chromosome inactivation in primary ovarian tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2452.

Abstract Background: Biopsy diagnosis of benign breast disease (BBD) based on the most severe lesion in a sample predicts future breast cancer risk and has implications for screening and management. Lobules are the functional unit of the breast and the structures from which BBD arises. We developed and preliminarily validated an automated computational pathology algorithm to discriminate normal from BBD breast lobules as a step toward automated comprehensive characterization of benign biopsies. Methods: 152 BBD biopsies (27 training, 125 validation) from the Mayo Clinic were examined using scanned digital images of H&E stained sections. For each image, a pathologist annotated up to 10 representative lobules as normal or BBD using standard pathology criteria. A deep learning algorithm to quantify lobular features was developed using 129 lobules from 27 subjects. Nine features were identified that discriminate normal vs BBD lobules, expressing lobule size, acini size and number, acinar lumen size, proportion of lobular stroma, and capillaries. Here, we validate their performance to discriminate normal vs BBD lobules in a set of 1250 lobules from 125 subjects using area under the ROC curve (AUC) analysis. Random forest analysis was used for multivariable modeling; model performance was assessed with a tenfold cross-validation approach. Results: Median subject age was 52 years. Among the 125 validation subjects, BBD findings were nonproliferative in 39%, proliferative in 45%, and atypical hyperplasia in 16%. Sections included 552 (44%) normal lobules and 698 BBD (56%) lobules, with representation of both lobule types on each section. In univariate analyses, four individual features showed good discrimination between normal vs. BBD lobules, yielding the following AUCs: lobule size= 0.74, mean acini size= 0.75, epithelial area= 0.75, and number of acini with large lumens= 0.76. Lobule size and epithelial area were highly correlated (Spearman rank correlation r = 0.95), but both of these features showed lower correlation with mean acini size and number of acini with large lumens (each r &lt; 0.5). With random forest modeling, number of acini with large lumens was the strongest discriminating feature, followed by mean acini size, lobule size, and epithelial area. With ten-fold cross validation of the multivariable random forest model, the overall AUC was 0.82 (95% CI: 0.79-0.85). Conclusion: Our validation showed that automated quantitative computational pathology assessment of breast lobules can discriminate normal versus BBD on a per lobule basis. This finding supports the feasibility of developing automated algorithms to classify every lesion in breast biopsies, expanding beyond visual assessment. Further studies using deep learning may reveal novel pathology features for classifying BBD biopsies with the potential to strengthen estimation of breast cancer risk. Citation Format: Amy C. Degnim, Thomas de Bel, Mark E. Sherman, Derek C. Radisky, Stacey J. Winham, Tanya L. Hoskin, Melody L. Stallings Mann, Marlene Frost, Robert A. Vierkant, Brendan T. Broderick, Ethan P. Heinzen, Rushin Brahmbhatt, Muhammad Arshad, Celine M. Vachon, Jodi M. Carter, Lori A. Denison, Daniel W. Visscher, Jeroen van der Laak. Discrimination of benign breast disease from normal lobules using an automated computational pathology algorithm [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2113.

Abstract Over one million women in the United States receive biopsy diagnoses of benign breast disease (BBD) each year, which confer a 1.5–4.0-fold increase in breast cancer risk. Studies in the general population suggest that nonsteroidal anti-inflammatory agents (NSAID) lower breast cancer risk; however, associations among women with BBD are unknown. We assessed whether NSAID use among women diagnosed with BBD is associated with lower breast cancer risk. Participants included 3,080 women (mean age = 50.3 ± 13.5 years) in the Mayo BBD surgical biopsy cohort diagnosed between January 1, 1992 and December 31, 2001 who completed breast cancer risk factor questionnaires that assessed NSAID use, and whose biopsies underwent detailed pathology review, masked to outcome. Women were followed from date of BBD biopsy to breast cancer diagnosis (main outcome) or censoring (death, prophylactic mastectomy, reduction mammoplasty, lobular carcinoma in situ or last contact). Median follow-up time was 16.4 ± 6.0 years. Incident breast cancer was diagnosed among 312 women over a median follow-up of 9.9 years. Regular non-aspirin NSAID use was associated with lower breast cancer risk [HR = 0.63; 95% confidence interval (CI) = 0.46–0.85; P = 0.002] with trends of lower risk (highest tertiles of use vs. nonuse) for greater number of years used [HR = 0.55; 95% CI = 0.31–0.97; Ptrend = 0.003), days used per month (HR = 0.51; 95% CI = 0.33–0.80; Ptrend = 0.001) and lifetime number of doses taken (HR = 0.53; 95% CI = 0.31–0.89; Ptrend = 0.003). We conclude that nonaspirin NSAID use is associated with statistically significant lower breast cancer risk after a BBD biopsy, including a dose–response effect, suggesting a potential role for NSAIDs in breast cancer prevention among patients with BBD.

Abstract Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. Experimental Design: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. Results: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with &gt;95% accuracy that was maintained in all analytic and biological validations. Conclusions: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications. See related commentary by McMullen et al., p. 5271