Predicting Breast Cancer Risk after Benign Percutaneous Biopsy

良性经皮活检后预测乳腺癌风险

• 批准号: 10430124
• 负责人: Amy C Degnim
• 金额: $ 63.92万
• 依托单位: MAYO CLINIC JACKSONVILLE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-05 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10430124
• 关键词: Age; Area; Benign; Biological Assay; Biological Markers; Biopsy; Breast Cancer Model; Breast Cancer Risk Assessment Tool; Breast Cancer Risk Factor; Breast Diseases; Cancer Control; Categories; Cells; Characteristics; Classification; Clinic; Clinical; Cox Models; Cytokeratin 8; Data; Development; Diagnosis; Epidemiology; Epithelial Cells; Face; Frequencies; Future; Gene Expression Profile; Genes; Goals; Histologic; Histopathology; Individual; Logistic Regressions; Machine Learning; Malignant Neoplasms; Mammographic Density; Measures; Methods; Modeling; Mutation; Needle biopsy procedure; Operative Surgical Procedures; Pathologic; Pathology; Pathway interactions; Patient Self-Report; Patients; Performance; Population; Population Heterogeneity; Prevention; Process; Questionnaires; RNA; ROC Curve; Radiology Specialty; Risk; Risk Estimate; Risk Factors; Risk Marker; Sampling; Severities; Severity of illness; Statistical Models; Structure; Surgical Models; TP53 gene; Terminal Ductal Lobular Unit; Testing; Time; Translating; Tumor Suppressor Genes; Update; Validation; Weight; Woman; Women' s Group; Work; base; breast cancer diagnosis; classification trees; cohort; disease diagnosis; follow-up; gradient boosting; immunohistochemical markers; improved; innovation; malignant breast neoplasm; molecular marker; nano-string; novel; predictive modeling; random forest; regression trees; risk prediction; risk prediction model; screening; senescence; statistical and machine learning; tissue biomarkers; tool

Project Abstract More than one million women are diagnosed with benign breast disease (BBD) by percutaneous biopsy annually in the U.S. and would benefit from improved breast cancer (BC) risk information as they face screening and prevention decisions. BBD is associated with increases in BC risk, ranging from 1.5-2.0 times for least severe categories to fourfold for most severe types. However, these risks apply to groups of women, not individuals, and individual risk varies considerably within BBD categories. Further, we have shown that breast cancer (BC) risk prediction models, such as the “Gail Model”, perform poorly among women with BBD. Previously, we developed the BBD-BC model for surgical biopsies, which provides individual risk estimates based on self-reported factors, detailed characteristics of BBD extent and severity, and assessment of involution (shrinkage and disappearance) of surrounding histologic structures (terminal duct lobular units (TDLUs)) from which most BC precursors arise. BBD-BC outperforms the Gail Model in predicting BC risk. However, given that radiologically-guided small (percutaneous) biopsies have largely replaced surgical biopsies for diagnosis, a new model based on this biopsy approach is needed. Further, the emergence of mammographic density as an important BC risk factor, development of novel methods to assess TDLU involution and increased use of biomarkers in routinely processed clinical samples offer an opportunity to develop an improved BC risk prediction tool for women with percutaneous biopsy diagnoses of BBD. The goal of this project is to build a BC risk prediction tool for women with BBD diagnosed on percutaneous needle biopsy that could be validated in diverse populations and implemented clinically. We propose to develop a cohort at Mayo that includes >7,000 women who were diagnosed with BBD on a percutaneous biopsy of whom >400 later developed BC. We will develop a model to predict BC that includes factors in the BBD-BC model for surgical biopsies. We will also assess mammographic density, measured as a volume and area, using validated methods. We will identify immunohistochemical markers that can be applied to BBD biopsies to predict future risk of developing BC and evaluate novel NanoString RNA assays, which measure expression of related genes as composite “signatures” reflecting cancer-like characteristics, proliferation, and a mutation-like score for the important TP53 tumor suppressor gene. Finally, we will develop an epidemiologic “case-cohort” that includes a random subset of women from the full cohort (n~500) and all the women that developed invasive BC (n~250). We will evaluate BC risk prediction in this case-cohort of 750 women to evaluate performance of risk models without biomarkers and with biomarkers using novel machine learning approaches that offer strengths compared with more typical statistical models. Using these data, we will build an absolute risk prediction model for the full cohort that can be tested in other populations.

项目摘要 100多万妇女通过经皮活检确诊为良性乳腺疾病(BBD) 每年在美国，并将受益于改善的乳腺癌(BC)风险信息，因为他们面临 筛查和预防决策。BBD与BC风险增加相关，范围为1.5-2.0倍 对于最严重的类型，最轻的类别增加到四倍。然而，这些风险适用于女性群体， 不是个人，个人风险在BBD类别中差异很大。此外，我们已经表明， 乳腺癌(BC)风险预测模型，如“盖尔模型”，在患有BBD的女性中表现不佳。 在此之前，我们开发了用于手术活检的bbd-bc模型，该模型提供个体风险估计。 根据自我报告的因素，BBD程度和严重程度的详细特征，以及 周围组织结构(末端导管小叶单位)退化(萎缩和消失) (TDLU))，其中大多数BC前体产生于此。BBD-BC在预测BC风险方面优于Gail模型。 然而，鉴于放射引导的小(经皮)活组织检查在很大程度上取代了外科手术 为了诊断活检，需要一种基于这种活检方法的新模型。此外，出现了 乳腺X线密度作为乳腺癌的重要危险因素，开发评估TDLU的新方法 常规处理的临床样本中生物标记物的退化和使用的增加提供了一个机会 为经皮活检诊断为BBD的女性开发一种改进的BC风险预测工具。这个 该项目的目标是为经皮针诊断为BBD的女性建立BC风险预测工具 可在不同人群中验证并在临床上实施的活组织检查。我们建议开发一种 Mayo的队列包括7,000名在经皮活检中被诊断患有BBD的女性 &gt；400后来开发了公元前。我们将开发一个模型来预测BC，该模型包括以下BBD-BC模型中的因素 外科活组织检查。我们还将评估乳房X光照相密度，以体积和面积衡量，使用 经过验证的方法。我们将确定可应用于BBD活检的免疫组织化学标记物 预测未来发展为BC的风险并评估新的纳米串RNA检测方法，该方法测量 相关基因作为反映癌症样特征、增殖和突变样特征的复合“标志” 重要的肿瘤抑制基因TP53的评分。最后，我们将制定一个流行病学的“病例队列”。 这包括整个队列(n~500)中的随机女性子集和所有发展为 侵袭性BC(n~250)。在这种情况下，我们将评估BC风险预测-750名女性的队列评估 使用新型机器学习方法对无生物标记物和有生物标记物的风险模型的性能 与更典型的统计模型相比，这些模型提供了优势。使用这些数据，我们将建立一个绝对的 可在其他人群中测试的全队列风险预测模型。

项目成果

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.

• DOI: 10.1038/s41523-021-00378-7
• 发表时间: 2022-01-19
• 期刊: NPJ breast cancer
• 影响因子: 5.9
• 作者: de Bel T;Litjens G;Ogony J;Stallings-Mann M;Carter JM;Hilton T;Radisky DC;Vierkant RA;Broderick B;Hoskin TL;Winham SJ;Frost MH;Visscher DW;Allers T;Degnim AC;Sherman ME;van der Laak JAWM
• 通讯作者: van der Laak JAWM

Breast Cancer Risk and Use of Nonsteroidal Anti-inflammatory Agents After a Benign Breast Biopsy.

• DOI: 10.1158/1940-6207.capr-20-0178
• 发表时间: 2020-11
• 期刊: Cancer prevention research (Philadelphia, Pa.)

Immune Responses and Risk of Triple-negative Breast Cancer: Implications for Higher Rates among African American Women.

• DOI: 10.1158/1940-6207.capr-19-0562
• 发表时间: 2020-11
• 期刊: CANCER PREVENTION RESEARCH
• 影响因子: 3.3
• 作者: Ogony, Joshua W.;Radisky, Derek C.;Ruddy, Kathryn J.;Goodison, Steven;Wickland, Daniel P.;Egan, Kathleen M.;Knutson, Keith L.;Asmann, Yan W.;Sherman, Mark E.
• 通讯作者: Sherman, Mark E.