Project 3: Inter-Relationships and Prognostic Significance of Breast Cancer Radiomic Risk Features, Tissue Microenvironment, and Adiposity

摘要/摘要 美国女性患乳腺癌的风险因种族和民族而异。 尽管如此，亚裔美国人和夏威夷土著/太平洋岛民(AANHPI)少数民族妇女一直 历史上在乳腺癌研究中的代表性不足。因此，在理解上存在重大差距。 这些人口差异的基础，包括夏威夷原住民和 在日裔美国人中发病率稳步上升，结果相对有利。肥胖和 乳房密度是公认的乳腺癌危险因素，在AANHPI妇女中差异很大，并直接 对乳房X光检查和一级预防的影响。到目前为止，我们的研究提供了强有力的 有证据表明，身体脂肪分布，包括内脏脂肪组织(VAT)，是乳房的重要预测因素 癌症风险。肥胖对乳房密度和乳房结构的其他方面的影响 通过乳房X光检查(例如，放射学特征)识别的疾病还不能很好地理解。我们的长期目标 是为了阐明在未被研究的少数民族AANHPI亚群(夏威夷土著， 密克罗尼西亚人、日本人、中国人、菲律宾人)，这可以转化为改进预防、早期发现和 治疗策略。我们的中心假设是已建立的辐射风险特征具有独一无二的 AANHPI亚组与乳腺癌发病率的相关性及其与组织的相关性 风险和预后的生物标志物以及肥胖，特别是增值税。研究资源包括全州范围的 夏威夷太平洋岛屿乳房摄影登记处与SEER夏威夷肿瘤登记处及其残留物相关联 组织资料库(RTR)和多种族队列研究(MEC)的夏威夷组成部分。我们的研究是 重点关注AANHPI的少数群体健康，目的如下：1)描述 组织蛋白生物标记物表达谱反映乳腺影像放射学风险特征 组织微环境与乳腺癌预后和疾病特异性生存；2)表征 乳房放射危险特征和包括增值税在内的不同肥胖度与 夏威夷原住民、日裔美国人和白人MEC人绝经后乳腺癌风险 参与者。3)校准常用的乳腺癌风险预测模型，包括已建立的 AAPHI和White的2D和3D乳房X光摄影(人工智能和机器学习)风险特征 女性总体和雌激素/孕激素受体和HER-2状态。这次会议的预期结果 建议的研究是为了加深我们对成像生物标志物之间独特关系的理解 来自高级机器学习方法和种族/民族、组织分子特征和肥胖症 表型，这将提高风险和预后模型的准确性，并更好地识别高危妇女，以便进一步 评估或量身定做的治疗。

SUMMARY / ABSTRACT The risk of breast cancer among U.S. women dramatically differs across racial and ethnic populations. Nonetheless, Asian American and Native Hawaiian/Pacific Islander (AANHPI) ethnic minority women have been historically underrepresented in breast cancer research. Consequently, there are major gaps in understanding the basis of disparities in these populations including high incidence and mortality among Native Hawaiians and a steadily rising incidence with comparatively favorable outcomes among Japanese Americans. Obesity and breast density, established breast cancer risk factors, vary widely across AANHPI women and have direct implications for mammographic screening and primary prevention. Our research to date provides strong evidence that body fat distribution, including visceral adipose tissue (VAT), is an important predictor of breast cancer risk. The influence of adiposity on breast density and other aspects of breast architecture that can be discerned through mammographic screening (e.g. radiomic features) is not well understood. Our long-term goal is to elucidate the breast cancer disparities seen in understudied minority AANHPI subgroups (Native Hawaiian, Micronesian, Japanese, Chinese, Filipina) that can be translated to improved prevention, early detection, and therapeutic strategies. Our central hypothesis is that established radiomic risk features have unique associations with breast cancer incidence in AANHPI subgroups and that they are correlated with tissue biomarkers of risk and prognosis and with obesity, especially VAT. Study resources include the statewide Hawai`i Pacific Islands Mammography Registry linked to the SEER Hawai`i Tumor Registry and its Residual Tissue Repository (RTR), and to the Hawai`i component of the Multiethnic Cohort Study (MEC). Our study is focused on the minority health of AANHPI, with the following aims: 1) Characterize the relationships of established breast imaging radiomic risk features with tissue protein biomarker expression profiles reflecting the tissue microenvironment and breast cancer prognosis and with disease-specific survival; 2) Characterize the joint relationships of breast radiomic risk features and different measures of adiposity, including VAT, with post-menopausal breast cancer risk among Native Hawaiian, Japanese American, and White MEC participants. 3) Calibrate commonly used risk prediction models for breast cancer by including established breast radiomic (AI and machine learning) risk features from 2D and 3D mammography in AAPHI and White women overall and by estrogen/progesterone receptor and HER-2 status. The expected outcome of the proposed study is to further our understanding of unique relationships between imaging biomarkers derived from advanced machine learning approaches and race/ethnicity, tissue molecular characteristics and adiposity phenotypes, which will improve risk and prognosis model accuracy and better identify high risk women for further assessment or tailored therapy.