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

项目 3：乳腺癌放射风险特征、组织微环境和肥胖的相互关系和预后意义

• 批准号: 10716156
• 负责人: JOHN Alan SHEPHERD
• 金额: $ 29.07万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10716156
• 关键词: 3-Dimensional; Abdomen; Adipose tissue; American; Architecture; Artificial Intelligence; Asian Americans; Biological Assay; Biological Markers; Blood; Body fat; Body mass index; Breast; Breast Cancer Model; Breast Cancer Patient; Breast Cancer Risk Factor; Breast Cancer gene; CD36 gene; CD8B1 gene; Calibration; Cancer Prognosis; Cd68; Cessation of life; Characteristics; Chinese; Clinical Markers; Cohort Studies; Disease; Disparity; ERBB2 gene; Early Diagnosis; Early treatment; Estrogen Receptors; Estrogens; Ethnic Origin; Ethnic Population; Evaluation; Fatty acid glycerol esters; Gene Expression Profile; Goals; Hawaii; Health; High Risk Woman; Image; Incidence; Japan; Japanese; Japanese American; Joints; Link; Machine Learning; Mammary Neoplasms; Mammographic Density; Mammographic screening; Mammography; Measures; Mediating; Minority; Minority Women; Molecular; Morbidity - disease rate; Native Hawaiian; Native Hawaiian or Other Pacific Islander; Obesity; Organ; Outcome; Pacific Islands; Participant; Pathology; Pattern; Personal Satisfaction; Phenotype; Population; Population Sciences; Postmenopause; Prevention; Primary Prevention; Progesterone Receptors; Prognosis; Race; Reporting; Research; Residual state; Resources; Risk; Risk Factors; Source; Subgroup; Survival Analysis; Therapeutic; Tissue Banks; Tissues; Translating; Tumor Markers; Visceral; Visceral fat; Visit; Woman; ZNF217 gene; anticancer research; breast cancer survival; breast density; breast imaging; cancer health disparity; cancer survival; clinically relevant; cohort; comparative; deep learning; ethnic difference; ethnic minority; experience; genetic risk factor; hormone receptor-positive; imaging biomarker; improved; individualized medicine; malignant breast neoplasm; mammography registry; migration; minority health; minority health disparity; modifiable risk; molecular modeling; molecular subtypes; mortality; multi-ethnic; nano-string; prognostic; prognostic model; prognostic significance; protein biomarkers; public health relevance; racial difference; racial diversity; racial population; radiomics; reproductive; risk prediction model; screening; subcutaneous; tissue biomarkers; tumor; tumor registry; virtual

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.

摘要/摘要 美国女性患乳腺癌的风险在不同种族和族裔人群中存在显着差异。 尽管如此，亚裔美国人和夏威夷原住民/太平洋岛民 (AANHPI) 少数族裔妇女一直 历史上在乳腺癌研究中代表性不足。因此，在认识上存在重大差距 这些人口差异的基础包括夏威夷原住民的高发病率和死亡率 日裔美国人的发病率稳步上升，结果相对较好。肥胖和 乳腺密度、已确定的乳腺癌危险因素在 AANHPI 女性中差异很大，并且直接影响 对乳房X光检查和初级预防的影响。我们迄今为止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），是乳房发育的重要预测因素 癌症风险。肥胖对乳房密度和乳房结构其他方面的影响 通过乳房X线照相筛查（例如放射组学特征）识别的疾病尚不清楚。我们的长期目标 旨在阐明在未充分研究的少数民族 AANHPI 亚组（夏威夷原住民、 密克罗尼西亚语、日语、中国语、菲律宾语）可以转化为改善预防、早期发现和 治疗策略。我们的中心假设是，已确定的放射组学风险特征具有独特的 AANHPI 亚组中乳腺癌发病率的相关性以及它们与组织的相关性 风险和预后以及肥胖的生物标志物，尤其是增值税。研究资源包括全州范围 夏威夷太平洋岛屿乳腺 X 线摄影登记处与 SEER 夏威夷肿瘤登记处及其残留物相关联 组织存储库 (RTR) 以及多种族队列研究 (MEC) 的夏威夷部分。我们的研究是 重点关注 AANHPI 的少数群体健康状况，目标如下： 1）描述 建立了乳腺成像放射组学风险特征，其中组织蛋白生物标志物表达谱反映了 组织微环境和乳腺癌预后以及疾病特异性生存； 2）表征 乳房放射学风险特征与不同肥胖指标（包括增值税）的联合关系 夏威夷原住民、日裔美国人和白人 MEC 的绝经后乳腺癌风险 参与者。 3) 通过纳入已建立的模型来校准常用的乳腺癌风险预测模型 AAPHI 和 White 中 2D 和 3D 乳房 X 线摄影的乳腺放射组学（人工智能和机器学习）风险特征 女性总体情况以及雌激素/孕激素受体和 HER-2 状态。预期成果 拟议的研究是为了进一步了解成像生物标志物之间的独特关系 来自先进的机器学习方法和种族/民族、组织分子特征和肥胖 表型，这将提高风险和预后模型的准确性，并更好地识别高风险女性以进一步 评估或定制治疗。