Multiregional imaging phenotypes and molecular correlates of aggressive versus indolent breast cancer

侵袭性乳腺癌与惰性乳腺癌的多区域成像表型和分子相关性

• 批准号: 10332716
• 负责人: Ruijiang Li
• 金额: $ 3.75万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10332716
• 关键词: Adjuvant Therapy; Anatomy; Biological; Biological Assay; Biological Markers; Breast; Breast Cancer Patient; Breast Cancer Risk Factor; Cancer Etiology; Characteristics; Clinical; Clinical Markers; Clinical Trials; Complement; Data; Diagnosis; Diagnostic; Disease; Evaluation; Exhibits; Failure; Functional disorder; Gene Expression; Gene Expression Profile; Genes; Genomics; Goals; Image; Image Guided Biopsy; Individual; Indolent; Invaded; MRI Scans; Magnetic Resonance Imaging; Methods; Modeling; Molecular; Morbidity - disease rate; Morphology; Outcome; Pathologic; Pathway interactions; Patients; Phenotype; Physiological; Physiology; Prognosis; Prognostic Factor; Prognostic Marker; Protocols documentation; Radiogenomics; Randomized; Recurrence; Regression Analysis; Reproducibility; Research; Retrospective cohort; Risk; Role; Staging; Techniques; Testing; Texture; The Cancer Genome Atlas; The Cancer Imaging Archive; Tissues; Uncertainty; United States; Validation; Variant; Woman; base; breast cancer progression; burden of illness; cancer recurrence; cancer survival; cancer type; chemotherapy; clinical translation; cohort; contrast enhanced; genomic biomarker; high risk; imaging biomarker; improved; individualized medicine; interest; malignant breast neoplasm; molecular phenotype; mortality; neglect; oncotype; overtreatment; patient stratification; precision oncology; prognostic; prognostic signature; prognostic value; prognostication; prospective; quantitative imaging; risk minimization; risk stratification; treatment response; tumor; tumor heterogeneity

ABSTRACT The goal of this research is to develop and validate prognostic imaging biomarkers for breast cancer. A major challenge in the management of breast cancer is distinguishing patients with indolent disease from those with aggressive lethal disease at diagnosis. Currently, there are no reliable biomarkers to distinguish these groups on an individual level. Consequently, all patients with breast cancer receive adjuvant therapies, but not all benefit equally. This one-size-fits-all approach causes overtreatment, leading to morbidity and mortality. The need for reliable biomarkers is highlighted by the randomized TAILORx trial, which identified a small group of low-risk breast cancer patients who had very low rates of recurrence without chemotherapy, based on the 21-gene Oncotype Dx assay. Unfortunately, a majority (67%) of patients fell in the intermediate-risk range according to the genomic assay, and uncertainty still remains regarding the need for chemotherapy among these patients. Clearly, better biomarkers are needed to improve prognostication and patient stratification in breast cancer. Built on extensive preliminary data, we hypothesize that imaging characteristics reflect underlying tumor pathophysiology, and that image-based phenotyping of both tumor and parenchyma will provide much improved accuracy for recurrence prediction. To test this hypothesis, we propose to: (1) develop and improve methods to explicitly quantify multiregional MRI phenotypes including those of intratumoral subregion and parenchyma, and systematically assess their reproducibility; (2) develop a prognostic imaging signature using a large retrospective cohort of >1000 patients curated by the Stanford Oncoshare Project, and validate it in the prospective multi-center I-SPY 1 cohort; (3) construct a radiogenomic signature to perform additional testing of its prognostic value in 13 public gene expression cohorts of >5000 breast cancer patients. To further improve prognostication, we will build a multifactorial model that integrates imaging with clinical and genomic markers. This research will advance the quantitative imaging field by moving beyond traditional gross-tumor features and incorporating additional parenchymal and intratumoral imaging characteristics. If successful, it will provide much needed, rigorously validated imaging biomarkers for breast cancer, which can be further tested for clinical utility in prospective trials. Ultimately, such biomarkers can be used to stratify patients and guide individualized therapy, by allowing clinicians to avoid overtreatment of indolent disease and intensify treatment in women with aggressive disease.

摘要 本研究的目的是开发和验证乳腺癌的预后成像生物标志物。 乳腺癌管理的一个主要挑战是区分惰性疾病患者 从那些被诊断患有侵袭性致命疾病的人身上。目前，还没有可靠的生物标志物， 在个人层面上区分这些群体。因此，所有乳腺癌患者都接受 辅助疗法，但并非所有的受益相同。这种一刀切的做法会导致过度治疗， 导致发病率和死亡率。对可靠的生物标志物的需求是由随机 TAILORx试验，确定了一小群低风险乳腺癌患者， 基于21基因Oncotype Dx检测，未化疗的复发率。不幸的是， 根据基因组测定，大多数（67%）患者处于中等风险范围， 关于这些患者是否需要化疗仍然存在不确定性。很明显， 需要生物标志物来改善乳腺癌的诊断和患者分层。建立在 根据广泛的初步数据，我们假设影像学特征反映了潜在的肿瘤 病理生理学，以及肿瘤和实质的基于图像的表型将提供 大大提高了复发预测的准确性。为了验证这一假设，我们建议：（1） 开发和改进明确量化多区域MRI表型的方法，包括 肿瘤内亚区和实质，并系统地评估其可重复性;（2）开发一种 预后成像特征，使用由美国国家癌症研究所管理的>1000例患者的大型回顾性队列， 斯坦福大学Oncoshare项目，并在前瞻性多中心I-SPY 1队列中进行验证;（3） 构建放射基因组签名，在13个公众中对其预后价值进行额外测试 >5000名乳腺癌患者的基因表达队列。为了进一步提高解释能力，我们将 建立一个多因素模型，将成像与临床和基因组标志物相结合。本研究 将通过超越传统的大肿瘤特征来推进定量成像领域， 合并了额外的实质和肿瘤内成像特征。如果成功，它将 为乳腺癌提供急需的、经过严格验证的成像生物标志物， 在前瞻性试验中进一步测试临床效用。最终，此类生物标志物可用于分层 患者和指导个性化治疗，通过允许临床医生避免过度治疗惰性 疾病和加强治疗的妇女与侵略性疾病。