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.

抽象的 这项研究的目的是开发和验证乳腺癌的预后成像生物标志物。 乳腺癌治疗的主要挑战是区分患有懒惰疾病的患者 从诊断时患有侵略性致命疾病的人。目前，没有可靠的生物标志物 在个人层面上区分这些群体。因此，所有乳腺癌患者接受 辅助疗法，但并非全部受益。这种千篇一律的方法会导致过度治疗， 导致发病率和死亡率。随机的生物标志物需要可靠的生物标志物 裁缝试验发现了一小部分低风险乳腺癌患者 基于21基因型DX测定法，复发率没有化学疗法。很遗憾， 大多数（67％）的患者根据基因组测定法患有中等风险范围，并且 这些患者对化疗的需求仍然存在不确定性。显然，更好 需要生物标志物来改善乳腺癌的预后和患者分层。建立在 广泛的初步数据，我们假设成像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 复发预测的准确性得到了很大提高。为了检验这一假设，我们建议：（1） 开发和改进方法以明确量化多区域MRI表型，包括 肿瘤内次区域和实质，并系统地评估其可重复性； （2）开发 使用大型回顾性队列的预后成像签名> 1000名患者 Stanford Oncoshare项目，并在潜在的多中心I-SPY 1同类中进行验证； （3） 构建一个放射基因组签名，以对13个公共的预后价值进行额外测试 > 5000名乳腺癌患者的基因表达人群。为了进一步改善预后，我们将 构建一个多因素模型，将成像与临床和基因组标记集成。这项研究 通过超越传统的总肿瘤功能，将推进定量成像领域 结合其他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，严格验证的成像生物标志物，这可以是 进一步测试了前瞻性试验中的临床实用性。最终，这种生物标志物可用于分层 患者并指导个性化治疗，允许临床医生避免过度治疗 疾病并加强侵略性疾病女性的治疗。