QUANTITATIVE IMAGING BIOMARKERS OF TREATMENT RESPONSE AND PROGNOSIS IN BREAST CANCER

乳腺癌治疗反应和预后的定量成像生物标志物

• 批准号: 10222593
• 负责人: Jia Wu
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10222593
• 关键词: Adjuvant; Adjuvant Therapy; Artificial Intelligence; Biological; Biological Assay; Biological Markers; Biopsy; Breast Cancer Patient; Characteristics; Clinical; Complement; Data; Descriptor; Diagnosis; Disease; ERBB2 gene; Ensure; Environment; Eosine Yellowish; Foundations; Future; Genetic Heterogeneity; Genomic approach; Genomics; Heterogeneity; Histologic; Histology; Image; Individual; Kinetics; Lead; MRI Scans; Magnetic Resonance Imaging; Malignant Neoplasms; Manuals; Measures; Medical Imaging; Methods; Modeling; Molecular; Molecular Profiling; Morbidity - disease rate; Morphology; Multiomic Data; Neoadjuvant Therapy; Normal tissue morphology; Oncology; Pathologic; Pathology; Patients; Pattern; Phenotype; Prognosis; Prospective cohort; Proteomics; Protocols documentation; Recurrence; Regimen; Reproducibility; Research; Risk; Role; Semantics; Site; Slide; Stains; Subgroup; Surrogate Markers; TNM; Testing; Texture; The Cancer Genome Atlas; Therapeutic; Toxic effect; Tumor Biology; Validation; Variant; Woman; Work; automated segmentation; base; cancer subtypes; chemotherapy; clinical translation; clinically relevant; cohort; computerized tools; contrast enhanced; effective therapy; feature extraction; genetic predictors; genomic predictors; high risk; image guided; imaging biomarker; imaging study; individualized medicine; innovation; intelligent algorithm; magnetic resonance imaging biomarker; malignant breast neoplasm; molecular marker; molecular pathology; mortality; multimodality; multiple omics; neglect; oncotype; outcome prediction; overtreatment; patient stratification; personalized management; precision medicine; predict clinical outcome; primary endpoint; prognostic; quantitative imaging; radiologist; response; side effect; success; survival prediction; synergism; targeted treatment; tool; transcriptomics; treatment response; tumor; tumor heterogeneity

ABSTRACT Breast cancer is a heterogeneous disease. Around 20% to 30% of women diagnosed with invasive breast cancer will have a recurrence and may eventually die of their disease. Currently, there are no reliable methods to identify which cancers will recur on an individual basis. Because of this, adjuvant therapies are given to nearly all patients with breast cancer, but benefit only a small proportion. A similar dilemma exists for neoadjuvant treatment, many patients fail to pathologically response to chemotherapy, and yet suffer from the associated toxicity. The conventional one-size-fits-all approach causes overtreatment, leading to morbidities and mortalities. To avoid these side effects, biomarkers that stratify patients with clinical relevance are critically needed for precision medicine in breast cancer. Molecular profiling is currently used to stratify breast cancer, but is limited by the requirement for invasive biopsy and confounded by intra-tumor genetic heterogeneity. Conversely, imaging provides a unique opportunity for the noninvasive interrogation of the tumor, its microenvironment, and invasion to surrounding normal tissues. We hypothesize that imaging characteristics reflect underlying tumor biology, and quantitative imaging features can provide independent valuable information, which are synergistic to known clinical, histologic, and genetic predictors. Accordingly, we have planned three specific aims to develop new quantitative imaging biomarkers for breast cancer, as well as clinically and biologically validate them. In Aim 1 we plan to develop automated computational tools to robustly quantify whole tumor, intratumor subregions, and parenchyma phenotypes from multimodal MRI. The curated breast cancer cohort (n=504) from our preliminary study will be analyzed, with available MRI scans and manually-delineated contours of tumor and parenchyma by board-certified radiologists. In Aim 2 we will build imaging feature-based models to predict recurrence-free survival and treatment response separately. By integrating with clinicopathologic and genomic predictors, the comprehensive models can predict clinical outcomes more accurately. The internal cohort (n=450) will be used for discovery, and the multi-center prospective cohort from I-SPY (n=186) will be used for validation. In Aim 3 we will elucidate the biological underpinnings behind our newly identified prognostic and predictive imaging biomarkers, by correlating them with biospecimen-derived phenotypes from the same tumor. In particular, we will investigate multi-omics molecular data as well as tumor morphology from H&E stained pathology slides. Three cohorts will be analyzed, including our internal cohort (n=450), the I-SPY cohort (n=186), and the TCGA cohort (n=1095). For three proposed aims, we have carried preliminary studies to prove the feasibility. By leveraging the richness of available well-annotated data and advanced artificial intelligence algorithms, it will increase the likelihood of success. Our proposed research will point new biomarkers of high value to better predict recurrence and treatment response at the individual level, and lead to better treatment decisions for women with breast cancer.

抽象的 乳腺癌是一种异质性疾病。大约20％至30％被诊断出患有侵入性乳腺癌的妇女 会复发，最终可能死于他们的疾病。当前，没有可靠的方法可以识别 癌症将单独复发。因此，几乎所有患者都给予辅助疗法 患有乳腺癌，但仅受益于一小部分。对于新辅助治疗而存在类似的困境，许多 患者在病理上对化学疗法的反应未能反应，但患有相关毒性。这 常规的一定大小的方法会导致过度治疗，导致病态和死亡。避免 这些副作用，将具有临床相关性的患者分层的生物标志物至关重要 乳腺癌的药。目前使用分子分析来分层乳腺癌，但受到 对侵入性活检的需求，并被肿瘤内遗传异质性混淆。相反，成像 为肿瘤，微环境和入侵提供了独特的机会 到周围正常组织。我们假设成像特征反映了潜在的肿瘤生物学，并且 定量成像功能可以提供独立的有价值信息，这些信息与已知 临床，组织学和遗传预测因子。因此，我们计划开发新的三个特定目标 用于乳腺癌的定量成像生物标志物，以及临床和生物学验证它们。在目标1中 我们计划开发自动计算工具，以稳健地量化整个肿瘤，肿瘤内区域和 来自多模式MRI的实质表型。我们的初步策划的乳腺癌队列（n = 504） 研究将进行分析，并进行可用的MRI扫描和手动划分的肿瘤和实质轮廓 由董事会认证的放射科医生。在AIM 2中，我们将构建基于成像功能的模型，以预测无复发的模型 生存和治疗反应分别。通过与临床病理学和基因组预测指标整合， 综合模型可以更准确地预测临床结果。内部队列（n = 450）将使用 为了发现，将使用I-SPY（n = 186）的多中心前瞻性队列进行验证。在目标3中 我们将阐明新确定的预后和预测成像背后的生物基础 生物标志物通过将它们与来自同一肿瘤的生物源性表型相关联。特别是我们 将研究H＆E染色病理幻灯片中的多矩分子数据以及肿瘤形态。 将分析三个队列，包括我们的内部队列（n = 450），I-SPY队列（n = 186）和TCGA 队列（n = 1095）。对于提出的三个目标，我们进行了初步研究以证明可行性。经过 利用可用的通知数据和高级人工智能算法的丰富性，它将 增加成功的可能性。我们拟议的研究将指出高价值的新生物标志物以更好 预测个人级别的复发和治疗反应，并为更好的治疗决定 乳腺癌的妇女。