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% 的女性被诊断患有浸润性乳腺癌 将会复发并最终可能死于疾病。目前还没有可靠的方法来鉴别 哪些癌症会因个人情况而复发。因此，几乎所有患者都会接受辅助治疗 与乳腺癌有关，但受益的只是一小部分。新辅助治疗也存在类似的困境，许多 患者未能对化疗产生病理反应，但却遭受相关的毒性。这 传统的一刀切的方法会导致过度治疗，导致发病和死亡。为了避免 这些副作用，迫切需要生物标志物对具有临床相关性的患者进行分层，以实现精确性 乳腺癌医学。分子谱分析目前用于对乳腺癌进行分层，但受到以下因素的限制： 需要进行侵入性活检，并受到肿瘤内遗传异质性的困扰。反之，成像 为肿瘤、其微环境和侵袭的非侵入性检查提供了独特的机会 到周围的正常组织。我们假设成像特征反映了潜在的肿瘤生物学，并且 定量成像特征可以提供独立的有价值的信息，这些信息与已知的协同作用 临床、组织学和遗传预测因子。因此，我们制定了三个具体目标来开发新的 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。目标 1 我们计划开发自动化计算工具来稳健地量化整个肿瘤、肿瘤内亚区域和 多模态 MRI 的实质表型。我们初步筛选的乳腺癌队列 (n=504) 将利用可用的 MRI 扫描以及手动描绘的肿瘤和实质轮廓来分析研究 由委员会认证的放射科医生进行。在目标 2 中，我们将构建基于成像特征的模型来预测无复发 生存率和治疗反应分开。通过与临床病理学和基因组预测因子相结合， 综合模型可以更准确地预测临床结果。将使用内部队列 (n=450) 用于发现，I-SPY 的多中心前瞻性队列（n=186）将用于验证。目标 3 我们将阐明我们新发现的预后和预测成像背后的生物学基础 生物标志物，通过将它们与来自同一肿瘤的生物样本衍生的表型相关联。特别是，我们 将研究多组学分子数据以及来自 H&E 染色病理切片的肿瘤形态。 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，证明了可行性。经过 利用丰富的注释良好的可用数据和先进的人工智能算法，它将 增加成功的可能性。我们提出的研究将指出新的高价值生物标志物更好地 预测个体水平的复发和治疗反应，并做出更好的治疗决策 患有乳腺癌的女性。