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: 10168918
• 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/10168918
• 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; Estrogen receptor positive; 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; 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 forecast; 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.

摘要 乳腺癌是一种异质性疾病。约20%至30%的女性被诊断为浸润性乳腺癌 会复发，并可能最终死于他们的疾病。目前，还没有可靠的方法来识别 哪些癌症会在个体基础上复发。正因为如此，几乎所有的患者都会接受辅助治疗。 乳腺癌患者，但受益比例很小。新辅助治疗也存在类似的困境，许多 患者对化疗没有病理反应，但仍遭受相关毒性。这个 传统的一刀切的方法会导致过度治疗，导致疾病和死亡。为了避免 这些副作用、对患者进行临床分层的生物标记物对于精确度来说是非常必要的。 乳腺癌的医学。分子图谱目前被用于乳腺癌的分层，但受到 对侵入性活检的要求，以及肿瘤内遗传异质性的混淆。相反，成像 为非侵入性询问肿瘤及其微环境和侵袭性提供了独特的机会 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 临床、组织学和遗传预测因子。因此，我们计划了三个具体目标来开发新的 乳腺癌的定量成像生物标记物，以及临床和生物学验证。在目标1中 我们计划开发自动化计算工具，以强有力地量化整个肿瘤、瘤内亚区域和 来自多模式磁共振成像的实质表型。我们初步筛选的乳腺癌队列(n=504) 研究将通过可用的MRI扫描和手动描绘的肿瘤和实质轮廓进行分析 由委员会认证的放射科医生提供。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 生存和治疗反应分开。通过结合临床病理和基因组预测因子， 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，来自I-SPY(n=186)的多中心预期队列将用于验证。在AIM 3中 我们将阐明我们新发现的预测和预测成像背后的生物学基础。 生物标记物，通过将它们与来自同一肿瘤的生物谱系衍生的表型相关联。特别是，我们 将研究多组学分子数据以及H&E染色病理切片中的肿瘤形态。 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)。对于三个拟议目标，我们已经进行了初步研究，以证明其可行性。通过 利用丰富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。我们提出的研究将把新的高价值生物标志物指向更好的 在个体层面预测复发和治疗反应，并导致更好的治疗决策 患有乳腺癌的女性。