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Radiogenomic Biomarkers of Breast Cancer Recurrence

乳腺癌复发的放射基因组生物标志物

基本信息

批准号：
10403957
负责人：
LEWIS A CHODOSH
金额：
$ 56.14万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-03 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10403957
关键词：
Address Archives Assessment tool Biological Assay Biological Markers Biology Breast Breast Cancer Treatment Cancer Prognosis Characteristics Clinical Complement Coupled Data Diagnosis Digital Mammography Dimensions Distant ERBB2 gene Epidermal Growth Factor Receptor Estrogen Receptors Estrogens Event Future Gene Expression Goals Heterogeneity Histopathology Human Image Imaging technology Immunohistochemistry Institution Magnetic Resonance Imaging Measures Modality Modeling Molecular Molecular Profiling Morbidity - disease rate Morphology Multimodal Imaging Outcome Paraffin Performance Phenotype Play Positron-Emission Tomography Predictive Value Primary Neoplasm Progesterone Prognosis Prognostic Marker Radiogenomics Recurrence Relapse Resistance Resolution Retrospective cohort Risk Risk Assessment Role Sampling Shapes Spiculate Surrogate Markers Testing Texture Time Tissue Banks Tissue Sample Tissues Training Treatment Failure Tumor Markers Tumor Subtype Tumor Tissue United States National Institutes of Health Validation Woman aggressive breast cancer aggressive therapy base breast imaging breast surgery cancer imaging cancer recurrence clinical care cohort computerized tools contrast enhanced cost follow-up imaging biomarker imaging modality improved in vivo malignant breast neoplasm molecular marker multimodality novel personalized medicine precision medicine predictive marker prognostic prognostic value prospective risk prediction model routine care screening side effect spatiotemporal technology development three-dimensional visualization time use tissue biomarkers transcriptome sequencing treatment group tumor tumor heterogeneity tumor progression ultrasound uptake vector

项目摘要

Project Summary The main goal of our project is to investigate the added value of multi-modality breast imaging in prognostic assessment for breast cancer. Accurate prognostic assessment is a key component of personalized treatment. Breast cancer prognosis has historically been determined based on tumor histopathology (i.e., size, grade, stage, etc) and immunohistochemistry (i.e., estrogen, progesterone, human epidermal growth factor receptors). Recently, molecular assays have also become available (i.e., OncotypeDX, MammaPrint, etc) that measure tumor gene expression as related to prognosis. Although a lot of progress has been made, there is still a need for substantial improvements in identifying women who are at risk for morbidity due to overly or insufficiently aggressive therapy. Currently, histopathology and the molecular characteristics of tumors are mainly analyzed based on selective tissue sampling. As it is increasingly recognized that intra-tumoral heterogeneity plays an important role in tumor progression and resistance to treatment, selective tissue sampling may be inadequate for fully capturing such heterogeneity, potentially resulting in incomplete information for guiding treatment. Imaging is increasingly used in routine care for screening, diagnosis, and treatment of breast cancer, with different modalities offering complementary information. This ability, coupled by a potential for high-resolution 3D visualization, has provided a new means for capturing vital aspects of tumor heterogeneity in-vivo, and therefore potentially complementary prognostic information. The overarching goal of our study is to address this fundamental question: Can tumor imaging phenotypes provide additional information to established histopathologic and emerging molecular markers for predicting breast cancer recurrence? We propose four aims: AIM1) Develop a multi-modality imaging phenotype vector that captures structural (e.g., shape, morphology, texture) and functional heterogeneity (e.g., contrast uptake) of primary tumors. AIM2) Determine the prognostic value of the imaging features in predicting breast cancer recurrence; predictive value of features will also be explored. AIM3) Develop an augmented recurrence risk assessment model that incorporates tumor imaging features with standard tumor histopathology and emerging molecular markers, and AIM4) Perform independent validation of our model with prospectively collected data. In our study, we will investigate the prognostic value of multi-modality imaging for women diagnosed with primary invasive breast cancer. We will utilize a cohort of women with imaging and tumor tissue biomarker data from an NIH trial completed at our institution, from which 10-year follow-up from initial diagnosis and treatment is currently available. Ultimately, by integrating imaging with tumor histopathology and molecular markers in an augmented recurrence risk assessment tool we will be able to help better guide treatment decisions for women diagnosed with breast cancer. Also, considering that multi-modality imaging is increasingly used as part of routine clinical care, our study could provide new imaging biomarkers to improve treatment decisions, at a minimal additional cost.

项目摘要我们项目的主要目标是研究多模态乳腺成像在预后方面的附加价值评估乳腺癌。准确的预后评估是个性化治疗的关键组成部分。乳腺癌预后历来是基于肿瘤组织病理学（即，大小，等级，分期等）和免疫组织化学（即，雌激素、孕酮、人表皮生长因子受体）。最近，分子测定也变得可用（即，OncotypeDX、MammaPrint等），肿瘤基因表达与预后相关。虽然已经取得了很大的进展，但仍有必要在确定因过度或不充分使用避孕药具而有发病风险的妇女方面取得了实质性进展，积极治疗目前主要从组织病理学和肿瘤的分子生物学特征进行分析基于选择性组织取样。随着越来越多的人认识到肿瘤内异质性在肿瘤的发生发展中起着重要作用，重要作用的肿瘤进展和耐药性，选择性组织取样可能是不够的用于完全捕获这种异质性，可能导致用于指导治疗的不完整信息。成像越来越多地用于乳腺癌的筛查、诊断和治疗的常规护理中，提供补充信息的不同模式。这种能力，加上高分辨率的潜力， 3D可视化提供了一种新的手段，用于捕获体内肿瘤异质性的重要方面，因此潜在地补充了预后信息。我们研究的首要目标是解决这一基本问题：肿瘤成像表型能否为已建立的预测乳腺癌复发的组织病理学和新出现的分子标志物？我们提出四个目标：AIM 1）开发多模态成像表型载体，其捕获结构（例如，成形，形态，质地）和功能异质性（例如，造影剂摄取）。AIM 2）确定影像学特征对乳腺癌复发的预测价值也将被探索。目标3）开发一种增强的复发风险评估模型，具有标准肿瘤组织病理学和新兴分子标记物的成像特征，以及AIM 4）执行独立验证我们的模型与前瞻性收集的数据。在我们的研究中，我们将调查多模态成像对诊断为原发性浸润性乳腺癌的妇女的预后价值我们将利用一组女性的影像学和肿瘤组织生物标志物数据，这些数据来自于在我们的这些机构目前提供从初步诊断和治疗开始的10年随访。最后，通过将成像与肿瘤组织病理学和分子标记物相结合，评估工具，我们将能够帮助更好地指导诊断为乳腺癌的妇女的治疗决定癌此外，考虑到多模态成像越来越多地用作常规临床护理的一部分，我们的这项研究可以提供新的成像生物标志物，以最小的额外成本改善治疗决策。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Special Section Guest Editorial: Radiogenomics in Prognosis and Treatment.

DOI：
10.1117/1.jmi.8.3.031901
发表时间：
2021-05
期刊：
Journal of medical imaging (Bellingham, Wash.)
影响因子：
0
作者：
Drukker K;Kontos D;Li H
通讯作者：
Li H

Editorial for "Evaluation of Monoexponential, Stretched Exponential and Intravoxel Incoherent Motion MRI Diffusion Models in Early Response Monitoring to Neoadjuvant Chemotherapy in Patients With Breast Cancer-A Preliminary Study".

“单指数、拉伸指数和体素内不相干运动 MRI 扩散模型在乳腺癌患者新辅助化疗早期反应监测中的评估——一项初步研究”的社论。

DOI：
10.1002/jmri.28118
发表时间：
2022
期刊：
Journal of magnetic resonance imaging : JMRI
影响因子：
0
作者：
McDonald,ElizabethS;Rosen,MarkA
通讯作者：
Rosen,MarkA

Functional 4-D clustering for characterizing intratumor heterogeneity in dynamic imaging: evaluation in FDG PET as a prognostic biomarker for breast cancer.

DOI：
10.1007/s00259-021-05265-8
发表时间：
2021-11
期刊：
European journal of nuclear medicine and molecular imaging
影响因子：
9.1
作者：
Chitalia R;Viswanath V;Pantel AR;Peterson LM;Gastounioti A;Cohen EA;Muzi M;Karp J;Mankoff DA;Kontos D
通讯作者：
Kontos D

Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols.