Radiogenomic tools for prediction of breast cancer neo-adjuvant chemotherapy response from pre-treatment MRI

项目摘要：美国每年有超过2万名患者接受新辅助化疗(NAC) 在手术作为局部晚期乳腺癌的标准治疗之前，但70%-90%的人最终会 未能实现完整的回应。如果在治疗前确诊，对标准反应差的患者 NAC可以立即接受更积极的治疗方案，绕过无效的 治疗窗口带来了不必要的痛苦，增加了病情进展的风险。当肿瘤发生变化时 在整个治疗过程中，可以使用临床动态对比增强(DCE)MRI进行监测，仍然没有 临床公认的NAC反应的治疗前预测指标。放射学分析，定义为高通量 定量图像特征的提取已被证明能够更早地预测 DCE-MRI具有更高的准确性。然而，大多数特征仅限于纹理和形状特征 结节，忽视了询问已知受牵连的肿瘤微环境特征的机会 在治疗反应中。此外，广泛采用放射组学治疗的一个关键障碍 反应预测是它的生物可解释性低，因为它的特征缺乏既定的分子和 形态基础。放射基因组学方法，寻求确定成像和 疾病的分子标记提供了更大的生物直觉，但通常不适用于临床结果。 我们提出了一种基于系统生物学的方法来根据基线乳房DCE-MRI预测NAC反应 具有很高的临床可解释性和稳健性。我们将开发新的放射组学特征，以响应- 肿瘤和肿瘤微环境中的相关肿瘤生物学(例如免疫反应和血管生成)， 然后验证它们预测治疗结果的能力以及它们在多尺度肿瘤生物学中的基础。 最终，提出的研究可以在不牺牲的情况下为NAC提供有效的、非侵入性的指导 这些特征的生物学可解释性，是临床采用这些工具的重要先决条件。目标1将 寻求开发一套系统生物学驱动的放射组学描述符来表征肿瘤内的生物学 以及乳腺DCE-MRI的微环境。我们之前已经证明了纹理的异质性特征 在肿瘤周围微环境中，增强后MRI可以预测治疗反应并与 有免疫反应。我们将扩展这些功能以捕获3D异构性的时间变化，两者都 肿瘤内和肿瘤周围。此外，我们将开发形态特征来表征组织 肿瘤相关血管网络。目标2将专注于发现和验证来自 基线DCE-MRI可以预测接受和不接受HER2靶向治疗的NAC的反应。目标3将 通过探索阐明AIM 2中确定的预测放射组学特征的分子和形态基础 它们与基因组和组织学尺度上的异常(免疫反应和血管生成)的关系 从治疗前的活组织检查样本中。

PROJECT SUMMARY: Over 20,000 patients in the US annually undergo neo-adjuvant chemotherapy (NAC) prior to surgery as a standard-of-care treatment for locally advanced breast cancers, but 70-90% will ultimately fail to achieve a complete response. If identified prior to treatment, patients who will respond poorly to standard NAC could be immediately placed on more aggressive treatment regimens, circumventing an ineffective treatment window that introduces unnecessary suffering and increases risk of progression. While tumor changes throughout treatment can be monitored using clinical dynamic contrast-enhanced (DCE) MRI, there remains no clinically-accepted pre-treatment predictors of NAC response. Radiomic analysis, defined as high-throughput extraction of quantitative image features, has been demonstrated to enable earlier prediction of response from DCE-MRI with improved accuracy. However, most of the features are limited to texture and shape features of the nodule, ignoring the opportunity to interrogate features of the tumor microenvironment known to be implicated in treatment response. Furthermore, a critical roadblock in the wide-scale adoption of radiomics for treatment response prediction is its low biological interpretability, as its features lack an established molecular and morphologic basis. Radiogenomic approaches, which seek to identify connections between imaging and molecular markers of disease, provide greater biologic intuition, but often without application to clinical outcomes. We propose a systems-biology based approach to predict NAC response from baseline breast DCE-MRI with high clinical interpretability and robustness. We will develop novel radiomic features targeted to response- associated tumor biology in the tumor and tumor microenvironment (e.g. immune response and angiogenesis), then validate both their capability to predict therapeutic outcomes and their basis in multi-scale tumor biology. Ultimately, the research proposed could provide effective, non-invasive guidance of NAC without sacrificing biological interpretability of the features, an important pre-requisite for clinical adoption of these tools. Aim 1 will seek to develop a set of systems-biology driven radiomic descriptors to characterize the biology within the tumor and its microenvironment from breast DCE-MRI. We have previously shown that texture heterogeneity features in the peri-tumoral microenvironment on post-contrast MRI are predictive of treatment response and associated with immune response. We will expand these features to capture temporal changes in 3D heterogeneity, both intra- and peri-tumorally. Additionally, we will develop morphological features to characterize organization of the tumor-associated vascular network. Aim 2 will focus on discovering and validating imaging signatures from baseline DCE-MRI which are predictive of response to NAC with and without HER2-targeted therapy. Aim 3 will elucidate the molecular and morphological basis of predictive radiomic features identified in Aim 2 by exploring their associations with aberrations on the genomic and histology scales (immune response and angiogenesis) from pre-treatment biopsy samples.