Integrating Clinical, Pathologic, and Immune Features to Predict Breast Cancer Recurrence and Chemotherapy Benefit

摘要 乳腺癌是全球女性癌症死亡的主要原因，有超过230万例。 每年都会确诊。大多数患者激素受体阳性，经抗雌激素治疗有效。 治疗，但一些患者有侵袭性疾病，并有复发和死亡的风险 化疗。基于基因表达的复发分析，如OncotypeDX，被设计用于预测 激素治疗后复发，并用于选择患者进行化疗。然而，这些化验是 昂贵(每项测试3,000美元)，执行需要相当长的时间，导致治疗延迟，并且测试是 在美国和全球的低资源环境中，未得到充分利用或坦率地说，无法获得。相反，每个病人 患有乳腺癌的患者有活检以确认诊断，这是由病理学家常规分析以确定的 乳腺癌的亚型和分级。深度学习是一种新兴的定量图像分析技术， 并且可以从病理中识别非直观的特征，包括基因表达模式。在前期工作中，我 已经证明，对病理样本的深入学习可以提供快速和经济有效的预测 OncotypeDX评分使用现成的数据，可以识别荷尔蒙复发风险较低的患者 心理治疗。 然而，OncotypeDX仍然不是化疗益处的完美预测指标，因为它被开发为 预测荷尔蒙治疗的复发。通过提炼我的深度学习生物标记物，将临床和 乳腺癌的免疫特征，可以提高预测化疗效益的准确性，从而 有能力进行个性化治疗。首先，我将利用最近国家数据中心临床数据的扩展 为癌症数据库的开发提供更准确的临床模型，对预后和化疗有好处。接下来，我 将使用多重免疫荧光更好地表征与以下相关的空间和细胞密度特征 化疗受益，并使用深度学习模型从标准苏木素和 伊红染色数字病理。最后，我将把这些临床和免疫模型与我现有的 学习病理模型，并在多机构队列中验证集成模型。这项工作的成果 将产生一个预测性和预测性的深度学习生物标记物，它可以很容易地做出准确的预测 可用的临床、病理和推断的免疫特征。这种方法有可能减少 由于周转时间短而导致化疗延迟，通过提高可获得性来应对医疗保健差距 通过量身定做预测化疗的生物标记物，提高治疗的个性化 利益。

Abstract Breast cancer is the leading cause of cancer death for women globally, with over 2.3 million cases diagnosed each year. Most cases are hormone receptor positive and effectively treated with anti-estrogen therapy, but some patients have aggressive disease and are at risk for recurrence and death without chemotherapy. Gene expression based recurrence assays, such as OncotypeDX, were designed to predict recurrence on hormonal therapy and are used to select patients for chemotherapy. However, these assays are expensive (> $3,000 per test), take considerable time to perform leading to treatment delays, and testing is underutilized or frankly unavailable in low resource settings in the US and globally. Conversely, every patient with breast cancer has a biopsy to confirm the diagnosis, which is routinely analyzed by pathologist to determine subtype of breast cancer and grade. Deep learning is an emerging technique for quantitative image analysis, and can identify non-intuitive features from pathology, including gene expression patterns. In preliminary work, I have demonstrated that deep learning on pathology samples can provide rapid and cost-effective prediction of OncotypeDX score using readily available data, and can identify patients at low risk of recurrence on hormonal therapy. However, OncotypeDX remains an imperfect predictor of chemotherapy benefit, as it was developed to predict recurrence on hormonal therapy. By refining my deep learning biomarker to incorporate clinical and immune features of breast cancer, I can improve accuracy in prediction of chemotherapy benefit and thus the ability to personalize treatment. First, I will capitalize on the recent expansion of clinical data in the National Cancer Data Base to develop a more accurate clinical models of prognosis and chemotherapy benefit. Next, I will use multiplex immunofluorescence to better characterize spatial and cell density features associated with chemotherapy benefit, and use deep learning models to infer these features from standard hematoxylin and eosin stained digital pathology. Finally, I will integrate these clinical and immune models with my existing deep learning pathologic model and validate the integrated model in a multi-institutional cohort. The result of this work will result in a prognostic and predictive deep learning biomarker that makes accurate predictions from readily available clinical, pathologic, and inferred immune features. This approach has the potential to reduce chemotherapy delays due to rapid turnaround time, combat healthcare disparities through improved availability of testing, and improve personalization of treatment by tailoring a biomarker for prediction of chemotherapy benefit.