Developing Digital Pathology Biomarkers for Response to Neoadjuvant and Adjuvant Chemotherapy in Breast Cancer

项目摘要 尽管在发现乳腺癌的生物学方面已经取得了巨大的进步，但选择 早期乳腺癌的化疗方案主要基于受体状态和分期。 然而，许多其他因素与反应有关，包括基因表达模式和肿瘤。 遗传学，但这些并不是患者都能获得的。苏木精-伊红染色的病理是 为所有乳腺癌患者例行公事地获取，并包含了丰富的超越等级的信息。为 例如，肿瘤浸润性淋巴细胞的类型和数量长期以来一直被认为是预测 对化疗的反应，但量化是具有挑战性的。 深度学习是在图像识别领域具有特别前景的新兴学科， 其中，模型可以通过反复暴露于样本图像来学习以识别任何候选特征 利息。利用深度学习，我们的团队和其他人成功地使用组织学预测了各种 与治疗反应有关的肿瘤特异性因素，包括受体状态、基因表达模式、 驱动器突变和肿瘤浸润性淋巴细胞。这些特征可以在以下位置准确检测到 护理，无需延长周转时间和与专业分子测试相关的费用。我们 假设深入学习组织学可以识别乳房新的形态和空间特征 癌症肿瘤反过来可以预测早期乳腺癌对化疗的反应。我们会带上 600多名接受新辅助化疗和以上患者的丰富机构队列的优势 2,000名具有长期生存数据的患者，以整理一个注释良好的数据库，适合深入学习 数字组织学。我们的患者队列还具有不同的人口统计特征，通常包括少数族裔患者 在公共数据集中的代表性不足，确保我们的研究结果适用于所有乳腺癌患者。我们 我将利用这个数据集来开发一个深入学习的组织学生物标记物，用于早期的化疗反应 乳腺癌。这种深度学习生物标记物将与标准反应标记物进行比较，以确定是否 组织学方面的深入学习提供了独立的预测价值，使更好地识别候选人 用于强化或去强化标准的以蒽环类和紫杉烷为基础的化疗。

Project Summary Although tremendous strides have been made in uncovering the biology of breast cancer, selection of chemotherapy regimens for early breast cancer is based predominantly on receptor status and stage. However, numerous other factors are associated with response, including gene expression patterns and tumor genetics, but these are not uniformly available for patients. Hematoxylin and eosin stained pathology is routinely obtained for all patients with breast cancer, and contains a wealth of information beyond grade. For example, the pattern and amount of tumor infiltrating lymphocytes has long been recognized as a predictor of response to chemotherapy, but quantification is challenging. Deep learning is an emerging discipline with particular promise in the domain of image recognition, wherein models can learn from repeated exposure to sample images to recognize any candidate features of interest. Using deep learning, our group and others have successfully used histology to predict a variety of tumor specific factors linked with response to treatment, including receptor status, gene expression patterns, driver mutations, and tumor infiltrating lymphocytes. These features can be accurately detected at point of care, without the extended turn-around time and expense associated with specialized molecular testing. We hypothesize that deep learning on histology can identify novel morphologic and spatial features of breast cancer tumors that in turn can predict response to chemotherapy in early breast cancer. We will take advantage of a rich institutional cohort of over 600 patients who received neoadjuvant chemotherapy and over 2000 patients with long term survival data to curate a well annotated database suited for deep learning on digital histology. Our patient cohort also features diverse demographics with inclusion of minority patients often underrepresented in public datasets, ensuring applicability of our findings to all patients with breast cancer. We will use this dataset to develop a deep learning histologic biomarker of chemotherapy response in early stage breast cancer. This deep learning biomarker will be compared to standard markers of response to determine if deep learning on histology provides independent predictive value, allowing better identification of candidates for intensification or de-intensification of standard anthracycline and taxane based chemotherapy.