Histologic image-based aggressiveness prediction in p16+ oropharyngeal carcinoma

描述（由申请人提供）：预计到2020年，p16+（HPV相关）口咽鳞状细胞癌（OSCC）患者数量将超过宫颈鳞状细胞癌。流行病学家称之为头颈癌“流行病”。与此同时，有一种新的观点认为，我们可能对p16+（HPV相关）OSCC患者进行了“过度治疗”，因为它通常在生物学上更惰性，肿瘤具有较少的染色体异常，约1/2的基因突变率和对治疗的有利反应。由于这些原因，许多人推测，治疗可以“降级”，以保持良好的患者生存，同时最大限度地减少治疗相关的发病率。然而，一个显着的少数患者p16+口腔鳞癌有侵略性的疾病，将复发，主要是在远处转移的形式，导致死亡。目前几乎没有临床和分子标记来区分p16+ OSCC。 该项目的重点是优化和评估基于定量组织形态学（QH）的图像分类器（QuHbIC），以确定哪些p16+ OSCC可能具有临床侵袭性，哪些OSCC患者的癌症不太可能复发。QuHbIC只需要标准苏木精和伊红（H&E）染色切片的数字化图像，通过先进的计算机视觉和模式识别工具，从中提取一系列描述肿瘤和间质细胞核的空间分布，形态，纹理和排列的特征。因此，“组织学生物标志物”的更多和更少的侵略性p16+口腔鳞癌将被确定。虽然分子遗传学方法已成为流行的肿瘤表征，H&E形态学仍然是非常有用的。实际上，肿瘤形态反映了肿瘤细胞中所有分子通路的总和，从而为预测肿瘤生物学、临床行为和治疗反应提供了令人难以置信的实用性。虽然病理学家对这些载玻片的视觉阅读可以预测行为，但利用计算机辅助定量的复杂组织形态学分析有可能从肿瘤的形态学中“解锁”更多关于肿瘤的信息。该项目的基本假设是：（a）疾病侵袭性的标志物编码在组织学中的视觉属性中。（活检或切除）癌症图像，以及其中一些“组织学生物标志物”（例如，核间变性和/或多核化）可以与疾病复发相关，而与其他临床和病理特征无关;和（B）这些“组织学生物标志物”可以通过计算机图像分析提取。 QuHbIC将通过来自圣路易斯华盛顿大学（华盛顿大学）的大量数字化H&E载玻片和长期临床随访数据进行培训和完善。将在可从华盛顿大学和约翰霍普金斯大学获得的扫描的H&E载玻片上进行分类器的独立评价。QuHbIC的成功验证可以为QuHbIC作为决策支持工具快速集成到临床工作流程中铺平道路，为帮助肿瘤学家制定 更明智的治疗决定。

DESCRIPTION (provided by applicant): By 2020 the number of patients with p16+ (HPV-related) oropharyngeal squamous cell carcinoma (OSCC) is predicted to surpass that for cervical SCC. Epidemiologists have termed this a head and neck cancer "epidemic." At the same time, there is an emerging view that we may be "over-treating" patients with p16+ (HPV-related) OSCC because it is typically more biologically indolent, with tumors having less gross chromosomal abnormalities, ~1/2 the gene mutation rate, and favorable responses to treatment. For these reasons, many speculate that therapies could be "de-escalated" to maintain favorable patient survival while minimizing treatment-related morbidity. However, a significant minority of patients with p16+ OSCC have aggressive disease that will recur, predominantly in the form of distant metastasis, resulting in death. There are currently few clinical-and no molecular-markers to discriminate more from less aggressive p16+ OSCC. The focus of this project is to optimize and evaluate a quantitative histomorphometric (QH)-based image classifier (QuHbIC) to identify which p16+ OSCC are likely to be clinically aggressive and which OSCC patients have cancers that are very unlikely to recur. QuHbIC only requires digitized images of standard hematoxylin and eosin (H&E) stained sections, from which a series of features describing spatial distribution, morphology, texture and arrangement of tumor and stromal cell nuclei will be extracted via advanced computer vision and pattern recognition tools. Thus "histologic biomarkers" for more and less aggressive p16+ OSCC will be identified. Although molecular genetic approaches have become popular for tumor characterization, H&E morphology is still remarkably useful. In reality, tumor morphology reflects the sum of all molecular pathways in tumor cells, thereby providing incredible utility for predicting tumor biology, clinical behavior, and treatment response. While the visual reading of such slides by pathologists can predict behavior, sophisticated histomorphometric analysis with computer-aided quantitation has the potential to "unlock" more revealing information about tumors just from their morphology. The hypotheses underlying this project are that (a) markers for disease aggressiveness are encoded in visual attributes in histological (biopsy or resection) images of cancer, and some of these "histologic biomarkers" (e.g. nuclear anaplasia and/or multi-nucleation) can be correlated with disease recurrence independent of other clinical and pathologic features; and (b) these "histologic biomarkers" can be extracted via computerized image analysis. QuHbIC will be trained and refined via a large cohort of digitized H&E slides with long term clinical follow up data from Washington University in St. Louis (Wash U). Independent evaluation of the classifier will be performed on scanned H&E slides available from both Wash U and Johns Hopkins University. The successful validation of QuHbIC could pave the way for rapid integration of QuHbIC into the clinical workflow as a decision support tool, providing critical information to assist oncologists in making more informed treatment decisions.