Histologic image-based aggressiveness prediction in p16+ oropharyngeal carcinoma

描述(申请人提供)：到2020年，p16+(HPV相关)口咽鳞状细胞癌(OSCC)的患者数量预计将超过宫颈SCC。流行病学家称这是一种头颈部癌症的“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)口腔鳞癌患者进行了“过度治疗”，因为它通常更具生物学惰性，肿瘤的大体染色体异常较少，基因突变率约为1/2，对治疗的反应良好。出于这些原因，许多人推测，治疗可以“降级”，以维持良好的患者生存，同时将与治疗相关的发病率降至最低。然而，相当少数的p16+口腔鳞癌患者有侵袭性疾病，这些疾病将复发，主要以远处转移的形式，导致死亡。目前几乎没有临床标记物，也没有分子标记物来区分侵袭性较弱的p16+口腔鳞癌。本项目的重点是优化和评估基于组织形态计量(QH)的定量图像分类器(QuHbIC)，以识别哪些p16+口腔鳞癌可能具有临床侵袭性，哪些口腔鳞癌患者的癌症极不可能复发。QuHbIC只需要标准苏木精-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，将从这些图像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，侵袭性更强和侵袭性更弱的p16+口腔鳞癌的“组织学生物标记物”将被识别出来。虽然分子遗传学方法已经成为肿瘤特征的流行方法，但HE形态学仍然非常有用。实际上，肿瘤形态反映了肿瘤细胞中所有分子途径的总和，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有用信息。虽然病理学家对这类幻灯片的视觉读数可以预测行为，但复杂的组织形态计量学分析和计算机辅助定量分析有可能仅从肿瘤的形态就“解锁”更多揭示肿瘤的信息。该项目的基本假设是：(A)疾病侵袭性的标记被编码在癌症组织学(活组织检查或切除)图像的视觉属性中，其中一些组织学生物标记物(例如，核再生和/或多核)可以与疾病复发相关，而不依赖于其他临床和病理特征；以及(B)这些组织学生物标记物可以通过计算机图像分析来提取。QuHbIC将通过一大批数字化的H&E幻灯片和圣路易斯华盛顿大学(华盛顿大学)的长期临床随访数据进行培训和改进。对分类器的独立评估将在华盛顿大学和约翰霍普金斯大学提供的扫描H&E幻灯片上进行。QuHbIC的成功验证可能为QuHbIC作为决策支持工具快速集成到临床工作流程铺平道路，为协助肿瘤学家制定 更知情的治疗决定。

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.