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。流行病学家把这称为头颈癌的“流行病”。与此同时，有一种新兴的观点认为，我们可能“过度治疗”了p16+ （hpv相关）OSCC患者，因为它通常更具生物学惰性，肿瘤具有较少的总体染色体异常，约1/2的基因突变率，并且对治疗反应良好。由于这些原因，许多人推测治疗可以“降级”以维持有利的患者生存，同时最大限度地减少治疗相关的发病率。然而，少数p16+ OSCC患者具有侵袭性疾病，会复发，主要以远处转移的形式，导致死亡。目前很少有临床标记，也没有分子标记可以更好地区分侵袭性较低的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只需要对标准苏木精和伊红（H&E）染色切片进行数字化处理，通过先进的计算机视觉和模式识别工具提取肿瘤和间质细胞核的空间分布、形态、纹理和排列等一系列特征。因此，将确定侵袭性p16+ OSCC的“组织学生物标志物”。尽管分子遗传学方法已成为流行的肿瘤表征，H&E形态学仍然非常有用。在现实中，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效用。虽然病理学家可以通过对这些幻灯片的视觉阅读来预测行为，但使用计算机辅助定量的复杂组织形态计量学分析有可能从肿瘤的形态中“解锁”更多的揭示性信息。该项目的假设基础是：(a)疾病侵袭性的标记是在癌症的组织学（活检或切除）图像的视觉属性中编码的，其中一些“组织学生物标记”（例如核发育不全和/或多核）可以独立于其他临床和病理特征与疾病复发相关；(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（Washington University in St. Louis）的长期临床随访数据的大量数字化健康与健康幻灯片进行培训和完善。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的成功验证可以为将QuHbIC作为决策支持工具快速整合到临床工作流程中铺平道路，为肿瘤学家的决策提供关键信息

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.