Lépine Charles, Klein Paul, Voron Thibault, Mandavit Marion, Berrebi Dominique, Outh-Gauer Sophie, Péré Hélène, Tournier Louis, Pagès Franck, Tartour Eric, Le Meur Thomas, Berlemont Sylvain, Teissier Natacha, Carlevan Mathilde, Leboulanger Nicolas, Galmiche Louise, Badoual Cécile
INSERM-U970, PARCC, Université de Paris, Paris, France.
Department of Pathology, Hôpital Européen Georges-Pompidou, APHP, Paris, France.
Front Oncol. 2021 Mar 8;11:596499. doi: 10.3389/fonc.2021.596499. eCollection 2021.
Juvenile-onset recurrent respiratory papillomatosis (JoRRP) is a condition characterized by the repeated growth of benign exophytic papilloma in the respiratory tract. The course of the disease remains unpredictable: some children experience minor symptoms, while others require multiple interventions due to florid growth. Our study aimed to identify histologic severity risk factors in patients with JoRRP. Forty-eight children from two French pediatric centers were included retrospectively. Criteria for a severe disease were: annual rate of surgical endoscopy ≥ 5, spread to the lung, carcinomatous transformation or death. We conducted a multi-stage study with image analysis. First, with Hematoxylin and eosin (HE) digital slides of papilloma, we searched for morphological patterns associated with a severe JoRRP using a deep-learning algorithm. Then, immunohistochemistry with antibody against p53 and p63 was performed on sections of FFPE samples of laryngeal papilloma obtained between 2008 and 2018. Immunostainings were quantified according to the staining intensity through two automated workflows: one using machine learning, the other using deep learning. Twenty-four patients had severe disease. For the HE analysis, no significative results were obtained with cross-validation. For immunostaining with anti-p63 antibody, we found similar results between the two image analysis methods. Using machine learning, we found 23.98% of stained nuclei for medium intensity for mild JoRRP vs. 36.1% for severe JoRRP ( = 0.041); and for medium and strong intensity together, 24.14% for mild JoRRP vs. 36.9% for severe JoRRP ( = 0.048). Using deep learning, we found 58.32% for mild JoRRP vs. 67.45% for severe JoRRP ( = 0.045) for medium and strong intensity together. Regarding p53, we did not find any significant difference in the number of nuclei stained between the two groups of patients. In conclusion, we highlighted that immunochemistry with the anti-p63 antibody is a potential biomarker to predict the severity of the JoRRP.
青少年复发性呼吸道乳头状瘤病（JoRRP）是一种以呼吸道内良性外生性乳头状瘤反复生长为特征的疾病。该病的病程仍然不可预测：一些儿童症状较轻，而另一些儿童由于肿瘤生长活跃则需要多次干预。我们的研究旨在确定JoRRP患者的组织学严重程度风险因素。对来自两个法国儿科中心的48名儿童进行了回顾性研究。严重疾病的标准为：每年手术内镜检查率≥5次、肿瘤扩散至肺部、发生癌变或死亡。我们进行了一项采用图像分析的多阶段研究。首先，利用苏木精和伊红（HE）染色的乳头状瘤数字切片，我们使用深度学习算法寻找与严重JoRRP相关的形态学模式。然后，对2008年至2018年间获取的喉乳头状瘤福尔马林固定石蜡包埋（FFPE）样本切片进行p53和p63抗体免疫组织化学检测。通过两种自动化工作流程根据染色强度对免疫染色进行定量：一种使用机器学习，另一种使用深度学习。24例患者患有严重疾病。对于HE分析，交叉验证未获得显著结果。对于抗p63抗体免疫染色，我们在两种图像分析方法之间发现了相似结果。使用机器学习，我们发现轻度JoRRP中强度染色细胞核占23.98%，而严重JoRRP中为36.1%（P = 0.041）；中强度和高强度染色细胞核合计，轻度JoRRP中占24.14%，严重JoRRP中为36.9%（P = 0.048）。使用深度学习，轻度JoRRP中强度和高强度染色细胞核合计占58.32%，严重JoRRP中为67.45%（P = 0.045）。关于p53，两组患者之间染色细胞核数量未发现任何显著差异。总之，我们强调抗p63抗体免疫化学是预测JoRRP严重程度的潜在生物标志物。
