Rogers Kai J, Abukhiran Ibrahim M, Syrbu Sergei, Tomasson Michael, Bates Melissa, Dhakal Prajwal, Bhagavathi Sharathkumar
Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
Division of Hematology/Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
Acad Pathol. 2023 Feb 1;10(1):100064. doi: 10.1016/j.acpath.2022.100064. eCollection 2023 Jan-Mar.
mutation status guides early therapeutic decisions in the treatment of clonal myeloid disorders and serves as a simple means of monitoring response to treatment. We aim here to develop a standardized protocol for evaluating 53 mutation status in myeloid disorders using immunohistochemistry assisted by digital image analysis and further compare this approach to manual interpretation alone. To accomplish this, we obtained 118 bone marrow biopsies from patients with hematologic malignancy and molecular testing for mutations associated with acute myeloid leukemia was performed. Clot or core biopsy slides were stained for p53 and digitally scanned. Overall mutation burden was assessed digitally using two different metrics to determine positivity, compared to the results of manual review, and correlated with molecular results. Using this approach, we found that digital analysis of immunohistochemistry stained slides performed worse than manual categorization alone in predicting mutation status in our cohort (PPV 91%, NPV 100% vs. PPV 100%, NPV 98%). While digital analysis reduced inter- and intraobserver variability when assessing mutation burden, there was poor correlation between the quantity and intensity of p53 staining and molecular analysis (R = 0.204). Therefore, digital image analysis of p53 immunohistochemistry accurately predicts mutation status as confirmed by molecular testing but does not offer a significant advantage over manual categorization alone. However, this approach offers a highly standardized methodology for monitoring disease status or response to treatment once a diagnosis has been made.
突变状态指导克隆性髓系疾病治疗中的早期治疗决策，并作为监测治疗反应的一种简单手段。我们的目的是开发一种标准化方案，用于在数字图像分析辅助下使用免疫组织化学评估髓系疾病中的p53突变状态，并进一步将这种方法与单纯的人工判读进行比较。为实现这一目标，我们从血液系统恶性肿瘤患者中获取了118份骨髓活检样本，并对与急性髓系白血病相关的突变进行了分子检测。凝血块或核心活检切片进行p53染色并数字扫描。使用两种不同指标对总体突变负荷进行数字评估以确定阳性，与人工复查结果进行比较，并与分子结果相关联。使用这种方法，我们发现在预测我们队列中的突变状态时，免疫组织化学染色切片的数字分析比单纯的人工分类表现更差（阳性预测值91%，阴性预测值100% 对比 阳性预测值100%，阴性预测值98%）。虽然数字分析在评估突变负荷时减少了观察者间和观察者内的变异性，但p53染色的数量和强度与分子分析之间的相关性较差（R = 0.204）。因此，p53免疫组织化学的数字图像分析能够准确预测经分子检测确认的突变状态，但相较于单纯的人工分类并没有显著优势。然而，一旦做出诊断，这种方法为监测疾病状态或治疗反应提供了一种高度标准化的方法。
