利用数字病理和免疫组织化学p53作为辅助髓系疾病的分子检测

IF 1.2 Q3 PATHOLOGY
Kai J. Rogers MD, PhD , Ibrahim M. Abukhiran MD , Sergei Syrbu MD, PhD , Michael Tomasson MD , Melissa Bates PhD , Prajwal Dhakal MD , Sharathkumar Bhagavathi MD
{"title":"利用数字病理和免疫组织化学p53作为辅助髓系疾病的分子检测","authors":"Kai J. Rogers MD, PhD ,&nbsp;Ibrahim M. Abukhiran MD ,&nbsp;Sergei Syrbu MD, PhD ,&nbsp;Michael Tomasson MD ,&nbsp;Melissa Bates PhD ,&nbsp;Prajwal Dhakal MD ,&nbsp;Sharathkumar Bhagavathi MD","doi":"10.1016/j.acpath.2022.100064","DOIUrl":null,"url":null,"abstract":"<div><p><em>TP53</em> 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 <em>TP</em>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 <em>TP53</em> 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<sup>2</sup> = 0.204). Therefore, digital image analysis of p53 immunohistochemistry accurately predicts <em>TP53</em> 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.</p></div>","PeriodicalId":44927,"journal":{"name":"Academic Pathology","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031312/pdf/","citationCount":"0","resultStr":"{\"title\":\"Utilizing digital pathology and immunohistochemistry of p53 as an adjunct to molecular testing in myeloid disorders\",\"authors\":\"Kai J. Rogers MD, PhD ,&nbsp;Ibrahim M. Abukhiran MD ,&nbsp;Sergei Syrbu MD, PhD ,&nbsp;Michael Tomasson MD ,&nbsp;Melissa Bates PhD ,&nbsp;Prajwal Dhakal MD ,&nbsp;Sharathkumar Bhagavathi MD\",\"doi\":\"10.1016/j.acpath.2022.100064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><em>TP53</em> 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 <em>TP</em>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 <em>TP53</em> 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<sup>2</sup> = 0.204). Therefore, digital image analysis of p53 immunohistochemistry accurately predicts <em>TP53</em> 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.</p></div>\",\"PeriodicalId\":44927,\"journal\":{\"name\":\"Academic Pathology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031312/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Pathology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2374289522000628\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PATHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Pathology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2374289522000628","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PATHOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

TP53突变状态指导克隆性骨髓疾病治疗的早期治疗决策,并作为监测治疗反应的简单手段。我们的目的是开发一种标准化方案,使用免疫组织化学辅助数字图像分析来评估髓系疾病中TP53突变状态,并进一步将这种方法与单独的手动解释进行比较。为了实现这一点,我们从血液系统恶性肿瘤患者身上获得了118份骨髓活检,并对与急性髓系白血病相关的突变进行了分子检测。对切片或核心活检切片进行p53染色并进行数字扫描。与手动审查的结果相比,使用两种不同的指标以数字方式评估总体突变负担,以确定阳性率,并与分子结果相关。使用这种方法,我们发现免疫组织化学染色载玻片的数字分析在预测我们队列中的TP53突变状态方面比单独手动分类表现更差(PPV 91%,NPV 100%vs.PPV 100%,NPV 98%)。虽然数字分析在评估突变负荷时降低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(R2=0.204)。因此,p53免疫组织化学的数字图像分析准确地预测了通过分子测试证实的TP53突变状态,但与单独手动分类相比没有显著优势。然而,这种方法提供了一种高度标准化的方法,用于在诊断后监测疾病状态或对治疗的反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Utilizing digital pathology and immunohistochemistry of p53 as an adjunct to molecular testing in myeloid disorders

Utilizing digital pathology and immunohistochemistry of p53 as an adjunct to molecular testing in myeloid disorders

Utilizing digital pathology and immunohistochemistry of p53 as an adjunct to molecular testing in myeloid disorders

Utilizing digital pathology and immunohistochemistry of p53 as an adjunct to molecular testing in myeloid disorders

TP53 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 TP53 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 TP53 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 (R2 = 0.204). Therefore, digital image analysis of p53 immunohistochemistry accurately predicts TP53 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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Academic Pathology
Academic Pathology PATHOLOGY-
CiteScore
2.20
自引率
20.00%
发文量
46
审稿时长
15 weeks
期刊介绍: Academic Pathology is an open access journal sponsored by the Association of Pathology Chairs, established to give voice to the innovations in leadership and management of academic departments of Pathology. These innovations may have impact across the breadth of pathology and laboratory medicine practice. Academic Pathology addresses methods for improving patient care (clinical informatics, genomic testing and data management, lab automation, electronic health record integration, and annotate biorepositories); best practices in inter-professional clinical partnerships; innovative pedagogical approaches to medical education and educational program evaluation in pathology; models for training academic pathologists and advancing academic career development; administrative and organizational models supporting the discipline; and leadership development in academic medical centers, health systems, and other relevant venues. Intended authorship and audiences for Academic Pathology are international and reach beyond academic pathology itself, including but not limited to healthcare providers, educators, researchers, and policy-makers.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信