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

Kai J. Rogers; Ibrahim M. Abukhiran; Sergei Syrbu; Michael Tomasson; Melissa Bates; Prajwal Dhakal; Sharathkumar Bhagavathi

doi:10.1016/j.acpath.2022.100064

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

Journal article

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Peer reviewed

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

Kai J. Rogers, Ibrahim M. Abukhiran, Sergei Syrbu, Michael Tomasson, Melissa Bates, Prajwal Dhakal and Sharathkumar Bhagavathi

Academic pathology, Vol.10(1), 100064

01/2023

DOI: 10.1016/j.acpath.2022.100064

PMCID: PMC10031312

PMID: 36970330

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Abstract

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.

Immunohistochemistry

AML

Digital pathology

p53

Details

Title: Subtitle: Utilizing digital pathology and immunohistochemistry of p53 as an adjunct to molecular testing in myeloid disorders
Creators: Kai J. Rogers - Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
Ibrahim M. Abukhiran - University of Iowa
Sergei Syrbu - University of Iowa
Michael Tomasson - Division of Hematology/Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
Melissa Bates - Division of Hematology/Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
Prajwal Dhakal - University of Iowa
Sharathkumar Bhagavathi - University of Iowa
Resource Type: Journal article
Publication Details: Academic pathology, Vol.10(1), 100064
DOI: 10.1016/j.acpath.2022.100064
PMID: 36970330
PMCID: PMC10031312
NLM abbreviation: Acad Pathol
ISSN: 2374-2895
eISSN: 2374-2895
Publisher: Elsevier Inc
Language: English
Date published: 01/2023
Academic Unit: Microbiology and Immunology; Hematology, Oncology, and Blood & Marrow Transplantation; Stead Family Department of Pediatrics; Pathology; Fraternal Order of Eagles Diabetes Research Center; Health, Sport, and Human Physiology ; Internal Medicine
Record Identifier: 9984365912402771

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