Evidence map›Paper›PMID 36788080›Full record

ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2023

Molecular-Based Immunohistochemical Algorithm for Uterine Leiomyosarcoma Diagnosis.

Amir Momeni-Boroujeni, Elham Yousefi, Ridin Balakrishnan, Stephanie Riviere, Elizabeth Kertowidjojo, Martee L Hensley, Marc Ladanyi, Lora H Ellenson, Sarah Chiang

Open access · greenAbstract read
In one paragraph

Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
12.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 42 citations in OpenAlex.

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  16. Towards a greater understanding of uterine sarcomas.Case reports in women's health · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Amir Momeni-BoroujeniDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Elham YousefiDepartment of Pathology and Cell Biology, Columbia University, New York, New York.
Ridin BalakrishnanDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Stephanie RiviereDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Elizabeth KertowidjojoDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Martee L HensleyDepartment of Medicine, Gynecologic Medical Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York; Department of Medicine, Weill Cornell Medical College, New York, New York.
Marc LadanyiDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Lora H EllensonDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Sarah ChiangDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York. Electronic address: chiangs@mskcc.org.
Memorial Sloan Kettering Cancer Center · USColumbia University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

The morphologic assessment of uterine leiomyosarcoma (LMS) may be challenging, and diagnostic immunohistochemical (IHC) analysis is currently lacking. We evaluated the genomic landscape of 167 uterine LMS by targeted next-generation sequencing (NGS) to identify common genomic alterations. IHC analyses corresponding to these genomic landmarks were applied to a test cohort of 16 uterine LMS, 6 smooth muscle tumors of uncertain malignant potential (STUMP), and 6 leiomyomas with NGS data and a validation cohort of 8 uterine LMS, 12 STUMP, 21 leiomyomas and leiomyoma variants, 7 low-grade endometrial stromal sarcomas, and 2 diagnostically challenging uterine smooth muscle tumors. IHC results were individually interpreted by 3 pathologists blinded to NGS data. Overall, 94% of LMS showed ≥1 genomic alteration involving TP53, RB1, ATRX, PTEN, CDKN2A, or MDM2, with 80% showing alterations in ≥2 of these genes. In the test cohort, an initial panel of p53, Rb, PTEN, and ATRX was applied, followed by a panel of DAXX, MTAP, and MDM2 in cases without abnormalities. Abnormal p53, Rb, PTEN, and ATRX IHC expression was seen in 75%, 88%, 44%, and 38% of LMS, respectively, in the test cohort. Two or more abnormal IHC results among these markers were seen in 81% of LMS. STUMPs demonstrated only 1 IHC abnormality involving these markers. No IHC abnormalities were seen in leiomyomas. In the validation cohort, abnormal p53, Rb, and PTEN IHC results were seen in LMS, whereas rare STUMP or leiomyomas with bizarre nuclei showed IHC abnormalities involving only 1 of the markers. Abnormalities in ≥2 markers were present in both diagnostically challenging smooth muscle tumors, confirming LMS. Concordance was excellent among pathologists in the interpretation of IHC (κ = 0.97) and between IHC and NGS results (κ = 0.941). Uterine LMS exhibit genomic landmark alterations for which IHC surrogates exist, and a diagnostic algorithm involving molecular-based IHC may aid in the evaluation of unusual uterine smooth muscle tumors.

Indexed as

AlgorithmsImmunohistochemistryLeiomyosarcomaUterine NeoplasmsFemaleHumansMolecular Diagnostic TechniquesReproducibility of Resultsgenomic landmarksgenomic profilingimmunohistochemistryuterine leiomyosarcoma

Identifiers

PMID36788080
PMCPMC10191186
OpenAlexW4313897277

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.