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Record W2018310339 · doi:10.1097/pas.0b013e318211abd6

Expression of Subtype-Specific Group 1 Leiomyosarcoma Markers in a Wide Variety of Sarcomas by Gene Expression Analysis and Immunohistochemistry

2011· article· en· W2018310339 on OpenAlexaff
Anne M. Mills, Andrew H. Beck, Kelli Montgomery, Shirley Zhu, Íñigo Espinosa, Cheng‐Han Lee, Subbaya Subramanian, Christopher D.�M. Fletcher, Matt van de Rijn, Robert B. West

Bibliographic record

VenueThe American Journal of Surgical Pathology · 2011
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaVancouver General Hospital
FundersNational Cancer Institute
KeywordsImmunohistochemistryLeiomyosarcomaPathologyBiologyTissue microarraySarcomaDesminHMGA2GeneMedicineGenetics

Abstract

fetched live from OpenAlex

Leiomyosarcomas (LMSs) constitute approximately one quarter of all sarcomas and are usually defined by morphologic criteria and/or immunoreactivity for actin or desmin. Among high-grade lesions, the distinction from undifferentiated pleomorphic sarcoma (UPS) can be problematic, and previous studies have shown that a significant number of LMS cases may be hiding under the diagnosis of UPS. We recently described 3 novel molecular LMS subtypes that are distributed similarly over LMSs of gyneocologic and non-gyneocologic origins. The group 1 subtype shows an improved disease-specific survival compared with the other 2 groups that is independent of histologic grade. Group 1 comprises approximately 25% of all LMSs, and is defined by a shared pattern of gene expression, a distinct pattern of genomic changes, and reactivity for at least 3 of 5 immunohistochemistry (IHC) markers (smooth muscle gamma actin, calsequestrin 2, human muscle cofilin2, myosin light chain kinase, and sarcolemmal membrane associated protein), as tested on 271 cases of LMS in tissue microarrays. These IHC markers have not been well characterized in non-LMS sarcomas. Here we provide a characterization of these 5 markers across normal tissues, an additional 59 cases of LMS, and a wide range of 565 non-LMS soft tissue tumors from 44 diagnostic categories, with a focus on UPS. When analyzed individually, the 5 markers were found to be expressed in many sarcomas other than LMSs. However, when analyzed by the same criteria used for the recognition of group 1 LMSs, in which a case is scored positive when at least 3 of 5 markers reacted, coordinate expression was seen in significant numbers of cases from only 3 diagnostic groups that included 22% of leiomyomas (n=22), 16% of gastrointestinal stromal tumors (n=43), and 18% of endometrial stromal sarcomas (n=11). In addition, 5% (n=57) of UPSs showed a staining pattern similar to that seen in group 1 LMSs. To further examine the possibility that group 1 LMS constitutes a small part of cases diagnosed as UPS, we examined the expression of the top 500 genes from the group 1 LMS expression signature in 29 UPSs by complementary DNA microarray. Unsupervised hierarchical clustering of 29 UPS expression showed that 2 (7%) had coordinated high levels of expression of genes from the group 1 LMS signature, a rate similar to that seen by IHC analysis. These findings show that group 1 LMS IHC markers smooth muscle gamma actin, calsequestrin 2, human muscle cofilin2, myosin light chain kinase, and sarcolemmal membrane associated protein when coordinately expressed have specificity for a subset of LMS when compared with other sarcomas, and may be useful for the recognition of group 1 LMS cases within cases diagnosed as UPS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.253
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations34
Published2011
Admission routes1
Has abstractyes

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