Evidence map›Paper›PMID 39751954›Full record

ArticleCancer immunology, immunotherapy : CII2025

Identification of immune suppressor candidates utilizing comparative transcriptional profiling in histiocytic sarcoma.

Jennifer A Lenz, Brandon Peng, Charles-Antoine Assenmacher, Austin King, Paul J Zhang, Robert G Maki, M Andres Blanco, Enrico Radaelli, Matthew J Atherton

Abstract readComparative Study
In one paragraph

Article in Cancer immunology, immunotherapy : CII, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
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.

Jennifer A LenzDepartment of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-3210-618X
Brandon PengDepartment of Biomedical Sciences, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Charles-Antoine AssenmacherDepartment of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Austin KingDepartment of Biomedical Sciences, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Paul J ZhangDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Robert G MakiMemorial Sloan Kettering Cancer Center, New York, NY, USA.
M Andres BlancoDepartment of Biomedical Sciences, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Enrico RadaelliDepartment of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Matthew J AthertonDepartment of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA. mattath@upenn.edu.ORCID http://orcid.org/0000-0002-9830-3151

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
UNIVERSITY OF PENNSYLVANIA CAN CTR SUPPORT GRANTP30CA016520 · NCI · UNIVERSITY OF PENNSYLVANIA · PI SHELLEY L BERGER · 1985 to 2026
$222.3M
Prognostic and Therapeutic Implications of IFNAR1 Signaling on CAR T Cell Therapy for CancerK08CA252619 · NCI · UNIVERSITY OF PENNSYLVANIA · PI ATHERTON, MATTHEW JOHN · 2021 to 2025
$768k
NCI NIH HHS K08 CA252619NCI NIH HHS K08CA252619NCI NIH HHS P30 CA008748NCI NIH HHS P30 CA016520
6 · The paper itself

Abstract

Histiocytic sarcoma (HS) is a rare yet lethal malignancy with no established standard of care therapies. A lack of pre-clinical models limits our understanding of HS pathogenesis and identification of therapeutic targets. Canine HS shares multiple clinical and genetic similarities with human HS, supporting its use as a unique translational model. Prior studies have investigated the immunogenicity of HS. Although increased tumor infiltrating lymphocyte (TIL) density is associated with favorable outcomes in canine HS, virtually all canine patients eventually succumb to progressive disease consistent with ultimate failure of anti-tumor immunity. To investigate potential regulators of the immune tumor microenvironment (TME), we undertook a comparative transcriptional approach of three long-lived cases of canine pulmonary HS with heavy T cell infiltrate and three short-lived cases of splenic HS that lacked significant T cell inflammation and compared these data to corresponding grossly normal tissues from dogs undergoing necropsy. This comparison identified PDCD1, encoding the immune checkpoint PD-1, and SPP1, encoding the secreted pro-tumorigenic protein osteopontin, as positive differentially expressed genes (DEGs) in canine HS. TXNIP, encoding the tumor suppressor TXNIP, was the most significant negative DEG. Comparative transcriptomic studies revealed conservation of enriched (including SPP1) and depleted (including TXNIP) DEGs between canine and human HS patients. Immunohistochemistry demonstrated osteopontin in the TMEs of canine and human HS. Collectively, we uncover PD-1, osteopontin, and TXNIP as putative actionable targets in HS and further establish canine HS as a preclinical platform to screen novel immunotherapeutic approaches for this deadly disease.

Indexed as

Histiocytic SarcomaTumor MicroenvironmentAnimalsDog DiseasesDogsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansLymphocytes, Tumor-InfiltratingMaleProgrammed Cell Death 1 ReceptorTranscriptomeProgrammed Cell Death 1 ReceptorHistiocytic sarcomaOsteopontinPD-1TXNIP

Identifiers

PMID39751954
PMCPMC11699166

What OpenQuestion holds

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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.