Evidence map›Paper›PMID 35593962›Full record

ReviewClinical reviews in allergy & immunology2022

Autoantibodies and Cancer Association: the Case of Systemic Sclerosis and Dermatomyositis.

David F Fiorentino, Livia Casciola-Rosen

Open access · greenAbstract readReview
In one paragraph

Review in Clinical reviews in allergy & immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.4field-weighted citation impact, top 7% 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

17 citing papers in PubMed, 24 citations in OpenAlex.

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  11. Cancer in connective tissue disease.Frontiers in immunology · 2025
    Review
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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

2 authors at 2 institutions in 1 country.

David F FiorentinoDepartment of Dermatology, Stanford University School of Medicine, Redwood City, CA, USA.
Livia Casciola-RosenDepartment of Medicine, Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. lcr@jhmi.edu.ORCID http://orcid.org/0000-0002-4172-1539
Johns Hopkins University · USStanford University · US

Funding

Autoantibodies Define Scleroderma Subgroups with Distinct Relationships to CancerR01AR073208 · NIAMS · JOHNS HOPKINS UNIVERSITY · PI LIVIA A CASCIOLA-ROSEN, Ami Aalok Shah · 2018 to 2026
$5.2M
Harnessing multivariate patient- and population-level disease trajectories to predict major clinical events in sclerodermaK24AR080217 · NIAMS · JOHNS HOPKINS UNIVERSITY · PI Ami Aalok Shah · 2022 to 2026
$847k
NIAMS NIH HHS K24 AR080217NIAMS NIH HHS R01 AR073208
6 · The paper itself

Abstract

Several rheumatic diseases have a perplexing association with cancer. Unraveling this mysterious connection is likely to provide deeper understanding regarding mechanisms governing the onset of both autoimmunity and cancer immunity, in addition to providing clinicians much needed guidance around whom and when to screen for occult malignancy. Systemic sclerosis (scleroderma) and dermatomyositis are two diseases in which the association with internal malignancy is well-described and can be considered as models from which to gain important insights that likely have broader applicability. The past 15 years have witnessed a striking acceleration in understanding how these two diseases are related to cancer emergence-an important crack in this inscrutable armor has been the discovery and characterization of disease-specific autoantigens that are closely tied with risk of cancer emergence. The best-described examples of this are antibodies against anti-RNA polymerase III (anti-POL3) and transcription intermediary factor 1-gamma (anti-TIF1γ). Patients with systemic sclerosis and cancer that are diagnosed within a short time interval of each other frequently have anti-POL3 antibodies. Antibodies against the minor spliceosome protein RNA-Binding Region Containing 3 (RNPC3) are also associated with increased cancer incidence in systemic sclerosis. Similarly, in the dermatomyositis spectrum, the majority of anti-TIF1γ-associated cancers are detected around the time of DM onset (most often within 1 year). Antibodies against Nuclear Matrix Protein 2 are also potentially associated with increased cancer emergence in dermatomyositis. The systemic sclerosis/anti-POL3 connection with close cancer onset led to the first experiments directly supporting the concept that rheumatic disease may in fact be a manifestation of cancer. It is now clear that studying these diseases through the lens of autoantibodies can reveal relationships and insights that would otherwise remain obscured. Extending these studies, new findings show that antibodies against RNA polymerase I large subunit are associated with protection against short interval cancers in anti-POL3-positive systemic sclerosis patients. These insights highlight the fact that autoantigen discovery related to cancer emergence remains an important priority; such new tools will enable the testing of specific hypotheses regarding mechanisms governing disease emergence and development of effective anti-tumor responses. Autoantibody phenotype will likely play an important role in the development of cancer screening guidelines that are critically needed by clinicians taking care of these patients. In this review, we will summarize the current state of knowledge regarding the different ways in which autoantibodies are connected with systemic sclerosis/dermatomyositis and malignancy and highlight potential paths forward.

Indexed as

DermatomyositisNeoplasmsRheumatic DiseasesScleroderma, SystemicAutoantibodiesAutoantigensHumansAutoantibodiesAutoantigensAutoantibodyAutoimmunityCancerDermatomyositisSclerodermaSystemic sclerosis

Identifiers

PMID35593962
PMCPMC10666558
OpenAlexW4280563350

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.