Clinical and epidemiological project in cancer-associated dermatomyositis : the role of anti-TIF1γ antibodies
Bibliographic record
Abstract
Idiopathic inflammatory myopathies (IIM) are a heterogeneous group of rare chronic autoimmune inflammatory diseases causing a debilitating muscle weakness but encountering even other symptoms such as arthritis, skin rash, lung involvement or dysphagia. IIM are a diagnostic challenge because of the clinical variability and the need to early identify the subgroups of disease with worst prognosis. A late diagnosis delays treatment start, which is detrimental to disease outcome. One of these subgroups is dermatomyositis (DM) which is characterized by a particular skin rash, specific histopathological findings, and typical autoantibodies. DM can be associated with cancer but clinically it is very difficult to identify patients with DM who are at risk to develop cancer. Anti-TIF1γ autoantibody has been identified as a marker of cancer risk in patients with DM and can be helpful to identify which patients with DM should undergo cancer screening. However, only half of the anti-TIF1γ positive patients develop cancer. Moreover, it is unclear if these autoantibodies are present before the cancer or develop as a consequence of cancer and if they have a prognostic role in cancer treatment. The aims of my thesis were 1) to update the epidemiology of IIM and cancer in Sweden, 2) to elucidate the temporal relationship between anti-TIF1γ autoantibody, DM diagnosis and cancer; 3) to study the prognostic value of anti-TIF1γ autoantibody levels; 4) to compare the reliability of different laboratory assays to detect anti-TIF1γ autoantibodies. In paper I we performed a registry based study of the risk of cancer in IIM in Sweden between 2002 and 2016 and observed that especially patients with DM presented a high cancer risk within one year from diagnosis compared to other forms of IIM and that cancer subtypes differed in the period before or after IIM diagnosis where ovarian and lung cancer were more frequent before IIM diagnosis while oral, cervical and skin cancers were more frequent after diagnosis. In paper II we studied a cohort of cancer associated IIM in Sweden and Spain and showed that anti-TIF1γ autoantibodies are specific for DM and associated with cancer within 3 years from DM diagnosis. Anti-TIF1γ autoantibodies may be detected before clinical symptoms of cancer and may disappear after successful treatment of cancer. Higher autoantibody titers were associated with a high mortality rate within one year of DM diagnosis. In paper III we investigated longitudinally collected sera from patients with DM with and without cancer and found that the levels of anti-TIF1γ autoantibodies were slightly higher in cancer associated DM and decreased over time but almost never became negative regardless of cancer status. Furthermore, anti-TIF1γ autoantibodies were in most cases present in the first available serum sample and rarely appeared after DM diagnosis. This means that re-testing for anti-TIF1γ is not useful during follow-up. In paper IV we analyzed the same cohort as in paper 3 for anti-TIF1γ antibodies with four different autoantibody assays (immunoprecipitation, in-house and commercial ELISA and lineblot) and confirmed that ELISA is a sensitive and reliable assay to find anti-TIF1γ antibody positive cancer associated DM. In conclusion, my thesis has contributed by describing a contemporary picture of cancer associated IIM and DM in Sweden, adding new knowledge about the risk for different cancer subtypes before and after IIM diagnosis. It has helped understanding the temporal behavior of anti-TIF1γ autoantibodies and the most reliable laboratory assays to measure them. Thanks to this thesis we today have more information on how to interpret the cancer risk in patients with DM and anti-TIF1γ antibodies. List of scientific papers I. Dani L*, Weng Ian Che*, Lundberg IE, Hellgren K, Holmqvist M. Overall and site-specific cancer before and after diagnosis of idiopathic inflammatory myopathies: a nationwide study 2002-2016. Semin Arthritis Rheum. 2021 Feb;51(1):331-337. *Equal contribution. https://doi.org/10.1016/j.semarthrit.2020.12.009 II. Dani L, Holmqvist M, Martínez MA, Trallero-araguas E, Dastmalchi M, Svensson J, Labrador-Horrillo M, Selva-o’Callaghan A, Lundberg IE. Anti-transcriptional intermediary factor 1 γ antibodies in cancer-associated myositis: a longitudinal study. Clin Exp Rheumatol. 2020 Jan-Feb;38(1):67-73. https://pubmed.ncbi.nlm.nih.gov/31365334 III. Dani L*, Selickaja S*, Galindo-Feria A, Svensson J, Venalis P, Leonard D, Hemberg M, Hanna B, Lundberg IE, Holmqvist M. Anti-transcriptional intermediary factor 1 γ antibodies in dermatomyositis with and without cancer - A longitudinal study. *Equal contribution. [Manuscript] IV. Selickaja S, Galindo-Feria A, Dani L, Mimori T, Rönnelid J, Holmqvist M, Lundberg IE, Venalis P. ELISA, protein immunoprecipitation and line blot assays for anti-TIF1-γ autoantibody detection in cancer-associated dermatomyositis. Rheumatology (Oxford). 2022 Nov 28;61(12):4991-4996. https://doi.org/10.1093/rheumatology/keac288
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".