Bibliographic record
Abstract
Malignancy risk in systemic lupus erythematosus (SLE) is about 15% higher than the general age-and-sex-matched population. Hematologic cancers, especially B-cell lymphomas, are about three-fold increased in SLE, while breast, uterine, ovarian, and prostate cancer are associated with lower risk. 1 2Increased susceptibility to both SLE and malignancy could result from genetic variations that drive molecular mechanisms, including increased expression of APRIL, higher levels of interleukins (IL-6 and IL-10), and polymorphism of tumour necrosis factor pathways (e.g. TNF α-induced protein 3), or DNA repair.1 3 4 Specific human leukocyte antigen alleles (e.g. HLA-DR2 and HLA-DR3) are linked to both SLE and lymphoma.5Cyclophosphamide may be a risk factor for hematological and non-melanoma skin cancers in SLE, but this explains only a small proportion of cancers in SLE.6 Antimalarial drugs are likely associated with lower risk of breast and skin cancers in SLE.7 Regarding newer therapies, one study of tacrolimus showed no increased cancer risk in SLE.8 Secondary hematologic malignancies, in non-lupus lymphoma patients treated with cellular therapies, highlights the need to further study these new agents as lupus therapeutics.6 9Smoking correlates with increased lung cancer risk, and lung fibrosis may also be a risk factor.7 Patients with SLE are at increased risk of developing the squamous intraepithelial lesions that precede cervical cancer development, as well as other malignancies strongly associated with human papilloma virus, including vulvar and anal carcinoma.1At present, promotion of preventive measures such as smoking cessation and regular cancer screening programmes (particularly for cervical dysplasia) in SLE are common-sense interventions. Cervical dysplasia screening could also bring to attention rare vulvar and vaginal cancers.10 European Alliance of Associations for Rheumatology (EULAR) guidelines recommend annual Papanicolaou smear tests in heavily immunosuppressed patients (e.g. cyclophosphamide).11 Individuals exposed to cyclophosphamide should undergo lifelong annual urine cytology, with prompt investigation of abnormal cytology, to rule out bladder cancer. Otherwise, patients with SLE should follow cancer screening according to local general population guidelines.References Bernatsky S, Ramsey-Goldman R, Labrecque J, et al. Cancer risk in systemic lupus: An updated international multi-centre cohort study. J Autoimmun. 2013;42:130–5. doi: 10.1016/j.jaut.2012.12.009.Clarke AE, Pooley N, Marjenberg Z, et al. Risk of malignancy in patients with systemic lupus erythematosus: systematic review and meta-analysis. Semin Arthritis Rheum. 2021;51(6):1230–41. doi: 10.1016/j.semarthrit.2021.09.009.Bernatsky S, Velasquez Garcia HA, Spinelli JJ, et al. Lupus-related single nucleotide polymorphisms and risk of diffuse large B-cell lymphoma. Lupus Sci Med. 2017;4(1):e000187. doi: 10.1136/lupus-2016-000187.Rosenberger A, Sohns M, Friedrichs S, et al. Gene-set meta-analysis of lung cancer identifies pathway related to systemic lupus erythematosus. PLoS One. 2017;12(3):e0173339. doi: 10.1371/journal.pone.0173339.Tessier-Cloutier B, Twa DD, Baecklund E, et al. Cell of origin in diffuse large B-cell lymphoma in systemic lupus erythematosus: Molecular and clinical factors associated with survival. Lupus Sci Med. 2019;6(1):e000324. doi: 10.1136/lupus-2019-000324.Bernatsky S, Ramsey-Goldman R, Joseph L, et al. Lymphoma risk in systemic lupus: effects of disease activity versus treatment. Ann Rheum Dis. 2014;73(1):138–42. doi: 10.1136/annrheumdis-2012-202099.Bernatsky S, Ramsey-Goldman R, Urowitz MB, et al. Cancer risk in a large inception systemic lupus erythematosus cohort: Effects of demographic characteristics, smoking, and medications. Arthritis Care Res (Hoboken). 2021;73(12):1789–95. doi: 10.1002/acr.24425.Ichinose K, Sato S, Igawa T, et al. Evaluating the safety profile of calcineurin inhibitors: cancer risk in patients with systemic lupus erythematosus from the LUNA registry-a historical cohort study. Arthritis Res Ther. 2024;26(1):48. doi: 10.1186/s13075-024-03285-x.Bernatsky S, Joseph L, Boivin JF, et al. The relationship between cancer and medication exposures in systemic lupus erythaematosus: A case-cohort study. Ann Rheum Dis. 2008;67(1):74–9. doi: 10.1136/ard.2006.069039.Tessier-Cloutier B, Clarke AE, Pineau CA, et al. What investigations are needed to optimally monitor for malignancies in SLE? Lupus. 2015;24(8):781–7. doi: 10.1177/0961203315575587.Andreoli L, Bertsias GK, Agmon-Levin N, et al. EULAR recommendations for women’s health and the management of family planning, assisted reproduction, pregnancy and menopause in patients with systemic lupus erythematosus and/or antiphospholipid syndrome. Ann Rheum Dis. 2017;76(3):476–85. doi: 10.1136/annrheumdis-2016-209770.Learning Objectives At the end of this presentation participants will be able to:Determine what cancers people with SLE may be at higher (or lower) risk ofIdentify SLE treatments that may pose higher risk for specific cancersExplain smoking as a cancer risk factor for SLE patients
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.239 | 0.079 |
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".