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Record W4411540694 · doi:10.58931/cibdt.2025.3139

Using Immunosuppressive Therapies to Treat Inflammatory Bowel Diseases (IBD) in the Post‑Cancer Setting

2025· article· en· W4411540694 on OpenAlexaff
Rana Kandel, Sanjay K. Murthy

Bibliographic record

VenueCanadian IBD Today · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineTofacitinibUlcerative colitisInflammatory bowel diseaseCancerRheumatoid arthritisImmunologyColorectal cancerImmune systemSkin cancerInfliximabInternal medicineOncologyDiseaseTumor necrosis factor alpha

Abstract

fetched live from OpenAlex

Inflammatory bowel diseases (IBD), including Crohn’s disease (CD), and ulcerative colitis (UC), are chronic immune-mediated inflammatory disorders (IMID) affecting both intestinal and extraintestinal organs. Chronic intestinal inflammation causes multifocal DNA damage, increasing the risks of intestinal cancers. While the widespread use of effective biologic and small molecule therapies and intensified immune modulating (IM) regimens in recent years may have contributed toward declining colorectal cancer risks, these treatments could have introduced unexpected cancer risks in organs not directly affected by IBD due to reduced immune surveillance. Among individuals with IBD, the use of thiopurines has been frequently associated with risks of lymphoma, non‑melanoma skin cancer (NMSC), and cervical cancer. Several large studies have also reported increased risks of lymphoma, and melanoma associated with anti-tumour necrosis factor alpha (anti‑TNF) therapies, although other studies have not shown these associations. A randomized controlled trial (RCT) in elderly individuals with rheumatoid arthritis (RA) and cardiovascular risk factors reported a slightly increased all‑cause cancer risk with the non-selective Janus kinase inhibitor (JAKi), tofacitinib. Other immunosuppressive (IS) therapies, including methotrexate, anti-interleukin (IL)‑12/23 or anti‑IL-23 therapies, anti-α4β7 integrin therapy, JAK-1-selective inhibitors (upadacitinib), and sphingosine-1-phosphate receptor agonists, have not been associated with increased cancer risks to date. However, some of these newer therapies have only been available for a few years. Given the low absolute risk of treatment‑related cancers, controlling underlying IBD with IS therapies is typically prioritized to improve quality of life and reduce IBD-related complications. However, the decision to start or continue IS therapy in individuals with current or prior malignancy is more complex, as immune surveillance may be more crucial for these patients. Clinical trials generally exclude patients with a cancer history, which limits the available evidence on cancer recurrence risks associated with specific therapies. Additionally, some cytotoxic chemotherapy regimens can control IBD for prolonged periods, suggesting that additional immunomodulation may be unnecessary, and potentially harmful, during cancer treatment. Conversely, hormonal, radiation, and immune checkpoint inhibitor therapies have been associated with increased risks of IBD flares. Therefore, a careful and collaborative approach with oncologists is essential for the optimal management of IBD patients diagnosed with cancer. Recently, the European Crohn’s and Colitis Organization (ECCO) and the American Gastroenterological Association (AGA) released practice recommendations regarding the use of IS therapies in individuals with IBD in the post‑cancer setting. This review summarizes the evidence regarding cancer risks associated with specific IBD therapies in this context and presents a management approach based on both scientific and practical considerations.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.252
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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