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HLA associations with immunotherapy related endocrine toxicity.

2024· article· en· W4399738014 on OpenAlexaffabout
Zoe Quandt, Christian W. Thorball, Pooja Middha, Douglas B. Johnson, Cosmin A. Bejan, Lydia Yao, Yaomin Xu, Flavia Hodel, Athina Stravodimou, E. Shearer-Kang, Geoffrey Liu, Melinda C. Aldrich, Adam J. Schoenfeld, Elad Ziv, Elizabeth J. Phillips, Jacques Fellay, Ewa A. Bergmann, G. Scott Chandler, Justin M. Balko, Ashis Saha

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineImmunotherapyToxicityHuman leukocyte antigenEndocrine systemImmunologyOncologyInternal medicineImmune systemHormoneAntigen

Abstract

fetched live from OpenAlex

2516 Background: Endocrine immune related adverse events (irAEs), including thyroid dysfunction (ICI-T), diabetes mellitus (ICI-DM) and hypophysitis (ICI-HP), caused by treatment with immune checkpoint inhibitors (ICIs) are largely irreversible and pose a burden to cancer patients. Variation in HLA predisposes to many autoimmune conditions, but data associating endocrine irAEs to HLA types is limited. Methods: We included 6300 patients from multiple centers across the United States, Canada, Europe and Australia who were treated with ICIs for multiple cancer types. The most common endocrine irAE in these patients was ICI-T (806 cases), followed by ICI-HP (163 cases) and ICI-DM (75 cases). Cases of ICI induced adrenal insufficiency were considered to be ICI-HP unless the patient had an elevated ACTH. Patients without the specific irAE were considered controls. Genotyping and HLA imputation were completed at each institution independently. Associations between HLA types and ICI-T, ICI-DM, and ICI-HP were tested in patients of European ancestry after adjustment for type of immunotherapy (PD-1/PD-L1 monotherapy, anti-CTLA-4 monotherapy, and combination), cancer type, sex, age and 5 principal components, at each center. The results were then meta-analyzed using fixed-effects inverse-variance weighted approach. False discovery rate (FDR) adjusted p-value of 0.05 was considered significant whereas those between 0.05 and 0.1 were considered nominally significant. Results: In the ICI-DM group, we identified 1 significant association with HLA DRB1*04:01 (OR=2.45, FDR=0.002) which is known to increase risk for type 1 DM (T1DM) in European ancestry. There were 5 additional nominal associations including DRB1*0301 (OR=2.16, FDR=0.07), a known HLA for T1DM in European ancestry. For ICI-HP, there were 7 significantly associated HLA types. Of particular interest are DRB1*14:01 (OR=3.98, FDR=0.02), DRB1*07:01 (OR=1.86, FDR=0.02) and C*07:02 haplotype (OR=1.79, FDR=0.02). While DR7 and DR14 are novel associations, C*07:02 is in linkage disequilibrium with DR15 and DQB1*06:02, both of which have previously been associated with ICI-HP. For ICI-T, there were no associated HLA types. Conclusions: In our study, we report HLA types associated with endocrine irAEs. In particular, we see HLA associations for ICI-DM that are known to be associated with T1DM. This suggests a potential shared mechanism between these forms of autoimmune DM. Additionally we found novel HLA associations with ICI-HP. These findings may have an impact on the clinical care of patients treated with ICI. However, further work is warranted to determine if HLA typing prior to ICI initiation should be considered for irAE risk prediction, irAE surveillance, irAE prevention, and possibly cancer treatment decisions.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.407
Teacher spread0.357 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2024
Admission routes2
Has abstractyes

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