Abstract 3460: Predicting cessation of immune checkpoint inhibitor therapy due to severe adverse events with an integrated autoimmune disease polygenic risk score
Notice bibliographique
Résumé
Abstract Background: Immune checkpoint inhibitors (ICIs), including anti-PD1/PD-L1 and anti-CTLA4 agents, have changed the landscape of cancer therapy. However, 6-30% of patients treated with ICIs can develop severe immune-related adverse events (irAEs) depending on the type of therapy used, often leading to cessation of treatment. Hundreds of genetic variants have been associated with autoimmune disorders, and recent genetic studies have developed polygenic risk scores (PRS) for composite risk of autoimmune disease (PRSAID). We used data from an ongoing observational study of patients with non-small cell lung cancer (NSCLC) treated with ICIs to evaluate the association between PRSAID and cessation of ICI therapy due to severe irAEs. Methods: Our cohort comprises 1,316 advanced NSCLC patients who received ICI therapy from four institutions between 2009 and 2022. Genotyping was performed using the Affymetrix Precision Medicine Array, and imputation was performed using 1000 Genomes Reference. To evaluate cessation of therapy due to severe irAEs, we conducted a thorough review of electronic health records. We identified permanent discontinuation of ICIs due to irAEs as cessation for at least 6 months without any disease progression. We tested the association of a previously published PRSAID (PMID: 35393596) with cessation of ICI due to any severe irAE. PRSAID was converted to a standard normal distribution. We conducted an unconditional logistic regression model to evaluate the association between a standardized PRSAID and discontinuation of ICI therapy due to severe irAEs. All models were adjusted for age at diagnosis, sex, type of ICI therapy, recruitment site, and 5 ancestry-informative principal components. Results: Of the 1,316 participants with genotype and outcome data, 171 (13.5%) of patients stopped ICI therapy due to severe irAEs. The most common irAEs leading to discontinuation were colitis, hepatitis, and pneumonitis. The PRSAID predicts the cessation of therapy due to severe irAEs with an odds ratio (OR) per standard deviation (SD) of 1.20 (95% confidence intervals [CI]=1.02-1.41, p=0.03). The association was stronger in individuals with high genetic risk (top 25th percentile compared to bottom 75th percentile of the PRSAID) (OR=1.60, 95% CI = 1.11-2.28, p=0.01). When we stratified the analysis by therapy type, we found an OR of 1.17 (95% CI-0.98-1.39, p = 0.08) for the anti-PD1/PD-L1 monotherapy groups and an OR of 1.48 (95% CI=0.94-2.40,p=0.09) for the dual anti-PD1/PD-L1 and anti-CTLA4 therapy group. Conclusions: An integrated PRS for autoimmunity can predict the cessation of ICI therapy due to severe irAEs in patients with advanced cancer. These findings suggest that PRS may help determine which patients may benefit from careful monitoring or even preventative measures for irAEs during ICI therapy. Citation Format: Pooja Middha, Rohit Thummalapalli, Zoe Quandt, Princess Margaret Lung Group, Matthew A. Gubens, Christine M. Lovly, Geoffrey Liu, Melinda C. Aldrich, Adam J. Schoenfeld, Elad Ziv. Predicting cessation of immune checkpoint inhibitor therapy due to severe adverse events with an integrated autoimmune disease polygenic risk score [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3460.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».