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Enregistrement W4405039664 · doi:10.1182/blood-2024-207293

Characteristics of Myelodysplastic Syndromes with Idiopathic Pulmonary Fibrosis

2024· article· en· W4405039664 sur OpenAlexaff
Inés Zugasti, Samuel Urrutia, Eduardo Edelman Saul, Georgina S. Daher-Reyes, Mohammad Asim Amjad, Jia Wu, Koji Sasaki, Ajay Sheshadri, Guillermo Garcia‐Manero

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Établissements canadiensPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineMyelodysplastic syndromesIdiopathic pulmonary fibrosisPulmonary fibrosisInternal medicineFibrosisGastroenterologyLungBone marrow

Résumé

récupéré en direct d'OpenAlex

Introduction The co-occurrence of myelodysplastic syndromes (MDS) and idiopathic pulmonary fibrosis (IPF) is highly specific of an underlying short telomere syndrome (STS), most of which harbor a TERT germline mutation. The understanding of germline MDS predisposition is increasing, however, the only evidence in this context is reported by Papiris et al., describing 5 patients co-affected with IPF and MDS. Most STS are diagnosed after the sixth decade, with IPF usually being the primary manifestation. This particular scenario is of relevance, as comorbidities like IPF alongside with other known predispositions may play a crucial role in hematologic malignancy management. The study aims to describe the molecular profile, clinical and pulmonary characteristics, and outcomes of a cohort of 141 patients with MDS and pulmonary fibrosis (PF) diagnosed in our center from November 2008 to February 2022. Methods This retrospective single-center study included patients diagnosed with MDS and PF from November 2008-February 2022. For mutational analysis, 28 or 81-gene NGS panel covering TERT and TERT but no other STS-related genes were used. Responses were assessed using the IWG2023 criteria. Chest CT images and functional respiratory tests at IPF diagnosis were individually reviewed by the Pulmonary Team of our center. Results Of the 141 patients identified with MDS and PF, 75 were diagnosed with IPF, and of those, 61 had available molecular data. The median age at MDS diagnosis was 71 years (52-91), and 75% were male. Fifty-one (84%) had MDS, 2 (4%) MDS/MPN, and 7 (12%) CMML. Based on IPSS-M, 19 (40%) were classified as very high-risk, 10 ( 21%) high-risk, 2 (4%) moderate-high, 4 (9%) moderate-low, 8 (17%) low and 4 (9%) as a very low. CPSS-Mol on the CMML patients stratified 3 (44%), 2 (28%), 2 (28%) to high, intermediate-2, and intermediate-1 risk categories, respectively. Twenty patients (33%) progressed to AML, and 13 (20%) underwent an HSCT. Results from the 81-gene panel, which includes TERT were available in 46 patients, 13 of whom harbored a TERT germinal mutation. This led to an incidence of suspected STS of 28.3% in individuals co-affected with IPF and MDS. Overall Response Rate (ORR) (IWG2023 criteria) was 62.9% (first line), 39.4% (second line), and 50% (third line). The median follow-up of the cohort was 39.01 months, and the median Overall Survival (OS) was 30.6 months (95% CI 22.4 - 48.4, n=61). TERT-mutated patients were diagnosed with MDS younger (median 69 vs. 73 years, p=0.033). Five (36%) of them had MDS-IB2, 3 (21%) MDS-biTP53, and 3 (21%) MDS-SF3B1 and MDS-LB, respectively (none CMML or MDS/MPS). Mutations in TERT were significantly associated with co-mutations in SRSF2 (p=0.043), without differences with other splicing genes. TERT-mutated patients also presented an earlier age of IPF diagnosis (p=0.039) and carried with more severe interstitial lung disease (ILD) (p=0.001). No differences were identified in smoke history, fibrogenic exposure, patterns on CT at IPF diagnosis, pulmonary function tests, or respiratory symptoms. Patients with wildtype TERT did not exhibit any cutaneous (p=0.000), hepatic involvement (p=0.024), nor premature aging signs (p=0.088). First-grade family history was significant for IPF (p=0.003). TERT and wildtype TERT mutated groups received equivalent MDS treatments; no differences were observed in responses. OS was not shorter for TERT-mutated patients (wildtype 29.2 months (95% CI 22.4 - 66.3) vs. TERT-mutated 19.7 months (95% CI 7.9 - NA); p=0.92). However, harboring a TERT mutation conferred a shorter OS only in very-high IPSS-M patients (median OS 20.3 months (95%CI 15.5-NA; n=9) vs. 8 months (95% CI 6.14 - NA, n=5); p=0.038). Patients with TERT mutation died significantly more of non-infectious respiratory causes (p=0.005). Univariate analysis for OS was significant for IPSS-M (p=0.000), 2022 WHO classification (p=0.001), and TP53 mutation (p=0.000). TERT mutations and pulmonary characteristics had no impact on survival. Conclusion The incidence of suspected STS in patients with concomitant MDS and IPF is 28.3%. MDS and IPF present at younger age in those with TERT mutation and may impact survival if they present with high-risk MDS. Larger cohorts are needed to validate these results. Nevertheless, this study has profound implications for clinical decisions, family counseling, and early advice on avoiding smoke and other potential fibrogenic exposures.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,227
Écart entre enseignants0,218 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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