PORTO-001 study: Changes in the oral microbiome and matrix metalloproteinases in patients with oropharyngeal squamous cell carcinoma undergoing definitive chemoradiotherapy.
Notice bibliographique
Résumé
e18034 Background: Head and neck cancer patients treated with concurrent chemoradiotherapy (CRT) develop oral mucositis, with varying severity. However, knowledge about risk factors, biomarkers, treatment, and prognosis of oral mucositis is still limited. Methods: We designed an exploratory study to examine the changes in the oral microbiome and salivary and plasma levels of matrix metalloproteinases (MMP) in patients with locoregionally advanced oropharyngeal squamous cell carcinoma (OPSCC) undergoing definitive CRT. Patients were followed from screening to week 7 of treatment with CRT, which consisted of 70 Gy/35 fractions of radiation with cisplatin given at 100 mg/m2 q3weeks or weekly 40 mg/m2. Clinical and demographic data were collected at specified timepoints. Samples were collected at screening and on week 4 of treatment with CRT. A tumor swab was collected for microbiome analysis. Blood and saliva samples were collected for measurements of 7 MMPs and 26 other cytokines. The study population was summarized descriptively. The change in the cytokines and the α-diversity of microbiome data (week 4 vs screening) were examined using Wilcoxon signed rank test. Results: A total of 11 patients were included. Nine (82%) were male. 5 patients (45%) were stage III, and there were 2 patients (18%) for each of stages I, II, and IV. A history of smoking was present in 5 (45.4%) patients. p16 was positive in 9 (81.8%) patients. There was a median weight loss of 7.0% (5.1 kg) between screening and week 7 visits. Almost half (45%) of patients developed grade 2 oral mucositis. All patients developed radiation dermatitis, with most cases ranging between grades 2 and 3. There were no statistically significant changes in the salivary levels of cytokines. However, 1 patient developed clinical oral mucositis before all the other patients, at week 1, and this was associated with an upsurge in the salivary levels of IFN-γ, IL-6, TNF-α, MMP-1, MMP-2 and MMP-7. For all patients, there were statistically significant changes (median (95% confidence interval)) in the blood plasma levels of IP-10 (-0.1 fluorescence intensity (FI) (-0.3, -0.04), p = 0.01), MIP-1ß (0.2 FI (0.07, 0.5), p = 0.02), SDF-1α (1.4 pg/ml (0.07, 3.7), p = 0.02), MMP-1 (-0.4 pg/ml (-0.5, -0.09), p = 0.01), MMP-3 (0.4 pg/ml (0.03, 0.5), p = 0.02), and MMP-8 (-0.2 FI (-0.3, -0.02), p = 0.04). There was a 29.1% ((-3.0%, -32.8%), p = 0.005) reduction in α-diversity of microbiome at week 4 from screening. Conclusions: In this small series of patients diagnosed with OPSCC, there was a change in the plasma levels of IP-10, MIP-1ß, SDF-1α, MMP-1, MMP-3, and MMP-8, as well as a reduction in the α-diversity of the oral microbiome. Larger studies are needed to confirm these results. If confirmed, these changes might imply a potential role as predictive or pharmacodynamic biomarkers for oral mucositis.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».