Circulating tumor cells enumeration and characterization in patients with lung cancer treated with immunotherapy.
Notice bibliographique
Résumé
e15030 Background: Programmed death-ligand 1 (PD-L1) is a predictive biomarker for immunotherapy in the treatment of non-small cell lung cancer (NSCLC). Assessing PD-L1 expression from the tumor specimen can be challenging because of tissue accessibility, heterogeneity, and dynamic changes in PD-L1 expression that may impact the status of PD-L1 during disease evolution and treatment. Hence, assessing PD-L1 status from archival tumor might not reflect its actual state on the tumor and having a real-time assessment of its expression with the use of non-invasive techniques such circulating tumor cells (CTCs) is useful. Methods: We conducted a single centre prospective study to detect CTCs in the blood of patients with stage III-IV NSCLC treated with immunotherapy using the standard CellSearch technology for CTC enumeration and the Epic Sciences technology for CTCs enumeration and assessment of PD-L1 protein expression on CTCs. CTCs were detected at baseline before treatment initiation and after 2 cycles of treatment. Study endpoints included CTCs detection and concordance between the 2 technologies, PD-L1 expression assessment on CTCs with the Epic Sciences technology, and concordance with tissue-based expression. Comparisons were made using Pearson correlation coefficient (PCC) and intraclass correlation coefficient (ICC) for count data and percentage of perfect agreement. Results: Between 2019 and 2022, 48 patients treated with immunotherapy were enrolled in the study. The mean ± standard deviation (SD) age was 66.8 ± 10.3 years, 63% were females, 42% received combination chemotherapy and immunotherapy, and 44% had adenocarcinoma histology. The tissue PD-L1 expression was high (≥50%), intermediate (1-49%), and low (<1%) in 36%, 31% and 33% of patients respectively. The mean ± SD baseline CTCs count per 7.5ml was 1.97 ± 4 with CellSearch and 1.38 ± 2.72 per ml with Epic Sciences. After 2 treatment cycles, 22% and 41% of patients had an increase in their CTCs count with CellSearch and Epic Sciences, respectively and 30% and 29% had a decrease in their CTCs. The PCC and ICC for baseline CTCs detected by CellSearch and Epic Sciences were 0.72 and 0.61, respectively and for CTCs detected after 2 cycles of treatment by the 2 technologies were -0.16 and 0.45 respectively. One patient had PD-L1 expression on their CTCs with the Epic Sciences technology and there was no association between the PD-L1 expression on CTCs and on matched tissue samples. Conclusions: This study showed a moderate to strong correlation between the 2 technologies for baseline CTCs detection and moderate to poor correlation for CTCs detection after 2 cycles of therapy. No association was found between CTCs and tissue PD-L1 expression. Future work needs to further investigate the role of PD-L1 expression on CTCs.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».