Abstract 563: cfDNA-based analysis of minimal residual disease and T-cell receptor clonality as predictors of relapse in stage 3 NSCLC treated with chemoradiotherapy and durvalumab
Notice bibliographique
Résumé
Abstract Introduction: Durvalumab immunotherapy has rapidly emerged as standard treatment for stage 3 NSCLC patients following definitive chemoradiotherapy (CRT). Multiple novel immunotherapeutic strategies are in development to enhance the chance of cure in this setting as well. There exists a critical need to identify blood-based biomarkers capable of predicting clinical benefit from adjuvant immunotherapy as well selecting patients at high-risk of relapse for further drug development. Cell-free DNA (cfDNA)-based analysis of both minimal residual disease (MRD) and T-cell receptor (TCR) clonality have immense potential to predict and monitor response to adjuvant immunotherapy. In this study, we have combined innovative cfDNA measures of MRD (CAPPseq), TCR clonality (CapTCR-seq) and methylation (cfMeDIPseq) as potential predictive biomarkers of disease progression in stage 3 NSCLC patients treated with CRT and durvalumab. Methods: Stage 3 NSCLC patients undergoing CRT and durvalumab were recruited prospectively to undergo serial blood collections at baseline, pre- and post- durvalumab. CAPPseq and cfMeDIPseq were performed as measures of MRD. TCR repertoire analysis (CapTCR-seq) was performed on cfDNA using hybrid-capture TCR sequencing and TCR diversity/clonality was estimated using the Shannon's index. Correlations between MRD, TCR clonality, response and progression-free survival (PFS) were examined using logistic/cox regression. Results: 79 stage 3 NSCLC patients have been prospectively recruited and undergone serial blood collection. CAPPseq, cfMeDIPseq and capTCR-seq have been completed in 22 patients (5 primary progression on CRT, 17 received durvalumab). Tumor cfDNA was detectable by CAPPseq at baseline in 14 patients. High correlation between tumor cfDNA detected by CAPPseq and cfMeDIPseq was found (R=0.68, p<0.0001). Failure to clear MRD with CRT plus durvalumab was associated with significantly increased risk of recurrence with a median PFS of 5.0 vs 15.0 months (p<0.0001). Lower TCR clonality measured pre-durvalumab trended with lower likelihood of response (OR 0.82, p=0.09) and worse PFS (HR 1.16 P=0.10). Importantly, a decrease in TCR clonality compared to baseline, signaling the lack of clonal expansion on treatment, was significantly associated with a worse PFS (p=0.05). A decrease in TCR clonality of 50% after CRT was associated with a worse PFS (HR 3.5, p=0.14). CAPPseq, cfMeDIPseq and capTCR-seq analyses are ongoing in the full cohort. Conclusions: Failure to clear MRD and decreasing TCR clonality as assessed by cfDNA was highly correlated with increased risk of recurrence and reduced PFS with consolidation durvalumab. This innovative approach has significant potential to define a new biomarker for the use and development of adjuvant immunotherapy. Citation Format: Sally CM Lau, Shirin Soleimani, Jinfeng Zou, Justin Burgener, Shelley Kuang, Stephanie WY Wong, Malcolm Ryan, Ben X. Wang, Stephanie Pedersen, Devalben Patel, Penelope A. Bradbury, Geoffrey Liu, Natasha Leighl, Ming S. Tsao, Pamela S. Ohashi, Scott V. Bratman, Trevor Pugh, Frances A. Shepherd, Adrian G. Sacher. cfDNA-based analysis of minimal residual disease and T-cell receptor clonality as predictors of relapse in stage 3 NSCLC treated with chemoradiotherapy and durvalumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 563.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 ».