Abstract PR003: ctDNA release kinetics and fragmentation to monitor treatment response and resistance in esophageal adenocarcinoma
Notice bibliographique
Résumé
Abstract Background: Esophageal adenocarcinoma (EAC) has a low 5-year survival rate and limited options for targeted therapies. Circulating tumor (ct)DNA isolated from liquid biopsies could help monitor patients to personalize treatment decisions. However, ctDNA studies in EAC remain sparse, and significant knowledge gaps exist in our understanding of ctDNA emission in response to therapy. The goal of this study was 1) to conduct a multimodal, longitudinal analysis of plasma cell free (cf)DNA in EAC patients and 2) to better understand the biology of ctDNA release during drug treatment. Methods: 1) A total of 189 longitudinal blood samples were collected from 41 EAC patients throughout treatment. cfDNA was isolated from baseline and post-neoadjuvant chemo samples for targeted deep sequencing alongside white blood cell controls. Shallow whole-genome sequencing was conducted on cfDNA for ichorCNA and fragmentomics analysis. 2) To dynamically monitor ctDNA fluctuations during EAC treatment, cfDNA was isolated from all other timepoints for digital (d)PCR quantification of the mutations identified from sequencing. ctDNA release kinetics were studied in vitro using three cell lines (OE19, FLO-1, A549), as well as our newly established cisplatin resistant model of OE19. ctDNA was analyzed by Qubit, dPCR, and Bioanalyzer. Annexin-V/PI flow cytometry was used to assess percentages of apoptotic and necrotic cells to correlate ctDNA to release mechanism. Results: 1) Sequencing has been completed for 21/41 patients and is ongoing for 20/41 patients. In the first 21 patients, somatic mutations were found in potential EAC driver genes, such as KRAS, TP53, ACVR2A, NOTCH1, and APC. Interestingly, in one patient’s cfDNA sample sequenced post neoadjuvant chemo, a novel mutation emerged in FBXW7, representing a potential resistance driver. ichorCNA tumor fraction (TF) values overall were low, and when using the recommended 0.03 TF estimate cutoff, there were significantly larger baseline TF values in metastatic (stage IVB) patients than those with localized disease (stage III) (p<0.05). 2) In vitro viable cancer cell numbers correlated to ctDNA levels, as measured by qubit and dPCR analysis. Additionally, cisplatin and 5-FU chemo treatments led to larger ctDNA release in all cells. ctDNA emission kinetics correlated to cytotoxicity (apoptosis and necrosis as shown through flow cytometry), with higher levels of ctDNA released by cisplatin-sensitive vs. resistant cells (p<0.05). Additionally, cisplatin treatment caused a shift in average ctDNA fragment size, with larger fragments observed during treatment, corresponding to an increased proportion of necrotic cells. Conclusions: This study reveals that multimodal cfDNA analysis can successfully be used in EAC patients to monitor treatment response and potentially identify resistance mechanisms. Moreover, in vitro models demonstrated a correlation of ctDNA emission and fragment length with chemo-cytotoxicity, shedding light on the release kinetics of DNA from cancer cells. Citation Format: Alexandra Bartolomucci, Laura Kienzle, Sarah Tadhg Ferrier, Lisa-Monique Edward, Jeffrey Bruce, Kwang-Bo Joung, Stephenie Prokopec, Wotan Zeng, Abirami Sharma, Kyle Dickinson, Nicholas Bertos, Jonathan Cools-Lartigue, Lorenzo Ferri, Trevor J. Pugh, Julia V. Burnier. ctDNA release kinetics and fragmentation to monitor treatment response and resistance in esophageal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR003.
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 | 0,001 |
| 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 | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».