Abstract P2-02-02: Dynamics of ctDNA changes during neoadjuvant chemotherapy in triple-negative breast cancer patients
Notice bibliographique
Résumé
Abstract Background: Liquid biopsies to monitor response to treatment are a minimally invasive and highly attractive method for clinical application. Detection of ctDNA in plasma is now highly sensitive thanks to the use of novel highly sensitive and specific techniques such as ddPCR. In the present study we set out to analyze the utility of using ctDNA to monitor response to treatment in patients receiving standard neoadjuvant chemotherapy in triple negative breast cancer. Methods: Serial blood was collected from triple negative breast cancer patients participating in the Q-CROC-03 clinical trial (NCT01276899). The trial recruited triple negative breast cancer patients undergoing standard neoadjuvant chemotherapy. Paired biopsies were collected prior and at the end of treatment and serial bloods collected throughout the study. Whole exome sequencing was performed on tissues collected and we identified mutated genes of interest. Cell free DNA (cfDNA) was extracted from 3 ml of plasma and 4-10 variants per patient were analyzed by ddPCR in serial plasma samples collected before and during treatment. Response was measured by evaluating residual cancer burden (RCB), and non-responders were RCBII-III, responders RCB0-I. Results: For the present analysis, we identified 60 variants in tumors from 12 patients (9 RCBII-III and 3 RCB0-I). Except for TP53, none of the genes were shared among the tumors. 20% of the variants were not detected in ctDNA at any time point and we did not find any correlation between cfDNA levels and tumor size or response to treatment. The average variant allele frequency (VAF) of all detected variants at baseline was higher in RCBII-III patients than in RCB0-I patients (7.0 vs 0.7 respectively). Interestingly, variants that were detected either only in the pre-chemo tumor or in the post-chemo tumor were frequently detected throughout neoadjuvant therapy, highlighting the ability of ctDNA to capture tumor heterogeneity. In almost all cases, we observed a dramatic decrease in ctDNA VAF after one cycle of chemotherapy, including 30% to non-detectable levels. By the 5th cycle of chemotherapy 97% of detected variants had decreased (average 95% decrease). This decrease in ctDNA VAF was independent of RCB score. In some RCBII-III cases, ctDNA VAF increased prior to surgery, reflecting residual tumor presence. Conclusion: ctDNA could be detected in plasma of all early TNBC patients undergoing neoadjuvant chemotherapy with the majority of variants detected in plasma collected at baseline prior to chemotherapy. Once treatment started, the abundance of ctDNA markedly decreased in plasma independently of tumor response. The effect of chemotherapy on levels of ctDNA needs further investigation. Citation Format: Cavallone L, Adriana A-M, Aldamry M, Lafleur J, Cathy L, Alirezaie N, Bareke E, Majewski J, Ferrario C, Mihalciou C, Roy J-A, Markus E, Robidoux A, Pelmus M, Aleynikova O, Discepola F, Basik M. Dynamics of ctDNA changes during neoadjuvant chemotherapy in triple-negative breast cancer patients [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-02-02.
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) | 0,002 | 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 ».