Abstract P5-05-01: Personalized Cancer Monitoring (PCM): a novel ctDNA tool to detect molecular residual disease in patients with early-stage breast cancer
Notice bibliographique
Résumé
Abstract Introduction: Identification of Molecular Residual Disease (MRD) in patients with breast cancer with circulating tumor DNA (ctDNA) presents a strategy to identify patients at high risk of relapse. Approaches that detect ctDNA at lower concentrations are required to increase sensitivity and improve on the lead time between ctDNA detection and clinical relapse. Here we present results using novel highly sensitive tumor-informed sequencing assays for ctDNA detection of MRD based on detection of multiple patient specific mutations in ctDNA. Methods: 62 stage II-III breast cancer patients (23 hormone receptor positive HER2 negative (HR+HER2-), 20 HER2+, 15 triple negative breast cancer (TNBC) and 4 unknown receptor status) enrolled in the ChemoNEAR sample collection study were included. All patients received neoadjuvant chemotherapy, followed up by surgery, with samples taken at diagnosis, and post-surgery every 3 months for the first two years, followed by every 6 months for up to five years. Tumor DNA from FFPE samples and germline was Whole Exome Sequenced to identify patient specific mutations and design anchored-multiplex PCR (AMP™) Personalized Cancer Monitoring (PCMTM) assays to track mutations in plasma. Cell free DNA was extracted from 613 plasma samples (median volume 4ml, range 0.5-4.5ml) and sequenced with PCMTM assays, with 37-177 variants (median 52) per panel, to a depth of 100,000x per locus. A proprietary algorithm was used to identify ctDNA. Results: At a median follow-up of 52.7 months post-surgery (range 15.3-96.4 months), ctDNA was detected in 25.8% (16/62) of patients, with detected ctDNA levels ranging from allele frequency (AF) of 0.01%, to 32.5%) (median 0.24% AF). Detection of ctDNA was associated with a high risk of future relapse (HR 65.4, 95% CI 14.5-293.7), with a median lead-time from ctDNA detection to clinical relapse of 13.7 months (range 3.9-58.9). MRD was identified in 76.9% (10/13) of patients who relapsed. ctDNA was detected prior to relapse in both patients with brain only relapse, but with a reduced lead time over clinical relapse (5.73 and 3.90 months), which was previously not achievable with digital PCR MRD-detection assays. Of patients with assessable baseline samples, 81% (39/48) had ctDNA detected. No patients with undetected ctDNA, or detectable ctDNA with AF< 0.1%, relapsed during follow-up, whereas ctDNA was detected at baseline in all 10 patients who relapsed during follow-up (p=0.1). Conclusions: PCMTM detected breast cancer relapse with a long lead-time over clinical relapse, and strong association with relapse free survival, an advancement over previously published data with digital PCR MRD detection. Prospective, interventional trials are now required to assess whether treatment on the basis of MRD detection improves outcome, including the TRAK ER Trial (NCT04985266). Citation Format: Isaac Garcia-Murillas, Giselle Walsh-Crestani, Edward Phillips, Rosalind Cutts, Sarah Hrebien, Kathryn Dunne, Kally Sidhu, Robert Daber, Amber C. Carter, Lorena De La Peña, Stephen Johnston, Alistair Ring, Simon Russell, Abigail Evans, Anthony Skene, Duncan Wheatley, Ian Smith, Nicholas Turner. Personalized Cancer Monitoring (PCM): a novel ctDNA tool to detect molecular residual disease in patients with early-stage breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-05-01.
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 | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».