Abstract GS4-07: Assessing prognosis after neoadjuvant therapy: A comparison between anatomic ypAJCC staging, residual cancer burden class and neo-bioscore
Notice bibliographique
Résumé
Abstract Background: Pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer is associated with improved survival. Further assessment of the extent of residual disease, using the pathological anatomic American Joint Committee on Cancer staging method (ypStage) or the Residual Cancer Burden (RCB) method, have been shown to add prognostic information for patients with residual disease. Neo-Bioscore, an alternate system to classify response to NAC, includes clinical stage at diagnosis and biology and defines eight prognostic groups. The goal of this study was to compared three scoring systems (anatomic ypStage (7th ed), RCB Class and Neo-Bioscore) and assess whether RCB Class and Neo-Bioscore provide additional prognostic value in the context above anatomic ypStage, the most commonly used method for post-neoadjuvant residual disease assessment. Methods: Data from 5161 patients treated with NAC was pooled from 12 sites. Patients without clinical and pathological staging were excluded, as were patients with HER2+ breast cancer who did not receive neoadjuvant HER2-targeted therapy, leaving 3730 for analysis. PCR was defined as no residual invasive tumor in breast and nodes, i.e. RCB-0 or ypT0/Tis and ypN0. Patients with discordant pCR status by RCB Class vs ypStage (n=9) were excluded. Associations between each scoring system and event-free survival (EFS) were evaluated using the log rank test. EFS at 5 years was estimated using the Kaplan Meier method. Associations between Neo-Bioscore and EFS were assessed in the pCR group. For patients with residual disease, we assessed RCB and Neo-Bioscore within each ypStage. Analysis was performed overall and within subtype. Subgroups with <5 patients were excluded from the survival analyses. Results: ypAJCC staging, RCB class and Neo-Bioscore were all associated with EFS in the overall population and within each subtype (log rank p<0.0001). Of note, 13 patients with a Neo-Bioscore of 7 all recurred or died within 19 months of follow-up. Overall, 34% (1264/3721) of patients achieved a pCR. Their Neo-Bioscore ranges from 0-5, where 3% (37/1264) has a Neo-Bioscore of 5 despite achieving pCR. The Neo-Bioscore was not associated with EFS in case of a pCR, with EFS estimates at 5 years of 95%, 94%, 92%, 93%, 90% and 92% for Neo-Bioscores 0-5 respectively. As HR and HER2 status are components of the score, the range of Neo-Bioscore in the pCR group differs by subtype. However, similar to the overall analysis, the Neo-Bioscore was not prognostic within subtypes in case of pCR. Overall, among the patients who did not achieve pCR, both RCB class and Neo-Bioscore were associated with EFS within ypStages I, II and III. However, the ypStage within which RCB and Neo-Bioscore are prognostic is different for each subtype. RCB class was prognostic in ypStage I in both HR+ subtypes: patients with ypStage-I/RCB-I had significantly improved survival compared to patients with ypStage-I/RCB-II (5-year EFS: 100% vs 83% in HR+HER2- and 95% vs 77% in HR+HER2+). In contrast, for patients with triple negative breast cancer, RCB class was prognostic within ypStage II and III. Analysis by clinical stage and the components of the three systems that contribute most to prognosis will be presented. Conclusions: The degree of response to NAC adds important information to pCR versus residual disease. The Neo-Bioscore was not prognostic among patients with pCR, suggesting that clinical stage (including subtype and grade) adds little information in the setting of a pCR. In contrast, both RCB and Neo-Bioscore provide additional prognostic information to the conventional ypAJCC staging among non-pCR patients, suggesting that clinical stage, tumor biology as well as extent of residual disease all contribute to prognosis in the setting of residual disease after NAC. Citation Format: Marieke EM van der Noordaa, Christina Yau, Sonal Shad, Marie Osdoit, Tessa G Steenbruggen, Diane de Croze, Anne-Sophie Hamy, Marick Lae, Fabien Reyal, Maria Del Monte-Millán, Miguel Martin, Sara Lopez Tarruella, I-SPY 2 TRIAL Consortium, Judy C Boughey, Matthew Goetz, Tanya Hoskin, Rebecca Gould, Vincent Valero, Gabe Sonke, Maartje van Seijen, Jelle Wesseling, John Bartlett, Stephan Edge, Mi-Ok Kim, Jean Abraham, Carlos Caldas, Helena Earl, Elena Provenzano, Stephen-John Sammut, David Cameron, Ashley Graham, Peter Hall, Lorna MacKintosh, Fang Fan, Andrew K Godwin, Kelsey Schwensen, Priyanka Sharma, Angela DeMichele, Janet Dunn, Louise Hiller, Larry Hayward, Jeremy Thomas, Kimberley Cole, Lajos Pusztai, Laura van 't Veer, Fraser Symmans, Laura Esserman. Assessing prognosis after neoadjuvant therapy: A comparison between anatomic ypAJCC staging, residual cancer burden class and neo-bioscore [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr GS4-07.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».