Biomarker Discordances and Alterations Observed in Breast Cancer Treated with Neoadjuvant Chemotherapy: Causes, Frequencies, and Clinical Significances
Notice bibliographique
Résumé
Purpose: Biomarker discordances and alterations can be encountered between tru-cut biopsy and residual tumor in breast cancer treated with neoadjuvant chemotherapy (NACTx). We aimed to investigate the effect of NACTx on major biomarker expression (ER, PR, HER2, Ki-67) and tumor grade, the frequency and causes of receptor discordances, and the clinical significance of changes in terms of adjuvant therapy need and chemosensitivity. Methods: In this retrospective study, ER, PR, HER2, and Ki-67 expression and tumor grades were compared between pre- and post-NACTx tumor samples using the Wilcoxon signed-rank test. The frequencies of receptor discordances and the need for new adjuvant therapy due to discordances were calculated. The effect of patient and tumor characteristics and NACTx regimens on discordances was investigated using multivariate analysis. Using histopathological examinations, residual tumors were divided into chemotherapy-responsive and chemotherapy-unresponsive tumors. Biomarker changes in both groups were analyzed for predictability of chemosensitivity. Results: Of the 169 patients who received NACTx, 102 patients having enough residual tumors in the surgical pathology specimen were enrolled in the study. Histopathologically, about 70% of tumors were partially responsive to NACTx and 30% were unresponsive (chemo-resistant). The concordance and discordance rates were 95.1% versus 4.9% for ER (p = 0.180), 97.1% versus 2.9% for PR (p = 0.083), and 89.2% versus 10.8% for HER2 (p = 0.763), respectively. In addition, 15% of hormone receptor (HR)-negative patients became HR(+) and 5.7% of HER2(−) patients became HER2(+) in the residual tumors, requiring adjuvant endocrine or anti-HER2 therapy. In particular, 18% of triple-negative patients became HR(+) and 12% became HER2(+). HER2 loss was detected in 40% of HER2(+) patients. Multivariate logistic regression analysis revealed that lower estrogen expression (p = 0.046), a smaller tumor size (p = 0.029), and anti-HER2 therapy (p < 0.001) have independent efficacy on ER discordance, PR discordance, and HER2 discordance, respectively. Ki-67 and PR expression significantly decreased in chemotherapy-responsive tumors (p = 0.001 and p = 0.004), and the tumor grade increased in chemotherapy-unresponsive tumors (p = 0.034). Conclusions: Approximately 3–5% of HR discordance and about 10% of HER2 discordance can be observed in breast cancer after currently used NACTx regimens. Discordances are bi-directional (from positive to negative and vice versa), and their causes are multifactorial; they should be assessed accordingly. The NACTx effect alone cannot explain observed discordances but can cause biomarker alterations. The change in receptor status from positive to negative, especially HER2 loss, is mainly associated with the NACTx effect. However, the shift from negative to positive is thought to be primarily related to intratumoral heterogeneity. Receptor statuses becoming positive are of more clinical importance due to adjuvant therapy requirements. Biomarker alterations in PR, Ki-67, and tumor grade can provide predictive information about tumor chemosensitivity.
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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 | 0,005 |
| 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 | 0,000 | 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) | 0,000 | 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 ».