Using chemotherapy response by KELIM score to predict response to first line maintenance PARP inhibitor therapy in non-BRCA mutant/homologous recombination deficiency (HRD) unknown high grade serous ovarian cancer (HGSOC).
Notice bibliographique
Résumé
e17547 Background: Maintenance PARP inhibitor therapy after response to first line chemotherapy is now standard of care in advanced HGSOC. Niraparib is available to all patients based on the PRIMA trial data; however, the progression free survival (PFS) benefit for patients without BRCA mutations or homologous recombination deficiency (HRD) is limited. Funded HRD testing is not accessible in many countries. Patient selection for PARPi therapy in non-BRCA mutant HGSOC is challenging. The calculated CA-125 ELIMination of Rate Constant K (KELIM) score is a mathematical model developed to evaluate CA-125 kinetics during chemotherapy. KELIM has been shown to correlate with chemosensitivity, with scores ≥1 associated with better clinical outcomes. This project aims to use real-world patient data to assess if surrogate markers, such as KELIM score and/or pathological chemotherapy response score, can predict response to 1st line maintenance PARPi in the absence of funded HRD testing. Methods: A retrospective review of non-BRCA mutant HGSOC cases on first line maintenance PARPi therapy at BC Cancer, Canada between April 2020 and June 2022. Only cases confirmed to be non-BRCA mutant (by both germline and tumour testing) were included in the study. Data collection was through electronic medical records and included patient demographics, chemotherapy intent (neoadjuvant vs. adjuvant), pathological (p) chemotherapy response score (CRS), progression-free survival (PFS) defined as start of niraparib to radiological evidence of disease progression. PFS analysis was performed in all patients provided 1 full cycle had been completed. The rate of not progressing at 12 months (PFS12) was calculated. KELIM score was calculated from at least 3 CA125 values taken within 100 days from the chemotherapy start date and using a validated calculation software. Results: 70 patients met the full eligibility criteria for analysis. All patients received niraparib as PARPi therapy. Mean age was 67 years and 40 patients (57%) were ≥ 65 years. Most patients were FIGO stage 3C at presentation (56%). Median number of niraparib cycles was 10 (range 1-28). 35/70 patients (50%) had disease progression at time of data analysis with a median follow-up of 13.2 months. 59 patients had evaluable KELIM scores. pCRS was not associated with any statistically significant differences in PFS. Patients with KELIM scores ≥1 had a trend to greater PFS from niraparib vs. those with KELIM <1 with median PFS of 15 months vs. 8.3 months respectively ( p=0.06). PFS12 rate was higher at 64% with KELIM scores ≥1 vs. 43% with KELIM <1; ( p=0.18). Conclusions: We show that KELIM score could be a useful tool to predict PARP inhibitor response and aid clinical decision-making by oncologists and patients in the real world setting where HRD testing is unfunded.
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 | 0,002 |
| 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».