783 Combined immunotherapy improves outcome for replication repair deficient (RRD) high-grade glioma failing anti-PD1 monotherapy: a report from the International RRD consortium
Notice bibliographique
Résumé
<h3>Background</h3> Response to immune checkpoint inhibition (ICI) is encouraging for patients with progressive, DNA replication-repair deficient, high-grade glioma (RRD-HGG).<sup>1</sup> However, the clinical outcomes and biological mechanisms for subsequent immune-directed salvage approaches after progression on anti-PD1 monotherapy remain unknown. <h3>Methods</h3> The International RRD Consortium performed a registry study of patients managed using central molecular, genomic, radiological review and treatment recommendations between 2015–2021. Treatment after progression on anti-PD1 monotherapy included re-irradiation where feasible, and continuation of anti-PD1 with either anti-CTLA4 (ipilimumab), or a MEK-inhibitor (trametinib). Outcomes included radiological response (iRANO), toxicity, second progression-free (PFS2) and overall survival (OS2). Companion biomarkers were analyzed centrally. <h3>Results</h3> Among 75 patients with RRD-HGG receiving PD-1 blockade, 20 remain progression-free at a median follow-up of 44.6-months. For 55 patients with 2<sup>nd</sup>-relapse/progressive tumors, continuation of ICI (n=38) resulted in median OS2 of 11.6-months (51% alive) versus 1.2-months when ICI was discontinued (n=17; no survivors, p<0.001). The combination of ipilimumab/nivolumab (n=24) resulted in response/stable disease in 75%, with median OS2 of 12.1-months. The addition of MEK-inhibitor led to response in 3/5 patients with prolonged survival. Re-irradiation improved OS2, especially for RRD-HGG with lower mutation burden (p=0.002), and those receiving ipilimumab (median OS2=33-months). Several important insights were gained from the biomarker-analyses. Survival was impacted by extreme mutation burden, but not genomic microsatellite instability. Delayed, sustained responses were observed in ultra-hypermutant RRD-HGG, associated with changes in mutational spectra and immune microenvironment. RRD-HGG showed elevated CTLA4 expression over time, explaining the responses to ipilimumab. The remarkable sensitivity to re-irradiation was explained by an absence of deleterious post-radiation indel signatures (ID8; COSMIC),<sup>2</sup> suggesting selective immune-editing. Early radiological immune ‘flare’ was observed in 33% of patients on combined immunotherapy and radiation who did not demonstrate flare on monotherapy, suggesting immune-synergism. Enrichment of RAS-MAPK mutations in genomically unstable RRD-HGG explained responses to MEK-inhibitors. Additionally, reinvigoration of peripheral immune response was observed. In all cohorts, immune adverse events were a major cause of treatment interruption, with higher prevalence in patients with bi-allelic mismatch-repair deficiency vis-à-vis Lynch syndrome. <h3>Conclusions</h3> We provide mechanistic rationale for the sustained benefit in RRD-HGG from immune-directed/synergistic salvage. Our data suggest that the continuous mutagenesis renders hypermutant RRD-HGG susceptible to ICI beyond initial progression. The combination with re-irradiation and additional immune/targeted agents can maximize survival in these children and young adults. Future research should focus on biology-driven rational immunotherapy combinations that also result in lower toxicity to maximize patient benefit. <h3>Acknowledgements</h3> AD would like to acknowledge the supports of the St Baldrick Foundation, Stand up to Cancer and Hold’em for Life Foundations for his fellowship and research. <h3>References</h3> Das A, Sudhaman S, Morgenstern D, <i>et al</i>. Genomic predictors of response to PD-1 inhibition in children with germline DNA replication repair deficiency. <i>Nat Med</i>. 2022;<b>28</b>:125–135. Kocakavuk E, Anderson KJ, Varn FS, <i>et al.</i> Radiotherapy is associated with a deletion signature that contributes to poor outcomes in patients with cancer. <i>Nat Genet</i>. 2021;<b>53</b>:1088–1096. <h3>Ethics Approval</h3> The study was approved by the SickKids Research Ethics Board (REB number: 1000048813) <h3>Consent</h3> Consent was obtained from study participants and/or their parents, as applicable.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 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) | 0,000 | 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 tête enseignante, 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 ».