Abstract A074 Uncovering chemoresistance mechanisms in CIC-DUX4 sarcoma using a novel xenograft model
Notice bibliographique
Résumé
Abstract Objective: Capicua-double homeobox 4 (CIC-DUX4)–rearranged sarcomas (CDS) are exceptionally rare and highly aggressive tumors that usually develop in soft tissues in children, adolescents, and adults. These sarcomas exhibit high rates of metastasis and quickly develop resistance to chemotherapy, posing significant treatment challenges. Patients are currently treated using Ewing sarcoma chemotherapy protocols, but those with CDS have a significantly poorer prognosis, with a median survival of less than 2 years. This underscores the urgent need for effective therapeutic strategies for CDS. In this study, we created chemoresistant CDS tumors to identify distinct transcriptomic patterns in these resistant tumors and to explore new therapeutic opportunities for combating CDS. Method: To discover drivers of chemo resistance we developed a fractionated high dose chemotherapy protocol to use on our cell line-derived xenograft (CDX) model. This regimen is based on the same genotoxic chemotherapy that is currently in use for CIC-DUX4 patients: VDC-IE (vincristine, doxorubicin, cyclophosphamide, ifosfamide and etoposide). Tumor progression and metastasis was monitored using bioluminescence imaging. When the tumors were palpable, we started chemotherapy and continued for 3 cycles. To assess transcriptional changes associated with resistance to the treatment, we performed RNA-seq on tumors from treated and vehicle arms. Differential expression analysis, Gene Set Enrichment Analysis (GSEA) and single-sample Gene Set Enrichment Analysis (ssGSEA) and somatic variant calling was done to identify which genes and pathways are enriched in the chemo resistant tumors and to understand whether the selective pressure of the treatment leads to an enrichment for specific cell types. We used deconvolution and enrichment approaches leveraging cell type signatures obtained from healthy bone marrow and muscle single-cell atlases. Results: Three cycles of chemotherapy treatment significantly improved overall survival of the mice undergoing chemotherapy. Transcriptomic profiling of the resistant tumors showed increased representation of cancer stem cell genes such as Sox2, KLF4, Prrx1, ALDH1A2, ABCC1 in chemotherapy resistant tumors. ssGSEA showed an increase in Ng2+ MSCs, Fibroblast/chondrocyte progenitor cell population signatures. In addition to stemness markers, the expression of IIGF1 and IGF2 were enriched in the treatment arm with LOG2Fold change of 1.7 and 2.2 (p-value 0.02, 0,002). We confirmed higher activation of mTOR in the treated tumors using IHC for phosphorylated S6 ribosomal protein, marker for the activity of mTOR, suggesting a new vulnerability in chemo resistant tumors. Conclusion: CDS is driven by a so far undruggable fusion gene which is patognomonic of the disease. These tumors quickly acquire resistance to chemotherapy and become aggressive. Here we have identified a mechanism-based therapeutic strategy to overcome this challenge. Our model suggests effectiveness of dual inhibition of IGF1R/mTOR to overcome chemoresistance. Citation Format: Masoumeh Aghababazadeh, Ana Castillo-Orozco, Niusha Khazaei, Wajih Jawhar, Geoffroy Danieu, Livia Garzia. Uncovering chemoresistance mechanisms in CIC-DUX4 sarcoma using a novel xenograft model [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A074.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».