Abstract A074 Uncovering chemoresistance mechanisms in CIC-DUX4 sarcoma using a novel xenograft model
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".