Abstract B001: Timing the development of chemoresistance in relapsed pediatric cancer with mutational signatures
Bibliographic record
Abstract
Abstract Survivors of pediatric cancer face lifelong battles with severe morbidities, including a significant risk of recurrence. Pre-existing genetic variation within primary tumors give certain cell populations an evolutionary advantage, increasing the likelihood of treatment resistance and relapse. The rarity of pediatric cancer and limited clinical metadata have restricted comprehensive studies on chemoresistance development. I aim to characterize the timing of chemoresistance in common childhood cancers. I developed a pipeline combining clonal evolution reconstruction with mutational signature extraction at the subpopulation level to elucidate changes in mutational processes over time. Analyzing 1,886 tumor genomes from 10 pediatric cancer cohorts revealed a common pattern of parallel evolution in most pediatric cancers, regardless of tissue origin. After de novo signature extraction, mutational signatures linked to specific cancer treatments were detected in post-treatment tumor samples. In a subset of 611 samples with detailed clinical data, mutational signatures associated with alkylating agents like cisplatin were found in 37.8% of samples, accounting for a mean 30.6% of the mutation burden, whereas antimetabolite agents like fluorouracil were found in 14.9% of samples and contributed 13.8% of the mutation burden. The higher prevalence and burden of alkylating agent signatures suggested a mechanism of direct and continuous DNA damage, while antimetabolite agents depend on active cell cycling. The specific chemotherapy’s mechanism of action dictates its genomic presentation in treated tumors. When placed in the context of the phylogenetic tree, we identified specific subpopulations with therapy signatures, indicating subpopulation-level resistance. In cases with multiple tumor samples, resistant subpopulations in recurrences or metastases were traced to early ancestral clones already present at diagnosis, suggesting that early-stage chemotherapy-resistant subpopulations expanded post-treatment. Finally, the timing of resistance phenotype development varies between pediatric cancer types. In osteosarcoma, pulmonary metastases separate and expand independently before developing cisplatin-associated signatures. In contrast, gliomas show therapy effects earlier; shared subpopulations exhibit therapy-associated signatures before the bifurcation and expansion of progressive subpopulations. Thus, the mechanisms responsible for metastasis likely differ from those driving resistance in osteosarcoma, while in glioma, the effect of therapy coincides with transformation to high-grade subtypes. In summary, investigating mutational signatures at the subclonal level unveils new insights into the clonal dynamics and development of chemoresistance in pediatric cancers, contributing to our understanding of cancer evolution and advancing the field of personalized medicine. Citation Format: Sasha Blay, Mehdi Layeghifard, Scott Davidson, David Chen, Astra Schwertschkow, Vijay Ramaswamy, Michael Taylor, Elli Papaemmanuil, Anita Villani, David Malkin, Ludmil Alexandrov, Mark Cowley, Adam Shlien. Timing the development of chemoresistance in relapsed pediatric cancer with mutational signatures [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 B001.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".