Abstract B056: Unveiling the temporal impact: Exploring dynamic changes in the pediatric solid tumor microenvironment through time
Bibliographic record
Abstract
Abstract Introduction: Immunogenomics investigates the tumor immune microenvironment (TIME), but its clinical application is limited by the lack of time-dependent bioinformatic models and the rarity of longitudinal tumor banking to characterize the dynamics of TIME throughout the disease evolution. Objectives: We aim to evaluate the TIME plasticity of pediatric solid tumors, analyze the modifications of TIME over time and identify time-dependent modifications. Method: We studied the transcriptomic data from longitudinal sampling of pediatric solid tumors (age < 21). The immune plasticity was assessed by immune gene profiling with k-means clustering and immune infiltrate enumeration (MCPCounter). We developed patient-adjusted longitudinal analyses to evaluate immune remodeling. A longitudinal general linear mixed model (GLMMSeq) was used, with default parameters, to extract the list of differentially expressed genes (DEG) over time. Gene set enrichment analyses (GSEA) were performed, using GLMMSeq results, to study metabolic pathways that undergo significant longitudinal dysregulation (adjusted p-value <0.05). The relation of immune changes to time was assessed by dynamic regression models (TimeReg package). Dysregulated genes and pathways, filtered to limit high correlation, were included in the regression models to find significant (p-value <0.05) associations with early or late relapses. Results: Twenty-seven patients with relapsed/refractory pediatric solid tumors were included, totaling 67 time points: 17 at diagnosis, 23 at first, 15 at second and 12 at subsequent relapses. Our analyses demonstrated an evolution in the immune infiltration and immune gene profile through time, suggesting TIME plasticity. The DEG analysis showed a downregulation of antitumoral immune genes (C9, EOMES) and an upregulation of tolerogenic genes (the CCL1 T-reg pro-attractive gene). Functional analyses identified downregulation of pro-inflammatory pathways (T-cell receptor complex, immunoglobulin complex) with upregulation of tolerogenic (macrophage cytokine production) and oncogenic pathways (IL6-JAK-STAT3 signaling, MYC target and IL2-STAT5 signaling). In upregulated pathways, the major contributors identified by core enrichment analysis were tolerogenic factors: immune checkpoints (CTLA4, IL4I1 and IL2RA), T-reg activator (TGFB1) and immunosuppressive gene (IL10RA). TimeReg analysis showed that late relapses were associated with modifications in the immunoglobulin production pathway and early relapses with MYC pathway dysregulation. This suggests that the changes in the tumor environment are influenced by the delay separating two time points and that immune desertion worsens with time. Conclusion: Our study showed that the TIME of pediatric solid tumors evolves towards immune depletion and tolerogenicity. The determinant of immune evolution and its relation to oncogenic pathways need to be further investigate. Citation Format: Virgile Raufaste-cazavieille, Stéphanie Bianco, Lara Hermann, Jeyani George Clement, Emeric Texeraud, Sylvie Langlois, Thomas Sontag, Pascal Tremblay-Dauphinais, Alex Richard-St-Hilaire, Charles Joly Beauparlant, Loïc Mangnier, Stéphanie Vairy, Sonia Cellot, Vincent-Philippe Lavallee, Thai Hoa Tran, Daniel Sinnett, Arnaud Droit, Raoul Santiago. Unveiling the temporal impact: Exploring dynamic changes in the pediatric solid tumor microenvironment through time [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 B056.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.000 |
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 teacher head, 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".