Abstract PR06: The immunogenomic landscape of pediatric primary solid tumors
Bibliographic record
Abstract
Abstract Introduction: Immune checkpoint inhibition is a novel approach of restimulating immune cells to overcome immunosuppressive microenvironment in tumors and has shown significant clinical benefit in 20-40% of adult patients. Although this success in adult cancers has resulted in a plethora of immunogenomic efforts to delineate mechanisms of response, such studies in pediatric cancers are lacking. The main objective of this study is to uncover the overall characteristics of immune microenvironment in pediatric solid tumors. Methods: We leveraged transcriptomes of 1,268 pediatric primary solid tumors acquired from multiple international initiatives, including Therapeutically Applicable Research to Generate Effective Treatments (TARGET, n = 269), International Cancer Genome Consortium (ICGC, n = 216) and Children Brain Tumor Tissue Consortium (CBTTC, n = 803). We compared our results with those of 7 adult cancer types from The Cancer Genome Atlas (TCGA, n = 2720). We applied methods of immune deconvolution, repurposed RNAseq data to recover infiltrating T- and B-cell clonotypes and studied checkpoint gene expression in pediatric tumors. In collaboration with Gabriela Miller’s Kids First Data Resource Centre, all analyses were performed and shared on CAVATICA computational platform (cavatica.sbgenomics.com). Results: Embryonal tumors with multilayered rosettes (ETMR) and medulloblastomas (MB) harbored the lowest levels of immune infiltration in our pediatric cohort. Immune deconvolution analysis revealed that neuroblastomas (NBL) had the highest T-cell infiltration amongst pediatric cancers. We found atypical teratoid/rhabdoid tumors (ATRT) had highest levels of CD8+ T cells among pediatric CNS tumors. While tumor mutational burden (TMB) was associated with immune cell infiltration in adult lung cancers and melanomas, we found no significant association in pediatric cancers. We found specific genetic alterations such as BRAF, H3F3A, and CTNNB1 had significant impact on the composition of the immune microenvironment. Analysis of T-cell repertoire revealed an inverse correlation between clonal diversity and TMB, suggesting a T-cell clonal expansion in high TMB samples. Finally, while the majority of NBL samples expressed LAG3, ~10% of samples had elevated levels of TIM3 gene, suggesting a distinct mode of immunosuppression in this subset. 2% of pediatric CNS tumors, mostly consisted of ATRTs, harbored high expression of PRF1 and GZMA genes suggestive of functionally capable immune cells in these cases. Conclusions: We report characteristics of the tumor microenvironment in pediatric tumors at primary diagnosis. We found that specific gene mutations, rather than mutational load, shape the composition of immune cell types. Furthermore, hypermutant samples exhibit evidence of clonal T-cell expansion. Our results suggest immune activity in 2% of CNS tumors and uncover a subtype of NBL expressing TIM3 checkpoint gene, calling for investigations to evaluate the effectiveness of immunotherapy. This abstract is also being presented as Poster A74. Citation Format: Arash Nabbi, Pengbo Sun, Sudhaman Sumedha, Kelsey Zhu, S.Y. Cindy Yang, Joseph N. Paulson, Marcel Kool, Komal Rathi, Karthik Kalletla, Pichai Raman, Yuankun Zhu, Adam C. Resnick, David T.W. Jones, Natalie Jäger, Stefan M. Pfister, Trevor J. Pugh. The immunogenomic landscape of pediatric primary solid tumors [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr PR06.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".