Abstract PR01: Three-hit model of Wilms’ tumor formation reveals immunogenic transcriptional subtypes
Bibliographic record
Abstract
Abstract Wilms’ tumor is the most common kidney cancer in children. Despite advances in care, children with metastatic, anaplastic, or relapsed disease still fare poorly. Recent studies have uncovered novel types of driver mutations, including in microRNA processing genes such as DROSHA. However, there are no ways to rationally guide therapy based on mutation, as the mechanisms by which they cause cancer remain poorly defined. Thus, here we investigated how specific types of driver mutations affect gene and protein expression. To understand how these mutations drive Wilms’ tumor formation, we first recategorized known mutations and copy number changes using a new classification schema. We found four mutation classes to be mutually exclusive with each other: microRNA processing, MYCN-activating, chromatin remodeling, and RNA splicing. These mutations were not mutually exclusive with common mutations in kidney development genes or loss-of-heterozygosity/imprinting (LOH/LOI) of chr11p15. We propose that this mutational pattern implies a “three-hit” model, whereby 11p15 LOH/LOI and mutations impairing kidney development predispose to but are often not sufficient for Wilms’ tumor formation. A third mutation then transforms the transcriptome via microRNA processing, MYCN, chromatin remodeling, or splicing. To study how these “third hits” affect gene expression, we next performed gene set enrichment analysis. As expected, we found that microRNA impairment leads to overexpression of microRNA target genes, and MYCN activation drives MYC target genes. Interestingly, we also found that loss of microRNA processing correlated with expression of oxidative phosphorylation genes, which may reveal a metabolic dependency in these tumors. In addition, mutations affecting splicing led to high levels of interferon-stimulated genes. Thus, the abnormal RNA species generated by altered splicing appear to trigger the innate immune response that normally responds to viral RNA. Finally, we measured how these mutations affect protein levels using reverse-phase protein arrays. Strikingly, Wilms’ tumors with either anaplastic histology or microRNA processing mutations express high levels of immune-related markers such as PD-1, PD-L1, phospho-Stat3, and phospho-NF-kB. In summary, many Wilms’ tumors develop a total of three types of mutations: 11p15 LOH/LOI, kidney development impairment, and a third hit that reshapes the transcriptome in an oncogenic fashion. These “third hits” may affect microRNA processing, MYCN activity, chromatin remodeling, or RNA splicing, and each type of mutation has distinct effects on gene expression. In particular, mutations in microRNA processing cause aberrant overexpression of microRNA target genes, leading to metabolic reprogramming and increased immunogenicity. As a result, some Wilms’ tumor mutations have widespread effects on the transcriptome that may be susceptible to immune checkpoint blockade. This abstract is also being presented as Poster B05. Citation Format: Kenneth S. Chen, Kavita Desai, James F. Amatruda. Three-hit model of Wilms’ tumor formation reveals immunogenic transcriptional subtypes [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 PR01.
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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 | 0.001 |
| 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 | 0.000 |
| 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".