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Abstract PR01: Three-hit model of Wilms’ tumor formation reveals immunogenic transcriptional subtypes

2020· article· en· W3047444557 on OpenAlexaboutno aff
Kenneth Chen, Kavita Desai, James F. Amatruda

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyWilms' tumormicroRNACancer researchGeneDroshaChromatin remodelingLoss of heterozygosityMutationTranscriptomeGeneticsChromatinGene expressionRNARNA interferenceAllele

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.078
GPT teacher head0.339
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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