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Record W3048066516 · doi:10.1158/1538-7445.pedca19-b39

Abstract B39: Modulation of insulin receptor alternative splicing to develop cancer therapeutics

2020· article· en· W3048066516 on OpenAlexaboutno aff
Safiya Khurshid, Matías Montes, Brianne Sanford, Peter J. Houghton, Frank Rigo, Dawn S. Chandler

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsnot available
Fundersnot available
KeywordsAlternative splicingGene isoformExonBiologyCancer researchRNA splicingCancerInsulin receptorCancer cellRhabdomyosarcomaAngiogenesisInsulinSarcomaEndocrinologyGeneMedicineRNAInsulin resistanceGeneticsPathology

Abstract

fetched live from OpenAlex

Abstract Insulin receptor (IN-R) undergoes alternative splicing to produce two isoforms: the full-length IN-RB and exon 11 skipped IN-RA isoform. While IN-RB has high affinity for insulin and much lower affinity for IGF1 or IGF2 ligands, IN-RA binds both insulin and IGF2 with high affinity and as such it exploits the IGF pathway to accelerate the onset of tumor-cell hallmarks such as proliferation and angiogenesis. Our data show that there is a significantly increased expression of IN-RA levels in multiple cancer patient cohorts, including rhabdomyosarcoma (RMS) and osteosarcoma (OS). We also found this increased IN-RA expression in tumor cell lines derived from multiple different pediatric tumor types such as RMS, OS, and Ewing’s sarcoma as compared to the control samples. Additionally, our data show that cellular stress such as hypoxia increases the expression of the IN-RA isoform. Adaptation to hypoxic environments is a hallmark of the neoplastic phenotype, and pediatric sarcomas exhibit an intrinsically hypoxic physiology. However, how the INR alternatively spliced mRNAs are regulated is not well understood, nor is the role that these isoforms play in the initiation and progression and therapeutic resistance of human cancer. Our central hypothesis is that hypoxia alters the expression of splicing factors, leading to the generation of the IN-RA isoform, which contributes to cancer progression beyond the micrometastatic stage. We used an insulin receptor mini-gene system to identify a binding site for the RNA binding protein cugbp, critical for the hypoxia-induced splicing regulation of IN-R. We targeted the cugbp binding site using an antisense oligonucleotide (ASO) walk to identify an ASO that binds to the cugbp binding site in the intron 10 of INR gene and modulates the levels of endogenous IN-RA. To allow the possibility for therapeutic intervention, we utilized this ASO in OS cell lines. We found that the application of ASO shifts the IN-R splicing towards IN-RB and significantly reduces proliferation, migration, and angiogenesis in cancer cell lines. In order to delineate the downstream consequences of this splicing change, we subjected the control and ASO-treated cells to mass spectrometric analysis. Preliminary analysis of this data reveals that proliferation markers such as Ki67, PI3-kinase pathway components, protein clusters such as cell-cell adhesion, and cell division, among others, are significantly downregulated in cells treated with ASO where the splicing has been restored to the IN-RB isoform. Our data open a new paradigm of how alternative splicing regulates cell signaling to mediate cancer-causing changes, and our ASO compounds show promising insight into how we can reinstate the splicing pattern and impede tumorigenesis. Citation Format: Safiya Khurshid, Matias Montes, Brianne Sanford, Peter Houghton, Frank Rigo, Dawn Chandler. Modulation of insulin receptor alternative splicing to develop cancer therapeutics [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 B39.

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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.148
GPT teacher head0.429
Teacher spread0.280 · 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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