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Overcoming miR-106a induced radioresistance in prostate cancer: Targeting senescence with KU-55933.

2018· article· en· W2791795647 on OpenAlexaff
Christianne Hoey, Jessica Ray, Xiaoyong Huang, Jouhyun Jeon, Paul C. Boutros, Stanley K. Liu

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsRadioresistanceClonogenic assayProstate cancerCancer researchDownregulation and upregulationMedicineDNA damageProstateSenescenceCancerRadiation therapyBiologyCellInternal medicineGeneGeneticsDNA

Abstract

fetched live from OpenAlex

77 Background: Prostate cancer is a leading cause of cancer related death in men worldwide, with recurrence being a major clinical problem after radiotherapy. There is an unmet need to better characterize radioresistant tumors and identify biomarkers to improve patient outcomes. Methods: We identified that miR-106a was overexpressed in radiation resistant cell lines compared to parental cells. We analyzed The Cancer Genome Atlas dataset to assess miR-106a expression in normal prostate, and low- to high-grade prostate tumors. To assess the functional role of miR-106a, we performed in vitro and in vivo assays for radiation response, including clonogenic survival, proliferation, senescence, and tumor xenograft growth after radiation. We performed gene array and pathway analyses to identify downstream effectors of miR-106a. Results: MiR-106a expression was significantly higher in prostate tumors with Gleason score > 7 compared to Gleason ≤ 7, suggesting miR-106a is involved in high grade disease. MiR-106a overexpression confers radioresistance in vitro and in vivo, by targeting LITAF. We now extend miR-106a’s effects to upregulation of ATM at the promoter level, thereby increasing ATM transcript and protein in the cell. Unexpectedly, we found that miR-106a’s mechanism of radioresistance through ATM upregulation does not alter DNA damage repair. ATM upregulation affects clonogenic survival through reduced senescence. KU-55933, a specific ATM kinase inhibitor, resensitizes miR-106a overexpressing cells to radiation by inducing senescence, a predominant mode of cell death in prostate cancer. Conclusions: Our research challenges the current paradigm of ATM and DNA damage repair by outlining another mechanism of radioresistance through alteration of senescence. Our findings suggest that miR-106a may be a promising biomarker for high-grade disease and radioresistant prostate cancer. In addition, we have identified a therapeutic intervention for miR-106a induced radioresistance. Improvements in bioavailability of KU-55933 may lead to its clinical use in combination with radiation therapy to radiosensitize miR-106a induced radioresistant prostate cancer.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.001

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 head0.051
GPT teacher head0.429
Teacher spread0.378 · 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
Published2018
Admission routes1
Has abstractyes

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