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Record W7037793448

Exploiting the SCF complex to identify novel therapeutic targets in high-grade serous ovarian cancer

2023· dissertation· en· W7037793448 on OpenAlexaff

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSynthetic lethalityPARP1Ovarian cancerIn silicoDiseasePhenotypeSerous fluidOlaparib
DOInot available

Abstract

fetched live from OpenAlex

An overarching, yet elusive goal for cancer researchers is to develop novel therapeutic strategies by identifying drug targets that will improve the lives and outcomes of cancer patients. High-grade serous ovarian cancer (HGSOC) remains the most lethal gynecological malignancy, with an estimated ~3,100 Canadians diagnosed and ~1950 succumbing to their disease each year. Unfortunately, >70% of HGSOC patients are diagnosed at stages III or IV and will eventually succumb with drug resistant disease. Therefore, new, more effective drug targets are urgently needed to address the poor morbidity and mortality associated with HGSOC. Recent genetic studies have determined that reduced expression of SKP1, CUL1 or RBX1 induces chromosome instability (CIN), or ongoing changes in chromosome complements, that is suspected to contribute to HGSOC pathogenesis. As copy-number losses of these genes occur in ~87% HGSOCs, the current study seeks to exploit these alterations using a synthetic lethal (SL) paradigm. Synthetic lethality is an innovative therapeutic strategy and is defined as the rare/lethal combination of two independently viable mutations/deletions. Accordingly, identifying SL interactors of the SCF complex (i.e., novel drug targets) would allow us to selectively exploit the aberrant genetics suspected to contribute to HGSOC pathogenesis. To identify 228 putative SL interactors of SKP1, CUL1 and RBX1, in silico and siRNA-based approaches were employed within FT secretory epithelial cellular contexts. Of these, CDK2 and PARP1 were prioritized for validation within RBX1+/- clones. To validate SL interactors, siRNAs and small-molecule inhibitors targeting PARP1 or CDK2 were combined with single-cell quantitative imaging microscopy (QuantIM) to assess SL phenotypes within RBX1+/- and NT-Control clones. QuantIM results reveal decreases in the number of RBX1+/- clones following silencing or inhibition of PARP1 or CDK2 relative to NT-Control clones, that corresponds with increases in γ-H2AX abundance, a marker of DNA DSBs. Collectively, the work presented in this thesis supports that PARP1 is a novel SL interactor of RBX1 and that CDK2 is an evolutionarily conserved SL interactor of the SCF complex. Furthermore, these findings highlight the potential clinical utility of utilizing Olaparib and/or SNS-032 for the treatment of HGSOCs exhibiting diminished expression of RBX1.

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 score0.003

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.089
GPT teacher head0.244
Teacher spread0.154 · 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
Published2023
Admission routes1
Has abstractyes

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