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Record W2498473468 · doi:10.1158/1538-7445.am2016-1240

Abstract 1240: Targeting epigenetic regulation in clear cell renal cell carcinoma

2016· article· en· W2498473468 on OpenAlexaff
Anthony Apostoli, Nazleen Lobo, Panagiotis Prinos, Dalia Baršytė-Lovejoy, Cheryl A. Smith, Laurie Ailles

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsStructural Genomics ConsortiumUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsClear cell renal cell carcinomaCancer researchEpigeneticsBAP1BiologyCancerRenal cell carcinomaPathologyGeneMedicineGeneticsMelanoma

Abstract

fetched live from OpenAlex

Abstract Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell cancer, is highly resistant to traditional radiation and chemotherapy. Recently, targeted therapies have extended progression-free survival, but responses are variable and no significant overall survival benefit has been achieved; thus, treatment options are scarce for patients bearing this disease. The majority of ccRCC cases (∼90%) are characterized by bi-allelic loss of the von Hippel Lindau (VHL) gene, followed by mutations in the epigenetic regulators PBRM1 (23%), SETD2 (8.4%), BAP1 (7.1%), and KDM5C (4.7%). The latter four genes implicate an integral role for chromatin remodeling in ccRCC, and thus signify a new realm of exploration and therapeutic targeting for this disease. Given the limited number of commercially available ccRCC cell lines, and reports that they may not accurately reflect primary tumors at the molecular level, our lab developed a novel method to generate primary patient-derived ccRCC (VHLmut) cells and matched normal renal proximal tubular epithelial (VHLwt) cells from human surgical specimens with high efficiency. This involved fluorescence-activated cell sorting of primary tumor single cell suspensions using the cell surface marker carbonic anhydrase IX (CA9), a HIF target which is upregulated upon VHL loss, to establish CA9+ VHLmut and CA9− VHLwt cells. Transcriptional profiling of VHLmut and VHLwt pairs found gene signatures that corresponded with patient matched primary ccRCC tumors and adjacent normal tissues in The Cancer Genome Atlas, demonstrating that these cells represent novel models for interrogation of ccRCC biology and testing of therapeutic strategies. Here, in the current study, these newly established cells were utilized to screen a focused library of 30 well-characterized drug-like compounds targeting specific components of chromatin remodeling complexes to determine the effect on cell growth in culture. At Day 0, 786-0 (−VHL) cells and VHLmut cells corresponding to four patient primary ccRCC tumors were plated at 2,500 cells/well in 96-well black clear-bottom plates (n = 5). Cells were then treated with the library of chromatin-targeting drugs on Day 1 and monitored until control-treated cells were 90-95% confluent. At endpoint, cells were fixed and stained with DRAQ5, a DNA intercalating dye that stains DNA stoichiometrically, for visualization on the LI-COR Odyssey CLx Imaging System. Across all five samples, there was a significant reduction in growth observed among cells treated with drugs targeting: i) BET bromodomains (BRD2, BRD3, BRD4 and BRDT), ii) EZH1/2, and iii) JMJD3, UTX and JARID1B. Ongoing work will determine mechanisms of action and whether these targets are also affected in matching VHLwt cells in order to identify compounds that may translate well in the clinic to treat patients with ccRCC. Citation Format: Anthony J. Apostoli, Nazleen Lobo, Panagiotis Prinos, Dalia Barsyte-Lovejoy, Cheryl Arrow Smith, Laurie Ailles. Targeting epigenetic regulation in clear cell renal cell carcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1240.

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 score0.004

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 integrity0.0000.000
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 head0.070
GPT teacher head0.354
Teacher spread0.284 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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