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Record W2886226874 · doi:10.1158/1538-7445.am2018-1817

Abstract 1817: Establishment and characterization of non-small cell lung cancer cell line variants selected for resistance to osimertinib

2018· article· en· W2886226874 on OpenAlexaff
Peter J. Ferguson, Mark Vincent, James Koropatnick

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsWestern University
Fundersnot available
KeywordsOsimertinibT790MCancer researchLung cancerEpidermal growth factor receptorCell cultureMedicineBiologyCancerOncologyGefitinibInternal medicineErlotinibGenetics

Abstract

fetched live from OpenAlex

Abstract Many non-small cell lung cancers (NSCLC) are oncogenically driven by mutant epidermal growth factor receptor (EGFR). EGFR tyrosine kinase inhibitors (TKIs) are commonly used as a first-line treatment against such tumors, but NSCLC tumors often recur with a mutant EGFR (often with a T790M mutation in exon 20) that confers resistance to these drugs [Ther Adv Respir Dis 10(6): 549-565, 2016]. Third generation EGFR TKIs (e.g., osimertinib) have been designed that are highly active aganst both T790M EGFR and the original non-T790M-mutated, oncogenic EGFR. Clinical resistance to third generation TKIs has been observed, but model systems in which to study the mechanisms of resistance are few. The human NSCLC cell line H1975, derived from an EGFR-TKI-resistant tumor, contains EGFR mutations T790M and L858R (exon 21). To explore mechanisms mediating resistance to third generation TKIs, we selected variants of the H1975 cell line for resistance to osimertinib to create models in which mechanisms of resistance can be characterized and tested for potential methods to overcome that resistance. Cells were exposed continuously to a single concentration of osimertinib, in some cases with the inclusion of verapamil to avoid possible selection of multidrug resistant cells. After 4 weeks, with weekly changes of drug-medium, clonal cell lines were selected in the presence of 6 and 10 μM osimertinib (one each) or in 5 μM osimertinib plus 10 μM verapamil (3 cell lines). These clonal lines were, respectively, 90-, 95-, 38-, 227-, and 244-fold resistant to osimertinib. The RAD51 inhibitor 2-(benzylsulfonyl)-1-(1H-indol-3-yl)-1,2-dihydroisoquinoline (IBR2), which enhances cytotoxicity of several EGFR inhibitors against numerous cell lines (Ferguson et al., JPET 2017, doi.org/10.1124/jpet.117.241661), decreased osimertinib-resistance by up to 80% in these cell lines. Sequence analysis indicates that cell line H1975/osi-6b retained the same EGFR sequence as that in the parent cell line. H1975/osi-5b/VPL appears to have undergone epithelial-to-mesenchymal transition. Further analyses of the resistant cell lines are being undertaken. These cell lines are a valuable resource in which to test methods to circumvent resistance to third generation EGFR TKIs. Citation Format: Peter J. Ferguson, Mark D. Vincent, James Koropatnick. Establishment and characterization of non-small cell lung cancer cell line variants selected for resistance to osimertinib [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1817.

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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.036
GPT teacher head0.402
Teacher spread0.366 · 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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