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Record W2111645707 · doi:10.7554/elife.06907

Evidence that synthetic lethality underlies the mutual exclusivity of oncogenic KRAS and EGFR mutations in lung adenocarcinoma

2015· article· en· W2111645707 on OpenAlexfundno aff
Arun M. Unni, William W. Lockwood, Kreshnik Zejnullahu, Shih-Queen Lee-Lin, Harold Varmus

Bibliographic record

VenueeLife · 2015
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsKRASBiologyOncogeneCancer researchPhenotypeAdenocarcinomaMutationMAPK/ERK pathwayMutantGeneSynthetic lethalityCancerSignal transductionGeneticsCell cycle

Abstract

fetched live from OpenAlex

Human lung adenocarcinomas (LUAD) contain mutations in EGFR in ∼15% of cases and in KRAS in ∼30%, yet no individual adenocarcinoma appears to carry activating mutations in both genes, a finding we have confirmed by re-analysis of data from over 600 LUAD. Here we provide evidence that co-occurrence of mutations in these two genes is deleterious. In transgenic mice programmed to express both mutant oncogenes in the lung epithelium, the resulting tumors express only one oncogene. We also show that forced expression of a second oncogene in human cancer cell lines with an endogenous mutated oncogene is deleterious. The most prominent features accompanying loss of cell viability were vacuolization, other changes in cell morphology, and increased macropinocytosis. Activation of ERK, p38 and JNK in the dying cells suggests that an overly active MAPK signaling pathway may mediate the phenotype. Together, our findings indicate that mutual exclusivity of oncogenic mutations may reveal unexpected vulnerabilities and therapeutic possibilities.

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 head0.108
GPT teacher head0.377
Teacher spread0.269 · 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

Citations205
Published2015
Admission routes1
Has abstractyes

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