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Record W4391311421 · doi:10.1101/2024.01.27.577524

Oncogenic RAS signaling is a tumor cell-intrinsic determinant of ferroptosis suppression via induction of the GCH1/BH4 axis

2024· preprint· en· W4391311421 on OpenAlexaff
Jonathan Lim, Frauke Stölting, Sofya Tishina, Leonie Thewes, Daniel Picard, Haifeng Zhang, Oksana Lewandowska, Tobias Reiff, Tal Levy, Barak Rotblat, Marc Remke, Johannes Brägelmann, Filippo Beleggia, Carsten Berndt, Silvia von Karstedt, Guido Reifenberger, Gabriel Leprivier

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersDr. Rolf M. Schwiete StiftungInstitut National Du CancerDeutsche KrebshilfeBundesministerium für Bildung und ForschungDeutsche Forschungsgemeinschaft
KeywordsDownregulation and upregulationSignal transductionCell biologyCancer researchBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Ferroptosis is an iron-dependent form of regulated cell death arising from excessive lipid peroxidation. The role of oncogenic RAS signaling in modulating the cellular response to ferroptosis is controversial. While seminal studies described that oncogenic RAS transformation drives a synthetic lethal vulnerability to archetypal ferroptosis inducers including erastin (eradicator of RAS and ST-expressing cells) and RSL3 (Ras selective lethal 3), more recent work suggest that oncogenic RAS signaling may confer ferroptosis resistance. Thus, the impact of oncogenic RAS expression on ferroptosis sensitivity is still poorly understood. Here, using orthogonal cellular systems across multiple classes of ferroptosis- inducing agents, as well as in silico therapeutic drug-response analyses, we provide unifying evidence that oncogenic RAS signaling suppresses ferroptosis. Integrated proteo- and transcriptomic analyses in oncogenic RAS-transformed cells further uncovered that RAS signaling upregulates the ferroptosis suppressor GTP cyclohydrolase I (GCH1) via transcriptional induction by the transcription factor ETS1 downstream of the RAS-MAPK signaling cascade. Targeted repression of Gch1 or of Gch1-controlled tetrahydrobiopterin (BH4) synthesis pathway is sufficient to sensitize RAS-mutant cell lines to ferroptosis in 2D and 3D cell models, as well as in tumor organoids and tumor xenografts, highlighting a mechanism through which RAS promotes resistance to ferroptosis induction. Furthermore, we found that GCH1 expression is clinically relevant and correlates with RAS signaling activation in human cancers. Overall, this study redefines oncogenic RAS signaling to be a ferroptosis suppressor, and identifies GCH1 as a mediator of this effect and a potential clinical target for the sensitization of RAS-driven cancers to ferroptosis-inducing agents. Significance Statement Although it is commonly accepted that ferroptosis induction is a mutant RAS-selective lethality, accumulating evidence suggests that oncogenic RAS protects cells against this form of cell death. However, a systematic survey establishing the relationship between RAS and ferroptosis sensitivity is lacking, and the molecular mechanisms this entails are still poorly understood. Here, we report across RAS-mutant isoforms, in diverse cellular models, and using multiple ferroptosis-inducing compounds that oncogenic RAS consistently suppresses ferroptosis. Further, we show that oncogenic RAS-mediated ferroptosis suppression is attributed to the upregulation of GCH1 and its downstream metabolite, tetrahydrobiopterin. Our study delivers a shift towards a new paradigm in which oncogenic RAS confers ferroptosis resistance, and a potential clinical strategy to re-engage ferroptosis sensitivity in RAS-driven cancers.

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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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
Published2024
Admission routes1
Has abstractyes

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