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Record W4386558863 · doi:10.1093/neuonc/noad137.003

PL02.1.A DELINEATING AND TARGETING A NOVEL METABOLISM-BASED POST-TRANSLATIONAL MECHANISM REGULATING THE ABUNDANCE OF THE ‘UNDRUGGABLE’ ONCOPROTEIN C-MYC IN MEDULLOBLASTOMA

2023· article· en· W4386558863 on OpenAlexaffabout
Emma Martell, Helgi Kuzmychova, Esha Kaul, Harshal Senthil, Saeeda Chowhury, Linda Morrison, Agnes Fresnoza, Jamie Zagozewski, Chitra Venugopal, C M Anderson, Sheila K. Singh, Versha Banerji, Tamra E. Werbowetski‐Ogilvie, Tanveer Sharif

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcMaster UniversityCancerCare ManitobaUniversity of Manitoba
Fundersnot available
KeywordsMedulloblastomaCancer researchEpigeneticsBiologyMechanism (biology)CancerOncogeneRegulatorBioinformaticsGeneticsGene

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Brain tumors are the leading cause of cancer death in children, and medulloblastoma (MB) is one of the most common pediatric central nervous system malignancies. Amplification of the c-MYC oncogene is frequently observed in the most aggressive and lethal subgroup of this disease, group 3 (G3), but not in other subgroups. Patients that have G3 MB tumors with high c-MYC abundance are more likely to present as metastatic and are prone to develop fatal recurrent tumors. Unfortunately, the functional ubiquity and disordered structure of c-MYC makes it difficult to target for cancer treatment. Therefore, it is critical to identify novel, out-of-the-box strategies to suppress oncogenic c-MYC in highly aggressive G3 MB brain tumors. Recently, metabolism has emerged as a major regulator of overall cellular signaling processes through post-translational and epigenetic mechanisms. While c-MYC is known to regulate cellular metabolism, whether metabolism plays a role in reciprocally supporting enhanced c-MYC abundance in cancer is unknown. We hypothesize that an intrinsic feedback mechanism may exist where metabolic activity modulates c-MYC abundance that could be exploited as a therapeutic strategy to improve outcomes for G3 MB patients. MATERIAL AND METHODS Using various well-characterized G3 MB cells, patient-derived G3 MB cells, orthotopic intracerebellar xenograft models, patient tumor bioinformatics, detailed biochemical characterization, and point-mutation analyses, we have identified a novel metabolism-dependent post-translational modification that regulates c-MYC stability in G3 MB. RESULTS In-depth molecular analyses unveiled that c-MYC is susceptible to oxidation and proteasomal degradation under conditions of metabolic stress. Targeting mitochondrial respiration via inhibition of complex-I promotes the accumulation of reactive oxygen species (ROS) and leads to rapid cysteine oxidation and proteasomal degradation of c-MYC in G3 MB cells, but not normal human brain astrocytes or neural stem cells (NSCs). Point mutation analysis combined with biochemical oxidation assays identified the specific cysteine residues of c-MYC that are susceptible to oxidation and ultimately responsible for enhanced c-MYC degradation following complex-I inhibition. Importantly, oral administration of a blood-brain barrier permeable complex-I inhibitor impaired the growth of intracerebellar MB xenograft tumors in mice, significantly prolonging animal survival. CONCLUSION Altogether, these findings unveil a novel mechanism through which metabolism regulates the post-translational stability of c-MYC and provides insights for designing rationale therapeutic strategies for the treatment of MB patients. SUPPORT This work is supported by a project grant from the Canadian Institutes of Health Research (CIHR).

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 score0.009

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.281
Teacher spread0.262 · 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 routes2
Has abstractyes

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