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Record W4399326604 · doi:10.1158/0008-5472.can-23-0519

Obesogenic High-Fat Diet and MYC Cooperate to Promote Lactate Accumulation and Tumor Microenvironment Remodeling in Prostate Cancer

2024· article· en· W4399326604 on OpenAlexafffund
Nadia Boufaied, Paolo Chetta, Tarek Hallal, Stefano Cacciatore, Daniela Lalli, Carole Luthold, Kevin Homsy, Eddie L. Imada, Sudeepa Syamala, Cornelia Photopoulos, Anna Di Matteo, Anna de Polo, Alessandra Maria Storaci, Ying Huang, Francesca Giunchi, Patricia A. Sheridan, Gregory Michelotti, Quang‐Dé Nguyen, Xin Zhao, Yang Liu, Elai Davicioni, Daniel E. Spratt, Simone Sabbioneda, Giovanni Maga, Lorelei A. Mucci, Claudia Ghigna, Luigi Marchionni, Lisa M. Butler, Leigh Ellis, François Bordeleau, Massimo Loda, Valentina Vaira, David P. Labbé, Giorgia Zadra

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversité LavalMcGill UniversityMcGill University Health Centre
FundersCancer Council South AustraliaFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoCanadian Institutes of Health ResearchAssociazione Italiana per la Ricerca sul CancroFondazione Umberto VeronesiDana-Farber Cancer InstituteGovernment of South AustraliaFoundation MedicineMcGill UniversityMcGill University Health CentreWorld Cancer Research Fund InternationalWorld Cancer Research FundHarvard T.H. Chan School of Public HealthBrown UniversityUniversità degli Studi di MilanoNational Cancer InstituteBrigham and Women's HospitalUniversità degli Studi di PaviaU.S. Department of Defense
KeywordsProstate cancerTumor microenvironmentCancer researchCancerGlycolysisBiologyLactate dehydrogenaseAnaerobic glycolysisLactate dehydrogenase ATumor progressionInternal medicineCancer cellEndocrinologyMedicineMetabolismBiochemistry

Abstract

fetched live from OpenAlex

Cancer cells exhibit metabolic plasticity to meet oncogene-driven dependencies while coping with nutrient availability. A better understanding of how systemic metabolism impacts the accumulation of metabolites that reprogram the tumor microenvironment (TME) and drive cancer could facilitate development of precision nutrition approaches. Using the Hi-MYC prostate cancer mouse model, we demonstrated that an obesogenic high-fat diet (HFD) rich in saturated fats accelerates the development of c-MYC-driven invasive prostate cancer through metabolic rewiring. Although c-MYC modulated key metabolic pathways, interaction with an obesogenic HFD was necessary to induce glycolysis and lactate accumulation in tumors. These metabolic changes were associated with augmented infiltration of CD206+ and PD-L1+ tumor-associated macrophages (TAM) and FOXP3+ regulatory T cells, as well as with the activation of transcriptional programs linked to disease progression and therapy resistance. Lactate itself also stimulated neoangiogenesis and prostate cancer cell migration, which were significantly reduced following treatment with the lactate dehydrogenase inhibitor FX11. In patients with prostate cancer, high saturated fat intake and increased body mass index were associated with tumor glycolytic features that promote the infiltration of M2-like TAMs. Finally, upregulation of lactate dehydrogenase, indicative of a lactagenic phenotype, was associated with a shorter time to biochemical recurrence in independent clinical cohorts. This work identifies cooperation between genetic drivers and systemic metabolism to hijack the TME and promote prostate cancer progression through oncometabolite accumulation. This sets the stage for the assessment of lactate as a prognostic biomarker and supports strategies of dietary intervention and direct lactagenesis blockade in treating advanced prostate cancer. SIGNIFICANCE: Lactate accumulation driven by high-fat diet and MYC reprograms the tumor microenvironment and promotes prostate cancer progression, supporting the potential of lactate as a biomarker and therapeutic target in prostate cancer. See related commentary by Frigo, p. 1742.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.041
GPT teacher head0.367
Teacher spread0.326 · 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 teacher head, 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

Citations44
Published2024
Admission routes2
Has abstractyes

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