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Record W3090602287 · doi:10.1101/2020.10.05.20206268

Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: a Mendelian randomization study

2020· preprint· en· W3090602287 on OpenAlexaff
Mark Gormley, James Yarmolinsky, Tom Dudding, Kimberley Burrows, Richard M. Martin, Steven J. Thomas, Jessica Tyrrell, Paul Brennan, Miranda Pring, Stefania Boccia, Andrew F. Olshan, Brenda Diergaarde, Geoffrey Liu, Danny Legge, Eloíza H. Tajara, Patrícia Severino, Martin Lacko, George Davey Smith, Emma E. Vincent, Rebecca C. Richmond

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsPrincess Margaret Cancer CentreSinai Health SystemLunenfeld-Tanenbaum Research InstitutePublic Health OntarioUniversity of Toronto
FundersNational Institute of Dental and Craniofacial ResearchMedical Research CouncilWorld Cancer Research FundFundação de Amparo à Pesquisa do Estado de São PauloUniversity of BristolDiabetes UKNational Institute for Health and Care ResearchWorld Cancer Research Fund InternationalNational Cancer InstituteCancer Research UKWellcome Trust
KeywordsMendelian randomizationEzetimibeMedicineOncologyInternal medicineHead and neck squamous-cell carcinomaGenome-wide association studyStatinCholesterolBioinformaticsCancerHead and neck cancerBiologyGeneticsSingle-nucleotide polymorphismGenetic variants

Abstract

fetched live from OpenAlex

Abstract Introduction Head and neck squamous cell carcinoma (HNSCC), which includes cancers of the oral cavity and oropharynx, is a cause of substantial global morbidity and mortality. Strategies to reduce disease burden include discovery of novel therapies and repurposing of existing drugs. Statins are commonly prescribed for lowering circulating cholesterol by inhibiting HMG-CoA reductase (HMGCR). Results from some observational studies suggest that statin use may reduce HNSCC risk. We appraised the relationship of genetically-proxied cholesterol-lowering drug targets and other circulating lipid traits with oral (OC) and oropharyngeal (OPC) cancer risk. Methods and findings We conducted two-sample Mendelian randomization (MR). For the primary analysis, germline genetic variants in HMGCR, NPC1L1, CETP, PCSK9 and LDLR were used to proxy the effect of low-density lipoprotein cholesterol (LDL-C) lowering therapies. In secondary analyses, variants were used to proxy circulating levels of other lipid traits in a genome-wide association study (GWAS) meta-analysis of 188,578 individuals. Both primary and secondary analyses aimed to estimate the downstream causal effect of cholesterol lowering therapies on OC and OPC risk. The second sample for MR was taken from a GWAS of 6,034 OC and OPC cases and 6,585 controls (GAME-ON). Analyses were replicated in UK Biobank, using 839 OC and OPC cases and 372,016 controls and the results of the GAME-ON and UK Biobank analyses combined in a fixed-effects meta-analysis. We found limited evidence of a causal effect of genetically-proxied LDL-C lowering using HMGCR, NPC1L1, CETP or other circulating lipid traits on either OC or OPC risk. Genetically-proxied PCSK9 inhibition equivalent to a 1 mmol/L (38.7 mg/dL) reduction in LDL-C was associated with an increased risk of OC and OPC combined (OR 1.8 95%CI 1.2, 2.8, p= 9.31 ×10 −05 ), with good concordance between GAME-ON and UK Biobank ( I 2 = 22%). Effects for PCSK9 appeared stronger in relation to OPC (OR 2.6 95%CI 1.4, 4.9) than OC (OR 1.4 95%CI 0.8, 2.4). LDLR variants, resulting in genetically-proxied reduction in LDL-C equivalent to a 1 mmol/L (38.7 mg/dL), reduced the risk of OC and OPC combined (OR 0.7, 95%CI 0.5, 1.0, p= 0.006). A series of pleiotropy-robust and outlier detection methods showed that pleiotropy did not bias our findings. Conclusion We found limited evidence for a role of cholesterol-lowering in OC and OPC risk, suggesting previous observational results may have been confounded. There was some evidence that genetically-proxied inhibition of PCSK9 increased risk, while lipid-lowering variants in LDLR, reduced risk of combined OC and OPC. This result suggests that the mechanisms of action of PCSK9 on OC and OPC risk may be independent of its cholesterol lowering effects, but further replication of this finding is required. Author summary Why was this study done? To determine if genetically-proxied cholesterol-lowering drugs (such as statins which target HMGCR) reduce oral and oropharyngeal cancer risk. To determine if genetically-proxied circulating lipid traits (e.g. low-density lipoprotein cholesterol) have a causal effect on oral and oropharyngeal cancer risk. What did the researchers do and find? There was little evidence that genetically-proxied inhibition of HMGCR (target of statins), NPC1L1 (target of ezetimibe) and CETP (target of CETP inhibitors) influences oral or oropharyngeal cancer risk. There was little evidence of an effect of circulating lipid traits on oral or oropharyngeal cancer risk. There was some evidence that genetically-proxied inhibition of PCSK9 increases, while lipid-lowering variants in LDLR reduces oral and oropharyngeal cancer risk. What do these findings mean? These findings suggest that the results of previous observational studies examining the effect of statins on oral and oropharyngeal risk may have been confounded. Given we found little evidence of an effect of other cholesterol lowering therapies, the mechanism of action of PCSK9 may be independent of cholesterol-lowering. Further replication of this finding in other head and neck cancer datasets is required.

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)0.034
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.059
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.328
Teacher spread0.300 · 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 designObservational
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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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