Ivana Holcatova

Background: Cholesterol pathways may influence head and neck squamous cell carcinoma (HNSCC) progression, but evidence on prognosis is inconsistent. We used Mendelian randomization (MR) to test whether genetically proxied inhibition of major LDL-C–lowering drug targets and circulating lipid traits affects overall survival (OS) in HPV-positive and non-HPV driven HNSCC. Methods: We proxied lifelong LDL-C lowering using 55 cis-acting SNPs in HMGCR, NPC1L1, PCSK9, and LDLR from the updated 2021 Global Lipids Genetics Consortium, and instrumented circulating lipid traits. Two-sample MR estimated effects on OS in 4,869 multi-centre HNSCC cases (1,291 HPV-positive; 3,578 non-HPV driven) using minimally adjusted Cox models. Sensitivity analyses additionally adjusted for tumour stage and treatment, assessed collider bias using an external HNSCC incidence GWAS, and examined between-centre heterogeneity and colocalization. Results: Using the updated GLGC 2021 instruments, genetically proxied HMGCR inhibition showed a directionally protective but non-significant association with OS in HPV-positive oropharyngeal HNSCC in the primary analysis (IVW HR=0.19, 95% CI 0.03–1.18; P=0.08), with directionally concordant weighted median results. No corresponding protective association was observed for HMGCR in non-HPV-driven disease (IVW HR=1.68, 95% CI 0.78–3.63; P=0.19). No clear evidence of association was observed for NPC1L1, PCSK9, LDLR, or circulating lipid traits in either HPV stratum. Colocalization did not support a shared causal variant. Conclusions: These analyses provide suggestive evidence that genetically proxied HMGCR inhibition may influence survival in HPV-positive oropharyngeal HNSCC. Impact: HMGCR-related pathways may be relevant to prognosis in HPV-positive oropharyngeal HNSCC, whereas clear survival effects of other cholesterol-lowering targets were not supported.

Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. The mutational signatures of mutagens in normal tissues offer opportunities to detect such exposures and survey them at population level. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that normal kidney cell genomes report an extensive repertoire of somatic mutational signatures. Microdissection of kidney structures revealed that proximal tubules exhibit higher mutation rates than other components of the nephron and most normal cell types despite low cell division rates. This is explained by marked enrichment of mutational signatures due to known exogenous carcinogenic mutagens including the plant-derived aristolochic acids, as well as several signatures of unknown causes including an unknown agent prevalent in Japan (SBS12), and signatures of uncertain origins (SBS40b and SBS40c). The results suggest the existence of multiple, common, systemically circulating mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

Supplementary Figure S23 shows heatmap columns that define enriched pathways and rows define signature genes. Enrichment significance is indicated by color intensity. The annotation bar on the top denotes the gene databases used for the enrichment test, and the annotation bar on the right indicates the subtypes of signature genes. Signature genes not associated with any significantly enriched pathway are listed at the bottom of the heatmap.

Supplementary Figure S21 shows (A) Estimation of the minimum gene set per NS-LUAD expression subtype by cross validation in the Sherlock cohort, evaluating 1 to 30 genes per subtype. Box plots show the number of genes per NS-LUAD subtype (x-axis) versus cross-validation error rates (y-axis). (B) Distribution of the error rates for predicting NS-LUAD expression subtypes using the 60-gene signature by cross validation in the Sherlock cohort. Training and testing set error rates are indicated by colors, and median values are marked by dashed lines.

Supplementary Figure S19 shows (A) Comparison of TIDE scores predicting response to ICB across NS-LUAD expression subtypes. B-E, Comparison of (B) predicted cytotoxic T lymphocyte (CTL) levels, (C) tumor mutational burden (TMB), (D) myeloid-derived suppressor cells (MDSC), and (E) cancer associated fibroblast (CAF) abundances across NS-LUAD expression subtypes. Mean values are indicated by the yellow lines. P-values from two-sided Mann-Whitney U-test are shown.

Supplementary Figure S6 shows violin plots that depict the expression levels of (A) the proliferation marker MKI67, and the mesenchymal markers (B) CDH2 and (C) TWIST1 in tumor samples with estimated purity of greater than 0.8. Mean values are indicated by the yellow lines in the violin plots. P-values from two-sided Mann-Whitney U-test are shown in corresponding figures.

Supplementary Figure S4 shows Summary of (A) IPA and (B) GSEA analyses of differentially expressed genes associated with the steady subtype. C-D, Summary of (C) IPA and (D) GSEA analyses of differentially expressed genes associated with the proliferativesubtype. E-F, Summary of (E) IPA and (F) GSEA analyses of differentially expressed genes associated with the chaoticsubtype. In IPA plots, orange and blue indicate positive and negative associations, respectively. In GSEA plots, bar height represents the normalized enrichment score, bar color indicates the FDR-adjusted p-value (FDR <0.1), and pathway annotations are shown on the left.

Supplementary Table S1 s shows summary of RNA-seq-based studies for NS-LUAD.

Supplementary Table S2 shows characteristics and clinical data of patients in the Sherlock cohort.