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Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade–specific epigenome and transcriptome landscapes

Abstract

Cervical cancer is the most common cancer affecting sub-Saharan African women and is prevalent among HIV-positive (HIV+) individuals. No comprehensive profiling of cancer genomes, transcriptomes or epigenomes has been performed in this population thus far. We characterized 118 tumors from Ugandan patients, of whom 72 were HIV+, and performed extended mutation analysis on an additional 89 tumors. We detected human papillomavirus (HPV)-clade-specific differences in tumor DNA methylation, promoter- and enhancer-associated histone marks, gene expression and pathway dysregulation. Changes in histone modification at HPV integration events were correlated with upregulation of nearby genes and endogenous retroviruses.

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Fig. 1: Mutational landscape of cervical cancers from Ugandan patients.
Fig. 2: Recurrent noncoding mutations.
Fig. 3: HPV-clade-specific molecular characteristics and prognosis.
Fig. 4: HPV-clade-specific histone mark landscapes.
Fig. 5: HPV integration alters local histone modifications and expression.

Data availability

All molecular and clinical data used in this publication can be found on the National Cancer Institute’s Genome Data Commons Publication Page at https://gdc.cancer.gov/about-data/publications/CGCI-HTMCP-CC-2020. Data from this publication are publicly available for download through dbGaP (phs000528), as part of the NCI Cancer Genome Characterization Initiative (CGCI; phs000235). Sample metadata are reported in Supplementary Table 2. TCGA cervical cancer data (file name: CESC.snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg.seg.txt) were obtained from http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/CESC/20160128/. Source data are provided with this paper.

Code availability

Bioinformatics analyses in this study were conducted with open-source software, with the exception of tumor purity and ploidy estimation, which was performed with Ploidetect (https://github.com/lculibrk/ploidetect).

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Acknowledgements

This project has been funded in whole or in part with US federal funds from the National Cancer Institute, National Institutes of Health, under contract no. HHSN261200800001E and HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US government. We gratefully acknowledge the Fred Hutchinson Cancer Research Center and the Uganda Cancer Institute for overseeing sample and data collection in Uganda. We are grateful for contributions from the other members of the HTMCP Cervical Cancer Working Group at the Department of Epidemiology, University of Alabama at Birmingham, the Pancreas Centre BC and various groups at Canada’s Michael Smith Genome Sciences Centre, including those from the Biospecimen, Library Construction, Sequencing, Bioinformatics, Technology Development, Quality Assurance, LIMS, Purchasing and Project Management teams. We thank the AIDS and Cancer Specimen Resource for logistical coordination and support of this project through NIH grants U01CA066535, U01CA096230 and UM1CA181255. L.C. and V.L.P. are the recipients of CIHR Frederick Banting and Charles Best Canada Graduate Scholarships GSD-164207 and GSD-152374, respectively. S.J.M.J. is the recipient of the Canada Research Chair in Computational Genomics. This research was supported by the Intramural Research Program of the NIH, National Cancer Institute (R.Y.). C.C. is supported by NIH grant P30AI027757. G.B.M. is supported by NCI grants U01CA217842 and P50CA098258. M.A.M. is the recipient of the Canada Research Chair in Genome Science. This work was supported in part by funding provided by the Canadian Institutes for Health Research (CIHR award FDN-143288) to M.A.M. A.I.O. was supported in part by the Endlichhofer Trust (OCCC 3120957) and a V Foundation grant (DVP2018-007).

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Contributions

A.G., V.L.P., Z.Z., R.B. and E.T. contributed equally to this work. J.S.R., A.I.O., D.S.G., A.J.M. and M.A.M. equally supervised this work. The HTMCP Cervical Cancer Working Group contributed collectively to this work. Project management and data coordination: K.N., M.A.D. and P.G. Cohort and clinical data collection: C.C., C. Nakisige, C. Namirembe, J.O., M.O., N.B.G., H.P., J.B. and J.M.G.-F. Pathology and molecular review: T.M.D., M.H.S., T.C.W. and R.B. Data were generated by Canada’s Michael Smith Genome Sciences Centre at BC Cancer and analyses were performed by V.L.P., Z.Z., R.B. and E.T. Contribution to analyses: G.B.M., R.Y., S.J.M.J., Y.M., K.L.M., A.G., S.K.C. and L.C. A.G., A.J.M., V.L.P., E.T. and M.A.M. wrote the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Marco A. Marra.

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Competing interests

G.B.M. reports the following potentially competing interests: SAB/consultant: AstraZeneca, Chrysallis Biotechnology, ImmunoMET, Ionis, Lilly, PDX Pharmaceuticals, Signalchem Lifesciences, Symphogen, Tarveda, Zentalis; stock/options/financial: Catena Pharmaceuticals, ImmunoMet, SignalChem, Tarveda; licensed technology: HRD assay to Myriad Genetics, DSP patents with Nanostring; sponsored research: Nanostring Center of Excellence, Ionis (provision of tool compounds). R.Y. reports the following potentially competing interests: research support from a CRADA with Celgene/BMS. T.C.W. reports the following potentially competing interests: consultant to Roche, BD and Inovio with respect to HPV diagnostic tests and therapeutic vaccines.

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Extended data

Extended Data Fig. 1 Additional characteristics of the HTMCP discovery and extension cohorts.

a. Coding mutations per Mb in samples exhibiting low (≤ 0.4) and high (> 0.4) APOBEC signatures. b. Difference in PIK3CA expression by HIV status. c. Mutations in the top 20 most mutated epigenetic modifiers, ordered by frequency of alterations for the cohort (n = 118). APOBEC signature proportion and homologous recombination deficiency (HRD) scores are reported above. HIV status, age at diagnosis, tumor histology (”other” includes neuroendocrine and undifferentiated) and stage are also annotated. d. Comparison of mutation frequencies of the 12 SMGs in the discovery vs. extension cohorts. Boxplots in a and b represent the median, upper and lower quartiles of the distribution and whiskers represent the limits of the distribution (1.5-times interquartile range), and statistics were determined using two-sided Wilcoxon rank sum tests.

Source data

Extended Data Fig. 2 Association of HPV clades with HIV status, gene expression, DNA methylation and survival.

a. HPV types in our cohort separated by HIV status (n = 72 positive samples, n = 45 negative), and clade. The x axis indicates the percentage of samples in that cohort infected by the indicated HPV type, and in brackets is the number of samples. b. Unsupervised clustering of the top 1,000 most variable genes across our cohort (n = 118 samples). q-values were determined using Benjamini-Hochberg (BH) corrected Fisher exact tests. c. Percentage of differentially methylated probes between clades (A7 = 51 samples, A9 = 56 samples) at different genomic features, by HPV clade. d. Log2 fold change and adjusted (BH) p-value of differentially expressed genes between clade A7- (n = 52) and A9-infected (n = 57) samples. e. Volcano plots showing the log2 fold change and adjusted p-value (BH) of differentially expressed genes between clade A7- (n = 52) and A9-infected (n = 57) samples associated with A9 hypermethylated (top), and A7 hypermethylated (bottom) differentially methylated regions (DMRs). f, g. top: Kernel density of E6 (f) and E7 (g) expression in the HTMCP cohort separates samples into high- and low expressing cases. bottom: gene ontologies enriched in differentially expressed genes in samples with low / high E6 (n = 68 / n = 48) (f) and E7 (n = 58 / n = 59) (g). h. Multivariate cox proportional hazards model for HPV clade, HIV status and disease stage for 66 patients. Hazard ratios and p-values reported for each variable were determined using log-rank tests. Where relevant, all statistical tests were two-sided.

Source data

Extended Data Fig. 3 Correlations between histone modifications and gene expression.

a. Cluster of clusters analysis for 54 consensus clustering solutions for all histone marks on 52 samples (solutions with k = 2 to 10 for each mark). The heatmap color indicates the sample probabilities in the consensus matrix. q-values for each variable were determined using Benjamini-Hochberg corrected Fisher exact tests. b. Schematic showing the cluster of clusters solution (k = 5 for H3K27ac and H3K4me3 and k = 4 for the other marks) for all histone marks and for the 3 active marks. Each dot represents a sample and dot color represents the cluster membership of the sample. Hollow circles indicate no available ChIP data for that sample. c. Fold change of H3K4me3 abundance and gene expression between clades associated with TSS of genes (−5/+20 kb) found at intersecting H3K4me3 and H3K27ac peaks. Sample Ns used for differential analyses (and derivation of adjusted p-values) were: expression A7 = 52, A9 = 57; H3K4me3 and H3K27ac A7 = 25, A9 = 22. Genes with BH-adjusted p-values <0.05 (DESeq, Methods) are highlighted. d. Expression of the genes reported in Fig. 4f separated by HPV clade. Boxplots represent the median, upper and lower quartiles of the distribution and whiskers represent the limits of the distribution (1.5-times interquartile range), and p-values were calculated by Wilcoxon rank sum tests. Where relevant, all statistical tests were two-sided.

Source data

Extended Data Fig. 4 HPV integration events and tumor microenvironments.

a. Number of HPV integration sites per event separated by HPV clade. b, c, e, f. Distribution of the number (b, e) and fold change in integrated samples (c, f) of genes (b, c) and ERVs (e, f) near integration events. d. Expression (RPKM) of selected genes near HPV integration events in each sample (n = 118). g. Fold change of ERVs nearby integration events separated based on the clusters identified in Fig. 5f. h. Histone mark coverage of a 115 kb genomic region containing ERVs. The line represents an integration event, and arrows indicate individual integration sites. Top tracks refer to a case with integration, and the bottom to a control case without integration. i. Total T-cell scores and estimated tumor content of samples with HPV integration events that are associated with significant changes in expression of ERVs or genes, and those that are not. j, k. CIBERSORT scores for all CD4 + T-cells (sum) and CD8 + T-cells (j), Follicular helper T-cells and neutrophils (k) separated by HIV status (HIV + n = 72, HIV- n = 45). Boxplots in a, d, g, ik represent the median, upper and lower quartiles of the distribution and whiskers represent the limits of the distribution (1.5-times interquartile range). All p-values were determined by Wilcoxon tests unless otherwise stated, and q-values were corrected using the Benjamini-Hochberg method. Where relevant, all statistical tests were two-sided.

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Gagliardi, A., Porter, V.L., Zong, Z. et al. Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade–specific epigenome and transcriptome landscapes. Nat Genet 52, 800–810 (2020). https://doi.org/10.1038/s41588-020-0673-7

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