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The mutational landscape of normal human endometrial epithelium

Abstract

All normal somatic cells are thought to acquire mutations, but understanding of the rates, patterns, causes and consequences of somatic mutations in normal cells is limited. The uterine endometrium adopts multiple physiological states over a lifetime and is lined by a gland-forming epithelium1,2. Here, using whole-genome sequencing, we show that normal human endometrial glands are clonal cell populations with total mutation burdens that increase at about 29 base substitutions per year and that are many-fold lower than those of endometrial cancers. Normal endometrial glands frequently carry ‘driver’ mutations in cancer genes, the burden of which increases with age and decreases with parity. Cell clones with drivers often originate during the first decades of life and subsequently progressively colonize the epithelial lining of the endometrium. Our results show that mutational landscapes differ markedly between normal tissues—perhaps shaped by differences in their structure and physiology—and indicate that the procession of neoplastic change that leads to endometrial cancer is initiated early in life.

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Fig. 1: Clonality of normal endometrial glands.
Fig. 2: Mutation burden correlates with age in normal endometrial glands.
Fig. 3: Histology images and reconstructed phylogenetic trees for two individuals in whom every normal endometrial gland contained at least one driver mutation.
Fig. 4: Phylogenetic trees of endometrial glands for donors aged 19 to 40 years.
Fig. 5: Timing of driver mutations in normal endometrial glands.

Data availability

Whole-genome sequencing data are deposited in the European Genome–Phenome Archive (EGA) with accession number EGAS00001002471.

Code availability

Code for statistical analyses on total substitution and driver mutation burdens is included in the Supplementary Information, and is deposited on GitHub at https://github.com/LuizaMoore/Endometrium. All other code is available on request.

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Acknowledgements

We thank the staff of WTSI Sample Logistics, Genotyping, Pulldown, Sequencing and Informatics facilities for their contribution; L. O’Neil, C. Latimer and P. Scott for technical support; F. Nadeu, N. Roberts and J. Wang for their advice on mutational signature extraction; A. R. J. Lawson, F. Abascal and S. Grossmann for their assistance with data analysis; and the Cambridge Biorepository for Translational Medicine for the provision of samples from deceased transplant organ donors. This work was supported by the Wellcome Trust. L.M. is a recipient of a CRUK Clinical PhD fellowship (C20/A20917) and Pathological Society of Great Britain and Ireland Trainee Small Grant (grant reference no. 1175). S.F.B. was supported by the Swiss National Science Foundation (P2SKP3-171753 and P400PB-180790). M.A.S. is supported by a Rubicon fellowship from NWO (019.153LW.038).

Author information

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Authors

Contributions

M.R.S. and L.M. designed the study and wrote the manuscript with contributions from all authors. K.S.-P., C.A.I.-D., J.J.B., K.T.M., M.J.-L. and L.M. obtained samples. P.E. and L.M. devised the protocol for laser-capture microscopy, DNA extraction and sequencing of endometrial glands. L.M. prepared sections, reviewed histology, and microdissected and lysed endometrial glands. Y.H. assisted with tissue processing and section preparation. L.M. performed data curation and analysis with help from D.L., T.H.H.C., M.A.S., S.C.D., K.J.D., T.B., R.R., T.J.M., J.N., P.S.T., S.F.B. and H.L.-S. T.H.H.C. reconstructed phylogenetic trees. S.C.D. performed formal clonality assessment with dpclust. M.A.S. devised filters for substitutions and structural variants. D.L., F.M. and S.M. assisted with signature analyses. I.M. assisted with statistical and dN/dS analyses. P.J.C. oversaw statistical analyses and performed analysis of structural variants. M.R.S. supervised the study.

Corresponding author

Correspondence to Michael R. Stratton.

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Competing interests

M.R.S. is on the Scientific Advisory Board for GRAIL.

Additional information

Peer review information Nature thanks Michael Lawrence and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Clonality of endometrial glands and driver mutations.

a, The majority of the sampled normal endometrial glands (n = 257 individual endometrial glands) were clonal with a median VAF for all identified indels of 0.3 or above. b, The presence of a driver mutation did not have a significant effect on the observed monoclonality of the glands (two-sided Mann–Whitney U-test, P = 0.1). Here, we compared the median VAF of endometrial glands with drivers (median = 0.33, range 0.17–0.5, n = 145) to that of glands without drivers (median = 0.31, range 0.16–0.5, n = 112). Box plots were constructed with the upper and lower edge of the box defining the 25th (Q1) and 75th (Q3) percentile, respectively, outliers (plotted as circles) are defined as values beyond the whiskers (upper, Q1 − 1.5 × interquartile range (IQR) and lower Q3 + 1.5 × IQR). c, All glands from the 19-year-old donor (donor PD37506) (n = 10 individual endometrial glands) were clonal with a median VAF ≥ 0.3, but there were no detectable driver mutations.

Extended Data Fig. 2 Assessment of clonal composition of individual endometrial glands using the mutation clustering method dpclust.

ac, Each column contains a summary of the clonality analysis for individual donors, showing the fraction of samples in which 1, 2 or 3 or more mutation clusters were found (a), the fraction of mutations assigned per cluster for each sample (b) and at the total number of SNVs per sample (c).

Extended Data Fig. 3 Phylogenetic trees of endometrial glands for donors aged 42 to 81 years.

Phylogenetic trees for twelve donors aged 42 to 81 years were also reconstructed using SBSs with branch length proportional to the number of variants; the stacked bar plots represent the attributed SBS mutational signatures that contributed to each branch. Signature extraction was not performed on branches with fewer than 100 substitutions. The ordering of signatures within each branch is for visualization purposes only. Every single studied gland from donors PD39952 (44 year old) and PD40659 (81 year old) had at least one driver mutation.

Extended Data Fig. 4 An example of copy-number neutral loss of heterozygosity in a normal endometrial gland.

a, A biallelic truncating mutation is seen in ZFHX3 (p.R715*), with every read carrying the variant. b, Associated copy-number neutral loss of heterozygosity is observed on chromosome 16.

Extended Data Fig. 5 Oncoplot of all driver mutations and their distribution across individual endometrial-gland samples and donors.

Each cell represents an individual endometrial gland sample and is colour-coded to represent the total number of detected driver mutations (0–3). PIK3CA was the most frequently mutated gene, with at least 1 mutation detected in 54% (15 out of 28) of women. In some glands, these mutations in PIK3CA co-occurred with mutations in ZFHX3, ARHGAP35, FGFR2, FOXA2 and other genes that are also selected for in endometrial cancer.

Extended Data Fig. 6 Timing of all driver mutations.

To time the driver mutations, we used the reconstructed SNV-based phylogenetic trees for 25 out the 28 donors. To estimate the time interval in which specific mutations occurred, we calculated a patient-specific mutation rate by taking the ratio of the mean mutation burden per endometrial gland of the patient and the age of the patient. The mutation number at the start and end of a branch in the phylogenetic tree was then converted to a lower and upper age by dividing these numbers by the estimated mutation rate. This approach relies on the assumption of a constant mutation rate for endometrial glands throughout the life of a patient. The same approach was used for dating indels. We dated the driver mutations that occurred in the trunks and branches.

Extended Data Fig. 7 Timing of driver mutations using patient-based and cohort-based estimates of mutation rates.

To estimate the time interval in which specific mutations occurred, we applied two approaches: (a) ‘patient-based’, in which we calculated a patient-specific mutation rate by taking the ratio of the mean mutation burden per endometrial gland of the patient and the age of the patient; (b) ‘cohort-based’, in which the mutation rate for each patient was derived from the linear mixed-effect model for total mutation rate that included data from the entire cohort. The mutation number at the start and end of a branch in the phylogenetic tree was then converted to a lower and upper age by dividing these numbers by the estimated mutation rate. Both approaches rely on the assumption of a constant mutation rate for endometrial glands throughout the life of the patient. The dotted line represents time from the upper bound of the estimate to sampling.

Extended Data Fig. 8 Comparison between normal endometrial epithelium and endometrial cancer.

a, b, Normal endometrial glands show a lower total mutation burden (substitutions (a) and indels (b)) than do endometrial cancers (data from PCAWG2). c, Genes under significant positive selection (dN/dS > 1) in normal endometrial epithelium (n = 257 individual endometrial glands) and endometrial cancer (n = 218 biologically independent samples). q values were calculated for the whole exome, and under a restricted hypothesis test of 369 known endometrial cancer genes28. d, Venn diagram showing the overlap between the genes under positive selection in normal endometrium and endometrial cancer from the results described in c. e, Identified driver mutations and their distribution in normal endometrial glands and the two major types of endometrial cancer (endometroid and serous carcinoma).

Supplementary information

Supplementary Information

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Moore, L., Leongamornlert, D., Coorens, T.H.H. et al. The mutational landscape of normal human endometrial epithelium. Nature 580, 640–646 (2020). https://doi.org/10.1038/s41586-020-2214-z

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