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Proteogenomic insights into early-onset endometrioid endometrial carcinoma: predictors for fertility-sparing therapy response

Nature Genetics volume 56, pages 637–651 (2024)Cite this article

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Abstract

Endometrial carcinoma remains a public health concern with a growing incidence, particularly in younger women. Preserving fertility is a crucial consideration in the management of early-onset endometrioid endometrial carcinoma (EEEC), particularly in patients under 40 who maintain both reproductive desire and capacity. To illuminate the molecular characteristics of EEEC, we undertook a large-scale multi-omics study of 215 patients with endometrial carcinoma, including 81 with EEEC. We reveal an unexpected association between exposome-related mutational signature and EEEC, characterized by specific CTNNB1 and SIGLEC10 hotspot mutations and disruption of downstream pathways. Interestingly, SIGLEC10Q144K mutation in EEECs resulted in aberrant SIGLEC-10 protein expression and promoted progestin resistance by interacting with estrogen receptor alpha. We also identified potential protein biomarkers for progestin response in fertility-sparing treatment for EEEC. Collectively, our study establishes a proteogenomic resource of EEECs, uncovering the interactions between exposome and genomic susceptibilities that contribute to the development of primary prevention and early detection strategies for EEECs.

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Fig. 1: Genomic landscape of EECs in TJFD study.
Fig. 2: Clinical and molecular characteristics of the stage IA EEEC.
Fig. 3: Germline mutations in EEECs are associated with specific genomic subtypes.
Fig. 4: Exposome-related mutational signature contributes massively to the early onset of EECs.
Fig. 5: 3D-Sig-Explorer reveals exposome-related mutational signature correlated with specific mutation sites of CTNNB1 and SIGLEC10 in EEECs.
Fig. 6: Proteogenomic characteristics of EEECs and LEECs.
Fig. 7: Proteogenomic features of progestin-insensitive EEECs after fertility-sparing treatment.
Fig. 8: Protein biomarkers for predicting progestin-insensitive EEECs with fertility-sparing treatment.

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Data availability

Raw sequencing data generated in this study are deposited in the Genome Sequence Archive for Human (https://ngdc.cncb.ac.cn/gsa-human/) with accession number HRA003319. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD046507. TCGA-UCEC and CPTAC-UCEC data were obtained from Cbioportal (https://www.cbioportal.org/) with the dataset identifier ucec_tcga_pan_can_atlas_2018 and ucec_cptac_2020. Panel sequencing of genomic data from the AACR GENIE Project was obtained from Synapse (https://www.synapse.org/) with the dataset identifier syn7222066. RNA-seq data from six public datasets (GSE1378, GSE1379, GSE6532, GSE9195, GSE12093 and GSE17705) of patients with breast cancer treated with endocrine therapy was obtained from Gene Expression Omnibus (GEO) repository. Source data are provided with this paper.

Code availability

Code required to reproduce the analyses in this paper is available through GitHub (https://github.com/Haz1y/EEEC_landscape) or Zenodo (https://doi.org/10.5281/zenodo.10756345)81.

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Acknowledgements

C.S. and G.C. acknowledge grants from the National Key R&D Program of China (2022YFC2704300 and 2022YFC2704301). X.C. acknowledges grants from the National Key R&D Program of China (2022YFC2704300 and 2022YFC2704305) and the Shanghai Technical Innovation Program (20Z1190070). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Zhe Hu, Zimeng Wu, Wei Liu, Yan Ning, Jingbo Liu, Wencheng Ding.

  2. These authors jointly supervised this work: Qinglei Gao, Kezhen Li, Gang Chen, Xiaojun Chen, Chaoyang Sun.

Authors and Affiliations

  1. Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China

    Zhe Hu, Zimeng Wu, Jingbo Liu, Junpeng Fan, Qinlan Li, Wenting Li, Xiaohang Yang, Yingyu Dou, Wei Wang, Wenju Peng, Funian Lu, Xucui Zhuang, Tianyu Qin, Xiaoyan Kang, Chenzhao Feng, Beibei Wang, Ding Ma, Qinglei Gao, Kezhen Li, Gang Chen & Chaoyang Sun

  2. National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P. R. China

    Zhe Hu, Zimeng Wu, Jingbo Liu, Wencheng Ding, Junpeng Fan, Qinlan Li, Wenting Li, Xiaohang Yang, Yingyu Dou, Wei Wang, Wenju Peng, Funian Lu, Xucui Zhuang, Tianyu Qin, Xiaoyan Kang, Chenzhao Feng, Beibei Wang, Ding Ma, Qinglei Gao, Kezhen Li, Gang Chen & Chaoyang Sun

  3. Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China

    Wei Liu, Shuyan Cai, Zhiying Xu, Qiaoying Lv, Qian Wang, Chao Wang & Xiaojun Chen

  4. Department of Pathology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, P. R. China

    Yan Ning

  5. The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, P. R. China

    Xinyu Wang

  6. Department of Obstetrics and Gynecology, Peking University People’s Hospital; Peking University People’s Hospital, Xicheng District, Beijing, P. R. China

    Zhiqi Wang & Jianliu Wang

  7. Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, P. R. China

    Jie Jiang

  8. OHSU Knight Cancer Institute, Portland, OR, USA

    Gordon B. Mills

Authors
  1. Zhe Hu
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  2. Zimeng Wu
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  3. Wei Liu
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  4. Yan Ning
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  5. Jingbo Liu
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  6. Wencheng Ding
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  7. Junpeng Fan
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  8. Shuyan Cai
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  9. Qinlan Li
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  10. Wenting Li
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  13. Wei Wang
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  21. Qiaoying Lv
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  22. Qian Wang
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  23. Chao Wang
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  25. Zhiqi Wang
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  26. Jianliu Wang
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  27. Jie Jiang
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  28. Beibei Wang
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  29. Gordon B. Mills
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  30. Ding Ma
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  31. Qinglei Gao
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  32. Kezhen Li
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  33. Gang Chen
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  34. Xiaojun Chen
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  35. Chaoyang Sun
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Contributions

C.S. and Z. Hu conceived the study idea. J.L. and W. Liu collected samples. Z. Hu conducted the multi-omic profiling, with the help of J.F., C.F. and J.P. Y.N., Q.W. and C.W. interpreted the pathological slides. J.L. and Z. Wu performed experimental validations, with the help of W. Li, X.Y., Q.L., S.C., W. Liu, Y.D., W.W., F.L., X.K., Z.X., X.Z. and T.Q. C.S. and Z. Hu wrote the manuscript with input from all the authors. G.B.M., D.M., Z.W., J.W., J.J. and X.W. provided expertise and feedback. C.S., X.C., G.C., K.L., Q.G. and W.D. supervised the project and provided valuable critical discussion. All authors reviewed and approved the manuscript before publication.

Corresponding authors

Correspondence to Qinglei Gao, Kezhen Li, Gang Chen, Xiaojun Chen or Chaoyang Sun.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Russell Broaddus and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The workflow of the TJFD-EC proteogenomic study.

The TJFD-EC study of 229 patients, including 14 AEHs and 215 EECs, was used for WES and proteomics analysis.

Extended Data Fig. 2 Quality assessments for WES and MS data.

(a) The body mass index (BMI) between EEECs and LEECs (all FIGO stages were included). The p value was calculated by two-sided Mann–Whitney U test. Data are represented as boxplots with the middle line indicating the median, while the upper and lower hinges indicate the 25% and 75% quartiles, respectively. (b, c) The WES quality control results for mapping quality, insert size and coverage. Notes: all of these results fall within the prescribed guidelines of ‘Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing’, published in Nature Biotechnology82. (d) The distribution of GIV scores in our WES samples. Notes: according to the thresholds recommended in the literature in Nature Biotechnology82, samples with a GIV score exceeding 1.5 are deemed to exhibit severe DNA damage. Specifically, only 4.2% of our samples surpass the GIV score threshold of 1.5. (e) The relative proportion of [C>T] transition in TCGA, CPTAC and TJFD datasets. Notes: we did not observe a significant enrichment of C>T mutations (FFPE-induced errors often exhibit a bias toward C>T transitions) in our dataset but even slightly lower trend of C>T mutations compared to TCGA study (p = 0.01). The p value was calculated by two-sided Mann–Whitney U test. Data are represented as boxplots with the middle line indicating the median, while the upper and lower hinges indicate the 25% and 75% quartiles, respectively. (f) Longitudinal quality control of mass spectrometry using tryptic digest of HeLa cells every three days. Correlation matrix of all measured HeLa samples based on Pearson’s correlation. (g) Histograms showing the distribution of peptides per protein (top panel) and the mass of proteins (bottom panel). (h) Principal component analysis (PCA) of proteomic data. Red triangles: tumors; blue dots: normal adjacent tissues. (i) The Venn diagram comparing the detected proteins in the CPTAC-EC study and our own TJFD study. (j) The pathway enrichment analysis on the 4189 unique proteins identified in the CPTAC dataset. Notes: only the TNF-α pathway in the hallmark genesets displayed significant enrichment (q < 0.1). This suggests that our comparative proteomic analysis with FFPE accurately reflects the biological characteristics of the samples, except for proteins involved in the activity of the TNF-α pathway.

Extended Data Fig. 3 Genomic characterization of TJFD and comparison with TCGA dataset.

(a) To eliminate the selection bias of patients in TJFD and TCGA databases caused by baseline demographic and clinical factors, propensity score matching was used to balance the two datasets. Baseline clinicopathological characteristics including FIGO stage, grade and TCGA subtype were fit into a multivariate logistic model, and the nearest neighbor algorithm and one-to-one match were set in the logistic model. R package ‘MatchIt’ was used for the calculation algorithm. (b, c) Clinical characteristics (b) and mutation profiles (c) between two datasets after propensity score matching (PSM). (d) Forest plot of differentially mutated genes between the two datasets before (left) and after PSM (right). Data are represented as a forest plot of ORs with 95% confidence intervals represented as circles and error bars. OR: odds ratio. The p values were calculated by two-sided chi-square test or Fisher’s exact test as appropriate.

Extended Data Fig. 4 Molecular characterization of early-onset EEC in the whole TJFD dataset.

To further investigate and validate the molecular characterization of early-onset EEC, the whole TJFD dataset (of all FIGO stages) was included. (a–d) Comparison of mutation landscape (a), tumor mutation burden (b), proportion of TCGA subtype (c) and frequently mutated genes (d) between age groups of the whole dataset. The p values were calculated by two-sided Mann–Whitney U test and two-sided Fisher’s exact test. For b, data are represented as boxplots with the middle line indicating the median, while the upper and lower hinges indicate the 25% and 75% quartiles, respectively. (e) To further depict the distribution difference of CTNNB1 mutation of early-onset EEC, the AACR GENIE dataset21 of 2401 endometrial carcinomas was included in analysis. The CTNNB1 mutations in late-onset tumors were distributed across the gene, while the early-onset tumors showed remarkable enrichment in the region of exon 3. (f) Correlation between the probability of specific somatic mutation and age-at-diagnosis in TJFD dataset. (g) The correlation in TCGA and CPTAC datasets was also performed for further validation. For f and g, logistics regression analysis was performed, and black dots represent the distribution of mutation status of specific genes in each patient alongside their respective ages.

Extended Data Fig. 5 Mutation signature associated with endogenous and environmental mutagens.

To reveal the association of de novo mutational signatures with the endogenous and exogenous mutagens, cosine similarity analysis against mutation signatures defined in COSMIC31, 32 and environmental agents36 was performed. (a) Comparison of de novo mutational signatures identified in this study with previously published COSMIC31 and SBS32 mutational signatures. (b) The mutational sig6 showed high similarity to COSMIC signatures about tobacco, aflatoxin, ROS, aromatic amines, ROS-like methyl eugenol and HR/NER deficiency. Moreover, de novo Sig6 was highly associated with environmental mutagens. (c) Comparison of the relative proportion of de novo mutation signatures between early-onset and late-onset EECs. The p values were calculated by two-sided t-test. Data are represented as boxplots with the middle line indicating the median, while the upper and lower hinges indicate the 25% and 75% quartiles, respectively. (d) Spearman’s correlation between the exposome-related mutation sig6 burden and age-at-diagnosis, and the green shadow represents 95% confidence interval. The p value from Spearman correlation with permutation test.

Extended Data Fig. 6 3D-Sig-Explorer uncovers exposure-molecular-phenotype links.

(a) Schematic overview of the 3D-Sig-Explorer model. The principle and computation details are described in Methods. (b) The mutational signatures contribution to LEEC-related genes.

Extended Data Fig. 7 The impact of SIGLEC10 on hormone treatment resistance in vitro and in vivo.

(a) Sanger sequencing validation of p.Q144K (or named as c.430C>A) mutation of SIGLEC10. (b) BioPlex database reveals protein interaction of SIGLEC10 and ESR1. (c) Validation of Siglec-10 overexpressing in cell line by western blot. (d) Cell proliferation of SIGLEC10Q144K, SIGLEC10wt, negative control (NC) and parental cells (CON), which were treated with progesterone (PRG, 25 μM) for 72 hours. The p values were calculated by the ANOVA test followed by Tukey’s honestly significant difference (HSD) post hoc test. (e, f) Representative images and quantification (n = 3 per group) of 3D tumor spheroids formation in negative control, SIGLEC10wt and SIGLEC10Q144K overexpressing RL95-2 and MCF-7 cells after treatment with PRG (20 μM) and medroxyprogesterone acetate (MPA, 10 μM) for 7 days. Scale bar, 20 μm. The p values were calculated by the ANOVA test followed by Tukey’s honestly significant difference (HSD) post hoc test. (g) Validation of Siglec-10 protein overexpressing in PDO model by western blot. (h) Multiplex immunofluorescence (mIF) images of Siglec-10, ER and PR in negative control, SIGLEC10wt and SIGLEC10Q144K overexpressing PDO models. Scale bar, 100 μm. (i) Tumor growth curves of subcutaneous EC models derived from negative control (top) and SIGLEC10Q144K overexpressing Ishikawa (bottom) after treatment with MPA (300 mg/kg/d, i.g.) for 21 days (n = 5 per group). The p values were calculated by two-sided t-tests. (j) Kaplan–Meier curves according to SRPK1 expression in GSE12093 and GSE17705 were shown. The cutoff for high and low level of SRPK1 was 1201.3 and 10.26426, respectively. For d and f, data represent the mean ± SD from three independent replicates. For i, data represent the mean ± SEM from five independent replicates. i.g.: oral gavage.

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Supplementary Information

Supplementary Methods.

Reporting Summary

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Supplementary Tables 1–5.

Source data

Source Data Fig. 7 and Extended Data Fig. 7

Unprocessed western blots.

Source Data Figs. 7 and 8

Statistical source data of the IHC quantification.

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Hu, Z., Wu, Z., Liu, W. et al. Proteogenomic insights into early-onset endometrioid endometrial carcinoma: predictors for fertility-sparing therapy response. Nat Genet 56, 637–651 (2024). https://doi.org/10.1038/s41588-024-01703-z

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