Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 212

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status

Open access
Coscia, F., Watters, K. M., Curtis, M., Eckert, M. A., Chiang, C. Y., Tyanova, S., Montag, A., Lastra, R. R., Lengyel, E., Mann, M.·2016·Nature Communications

Abstract

A cell line representative of human high-grade serous ovarian cancer (HGSOC) should not only resemble its tumour of origin at the molecular level, but also demonstrate functional utility in pre-clinical investigations. Here, we report the integrated proteomic analysis of 26 ovarian cancer cell lines, HGSOC tumours, immortalized ovarian surface epithelial cells and fallopian tube epithelial cells via a single-run mass spectrometric workflow. The in-depth quantification of >10,000 proteins results in three distinct cell line categories: epithelial (group I), clear cell (group II) and mesenchymal (group III). We identify a 67-protein cell line signature, which separates our entire proteomic data set, as well as a confirmatory publicly available CPTAC/TCGA tumour proteome data set, into a predominantly epithelial and mesenchymal HGSOC tumour cluster. This proteomics-based epithelial/mesenchymal stratification of cell lines and human tumours indicates a possible origin of HGSOC either from the fallopian tube or from the ovarian surface epithelium.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/ncomms12645

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 13 references

Research works printed but not linked: 56

  1. Koonings, P. P., Campbell, K., Mishell, D. R. Jr & Grimes, D. A. Relative frequency of primary ovarian neoplasms: a 10-year review. Obstet. Gynecol. 74, 921–926 (1989).
  2. Callahan, M. J. et al. Primary fallopian tube malignancies in BRCA-positive women undergoing surgery for ovarian cancer risk reduction. J. Clin. Oncol. 25, 3985–3990 (2007). · DOI · Paper page
  3. Kindelberger, D. W. et al. Intraepithelial carcinoma of the fimbria and pelvic serous carcinoma: evidence for a causal relationship. Am. J. Surg. Pathol. 31, 161–169 (2007). · DOI · Paper page
  4. Piek, J. M. et al. Dysplastic changes in prophylactically removed fallopian tubes of women predisposed to developing ovarian cancer. J. Pathol. 195, 451–456 (2001). · DOI · Paper page
  5. Bowtell, D. D. et al. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer. Nat. Rev. Cancer 15, 668–679 (2015). · DOI · Paper page
  6. Marks, J. R. et al. Overexpression and mutation of p53 in epithelial ovarian cancer. Cancer Res. 51, 2979–2984 (1991).
  7. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011). · DOI · Paper page
  8. Tothill, R. W. et al. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin. Cancer Res. 14, 5198–5208 (2008). · DOI · Paper page
  9. Domcke, S., Sinha, R., Levine, D. A., Sander, C. & Schultz, N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nat. Commun. 4, 2126 (2013). · DOI · Paper page
  10. Mitra, A. K. et al. In vivo tumor growth of high-grade serous ovarian cancer cell lines. Gynecol. Oncol. 138, 372–377 (2015). · DOI · Paper page
  11. Elias, K. M. et al. Beyond genomics: critical evaluation of cell line utility for ovarian cancer research. Gynecol. Oncol. 139, 97–103 (2015). · DOI
  12. Thakur, S. S. et al. Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell. Proteomics 10, M110 003699 (2011). · DOI · Paper page
  13. Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011). · DOI · Paper page
  14. Ross, J. S. et al. Comprehensive genomic profiling of epithelial ovarian cancer by next generation sequencing-based diagnostic assay reveals new routes to targeted therapies. Gynecol. Oncol. 130, 554–559 (2013). · DOI
  15. Tsuchiya, A. et al. Expression profiling in ovarian clear cell carcinoma: identification of hepatocyte nuclear factor-1 beta as a molecular marker and a possible molecular target for therapy of ovarian clear cell carcinoma. Am. J. Pathol. 163, 2503–2512 (2003). · DOI · Paper page
  16. Geiger, T., Cox, J. & Mann, M. Proteomic changes resulting from gene copy number variations in cancer cells. PLoS Genet. 6, e1001090 (2010). · DOI
  17. Anglesio, M. S. et al. Type-specific cell line models for type-specific ovarian cancer research. PLoS ONE 8, e72162 (2013). · DOI · Paper page
  18. Schaner, M. E. et al. Gene expression patterns in ovarian carcinomas. Mol. Biol. Cell 14, 4376–4386 (2003). · DOI · Paper page
  19. Ohyagi-Hara, C. et al. miR-92a inhibits peritoneal dissemination of ovarian cancer cells by inhibiting integrin alpha5 expression. Am. J. Pathol. 182, 1876–1889 (2013). · DOI
  20. Deeb, S. J. et al. Machine learning-based classification of diffuse large B-cell lymphoma patients by their protein expression profiles. Mol. Cell. Proteomics 14, 2947–2960 (2015). · DOI
  21. Tyanova, S. et al. Proteomic maps of breast cancer subtypes. Nat. Commun. 7, 10259 (2016). · DOI · Paper page
  22. Cheon, D. J. et al. Differential expression of argininosuccinate synthetase in serous and non-serous ovarian carcinomas. J. Pathol: Clin. Res. 1, 41–53 (2015).
  23. Watanabe, G. et al. alphaB-crystallin: a novel p53-target gene required for p53-dependent apoptosis. Cancer Sci. 100, 2368–2375 (2009). · DOI
  24. Ellis, M. J. et al. Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. Cancer Discov. 3, 1108–1112 (2013). · DOI · Paper page
  25. Edwards, N. J. et al. The CPTAC data portal: a resource for cancer proteomics research. J. Proteome Res. 14, 2707–2713 (2015). · DOI · Paper page
  26. Yang, D. et al. Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer. Cancer Cell 23, 186–199 (2013). · DOI · Paper page
  27. Ince, T. A. et al. Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours. Nat. Commun. 6, 7419 (2015). · DOI · Paper page
  28. Cox, J. & Mann, M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics 13, (Suppl 16): S12 (2012). · DOI · Paper page
  29. Schug, T. T., Berry, D. C., Shaw, N. S., Travis, S. N. & Noy, N. Opposing effects of retinoic acid on cell growth result from alternate activation of two different nuclear receptors. Cell 129, 723–733 (2007). · DOI · Paper page
  30. Schug, T. T., Berry, D. C., Shaw, N. S., Travis, S. N. & Noy, N. Dual transcriptional activities underlie opposing effects of retinoic acid on cell survival. Cell 129, 723–733 (2007). · DOI · Paper page
  31. Yoshida, H. et al. Accelerated degradation of PML-retinoic acid receptor alpha (PML-RARA) oncoprotein by all-trans-retinoic acid in acute promyelocytic leukemia: possible role of the proteasome pathway. Cancer Res. 56, 2945–2948 (1996).
  32. Sidell, N. Retinoic acid-induced growth inhibition and morphologic differentiation of human neuroblastoma cells in vitro. J. Natl Cancer Inst. 68, 589–596 (1982).
  33. Toyama, A. et al. Proteomic characterization of ovarian cancers identifying annexin-A4, phosphoserine aminotransferase, cellular retinoic acid-binding protein 2, and serpin B5 as histology-specific biomarkers. Cancer Sci. 103, 747–755 (2012). · DOI
  34. Volkmann, J. et al. High expression of crystallin alphaB represents an independent molecular marker for unfavourable ovarian cancer patient outcome and impairs TRAIL- and cisplatin-induced apoptosis in human ovarian cancer cells. Int. J. Cancer 132, 2820–2832 (2013). · DOI
  35. Lin, J. H. et al. Connexin 43 enhances the adhesivity and mediates the invasion of malignant glioma cells. J. Neurosci. 22, 4302–4311 (2002). · DOI · Paper page
  36. Kapoor, P. et al. Breast cancer metastatic potential: correlation with increased heterotypic gap junctional intercellular communication between breast cancer cells and osteoblastic cells. Int. J. Cancer 111, 693–697 (2004). · DOI
  37. Elzarrad, M. K. et al. Connexin-43 upregulation in micrometastases and tumor vasculature and its role in tumor cell attachment to pulmonary endothelium. BMC Med. 6, 20 (2008). · DOI
  38. Sawada, K. et al. Loss of E-cadherin promotes ovarian cancer metastasis via alpha 5-integrin, which is a therapeutic target. Cancer Res. 68, 2329–2339 (2008). · DOI · Paper page
  39. Darai, E. et al. Expression of cadherins in benign, borderline, and malignant ovarian epithelial tumors: a clinicopathologic study of 60 cases. Hum. Pathol. 28, 922–928 (1997). · DOI
  40. Maines-Bandiera, S. L. & Auersperg, N. Increased E-cadherin expression in ovarian surface epithelium: an early step in metaplasia and dysplasia? Int. J. Gynecol. Pathol. 16, 250–255 (1997). · DOI
  41. Verhaak, R. G. et al. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J. Clin. Invest. 123, 517–525 (2013).
  42. Auersperg, N. The origin of ovarian cancers--hypotheses and controversies. Front. Biosci. (Schol Ed) 5, 709–719 (2013). · DOI
  43. Flesken-Nikitin, A., Choi, K. C., Eng, J. P., Shmidt, E. N. & Nikitin, A. Y. Induction of carcinogenesis by concurrent inactivation of p53 and Rb1 in the mouse ovarian surface epithelium. Cancer Res. 63, 3459–3463 (2003).
  44. Bowen, N. J. et al. Emerging roles for PAX8 in ovarian cancer and endosalpingeal development. Gynecol. Oncol. 104, 331–337 (2007). · DOI · Paper page
  45. Bowen, N. J. et al. Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as ovarian cancer initiating cells. BMC Med. Genomics 2, 71 (2009). · DOI · Paper page
  46. Auersperg, N., Woo, M. M. & Gilks, C. B. The origin of ovarian carcinomas: a developmental view. Gynecol. Oncol. 110, 452–454 (2008). · DOI
  47. Pothuri, B. et al. Genetic analysis of the early natural history of epithelial ovarian carcinoma. PLoS ONE 5, e10358 (2010). · DOI
  48. Kim, J. et al. High-grade serous ovarian cancer arises from fallopian tube in a mouse model. Proc. Natl Acad. Sci. USA 109, 3921–3926 (2012). · DOI · Paper page
  49. Lengyel, E., Fleming, S., McEwen, K. A., Montag, A. & Temkin, S. M. Serial sectioning of the fallopian tube allows for improved identification of primary fallopian tube carcinoma. Gynecol. Oncol. 129, 120–123 (2013). · doi:10.1016/j.ygyno.2012.12.006
  50. Karst, A. M. & Drapkin, R. Primary culture and immortalization of human fallopian tube secretory epithelial cells. Nat. Protoc. 7, 1755–1764 (2012). · DOI · Paper page
  51. Merritt, M. A. et al. Gene expression signature of normal cell-of-origin predicts ovarian tumor outcomes. PLoS ONE 8, e80314 (2013). · DOI
  52. Scheltema, R. A. et al. The Q Exactive HF, a benchtop mass spectrometer with a pre-filter, high-performance quadrupole and an ultra-high-field Orbitrap analyzer. Mol. Cell. Proteomics 13, 3698–3708 (2014). · DOI · Paper page
  53. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods doi: 10.1038/nmeth.3901 (2016). · DOI · Paper page
  54. Chang, C. C. & Lin, C. J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27 (2011). · DOI · Paper page
  55. Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001). · DOI · Paper page
  56. Vizcaino, J. A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, D447–D456 (2016). · DOI · Paper page

Cited by

See all 212

Metadata

Authors
Coscia, F., Watters, K. M., Curtis, M., Eckert, M. A., Chiang, C. Y., Tyanova, S., Montag, A., Lastra, R. R., Lengyel, E., & Mann, M.
Venue
Nature Communications
Published
2016-08-26
Volume
7
Issue
1
Pages
12645-12645
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2517861366
Cited by
212
References
13
FWCI
28.84
Type
journal article
Language
en
Open access
gold
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status. https://app.cochat.ai/papers/10.1038/ncomms12645
The paper
Coscia, F., Watters, K. M., Curtis, M., Eckert, M. A., Chiang, C. Y., Tyanova, S., Montag, A., Lastra, R. R., Lengyel, E., & Mann, M. (2016). Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status. Nature Communications. https://doi.org/10.1038/ncomms12645

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.