Transcriptional Characterization of Stage I Epithelial Ovarian Cancer: A Multicentric Study
Abstract
:1. Introduction
2. Results and Discussion
2.1. Patient Cohorts and Sample Collection
2.2. Stage I Classification Using Advanced Ovarian Cancer Expression Subtypes
2.3. Immuno-Phenotype of Stage I EOC Patients
2.4. Transcriptional Alterations of Ovarian Cancer Stage I
2.5. Histotype-Specific Transcripts Validation
3. Materials and Methods
3.1. Patient Cohorts
3.2. Expression Analyses
3.3. Ovarian Cancer Molecular Signature Identification
3.4. Immuno-Phenotype Analyses
3.5. Network Analysis
3.6. qRT-PCR Data Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
EOC | Epithelial Ovarian Cancer |
FIGO | International Federation of Gynecological and Obstetrics |
IPS | Immunophenoscore |
IPG | Immunophenogram |
KM | Kaplan–Meier |
MUC | Mucinous |
SER HIGH | High Grade Serous |
SER LOW | Low Grade Serous |
CC | Clear Cell |
END | Endometrioid |
DIF | Differentiated subtype |
MES | Mesenchymal subtype |
MHC | Major Histocompatibility Complex |
CTL | Cytotoxic T Lymphocyte |
HLA | Human Leukocyte Antigen |
GO | Gene Ontology |
IMR | Immunoreactive subtype |
PRO | Proliferative subtype |
References
- Wang, Y.K.; Bashashati, A.; Anglesio, M.S.; Cochrane, D.R.; Grewal, D.S.; Ha, G.; McPherson, A.; Horlings, H.M.; Senz, J.; Prentice, L.M.; et al. Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes. Nat. Genet. 2017, 49, 856–865. [Google Scholar] [CrossRef] [PubMed]
- Phelan, C.M.; Kuchenbaecker, K.B.; Tyrer, J.P.; Kar, S.P.; Lawrenson, K.; Winham, S.J.; Dennis, J.; Pirie, A.; Riggan, M.J.; Chornokur, G.; et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat. Genet. 2017, 49, 680–691. [Google Scholar] [CrossRef] [PubMed]
- Hanley, G.E.; McAlpine, J.N.; Miller, D.; Huntsman, D.; Schrader, K.A.; Blake Gilks, C.; Mitchell, G. A population-based analysis of germline BRCA1 and BRCA2 testing among ovarian cancer patients in an era of histotype-specific approaches to ovarian cancer prevention. BMC Cancer 2018, 18, 254. [Google Scholar] [CrossRef] [PubMed]
- Labidi-Galy, S.I.; Papp, E.; Hallberg, D.; Niknafs, N.; Adleff, V.; Noe, M.; Bhattacharya, R.; Novak, M.; Jones, S.; Phallen, J.; et al. High grade serous ovarian carcinomas originate in the fallopian tube. Nat. Commun. 2017, 8, 1093. [Google Scholar] [CrossRef]
- Kurman, R.J.; Shih, I.E.M. The origin and pathogenesis of epithelial ovarian cancer: A proposed unifying theory. Am. J. Surg. Pathol. 2010, 34, 433–443. [Google Scholar] [CrossRef]
- Prat, J. Ovarian carcinomas: Five distinct diseases with different origins, genetic alterations, and clinicopathological features. Virchows Arch. 2012, 460, 237–249. [Google Scholar] [CrossRef]
- Karnezis, A.N.; Cho, K.R.; Gilks, C.B.; Pearce, C.L.; Huntsman, D.G. The disparate origins of ovarian cancers: Pathogenesis and prevention strategies. Nat. Rev. Cancer 2017, 17, 65–74. [Google Scholar] [CrossRef]
- Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 2011, 474, 609. [Google Scholar] [CrossRef]
- Charoentong, P.; Finotello, F.; Angelova, M.; Mayer, C.; Efremova, M.; Rieder, D.; Hackl, H.; Trajanoski, Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017, 18, 248–262. [Google Scholar] [CrossRef]
- Hackl, H.; Charoentong, P.; Finotello, F.; Trajanoski, Z. Computational genomics tools for dissecting tumor–immune cell interactions. Nat. Rev. Genet. 2016, 17, 441. [Google Scholar] [CrossRef]
- Bowtell, D.D.; Bohm, S.; Ahmed, A.A.; Aspuria, P.J.; Bast, R.C.; Beral, V.; Berek, J.S.; Birrer, M.J.; Blagden, S.; Bookman, M.A.; et al. Rethinking ovarian cancer II: Reducing mortality from high-grade serous ovarian cancer. Nat. Rev. Cancer 2015, 15, 668–679. [Google Scholar] [CrossRef] [PubMed]
- Martini, P.; Sales, G.; Massa, M.S.; Chiogna, M.; Romualdi, C. Along signal paths: An empirical gene set approach exploiting pathway topology. Nucleic Acids Res. 2012, 41, e19. [Google Scholar] [CrossRef] [PubMed]
- Calura, E.; Paracchini, L.; Fruscio, R.; Di Feo, A.; Ravaggi, A.; Peronne, J.; Martini, P.; Sales, G.; Beltrame, L.; Bignotti, E.; et al. A prognostic regulatory pathway in stage I epithelial ovarian cancer: New hints for the poor prognosis assessment. Ann. Oncol. 2016, 27, 1511–1519. [Google Scholar] [CrossRef] [PubMed]
- Marchini, S.; Cavalieri, D.; Fruscio, R.; Calura, E.; Garavaglia, D.; Fuso Nerini, I.; Mangioni, C.; Cattoretti, G.; Clivio, L.; Beltrame, L.; et al. Association between miR-200c and the survival of patients with stage I epithelial ovarian cancer: A retrospective study of two independent tumor tissue collections. Lancet Oncol. 2011, 12, 273–285. [Google Scholar] [CrossRef]
- Martini, P.; Paracchini, L.; Caratti, G.; Mello-Grand, M.; Fruscio, R.; Beltrame, L.; Calura, E.; Sales, G.; Ravaggi, A.; Bignotti, E.; et al. lncRNAs as Novel Indicators of Patients’ Prognosis in Stage I Epithelial Ovarian Cancer: A Retrospective and Multicentric Study. Clin. Cancer Res. 2017, 23, 2356–2366. [Google Scholar] [CrossRef]
- Calura, E.; Fruscio, R.; Paracchini, L.; Bignotti, E.; Ravaggi, A.; Martini, P.; Sales, G.; Beltrame, L.; Clivio, L.; Ceppi, L.; et al. MiRNA landscape in stage I epithelial ovarian cancer defines the histotype specificities. Clin. Cancer Res. 2013, 19, 4114–4123. [Google Scholar] [CrossRef]
- Calura, E.; Martini, P.; Sales, G.; Beltrame, L.; Chiorino, G.; D’Incalci, M.; Marchini, S.; Romualdi, C. Wiring miRNAs to pathways: A Topological approach to integrate miRNA and mRNA expression profiles. Nucleic Acids Res. 2014, 42, e96. [Google Scholar] [CrossRef]
- Tothill, R.W.; Tinker, A.V.; George, J.; Brown, R.; Fox, S.B.; Lade, S.; Johnson, D.S.; Trivett, M.K.; Etemadmoghadam, D.; Locandro, B.; et al. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin. Cancer Res. 2008, 14, 5198–5208. [Google Scholar] [CrossRef]
- Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674. [Google Scholar] [CrossRef]
- Coussens, L.M.; Zitvogel, L.; Palucka, A.K. Neutralizing tumor-promoting chronic inflammation: A magic bullet? Science 2013, 339, 286–291. [Google Scholar] [CrossRef]
- Chen, B.; Khodadoust, M.S.; Liu, C.L.; Newman, A.M.; Alizadeh, A.A. Profiling tumor infiltrating immune cells with CIBERSORT. Cancer Syst. Biol. 2018, 1711, 243–259. [Google Scholar]
- Gooden, M.; Lampen, M.; Jordanova, E.S.; Leffers, N.; Trimbos, J.B.; van der Burg, S.H.; Nijman, H.; van Hall, T. HLA-E expression by gynecological cancers restrains tumor-infiltrating CD8+ T lymphocytes. Proc. Natl. Acad. Sci. USA 2011, 108, 10656–10661. [Google Scholar] [CrossRef] [PubMed]
- Blank, C.; Haanen, J.; Ribas, A.; Schumacher, T. CANCER IMMUNOLOGY The “cancer immunogram”. Science 2016, 352, 658–660. [Google Scholar] [CrossRef] [PubMed]
- Dimitrova, N.; Nagaraj, A.B.; Razi, A.; Singh, S.; Kamalakaran, S.; Banerjee, N.; Joseph, P.; Mankovich, A.; Mittal, P.; DiFeo, A.; et al. InFlo: A novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer. Oncogene 2017, 36, 2472–2482. [Google Scholar] [CrossRef]
- Parolia, A.; Cielik, M.; Chinnaiyan, A.M. Competing for enhancers: PVT1 fine-tunes MYC expression. Cell Res. 2018, 28, 785–786. [Google Scholar] [CrossRef]
- Boudjadi, S.; Carrier, J.C.; Groulx, J.F.; Beaulieu, J.F. Integrins expression is controlled by c-MYC in colorectal cancer cells. Oncogene 2016, 35, 1671–1678. [Google Scholar] [CrossRef]
- Wei, L.; Yin, F.; Zhang, W.; Li, L. ITGA 1 and cell adhesion-mediated drug resistance in ovarian cancer. Int. Clin. Exp. Pathol. 2017, 10, 5522–5529. [Google Scholar]
- Nirschl, C.J.; Suarez-Farinas, M.; Izar, B.; Prakadan, S.; Dannenfelser, R.; Tirosh, I.; Liu, Y.; Zhu, Q.; Devi, K.S.P.; Carroll, S.L.; et al. IFN-Dependent Tissue-Immune Homeostasis Is Co-opted in the Tumor Microenvironment. Cell 2017, 170, 127–141. [Google Scholar] [CrossRef]
- Bolstad, B.M.; Irizarry, R.A.; Åstrand, M.; Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19, 185–193. [Google Scholar] [CrossRef]
- Helland, Å.; Anglesio, M.S.; George, J.; Cowin, P.A.; Johnstone, C.N.; House, C.M.; Sheppard, K.E.; Etemadmoghadam, D.; Melnyk, N.; Rustgi, A.K.; et al. Deregulation of MYCN, LIN28B and LET7 in a molecular subtype of aggressive high-grade serous ovarian cancers. PLoS ONE 2011, 6, e18064. [Google Scholar] [CrossRef]
- Bentink, S.; Haibe-Kains, B.; Risch, T.; Fan, J.B.; Hirsch, M.S.; Holton, K.; Rubio, R.; April, C.; Chen, J.; Wickham-Garcia, E.; et al. Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer. PLoS ONE 2012, 7, e30269. [Google Scholar] [CrossRef]
- Verhaak, R.G.; Tamayo, P.; Yang, J.Y.; Hubbard, D.; Zhang, H.; Creighton, C.J.; Fereday, S.; Lawrence, M.; Carter, S.L.; Mermel, C.H.; et al. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J. Clin. Investig. 2013, 123, 517. [Google Scholar] [CrossRef]
- Konecny, G.E.; Wang, C.; Hamidi, H.; Winterhoff, B.; Kalli, K.R.; Dering, J.; Ginther, C.; Chen, H.W.; Dowdy, S.; Cliby, W.; et al. Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer. JNCI J. Natl. Cancer Inst. 2014, 106, dju249. [Google Scholar] [CrossRef]
- Newman, A.M.; Liu, C.L.; Green, M.R.; Gentles, A.J.; Feng, W.; Xu, Y.; Hoang, C.D.; Diehn, M.; Alizadeh, A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 2015, 12, 453. [Google Scholar] [CrossRef] [Green Version]
- Raivo, K. Pheatmap: Pretty Heatmaps. Available online: https://cran.r-project.org/web/packages/pheatmap/index.html (accessed on 1 December 2019).
- Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016. [Google Scholar]
- Sales, G.; Calura, E.; Cavalieri, D.; Romualdi, C. g raphite-a Bioconductor package to convert pathway topology to gene network. BMC Bioinform. 2012, 13, 20. [Google Scholar] [CrossRef] [Green Version]
- Sales, G.; Calura, E.; Romualdi, C. meta Graphite—A new layer of pathway annotation to get metabolite networks. Bioinformatics 2018, 35, 1258–1260. [Google Scholar] [CrossRef]
- Hsu, S.D.; Lin, F.M.; Wu, W.Y.; Liang, C.; Huang, W.C.; Chan, W.L.; Tsai, W.T.; Chen, G.Z.; Lee, C.J.; Chiu, C.M.; et al. miRTarBase: A database curates experimentally validated microRNA–target interactions. Nucleic Acids Res. 2010, 39, D163–D169. [Google Scholar] [CrossRef] [Green Version]
- Xiao, F.; Zuo, Z.; Cai, G.; Kang, S.; Gao, X.; Li, T. miRecords: An integrated resource for microRNA–target interactions. Nucleic Acids Res. 2008, 37, D105–D110. [Google Scholar] [CrossRef]
- James, F.R.; Jiminez-Linan, M.; Alsop, J.; Mack, M.; Song, H.; Brenton, J.D.; Pharoah, P.D.P.; Ali, H.R. Association between tumor infiltrating lymphocytes, histotype and clinical outcome in epithelial ovarian cancer. BMC Cancer 2017, 17, 657. [Google Scholar] [CrossRef]
Clinical Annotation | Training Set | Validation Set |
---|---|---|
HISTOTYPES | ||
Clear cell | 16 (21%) | 22 (16.6%) |
Endometroid | 19 (25%) | 55 (41.7%) |
Mucinous | 17 (22.4%) | 21 (15.9%) |
Serous high-grade | 16 (21.0%) | 26 (76.4%) |
Serous low-grade | 8 (10.6%) | 8 (23.5%) |
GRADES | ||
G1 | 18 (23.7%) | 32 (32.6%) |
G2 | 18 (23.7%) | 31 (31.7%) |
G3 | 38 (50.0%) | 35 (35.7%) |
NA | 2 (2.6%) | 0 (0%) |
RELAPSING | ||
No | 55 (72.4%) | 105 (79.5%) |
Yes | 21 (27.6%) | 24 (18.2%) |
NA | 0 (0%) | 3 (2.3%) |
VITAL STATUS AT THE LAST FOLLOW UP | ||
Alive | 57 (75%) | 102 (77.3%) |
Dead of EOC | 15 (19.8%) | 9 (6.8%) |
Dead of other cause | 2 (2.6%) | 13 (9.9%) |
Unknown | 0 (0%) | 4 (3%) |
Awarded | 2 (2.6%) | 4 (3%) |
FIGO SUBSTAGE | ||
A | 25 (32.9%) | 27 (20.5%) |
B | 4 (5.3%) | 6 (4.5%) |
C | 47 (61.8%) | 52 (39.4%) |
NA | 0 (0%) | 47 (35.6%) |
Mean age in years [min-max] | 53 [21–82] | 55 [17–80] |
Mean follow up in years [min-max] | 9 [1–18] | 6 [0–17] |
Total number of patients | 76 | 132 |
Validation Set | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Muc vs SerHigh | Muc vs SerLow | Muc vs End | Muc vs Cc | SerHigh vs End | SerLow vs End | SerHigh vs Cc | SerLow vs Cc | End vs Cc | SerH vs SerL | |||||||||||
p | adj-p | p | adj-p | p | adj-p | p | adj-p | p | adj-p | p | adj-p | p | adj-p | p | adj-p | p | adj-p | p | adj-p | |
hsa-let-7a-5p | 0.0012 | 0.0091 | 0.9849 | 0.9863 | 0.0602 | 0.3635 | 0.0016 | 0.0074 | 0.0520 | 0.2758 | 0.1683 | 0.5946 | 0.4018 | 0.6980 | 0.0169 | 0.0805 | 0.1688 | 0.5215 | 0.0221 | 0.2328 |
hsa-miR-192-5p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.6842 | 0.9218 | 0.8806 | 0.9527 | 0.1767 | 0.6247 | 0.7796 | 0.8982 | 0.6812 | 0.8535 | 0.3771 | 0.7724 |
hsa-miR-194-5p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.8083 | 0.9218 | 0.7085 | 0.8940 | 0.1768 | 0.6247 | 0.9347 | 0.9527 | 0.5082 | 0.7506 | 0.4415 | 0.7784 |
hsa-miR-214-3p | 0.0000 | 0.0005 | 0.6570 | 0.9118 | 0.0014 | 0.0153 | 0.0000 | 0.0001 | 0.3073 | 0.8143 | 0.0040 | 0.1054 | 0.8778 | 0.9481 | 0.0000 | 0.0001 | 0.2122 | 0.5367 | 0.0002 | 0.0096 |
hsa-miR-26a-5p | 0.0072 | 0.0346 | 0.9291 | 0.9863 | 0.0790 | 0.4037 | 0.0033 | 0.0135 | 0.3287 | 0.8280 | 0.1551 | 0.5874 | 0.9999 | 0.9999 | 0.0095 | 0.0630 | 0.2958 | 0.6990 | 0.0283 | 0.2328 |
hsa-miR-29a-3p | 0.1899 | 0.4575 | 0.0512 | 0.3874 | 0.3262 | 0.6668 | 0.0957 | 0.2205 | 0.7376 | 0.9218 | 0.0132 | 0.1754 | 0.7564 | 0.9481 | 0.0025 | 0.0187 | 0.5136 | 0.7506 | 0.0070 | 0.1189 |
hsa-miR-29b-3p | 0.0044 | 0.0260 | 0.1692 | 0.9118 | 0.0031 | 0.0274 | 0.0003 | 0.0016 | 0.9426 | 0.9796 | 0.0024 | 0.1054 | 0.4083 | 0.6980 | 0.0007 | 0.0097 | 0.4482 | 0.7506 | 0.0035 | 0.0917 |
hsa-miR-30a-3p | 0.8097 | 0.9331 | 0.5548 | 0.9118 | 0.8213 | 0.9572 | 0.0000 | 0.0000 | 0.9633 | 0.9818 | 0.7056 | 0.8940 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.4771 | 0.7784 |
hsa-miR-30a-5p | 0.9686 | 0.9881 | 0.5980 | 0.9118 | 0.7880 | 0.9492 | 0.0000 | 0.0000 | 0.7934 | 0.9218 | 0.6702 | 0.8940 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.5494 | 0.8089 |
hsa-miR-96-5p | 0.4640 | 0.6767 | 0.9863 | 0.9863 | 0.5985 | 0.8572 | 0.0003 | 0.0018 | 0.7916 | 0.9218 | 0.6623 | 0.8940 | 0.0011 | 0.0140 | 0.0020 | 0.0176 | 0.0004 | 0.0058 | 0.5140 | 0.7784 |
CDK6 | 0.0004 | 0.0032 | 0.7787 | 0.9863 | 0.0124 | 0.0940 | 0.0017 | 0.0074 | 0.4418 | 0.9218 | 0.0986 | 0.4751 | 0.9367 | 0.9547 | 0.0284 | 0.1157 | 0.5240 | 0.7506 | 0.0090 | 0.1189 |
CDKN1A | 0.0064 | 0.0339 | 0.5615 | 0.9118 | 0.4863 | 0.8054 | 0.0217 | 0.0715 | 0.0093 | 0.1910 | 0.8607 | 0.9527 | 0.2890 | 0.6831 | 0.2496 | 0.4267 | 0.0413 | 0.2739 | 0.1272 | 0.4496 |
CDKN2A | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.2831 | 0.7896 | 0.7871 | 0.9426 | 0.0129 | 0.0857 | 0.0463 | 0.1504 | 0.0020 | 0.0209 | 0.5659 | 0.8107 |
E2F3 | 0.3567 | 0.6301 | 0.8805 | 0.9863 | 0.3187 | 0.6668 | 0.0001 | 0.0004 | 0.0788 | 0.3797 | 0.4780 | 0.8940 | 0.0028 | 0.0249 | 0.0118 | 0.0697 | 0.0000 | 0.0003 | 0.6869 | 0.8307 |
MDM2 | 0.0000 | 0.0000 | 0.0001 | 0.0016 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.5109 | 0.9218 | 0.5543 | 0.8940 | 0.0008 | 0.0140 | 0.1640 | 0.3858 | 0.0138 | 0.1218 | 0.2344 | 0.5730 |
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Share and Cite
Calura, E.; Ciciani, M.; Sambugaro, A.; Paracchini, L.; Benvenuto, G.; Milite, S.; Martini, P.; Beltrame, L.; Zane, F.; Fruscio, R.; et al. Transcriptional Characterization of Stage I Epithelial Ovarian Cancer: A Multicentric Study. Cells 2019, 8, 1554. https://doi.org/10.3390/cells8121554
Calura E, Ciciani M, Sambugaro A, Paracchini L, Benvenuto G, Milite S, Martini P, Beltrame L, Zane F, Fruscio R, et al. Transcriptional Characterization of Stage I Epithelial Ovarian Cancer: A Multicentric Study. Cells. 2019; 8(12):1554. https://doi.org/10.3390/cells8121554
Chicago/Turabian StyleCalura, Enrica, Matteo Ciciani, Andrea Sambugaro, Lara Paracchini, Giuseppe Benvenuto, Salvatore Milite, Paolo Martini, Luca Beltrame, Flaminia Zane, Robert Fruscio, and et al. 2019. "Transcriptional Characterization of Stage I Epithelial Ovarian Cancer: A Multicentric Study" Cells 8, no. 12: 1554. https://doi.org/10.3390/cells8121554