Novel Approaches in Ovarian Cancer Research against Heterogeneity, Late Diagnosis, Drug Resistance, and Transcoelomic Metastases
Abstract
:1. Introduction
2. Focus on Genomics
2.1. Somatic and Germline Mutations, Amplifications, and Structural Instability
2.2. Tumour Evolution and Heterogeneity
3. Focus on Transcriptomics
4. Focus on Epigenomics
5. Focus on Proteomics and Metabolomics
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
ARID1A | AT-rich interaction domain 1A |
ATM | ATM serine/threonine kinase |
ATR | ATR serine/threonine kinase |
AUC | Area Under The Curve |
BRAF | B-Raf proto-oncogene, serine/threonine kinase |
BRCA1/2 | BRCA1/2 DNA repair associated |
CA125 | cancer antigen 125 |
CCNE1 | cyclin E1 |
CD133 | prominin 1 |
CD44 | CD44 molecule (Indian blood group) |
CDK12 | cyclin-dependent kinase 12 |
CDK14 | cyclin-dependent kinase 14 |
ChIP-Seq | chromatin immunoprecipitation-sequencing |
CNV | copy number variations |
CpG | cytosine–guanine dinucleotides |
CTCs | cancer tumour cells |
ctDNA | circulating tumour cells |
CTNNB1 | catenin beta 1 |
DDR | DNA damage response |
DNMT | DNA methyltransferase |
DNMTi | DNA methyltransferase inhibitor |
EMSY | EMSY transcriptional repressor, BRCA2 interacting |
EMT | epithelial–mesenchymal transition |
EOC | epithelial ovarian cancer |
EpCAM | epithelial cell adhesion molecule |
ERASMOS | Effects of Regional Analgesia on Serum miRNA after Oncology Surgery Study |
ERBB2 | erb-b2 receptor tyrosine kinase 2 |
FIGO | The International Federation of Gynecology and Obstetrics |
FN1 | fibronectin 1 |
FOXM1 | forkhead box M1 |
gDNA | genomic DNA |
GEO | Gene Expression Omnibus (database) |
HDAC | histone deacetylase |
HGP | The Human Genome Project |
HG-SOC | high-grade serous ovarian cancer |
HOXA10/11 | homeobox A10/A11 |
HPLC | High-performance liquid chromatography |
IL6/JAK/STAT3 | interleukin 6/Janus kinase/signal transducer and activator of transcription 3 |
KLK6 | kallikrein-6 |
KRAS | KRAS proto-oncogene, GTPase |
LG-SOC | low-grade serous ovarian cancer |
lncRNA | long non-coding RNA |
M | metastatic region |
MAPK/Erk | mitogen-activated protein kinases/extracellular signal-regulated kinase |
MDR1 | multidrug resistance protein 1 |
miRNA | microRNA |
MLH1 | mutL homolog 1 |
mRNA | messenger RNA |
MS | Mass Spectrometry |
ncRNA | non-coding RNA |
NECC | New England Case Control study |
NF1 | neurofibromin 1 |
NFκB | Nuclear Factor Kappa B |
NGS | Next Generation Sequencing |
NMR | Nuclear magnetic resonance spectroscopy |
NRAS | NRAS proto-oncogene, GTPase |
OC | ovarian cancer |
ORM1 | Orosomucoid 1 |
P1/2 | primary tumour site |
PI3K/AKT | phosphatidylinositol 3-kinase/protein kinase B |
PI3K/RAS | phosphatidylinositol 3-kinase/RAS type GTPase family |
PIK3CA | phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha |
PMP | pelvic mass protocol |
PPP2R1A | protein phosphatase 2 scaffold subunit Alpha |
PTEN | phosphatase and tensin homolog |
RAD51B | RAD51 paralog B |
RAD51C | RAD51 paralog C |
RASSF1A | Ras association domain family 1 isoform A |
RB1 | RB transcriptional corepressor 1 |
RBPMS | RNA-binding protein, mRNA processing factor |
scRNA-Seq | single-cell RNA sequencing |
SERPINA1 | serpin family A member 1 |
SNPs | single-nucleotide polymorphisms |
SNV | single nucleotide variant |
SWI/SNF | switch/sucrose non-fermentable chromatin remodelling complex |
TCGA | The Cancer Genome Atlas |
TP53 | tumour protein 53 |
WNT | WNT signalling gene family |
Wnt/β-Catenin | canonical Wnt pathway |
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Subtype of EOC | Mutations | Other | Publications |
---|---|---|---|
HG-SOC | TP53, BRCA1/2, NF1, CDK12, RB1, PTEN, RAD51B, homologous recombination repair genes | PI3K/Ras, notch, FOXM1 pathways alterations | [13,26,27,35,36,37] |
LG-SOC | BRAF, KRAS, NRAS, ERBB2 | [13,35,36,38,39] | |
Endometrioid | ARID1A, PIK3CA, PTEN, PPP2R1A, β-Catenin | MMR deficiency, microsatellite instability | [13,31,35,36,40] |
Clear cell | PIK3CA, PTEN, CTNNB1, PP2R1A ARID1A, TP53, SWI/SNW | Chromatin remodelling factor inactivation, microsatellite instability | [13,31,35,36,41,42,43] |
Mucinous | KRAS, ERBB2 | [13,33,35,36,43,44] |
Genome Type | Methylation of Intergenomic Regions | Methylation of Regulatory Regions | Methylscape Biomarker | In-Solution Properties of Purified gDNA | Surface-Based Properties |
---|---|---|---|---|---|
Cancer genome | Low methylation | High methylation | Clustered methylation | DNA solvation | High adsorption |
Normal genome | High methylation (individual CpGs ~150 kbp apart) | Low methylation | Dispersed methylation | DNA aggregation | Low adsorption |
Problems | Approach | Method | Expected Application | Example Studies |
---|---|---|---|---|
Heterogeneity | Studying cell population patterns between ovarian cancer tumours of different grade, as well as between primary and metastatic tumours | Single-cell RNA sequencing | Understanding the leading cell population; may conclude in finding a specific target for diagnosis and precise treatment | [60] |
Proteomic profiling and statistical comparison between ovarian cancer cells and controls | Single-run MS | Potential biomarkers for diagnosis or outcome prediction | [80] | |
Late diagnosis | Training of machine to become a neural network with the lowest number of miRNAs needed for best diagnosis by correlation with clinical data | Machine learning algorithm based on miRNA expression data (microarrays, RNA sequencing) | Building of sensitive non-invasive diagnostic tools | [56] |
Using the physicochemical properties between alterations in genome methylation and gold surface | gDNA isolation and DNA–gold affinity | Development of easy, fast, and non-invasive diagnostic tools | [65] | |
Drug resistance | Building of endogenous RNA network | Support vector machine classifier (using data of mRNA, miRNA, and lncRNA vs. clinical data) | Development of a good model to predict disease reoccurrence in advance and to find potential biomarkers for the development of drug resistance | [52] |
Proteomic and metabolomics investigation and further statistical analysis to recognise differences between controls, platinum-resistant tumour, and platinum-sensitive tumour | 2D-LC-ESI-MS/MS, LC-MS | Development of biomarkers for recognition of chemoresistant ovarian cancer | [81] | |
Comparison of the primary sensitive and refractory resistant tumour | Whole-genome sequencing; transcriptome, methylation, and microRNA (miRNA) expression analyses | Designing of novel drugs for resensitisation or targeted therapy | [27] | |
Metastasis | Phylogenetic analyses identifying constituent clones and quantifying their relative abundances at multiple intraperitoneal sites | Whole-genome and single-nucleus sequencing | Understanding the process of metastasis migration and understanding the population spread, which could lead to better treatment management in the future | [46] |
Comparison of the mutation landscape, and copy number analysis between primary and metastatic sites | High-depth whole-exome sequencing | Understanding the ways of genomic evolution in transcoelomic metastasis | [45] | |
Establishment, isolation, cloning, and propagation of the cellular content of ovarian multilayered spheroids (cancer stem cells) to study their clonogenic, tumourigenic, and invasive properties | In vitro and in vivo study, RT-PCR | Describing cellular mechanisms and the influence of cancer stem cells on the aggressiveness of ovarian cancer | [63] | |
Targeting | Treatment of heavily pretreated and chemoresistant patients with the addition of DNMT inhibitor | Clinical trial | Development of treatment which helps to restore the sensitivity to carboplatin (classic treatment) | [70,71] |
Finding SNV, CNV, alteration in mRNA expression, miRNA expression | Exome sequencing, RNA sequencing, integrated data analysis | Finding driver mutations and key disrupted pathways in pathogenesis for precision medicine | [26,49] | |
Analysis of copy number signatures (including many copy number features) | Shallow whole-genome sequencing | Finding ways to predict overall survival and the probability of drug-resistance and relapse at the point of diagnosis | [28] | |
10-mRNA-score model constructed so that it strongly correlates with the level of DNA mutations and predicts the genome instability | Construction of RNA network | Prediction model of poor outcome, which could identify important pathways for targeting disease | [55] |
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Erol, A.; Niemira, M.; Krętowski, A.J. Novel Approaches in Ovarian Cancer Research against Heterogeneity, Late Diagnosis, Drug Resistance, and Transcoelomic Metastases. Int. J. Mol. Sci. 2019, 20, 2649. https://doi.org/10.3390/ijms20112649
Erol A, Niemira M, Krętowski AJ. Novel Approaches in Ovarian Cancer Research against Heterogeneity, Late Diagnosis, Drug Resistance, and Transcoelomic Metastases. International Journal of Molecular Sciences. 2019; 20(11):2649. https://doi.org/10.3390/ijms20112649
Chicago/Turabian StyleErol, Anna, Magdalena Niemira, and Adam Jacek Krętowski. 2019. "Novel Approaches in Ovarian Cancer Research against Heterogeneity, Late Diagnosis, Drug Resistance, and Transcoelomic Metastases" International Journal of Molecular Sciences 20, no. 11: 2649. https://doi.org/10.3390/ijms20112649