Ovarian cancer (OC) has the highest mortality rate of all gynecologic malignancies. The overall five-year survival is 46% and varies depending on the stage and histological type of the tumor. High-grade serous carcinoma (HGSOC) accounts for 75% of all epithelial ovarian malignancies and is diagnosed mainly at FIGO stage III (51%) or IV (29%), reflecting the aggressive nature.
Ovarian cancer (OC) has the highest mortality rate of all gynecologic malignancies . The overall five-year survival is 46% and varies depending on the stage and histological type of the tumor . High-grade serous carcinoma (HGSOC) accounts for 75% of all epithelial ovarian malignancies and is diagnosed mainly at FIGO stage III (51%) or IV (29%), reflecting the aggressive nature . In contrast, nonepithelial and more rare epithelial tumors such as endometrioid, mucinous. and clear-cell carcinomas are more frequently diagnosed at FIGO stages I–II . Consequently, the five-year survival for HGSOC is 43%, compared with 82%, 71%, and 66% for endometrioid, mucinous, and clear-cell carcinoma, respectively. The five-year OS rate is only 9% for FIGO stage IV HGSOC patients .
Until recently, OC classification was based on morphology and immunohistochemistry (IHC), but more modern diagnostic approaches take into account molecular genetics, protein post-translational transformations, and immune cell infiltrates . Over the last few decades, two distinct pathogenesis models were defined dividing ovarian malignancies into ovarian-origin OC and extra ovarian-origin OC. Ovarian-origin malignancies are very rare, mostly occurring at a young age or in childhood, and are presented by two main groups: (1) sex-cord stromal tumors tend to manifest as low-grade disease with a nonaggressive clinical course and are usually diagnosed at the early stages; (2) predominantly malignant germ cell tumors stand out due to their very fast tumor growth and the progression of clinical symptoms. Therefore, detailed screening tests do not seem mandatory for this category of tumors. The majority of epithelial ovarian cancers (EOCs) and epithelial–stromal ovarian tumors are suspected to be of extra ovarian origin, as the derivative cell is not ovarian (serous, mucinous, endometrioid, clear cell, and others). For clinical decision making, surface epithelial malignancies were further divided into two categories as a function of their pathogenetic pathways: type I and type II .
Most malignant tumors of the ovary are surface epithelial (90%). In 2014, the World Health Organization (WHO) recognized five principal epithelial OC histotypes: high-grade serous, low-grade serous, endometrioid, clear cell, and mucinous carcinoma. Other malignancies such as carcinosarcoma, adenosarcoma, and endometrioid stromal sarcoma are very rare; therefore, there is very little data concerning their pathogenesis and molecular features. Moreover, not otherwise specified ovarian tumors such as neuroendocrine, rete ovarii adenocarcinoma, Wilm‘s tumor, and others are exceptionally rare with an incidence of less than 0.1%. The most frequent mutation characteristics according to tumor morphology are presented in Table 1 .
|Histology||Cells of Origin||Precursors||More Frequent Somatic Mutations|
|Low-Grade Serous Carcinoma||Fallopian tube progenitor cell or secretory cell||Serous cystadenoma, adenofibroma, atypical proliferative serous tumor, noninvasive micropapillary serous borderline tumor||KRAS (30%), BRAF (30%), NRAS, EIF1AX, USP9X, ERBB2, FRAR1, NF1, HRAS|
|Mucinous Carcinoma||Unknown||Mucinous adenoma, mucinous borderline tumor||CDKN2A (76%), KRAS and TP53 (both 64%), ERBB2 (26%), RNF43, BRAF, PIK3CA, ARID1A (8–12%)|
|Endometrioid Carcinoma||Endometrial epithelial cells||Endometriosis and endometrial cell-like hyperplasia, endometrioid borderline tumor||ARID1A (30%), PIK3CA (30%), TERT, CTNNB1, TP53|
|Clear-Cell Carcinoma||Endometrial epithelial cells||Endometriosis, endometrioid borderline tumors||PIK3CA (50%), ARID1A (50%), KRAS, MET, PTEN, CTNNB1, RPL22, TP53|
|High-Grade Serous Carcinoma||Fallopian tube progenitor cell or secretory cell||SCOUT, P53 signature, STIC||TP53 (96–98%)
BRCA1/BRCA2 (10%, 25% somatic + germline);
CNAs of CCNE1 amplification, PTEN deletion, RB1 and NF1 loss
|Carcinosarcomas||Unknown||Carcinomatous component||TP53, CTNNB1|
Type II tumors are characterized as highly aggressive neoplasms accounting for 75% of all EOCs, which are usually diagnosed at a late stage. They include high-grade serous carcinoma (HGSOC)—the most common type—and rare types such as high-grade endometrioid, undifferentiated carcinomas, and malignant epithelial mesenchymal tumors (carcinosarcomas). Type II ovarian tumors have a high level of genetic instability; the majority harbors TP53 mutations . Recent data suggest that HGSOC tumors originate from the epithelium of the fallopian tube. Mutation of TP53 is the first known molecular event in the transformation of fallopian tube secretory cells to serous tubal intraepithelial carcinomas (STICs), which leads to HGSOC initiation. Mutated TP53 can be identified as an early tumor precursor of HGSOC. It has been estimated that it takes approximately seven years from STIC to clinically evolve into HGSOC . Almost 80% of women present with advanced (stages III-IV) disease and poor prognosis (the five-year survival rate is around 25%). Since up to 98% of all HGSOC cases are characterized by TP53 somatic mutations, this biomarker is widely investigated as a potential diagnostic tool for OC diagnostics .
We performed a literature search in NCBI PubMed from January 2014 to September 2020 with a specific emphasis on liquid biopsy biomarkers for early OC detection. We used the keywords “ovarian cancer” together with “circulating free DNA”, ”circulating tumor DNA”, ”circulating tumor cells”, “small non coding RNA”, “microRNA”, “PIWI- interactingRNA”, “Transfer-RNA-derivated small RNA”, “liquid biopsy”, “TEPS”, and “uterine lavage”. We identified 2193 abstracts in NCBI PubMed and selected 30 reports considered inclusion criteria—evaluating the efficacy of liquid biopsies as a diagnostic tool for OC detection. We summarize the results of these studies in Table 2 . This work provides deeper understanding of the aspects of OC pathogenesis and existing challenges for liquid biopsy applications in clinical practice.
|Author (Year), References||Number of OC Patients||Specimen||Method||Genetic Marker/Antigen||Detection Rate (%)||Detection Rate (%) (I-II Stage)||Sensitivity (%)||Specificity (%)|
|K.K Lin et al. (2019) ||112 germline or somatic BRCA-mutant HGOC||Plasma (ctDNA)||Targeted-NGS||BRCA1, BRCA2, TP53||96 for TP53||NR||NR||NR|
|Y. Wang et al. (2018) ||83 OC||Plasma (ctDNA)||Pap SEEK-PCR-based error-reduction technology Safe-SeqS||18 genes + assay for aneuploidy||43||35||NR||100|
|Y. Wang et al. (2018) ||83 OC||Plasma (ctDNA) + Pap Brush samples||Pap SEEK-PCR-based error-reduction technology Safe-SeqS||18 genes + assay for aneuploidy||63||54||NR||100|
|P.A. Cohen et al. (2018) ||54 OC||Plasma (ctDNA) + proteins||CancerSEEK
|16 gene panel + 41 protein biomarkers||98||38||NR||>99
AUC = 0.91
|J. Phallen et al. (2017) ||42 OC||Plasma (ctDNA)||Targeted NGS (TEC-Seq) and ddPCR||55 gene panel||71||68||NR||100|
|E. Pereira et al. (2015) ||22 HGSOC||Serum (ctDNA)||ddPCR, NGS, WES||TP53, PTEN, PIK3CA, MET, KRAS, FBXW7, BRAF||93.8||NR||81-91||60-99|
|A. Piskorz et al. (2016) ||18 OC||Plasma (ctDNA)||Targeted NGS||TP53||100||NR||NR||NR|
|R.C. Arend et al. (2018) ||14 OC||Plasma (cfDNA)||Targeted NGS||50 gene||100||NR||NR||NR|
|J.D. Cohen et al. (2016) ||32 HGSOC||Plasma cfDNA
|A. Vanderst-ichele et al. ||57 OC and bordline tumors||Plasma cfDNA||WGS||CNV||67||NR||NR||99.6
AUC = 0.89
|Y. Wang et al. (2018) ||245 OC||Cervix Pap brush samples (DNA)||Pap SEEK-PCR-based error-reduction technology Safe-SeqS,||18 genes + assay for aneuploidy||NR||33||34||99|
|Tao Brush (DNA)||Pap SEEK-PCR-based error-reduction technology Safe-SeqS||18 genes + assay for aneuploidy||NR||45||47||100|
|Salk et al. (2019) ||10 OC||Uterine lavage (DNA)||Duplex Sequencing||TP53||80||NR||70||100|
|E.Maritschnegg (2018) ||33 OC||Uterine lavage (DNA)||Deep-sequencing||AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, KRAS, NRAS, PIK3CA, PIK3R1, POLE, PPP2R1A, PTEN, TP53||80 for TP53||NR||NR||NR|
|E.Maritschnegg (2015) ||30 OC||Uterine lavage (DNA)||Massively parallel sequencing||AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2,||60 for TP53||100 for TP53||NR||NR|
|With ddPCR and SafeSeqS||KRAS, NRAS, PIK3CA, PIK3R1, POLE, PPP2R1A, PTEN, TP53||80 for TP53|
|B.K Erickson et al. (2014) ||5 OC||Vaginal tampon (DNA)||Massively parallel sequencing||NR||60||NR||60||NR|
|Kinde et al. (2013) ||22 OC||Liquid Pap smear tests (DNA)||Massively parallel sequencing||NR||41||NR||NR||NR|
|N. Li et al (2019) ||30 EOC||Plasma (CTC)||Magnetic nanospheres (MNs) + IHC||EpCAM, FRα||92||NR||75||90
AUC = 0.8
|Zhang et al. (2018) ||109 EOC||Plasma (CTC)||Imunomagnetic beads (EpCAM, HER2 and MUC1) + multiplex RT-PCR||EpCAM, HER2, MUC1, WT1, P16, PAX8||90||93||NR||NR|
|Q Rao et al. (2017) ||23 EOC||Plasma (CTC)||Microfluidic system with immunomagnetic beads (EpCAM) + IHC||EpCAM, CK3-6H5, panCK||87||NR||NR||NR|
|M. Lee et al. (2017) ||54 EOC||Plasma (CTC)||Incorporating a nanoroughened microfluidic platform + IHC||EpCAM, TROP-2, EGFR, Vimentin, N-cadherin||98.1||NR||NR||NR|
|Dong Hoon Suh et al. (2017) ||87 EOC, bordline, benigh||Plasma (CTC)||Tapered-slit membrane filters + IHC||EpCAM, CK9||56.3||NR||77.4||55.8
AUC = 0.61–0.75
|I. Chebouti et al. (2017) ||95 EOC||Plasma (CTC)||Adna Test Ovarian Cancer and EMT-1 Select/Detect + Multiplex RT-PCR||EpCAM, ERCC1, MUC1, MUC16, PI3Ka, Akt-2, Twist||82||NR||>90||>90|
|K. Kolostova et al. (2016) ||40 OC||Plasma (CTC)||MetaCell + IHC/qPCR||ICC: NucBlueTM, CelltrackerTM.
EpCAM, MUC1, MUC16, KRT18, KRT19, ERCC1, WT1
|K. Kolostova et al (2015) ||118 OC||Plasma (CTC)||MetaCell + IHC/qPCR||ICC: NucBlueTM, CelltrackerTM.
EpCAM, MUC1, MUC16, KRT18, KRT19,
|M. Pearl et al. (2015) ||31 EOC||Plasma (CTC)||CAM uptake-cell enrichment + IHC/RT-qPCR||EpCAM, Ca 125, CD44, seprase
EpCAM, CD44, MUC16, FAP
|Pearl et al. (2014) ||129 EOC||Plasma (CTCs)||CAM uptake – cell enrichment + IHC||EpCAM, Ca 125, CD44, seprase||88. 6||41.2||83||95.1|
|Gao et al. (2015) ||143 all 74 EOC||Serum microRNA||qRT-PCR||miR-200c||NR||NR||72||70, AUC = 0.79|
|miR-141||69||72, AUC = 0.75|
|Meng et al. (2016) ||163 EOC||Serum microRNA||TaqMan microRNA assays and ELISA||miR-200a||NR||NR||83||90, AUC = 0.91|
|miR-200b||52||100, AUC = 0.81|
|miR-200C||31||100, AUC = 0.65|
|3miRNAs set||88||90, AUC = 0.92|
|Yokoi et al. in (2017) ||269 all 155EOC||Serum microRNA||qRT-PCR + statistical cross-validation methods||8 miRNA combination||NR||86||92||91, AUC = 0.96|
|Yokoi et al. in (2018) et al. ||EOC 333||Serum microRNA||Microarrays||10 miRNAs set miRNA-320a, -665, -1275, -3184-5p, -3185, -3195, -4459, 4640-5p, -6076, and -6717-5p.
EOS vs. non cancer
|NR||NR||99||100, AUC = 0.72–1.0|
|Kim S. (2019) ||68 all 39HGOC||Serum microRNA||qRT-PCR||miRNA-145||NR||NR||91.7||86.8, AUC = 86.8|
|miRNA-200C||72.9||90.0, AUC = 77.9|
An approach for the lavage of the uterine cavity to detect cancer cells that have been shed was developed by Paul Speiser, Professor at Medical College of Vienna, and colleagues .
A study published by Kinde et al. in 2013 analyzed the liquid Pap test from the uterine cervix for detecting ovarian and uterine cancers. Massively parallel sequencing for tumor-specific mutations using a 12-gene panel was performed on DNA extracted from liquid Pap smear tests. This technique was successfully applied to 100% of patients. Detectible DNA mutations were found in 24 (100%) for endometrial cancer patients and in 9 of 22 (41%) OC, mainly in late stages . A pilot study showed that tumor cells and fragments containing tumor DNA can be found and collected in the vagina using a vaginal tampon and studied by using genetic analysis. They succeeded in revealing TP53 mutations in 60% of advanced HGSOCs . Y. Wang et al. 2018 published data of DNA analysis in Pap brush samples from 245 OC patients, and the detection sensitivity was 33%, including 34% for patients with stage I–II disease .
PIWI-interacting RNA (piRNAs) interact with PIWIs—germline-specific Ago family nuclear RNA-binding proteins—and form piRNA-induced silencing complexes (piRISCs). The latest data demonstrate the contribution of piRNAs and PIWI proteins to the main carcinogenesis events: cell proliferation, resisting cell death, genome instability, invasion, and metastasis. PIWIs are essential for germline tissues and gametogenesis. Due to their restricted expression in reproductive tissue and tumors, PIWIs are classified as cancer/testis antigens (CTA). They are considered as excellent objects for diagnostic/prognostic biomarkers and targeted therapies. piRNAs regulate mechanistic RNA-based inhibition of transposable elements in germlines. They can target nontransposable elements as well—such as protein-coding messenger RNAs (mRNAs) —and modulate their expression, not only in germlines, but also in somatic cells, by a mechanism similar to that of miRNAs. piRISCs contribute to cancer development and progression by promoting a stem-like state of cancer cells, or cancer stem cells. The expression of germline genes in cancer reflects the ectopic activation in somatic tissues of a naturally silenced developmental program managing the escape from cell death, immune circumvention, and invasiveness . In gynecologic malignancies, the study of piRNA pathophysiological significance, expression levels, and diagnostic performance remains exploratory.
Extracellular vesicles (EVs) contain cell surface proteins, as well as miRNAs and other molecules. EV-associated proteins and lncRNAs were investigated as potential biomarkers and showed greater sensitivity comparing to conventional biomarkers, but there are no data about the value to OC patients .
Innovative technologies based on very small samples are likely to drastically change medical practice in the near future. Presently available liquid biopsy assessments are not ready for use in clinical practice. Significant efforts remain to create reliable tests for early OC detection. Uterine lavage techniques are easy to apply and safe, and this approach appears very promising for implementation in daily clinical practice. miRNAs are promising biomarkers for cancer diagnosis and prognosis, and large-scale prospective clinical studies are ongoing. Research efforts directed toward single-cell analysis are likely to shed more light on diagnostic biomarkers and potential therapeutic targets in the future.