Novel biomarkers in ovarian carcinoma
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Novel biomarkers in ovarian carcinoma
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Novel biomarkers in ovarian carcinoma
- 1. Novel biomarkers in ovarian carcinoma PROF. MOUSTAFA RIZK ALEXANDRIA UNIVERSITY 8-3-2017 3/7/2020 5:00 AM 1
- 4. Diagnostic approach Laboratory Serum CA125 Increased in about 80–85% of women with advanced ovarian cancer Only 50% of patients with stage I ovarian cancer will have an elevated CA-125 level. Mostly considered as a useful biomarker for follow-up (e.g., monitoring of progression and regression) But has neither sufficient sensitivity nor specificity for early detection 3/7/2020 5:00 AM 4
- 5. Other tumor markers for ovarian cancer 3/7/2020 5:00 AM 5 CA19-9 CA72-4 CA15-3 Haptoglobin- alpha Mesothelin Lysophosphatidic acid Osteopontin
- 6. Diagnostic approach Laboratory Serum CA125 Imaging Transvaginal sonography Invasive Laparoscopy 3/7/2020 5:00 AM 6 25% of the OC in the early stage 100% of the OC within the early stage
- 7. Risk of Malignancy Index (RMI) 3/7/2020 5:00 AM 7 The best cutoff value for RMI is 200 RMI score = Ultrasound score x menopausal score x CA125 level in U/ml
- 8. Strategies for early detection require high sensitivity for early stage disease (>75%) , and must have extremely high specificity (99.6%) to attain a good PPV. There is no single screening test nor any existing screening paradigm that currently has such high specificity. Thus, discovery of specific molecular biomarkers /panels is emerging as an important requirement for early detection of ovarian cancer. 3/7/2020 5:00 AM 8
- 9. Potential biomarkers for ovarian cancer diagnosis A-Gene-based ovarian cancer biomarkers 1. Inherited gene mutations 2. Epigenetic changes 3. Gene expression B-Protein-based ovarian cancer biomarkers 1. Proteomic pattern diagnostics 2. Serum proteomic profiling 3. Single or panel novel biomarkers C-Emerging ovarian cancer biomarkers 1. MicroRNA-based ovarian cancer biomarkers 2. Metabolite-based ovarian cancer biomarkers 3/7/2020 5:00 AM 9
- 10. A-Gene-based ovarian cancer biomarkers 1-Inherited gene mutations(10%): Cancer Type General Population Risk Risk for Malignancy BRCA1 BRCA2 Breast 12% 46%-87%% 38%-84% Second primary breast 2% within 5 years 21.1% within 10 yrs 83% by age 70 10.8% within 10 yrs 62% by age 70 Ovarian 1%-2% 39%-63% 16.5%-27% Male breast 0.1% 1.2% Up to 8.9%% Prostate 6% through age 69 8.6% by age 65 15% by age 65 20% lifetime Pancreatic 0.50% 1%-3% 2%-7% Melanoma (cutaneous & ocular) 1.6% Elevated Risk 3/7/2020 5:00 AM 10 Van den Broek AJ, et al. Worse breast cancer prognosis of BRCA1/BRCA2 mutation carriers: what's the evidence? A systematic review with meta- analysis. PLoS One. 2015;10:e0120189. a-Germline mutations of the BRCA1 and BRCA2 tumor suppressor genes (90%)
- 12. A-Gene-based ovarian cancer biomarkers 3/7/2020 5:00 AM 12 1-Inherited gene mutations(10%): b- Germline mutations of the DNA mismatch repair(10%) o ~7% of hereditary ovarian cancer cases o 5% of all colorectal cancer cases o Most common cancers: colon,endometrial o Increased incidence of other adenocarcinomas, including stomach, small bowel HNPCC or Lynch Syndrome Mismatch repair genes (MMR) including MLH1, MSH2, and MSH6
- 13. Mismatch Repair Hereditary NonPolyposis Colorectal Cancer Increased incidence of cancers of the colon, endometrium, ovary, stomach, and upper urinary tract Autosomal dominant HNPCC due to germline mutations in mismatch repair genes hMSH2, hMLH1, MSH6, (PMS1, PMS2)
- 14. A-Gene-based ovarian cancer biomarkers 3/7/2020 5:00 AM 14 2 - Epigenetic changes: I. DNA methylation II. Histone modifications. Hypermethylation of at least one of a panel of 6 tumor suppressor gene promoters: BRCA1 RAS association domain family protein 1A (RASSF1A) Adenomatous polyposis coli (APC) p14ARF p16INK4a Death associated protein-kinase (DAPKinase) Most studies to date have focused on candidate gene approaches to identify: Hypermethylated Silenced candidate tumor suppressor genes Specific regions of hypomethylation in ovarian cancer Epigenetic markers can be assayed in circulating DNA of the blood, which provides the promise of a non-invasive test
- 15. A-Gene-based ovarian cancer biomarkers Identifying different subtypes of ovarian cancer Identifying cancer likely to be responsive to therapy •Claudin 3 (CLDN3) •WAP four-disulfide core domain 2 (WFDC2or HE4) •Folate receptor 1 (FOLR1) •collagen type XVIII a1 •Cyclin D1 (CCND1) 3/7/2020 5:00 AM 15 3-Gene expression Microarray technology Data analysis software SAGE Distinguishing normal ovarian tissue from ovarian tumors
- 16. •Although gene-based biomarkers are known to have potential for ovarian cancer, there is still no novel cancer specific biomarker in clinic. •This is due to the fact that gene levels are not always linked directly to levels of proteins, the molecules that biologically do functions. 3/7/2020 5:00 AM 16
- 17. B-Protein-based ovarian cancer biomarkers Proteomics has emerged as a powerful technology to decipher biological processes. It means large-scale characterization of proteins including more complicated features like isoforms, modifications, interactions and functional structures. 3/7/2020 5:00 AM 17 1. Proteomic pattern diagnostics
- 18. 3/7/2020 5:00 AM 18 Published studies show that proteomic pattern analysis in ovarian cancer has the potential to be a novel, highly sensitive diagnostic tool for detection at an early stage.
- 19. Among several different MS-based proteomics approaches, currently: 1. Matrix-assisted laser desorption and ionization time-of-flight (MALDI-TOF) 2. Surface-enhanced laser desorption and ionization time-of-flight (SELDI-TOF) are two of the most frequently used methods for new biomarker discovery 3/7/2020 5:00 AM 19
- 20. With the impressive results in terms of specificity and sensitivity in ovarian cancer detection, some criticism regarding 1. Instrument reproducibility 2. Quality control 3. Standard operating procedures for sample collection, handling and shipping have been raised. Recently researchers have emphasized more and more on the importance of reliability and reproducibility of a MS technology in protein profiling 3/7/2020 5:00 AM 20
- 21. 2-Single or panel novel biomarkers The OVA1 panel 3/7/2020 5:00 AM 21 CA-125 Beta-2 microglobulin Transferrin ApolipoproteinA1 Transthyretin OVA1 above 5.0 in premenopausal people and 4.4 in postmenopausal people indicates a high risk for cancer.
Editor's Notes
- Ovarian cancer is the most lethal of all common gynecologic malignancies, with more than 204,000 new cases and 125,000 deaths each year, accounting for 4% of all cancer cases and 4.2% of all cancer deaths in women around the world Contributing to the poor prognosis of ovarian cancer is the lack of symptoms in the early stages of the disease.
- More than 70% of the women are diagnosed with late stage disease [International Federation of Gynecology and Obstetrics (FIGO) stage III or IV], after distant metastasis has occurred. The 5-year survival rate for women diagnosed with late stage disease is less than 20% even with extensive surgery and chemotherapy, compared to up to 90% for women diagnosed with early stage disease .
- CA-125, which is significantly associated with ovarian cancer, is the only serum molecule now normally used in the clinical practice. While CA-125 serum levels are increased in about 80–85% of women with advanced ovarian cancer, only 50% of patients with stage I ovarian cancer will have an elevated CA-125 level. Therefore, CA-125 is mostly considered as a useful biomarker for follow-up (e.g., monitoring of progression and regression) of patients with established ovarian cancer, but has neither sufficient sensitivity nor specificity for early detection . Positive predictive value is the probability that subjects with a positive screening test truly have the disease. Negative predictive value is the probability that subjects with a negative screening test truly don't have the disease.
- Serum CA125 integrated with transvaginal sonography can only detect about 25% of the OC in the early stage . Laparoscopy can identify nearly 100% of the OC within the early stage, but the high cost and invasive properties tremendously impede its feasibility in clinical practice . Given the prevalence of ovarian cancer, strategies for early detection must have high sensitivity for early stage disease (>75%), as well as an extremely high specificity (99.6%) to attain a good positive predictive value.
- There are two scoring systems for assessing malignancy risk, the Risk of Malignancy Index 1 (RMI 1) and the Risk of Malignancy Index 2 (RMI 2), each of which calculates scores using ultrasound features, menopausal status and preoperative CA125 level according to the equation: RMI score = ultrasound score x menopausal score x CA125 level in U/ml. (107) The RMI 2 score gives greater weight to the ultrasound findings and menopausal status than the RMI 1. The best cutoff value for RMIs 1 and 2 is 200.
- Such high specificity will not likely be met by use of a single screening test alone, and cannot yet be met with any existing screening paradigm. Thus, discovery of novel ovarian cancer specific molecular biomarkers/panels is emerging as an important platform toward early detection. The present review summarizes various types of ovarian cancer markers investigated at present, including gene-, protein-based and emerging ovarian cancer biomarkers (such as microRNA-, metabolite-based).
- Contributing to the poor prognosis of ovarian cancer is the lack of symptoms in the early stages of the disease. More than 70% of the women are diagnosed with late stage disease [International Federation of Gynecology and Obstetrics (FIGO) stage III or IV], after distant metastasis has occurred. The 5-year survival rate for women diagnosed with late stage disease is less than 20% even with extensive surgery and chemotherapy, compared to up to 90% for women diagnosed with early stage disease . Therefore, detection of ovarian cancer at an early stage is critical for curative treatment interventions. Unfortunately, current diagnosis methods for the detection of early stage ovarian cancer are inadequate. Only 25% of all ovarian cancer is found at early stage [2] and [3]. CA-125, which is significantly associated with ovarian cancer, is the only serum molecule now normally used in the clinical practice. While CA-125 serum levels are increased in about 80–85% of women with advanced ovarian cancer, only 50% of patients with stage I ovarian cancer will have an elevated CA-125 level. Therefore, CA-125 is mostly considered as a useful biomarker for follow-up (e.g., monitoring of progression and regression) of patients with established ovarian cancer, but has neither sufficient sensitivity nor specificity for early detection .
- Risk of Malignancy in Individuals with a Germline BRCA1 or BRCA2-Pathogenic Variant. At least 10% of all epithelial ovarian cancers (EOCs) are hereditary, with germline mutations of the breast cancer 1/2 (BRCA1 and BRCA2) tumor suppressor genes accounting for approximately 90% of cases. Research showed that both BRCA proteins participate in transcriptional regulation of gene expression as well as the recognition or repair of certain forms of DNA damage, particularly double-strand breaks. Mutations of BRCA1 and BRCA2 are mainly of the frameshift or nonsense variety.
- Cancers are thought to arise from genetic alterations, environmental factors and a combination of both. Malignant transformation of normal ovarian epithelial cells is caused by genetic alterations that disrupt regulation of proliferation, programmed cell death and senescence. The vast majority of ovarian tumors arise due to accumulation of genetic damage, but the specific genetic pathways for the development of epithelial ovarian tumors, borderline and malignant, are largely unknown. Considering that a close connection exists between genetic changes and ovarian tumorigenesis, it is obvious that research on gene level (including studies of inherited gene mutations, epigenetic changes and gene expression) would also provide potential ovarian cancer biomarkers. DNA- or RNA-based cancer biomarkers utilize microarrays, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), DNA sequencing, fluorescent in situ hybridization (FISH) etc. to detect the genetic alterations occurring in the cancerous state.
- Risk of Malignancy in Individuals with a Germline BRCA1 or BRCA2-Pathogenic Variant. At least 10% of all epithelial ovarian cancers (EOCs) are hereditary, with germline mutations of the breast cancer 1/2 (BRCA1 and BRCA2) tumor suppressor genes accounting for approximately 90% of cases. Research showed that both BRCA proteins participate in transcriptional regulation of gene expression as well as the recognition or repair of certain forms of DNA damage, particularly double-strand breaks. Mutations of BRCA1 and BRCA2 are mainly of the frameshift or nonsense variety.
- Epigenetic mechanisms including DNA methylation and histone modifications are important means of gene regulation and play essential roles in tumor initiation and progression. Measurement of the methylation status of the promoter regions of specific genes can aid early detection of cancer, determine prognosis and predict therapy responses. The identification of specific genes that are altered by epigenetic events is currently under active investigation in ovarian cancer. For example, Tumor-specific hypermethylation of at least one of a panel of six tumor suppressor gene promoters, including BRCA1, RAS association domain family protein 1A (RASSF1A), adenomatous polyposis coli (APC), p14ARF, p16INK4a, and death associated protein-kinase (DAPKinase), was found in tumor DNA obtained from 50 patients with ovarian or primary peritoneal tumors. An identical pattern of gene hypermethylation was detected in the matched serum DNA from 41 of 50 patients (82% sensitivity), including 13 of 17 cases of stage I disease. In contrast, no hypermethylation was observed in nonneoplastic tissue or serum from 40 control women (100% specificity) [7] . Most studies to date have focused on candidate gene approaches to identify hypermethylated and silenced candidate tumor suppressor genes, but there is also a growing literature on specific regions of hypomethylation in ovarian cancer. Moreover, epigenetic markers can be assayed in circulating DNA of the blood, which provides the promise of a non-invasive test [7] .
- Quantitative or semi-quantitative measurement of the expression of particular genes in serum or tumor tissue has the potential for helping tumor diagnosis. In the last decade, the field of gene expression has progressed rapidly due in large part to the development of microarray technology, which enables us to measure the expression of tens of thousands of genes in a given tissue sample through a single experiment. This high-throughput technology, when coupled with powerful data analysis software, allows to rapidly compare gene expression between normal and malignant cells and to identify genes that are differentially regulated during cancer development. Microarray data can also be used to categorize tumors on the basis of their transcriptional profile, which may provide important biological, diagnostic and prognostic information. The current state of knowledge about the potential clinical value of gene expression profiling in ovarian cancer is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian tumors, identifying different subtypes of ovarian cancer and identifying cancer likely to be responsive to therapy. In EOC, gene-expression profiling has been used to provide prognostic information, to predict response to first-line platinum-based chemotherapy, and to discriminate between different histological subtypes [8] . In addition to microarray technology, serial analysis of gene expression (SAGE) represents another major class of technology currently available for the quantitative analysis of gene expression in ovarian cancer. SAGE facilitates the measurement of mRNA transcripts and generates a non-biased gene expression profile of normal and pathological disease tissue. Particularly, the SAGE technique has the capacity of detecting the expression of novel transcripts allowing for the identification of previously uncharacterized genes, thus providing a unique advantage over the traditional microarray-based approach for expression profiling. In ovarian cancer, several known and novel genes whose expressions are elevated have been identified by SAGE technology. These genes included claudin 3 (CLDN3) [9] , WAP four-disulfide core domain 2 (WFDC2, also known as HE4) [9] , folate receptor 1 (FOLR1) [9] , collagen type XVIII a1 (COL18A1) [9] , cyclin D1 (CCND1) [9] , FLJ12988 [9] .
- Use of blood test panels may help in diagnosis.The OVA1 panel includes CA-125, beta-2 microglobulin, transferrin, apolipoprotein A1, and transthyretin. OVA1 above 5.0 in premenopausal people and 4.4 in postmenopausal people indicates a high risk for cancer
- . The major proteomics technique that fundamentally supported the discovery of cancer biomarkers is MS which can determine precise mass and charge of protein, thus identity of the actual precursor proteins or protein profiles. Among several different MS-based proteomics approaches, currently, matrix-assisted laser desorption and ionization time-of-flight (MALDI-TOF) and surface-enhanced laser desorption and ionization time-of-flight (SELDI-TOF) are two of the most frequently used methods for new biomarker discovery [10] . Proteomic applications to ovarian cancer diagnosis have followed two paths [11] : one, called “proteomic pattern diagnostics” or “serum proteomic profiling”, is based on complex mass spectrometric differences between proteomic patterns of samples with and without cancer identified by bioinformatics. Many previously published studies showed that proteomic pattern analysis in ovarian cancer has the potential to be a novel, highly sensitive diagnostic tool for detection at an early stage [12] . However, with the impressive results in terms of specificity and sensitivity in ovarian cancer detection, some criticism regarding instrument reproducibility, quality control and standard operating procedures for sample collection, handling and shipping have been raised. Recently researchers have emphasized more and more on the importance of reliability and reproducibility of a MS technology in protein profiling.