Category: Ovarian Cancer

Ovarian cancer is the growth of abnormal cells in one or both ovaries in a women’s reproductive system. These cells multiply out of control forming a tumour and if left untreated, the tumour can metastasise to other parts of the body. Being the most lethal gynaecological malignancy, ovarian cancer is the fifth leading cause of cancer death in the developed world affecting females, with approximately every year 1,600 Australian women diagnosed with cancer. Consequently, the Australian government is supporting the National Health and Medical Research Council (NHMRC) by providing an immense amount of funding for cancer research. Subsequently, Australian researchers have received funding such as David Bowtell, Ian Campbell and Judith Clements who made contributions for identifying the key molecular inhibitory pathways of the disease with potential therapeutic targets for treatment, with the integration of international scientists. In the meantime, KLK-8, CA125 and Harmine are researched to be the most effective targets for treatment inhibiting the Ras pathway and treating the disease, therefore, consisting of potential utility for early detection, risk, prognosis, diagnosis therapy and monitoring. Although current research has been implemented, more thorough research from Australian scientists is needed for developing targeted medicine to seek potential health gain by contributing towards the burden of disease in Australia

Funding of Ovarian Cancer in Australia

With the recognition of NHPA, ovarian cancer has the lowest survival rate of any women’s cancer and little advancement of treatment options (Cancer Australia, 2019). The Government has provided over $800 million in funding through the NHMRC (Australia’s peak body for medical research) for cancer research, in particular, ovarian cancer funding of over $8 million to reduce the burden of this devastating disease (Research Funding Data, 2017). The benefit of funding is returned by the Australian investigators who received funding are taking innovative approaches to tackle ovarian cancer through different therapeutic targets such as screening drugs targeting cell population by locating the sensitivity of cancer cells to ensure patients have another chance to respond to treatment (Lam et al, 2015 REV, National Health and Medical Research Council, 2017).

Contributions made by Australian Researchers towards ovarian cancer

Ovaries can become cancerous like other organs in the body. Detection of ovarian cancer is challenging and is generally found at an advanced stage or has already metastasised (Torpy, 2011 REV). Ovarian cancer (OC) refers to a heterogeneous disease, strong association with mutations in BRCA1 or BRCA2 (figure 1), low parity and genetic factors (Svahn et al, 2013 REV). Being the most gynaecological malignancy, OC is the fifth leading cause of cancer death affecting women aged 35 to 74 years also with an overall 40% of 5-year survival rate. Worldwide, women diagnosed annually with cancer is 225,000 and 140,000 die from the disease (Matulonis et al, 2014 REV). Ovarian cancer is classified into three types, this depends on where cancer begins on the type of cell (Desai, 2014 REV). Epithelial type begins on the outside of the ovary (90% of most cases), Germ cell type begin from cells producing eggs (4% of most cases) and stromal type begin from supporting tissues within the ovary (rare cases) (Desai, 2014 REV).

Due to the poor survival of women diagnosed with ovarian cancer, there is persistent clinical demand in the search for effective prognostic biomarkers to evaluate outcomes of this cancer conducted by Australian researchers. Research by David Bowtell and colleagues (2017, AUS) identified the mutation of TP53 tumour-suppressor antibody serves as a biomarker promising for early detection for patients diagnosed with epithelial ovarian cancer (Yang et al, 2017 AUS). In conjunction with this biomarker, researchers from China are also focussing on the clinical significance of TP53 mutations for the development of a therapeutic target in screening and prognosis of epithelial ovarian cancer (Zhang et al, 2016 RES). Research conducted by Ian Campbell and associates (2018, AUS) focuses on investigating the germline variation genes that encode small GTPase proteins based on the Ras superfamily (Rho) associated with the risk of epithelial ovarian cancer susceptibility (Earp et al, 2018 AUS). The GTP binding proteins regulate cell proliferation and signal transduction by activating downstream effectors MAP-kinase Raf-1 (activates MEK/ERK pathway) for anti-apoptosis pathway (figure 1) (Earp et al, 2018 AUS). Consequently, international researchers from China have established therapeutic drugs to suppress mutationally activated Ras, specifically targeting the Ras/MAPK pathway (Ji et al, 2019 RES). Australian researcher Judith Clements and her colleagues (2018, AUS) contributed by their findings of tissue kallikrein peptidases (gene family encoding proteins in disease states) KLK4-7 as important therapeutic targets in ovarian cancer (figure 1) by exerting key modulatory effects on cancer-related proteins and genes (Wang et al, 2018 AUS). Clements coincides with German researchers focusing on over-expression of these kallikrein-related peptidases as novel therapeutic targets (Loessner et al, 2018 RES).

The role of MAPK/ERK and PI3K signalling pathways in ovarian cancer

Signal transduction is recognised as a potential target for cancer treatment and much effort has been made by Australian scientists to produce therapeutic agents to target signal transduction pathways (Wang et al, 2018 AUS, Yang et al, 2017 AUS) (figure 1). Based on the Reactome and Kegg databases along with research provided by the Australian scientists, the major enriched pathways for ovarian carcinomas were the Phosphoinositol 3 kinase (PI3K) pathway and the RAS pathway for cell proliferation and survival (Reibenwein and Krainer, 2008 REV). Mutations and dysregulations in ovarian carcinomas such as KRAS and BRAF (Earp et al, 2018 AUS) and also disruption of the tumour suppressor gene E-cadherin due to being oncogenic potential (Earp et al, 2018 AUS). This results in the loss of cell-cell adhesion which gains further motile and invasive behaviour this is the process known as the epithelial-to-mesenchymal transition (EMT) (figure 1) which is a crucial event under pathological conditions involving cancer progression (Gheldof and Berx, 2013 REV).

RAS signalling pathway is involved with the transduction of signals of several growth factors, cytokines and proto-oncogenes (Earp et al, 2018 AUS). For ovarian cancer cells, MAPK’s play a critical role in stimulating the growth of the cells by the membrane receptor signals for Gonadotrophins (figure 1) (Smolle et al, 2013 REV). RAS proteins regulate signalling transduction and are also critical regulators of several aspects of malignant transformation and cell growth (De Luca et al, 2012 REV). The PI3K pathway is activated and altered in 70% of ovarian cancer cases (De Luca et al, 2012 REV). The abnormal activation of the PI3K pathway results in increased activity of tumour onset, progression and invasion in ovarian cancer (fig 1). In accordance with the inhibition of the PI3K/Akt/mTOR signalling pathway it was found that it can re-sensitise chemoresistant cancer cells to chemotherapeutic drugs, there are established rational therapies by the Australian scientists and international research that target and inhibit both RAS pathway inhibiting tumour growth, spread and survival (Tanaka et al, 2011, Earp et al, 2018 AUS) (figure 1).

Implementing potential therapeutic targets of KLK-8 and CA125

Higher Human Kallikrein Gene 8 (KLK8) researched by Judith Clements (2018, AUS) is a new discovery from the members of the kallikrein-related peptidases undertaken as a potential biomarker in 147 malignant ovarian tissues refining our understanding of determinants of the prognosis in OC. (Wang et al, 2018 AUS, Loessner et al, 2018 RES). Due to these proteomics-based expression profiling technologies, increased amounts of prognostic biomarkers associated with tumours in relevance to ovarian carcinomas have been discovered contributing a promising therapeutic for the monitoring, prognosis and diagnosis of the disease (Wang et al, 2018 AUS). Coinciding with the recognition of the antigen CA125 (serum marker) also being a target for immunotherapy and an early detection marker (Scholler and Urban, 2007 REV). CA125 contain functional properties and specific locations which make it a choice for therapeutic targets also injecting anti-CA125 is considered a potential benefit of survival (Scholler and Urban, 2007 REV).

International researchers are implementing the natural drug Harmine which can be useful to treat ovarian cancer due to being associated with oncogenic Ras mutations (Ji et al, 2019 RES). This herbal drug belongs to the alkaloid family beta-carboline found in medicinal plants functioning to inhibit Ras in order for suppressing tumour growth (Ji et al, 2019 RES). Coinciding with the Australian scientist Ian Campbell who focuses on inhibiting the Ras pathway and ovarian cancer susceptibility (Earp et al, 2018 AUS).

An ovary is a reproductive gland part of the female reproductive system that is responsible for producing oocytes and the hormones estrogen and progesterone. An ovum is released from the ovary each month with the intention of meeting sperm in the fallopian tube, traveling to the uterus, and implanting for pregnancy. According to the American Cancer Society, “about 22,240 women will receive a new diagnosis of ovarian cancer” (“Key statistics for ovarian cancer”, 2018). Of those 22,240 women, more than half of them will lose the battle to this cancer. Not only is ovarian cancer one of the most common cancers a woman can develop, but it is also one of the most fatal.

Etiology and Risk Factors

Risk factors are conditions in which a person’s chance of obtaining a disease is increased and these conditions can be modifiable or unmodifiable. Some associated risk factors of ovarian cancer include women of older age, especially those experiencing menopause, family history, an earlier starting age of menstruation and a later age of beginning menopause, infertility issues, and the use of replacement hormones (McCully & Perry-Philo, 2015, p. 997). Lifestyle influences or modifiable risk factors like a poor diet and decreased physical activity also play a role in cancer. But, the biggest risk factor is gene mutations that are inherited and can be identified by genetic testing. The BRCA gene makes tumor suppressor proteins and having a mutation in this gene increases the likelihood of developing ovarian cancer. One way to decrease the chances of developing ovarian cancer is the use of oral contraceptives. This keeps the menstruation cycle regular and “results in less ovulation during a woman’s lifetime” (Nezhat, Apostol, Nezhat, C., & Pejovic, 2015, p. 263).

Pathophysiological Process

Any type of cancer beings by the uncontrollable replication of abnormal cells. Ovarian cancer is malignant which means that these abnormal cells travel to other parts of the body rather than staying in the ovary. There are three different classifications of ovarian cancer and they include epithelial, stromal, and germ cell (Nezhat, Apostol, Nezhat, C., & Pejovic, 2015, p. 262).

The most common type of ovarian cancer begins at the epithelium or tissue that encases the ovary. These epithelial cells multiply quickly and it is extremely easy for these cells to travel to other places in the body. Stromal cells are responsible for producing the hormones estrogen and progesterone and these tumors are located inside the ovary and develop from connective tissue. Lastly, germ cell tumors are cells that are responsible for reproduction and mature into an ovum. Little is known about the beginning formation and early stages of these tumors because they are asymptomatic until the tumors metastasize and travel elsewhere. The signs and symptoms are related to the later development of the disease.

Clinical Manifestations and Complications

Clinical manifestations of ovarian cancer usually don’t become apparent until cancer metastasizes and is at its later stages. This is why ovarian cancer has such a poor prognosis and is known as an insidious killer. The most common signs and symptoms experienced are weight loss, trouble eating, bloating, abdominal/pelvic pain, and frequency and urgency regarding urination. These symptoms are very vague and could happen normally in a healthy individual which is a reason why someone wouldn’t immediately go get checked if one of them is present. These clinical manifestations become persistent and more problems start to develop including fatigue, back pains, pain during intercourse, constipation, menstruation changes, and abdominal edema (Nezhat, Apostol, Nezhat, C., & Pejovic, 2015, p. 264). If the cancer isn’t caught early, which it usually isn’t, then many complications can arise. The cancer cells will travel to other parts of the reproductive system and even throughout the whole body. This can cause a woman to lose her reproductive organs and make it impossible to have children which can take a huge emotional toll on some families. Also, if the reproductive organs are removed during surgery, the patient, no matter what age, will experience menopause if they haven’t done so already. And of course, death is the most extreme result of ovarian cancer, which is sadly a common occurrence.

Diagnostics

Women living with ovarian cancer for long periods of time before going to the doctor for the abnormalities they are noticing. Since it doesn’t usually get detected until its later stages, it is extremely important that women get yearly pelvic examinations. During this exam, a physician will palpate the vagina to make sure the anatomy is normal and that there are no obvious masses or other abnormalities. If something abnormal is found during an exam it is common to then use imaging to confirm the findings. Some imaging that may be used is a CT scan, ultrasound, and MRI. These images allow the physician to view the ovaries since they cannot be viewed with a physical exam. In addition, blood can be drawn to check for the tumor marker CA-125. Tumor markers are substances that are made by cancer cells that travel throughout the blood that can indicate a certain type of cancer (McCully & Perry-Philo, 2015, p. 997). With the physical examination, imaging, and a blood test, ovarian cancer can then be diagnosed.

Conclusion

In conclusion, ovarian cancer has impacted many women’s lives and continues to do so. Since there are many risk factors for this disease and since it is usually detected at later stages it is important to keep up with physical examinations and early screening methods. Treatment for ovarian cancer is usually surgical remover of the tumor and chemotherapy. The surgical removal could result in the removal of just the tumor, one or two ovaries, or even the removal of the ovaries along with the uterus. Any type of surgery is difficult but especially one where it may seem like the patient’s womanhood is being lost. When a woman loses a reproductive organ, it can do a great amount of emotional damage for her and those close to her. This is why it is essential to consider the holistic care of a patient experiencing the physical and emotional pain of this insidious killer called ovarian cancer.

An effective screening method for detection of early-stage ovarian cancer requires a specificity of at least 99.6%, sensitivity of at least 75% and a positive predictive value of at least 10% (Clarke-Pearson DL, 2009). Because of the requirement to have high specificity in order to be considered as a screening tool in the general population, this Is a great challenge. Rising

CA125 values are associated with progressive growth of ovarian cancer, whereas stable CA125 values, even when elevated, are associated with benign conditions(Lu et al, 2013). CA 125 is the marker for late stage of the ovarian cancer. It is less sensitive In early stage and in premenopausal women.

Because survival rates, which are subject to lead-time bias, were reported rather than mortality, these results do not, however, prove that screening reduced deaths from the disease and the perceived survival benefit may instead be attributed to an earlier diagnosis of disease with no impact on lifespan.

In Ueland, et al. 2011 and Coleman et al. 2016, significance of using a combination panel was effectively reported. These studies validate multivariate index assay, which is a combination of multiple markers along with CA125. These studies were intended to replace the use of CA125 alone with multivariate index assay in detecting ovarian cancer. OVA1 (Ueland et al.2011) and second generation OVA1(Coleman et al.2016) showed improvement in sensitivity and negative predictive value compared to CA125 alone. The sensitivity in early stage disease is 94% compared to 61% of CA125. Ueland et al report identified 76% of the malignancies missed by CA125. This is consistent with the study by Longoria et al. 2014, which suggested that OVA1 correctly predicted 78% of the malignancies missed by CA125.

They both decreased specificity and positive predictive value to a great amount, which would potentially cause more false positive results. Strengths of these studies are the large cohort, prospective multi-institutional data collection, which helped to have a diverse population. The studies also included both pre-menopausal and post-menopausal women, which further helped in the implementation of multivariate index assay in the pre-menopausal group, where CA125 is less sensitive by its own.

But these assays are only recommended for women presenting with adnexal masses, who are scheduled for the removal of the mass. Therefore, multivariate index assay can only appropriately predict malignancy in the early stages of the disease at the time of surgery, by complementing physician’s preoperative assessment. Because prevalence is high among the patients with current adnexal mass, this cohort increases PPV and decreases NPV

In Hamed et al. 2013, adding an additional marker to CA125 showed an improved sensitivity, but decreased specificity (96.7% sensitive & 80% specific versus 83.3% & 85%). This data is consistent with another study by Moore et al, 2008 in terms of overall trend, which reported a sensitivity of 94.3% and specificity of 75%, when HE4 and CA-125 are combined to detect malignancy. HE4 has a greater advantage over CA125, as it is not elevated in any other benign gynecological diseases. It was also able to differentiate healthy patients from benign diseases. It is also worth noting that HE4 can also be elevated in other non -gynecological cancer. Since it is not clear in the study that the researchers excluded women with non-gynecological disease symptoms or signs, the potential of using HE4 must be validated with more research. The study excluded any women with any underlying pathology, and was matched for all characteristics to the control group. The comparison of both benign and control group helped to identify major changes in the elevation of markers that can be distinguished from the ovarian cancer group. This lead to the conclusion that HE4 was superior to CA-125 in separating benign, borderline ovarian tumors, and cancers of the fallopian tubes as well This study does not distinguish the effectiveness of HE4 based on the menopausal status. This may play a factor in the wide range of HE4 serum levels (60 pmol/Lto 150 pmol/L).The study also had a cut off value for HE4 as 150 pmol/l, which could be varied in different ages of women. Thus if the study instead looked at the changes in the baseline values of the women, like ROCA, the data would have held more power. In a study nested within PLCO, HE4 was the second best marker after CA125 with a sensitivity of 73% compared to 86% for CA125 [16].

Data from Bluyss et al. 2015 was in favor of adding HE4 and Glycodelin (a novel marker) to CA125, even though there were not much difference between the values of CA125 and in combination with others. Sensitivity was increased from 0.894 to 0.915 and area under the curve increased from 0.957 to 0.967, after adding glycodelin and HE4 to CA125. The researchers attributed this lack of difference to the inclusion of more than 15% false negatives to the cohort that were not identified in the source sample of UKCTOCS. MMP-7 & CYFRA 21-1 were considered to be non-significant markers as they had p>0.05 and the lowest area under the curve value respectively. Not a lot of research has been done to prove the effectiveness in a biomarker panel, which therefore calls for further research. Strengths of this study were large sample size, randomization of each case matched on age, exclusion of women with a previous diagnosis of cancer.

Będkowska et al. 2017, also reported an improved early detection of ovarian cancer using the combination of CA125 with other markers than alone. A combined panel of MMP-7, HE4 and CA125 was suggested for utilization in all stages of the disease, especially in stage I & II. Sensitivity of MMP-7 was superior to CA125 in the early stages. Diagnostic power is indicated by the ROC curve, with a higher area under the curve value for the combination panel. Unfortunately, effectiveness of TIMP-1, another marker that was compared, was not able to be proved. It could not differentiate between benign and malignant ovarian tumors. A limitation to this study is the inability of the data to be compared to other researches since there is no. strength of this study is exclusion of participants with other gynecological disease and endometriosis, as CA125 and HE4 are shown to be elevated in these conditions as well. But the study failed to mention if there had been any history of other types of cancer in these women, nor if they were excluded. The study only includes patients from one institution and therefore should be mindful of selection bias.

Simmons et al. 2017 described the potential of a panel of markers which increased sensitivity by detecting cases missed by CA125 alone. The panel consisted of top 4 biomarkers from eight markers that were tested. CA125, HE4, CA72-4 and MMP-7 became the combination panel with a sensitivity of 83.2% at 98% specificity, improving on CA125 alone. A unique characteristic of this study is the use of baseline values for each of the markers and comparing variation in the levels pertaining to cancer types and menopausal status. This longitudinal analysis of the markers helped in the improved sensitivity, reduced mortality, and improved lead time. This fact was akin first established in a randomized UKTOCS and in the establishment of ROCA with CA125.

A data regarding CA125 and HE4 combination in this study was not in accordance with the value reported by Hamed et al, 2013.In this study, the CA125 and HE4 combination achieved a sensitivity of 79.7% at 98% specificity, whereas Hamed et al, 2013 showed a decreased specificity and increased sensitivity. The study used samples from UKTOCS trial, which had the benefit of a large, multi-institutional blinded study.

H.Y et al, 2017 conducted a study using GOLPH3, CA19.9, and CA125 to observe the usefulness of combination marker panel. This panel indeed showed a statistically significant result (p Results from Horala et al. 2016 showed a three-marker panel consisting of osteopontin, CA125 and HE4 with better area under the curve value of 0.958 than CA125 alone (area under the curve of 0.932). This helped in differentiating between benign and malignant ovarian cancer. Strength of this study is appropriate use of benign and healthy control groups to search for a screening marker. But this marker cannot be used as a marker for general population, but could possibly be useful in high risk population(BRCA1 or BRCA2).

This is the first study with serum angiognenesis factors, and therefore further stidies must be conducted in a large cohort to investigate the usefulness. and as with other markers osteopontine levels are also significantly elevated in other malignancies.

Introduction

Ovarian cancer (OC) is the 9th most common cancer and the 9th most common cause of cancer-related death in women, with an overall 5-year survival of approximately 40%. In 2018, 295,414 new cases of OC were documented worldwide, 184,799 of which resulted in death (1). High-grade serous ovarian carcinoma (HGSOC) is the most common and deadliest subtype of epithelial ovarian cancer (EOC), accounting for 70-80% of OC deaths (2). Originating from premalignant lesions in the epithelium of the fallopian tubes, HGSOC is known for its important genomic instability (3). Aside from universal TP53 mutation, HGSOC has numerous aberrations that vary among different tumours (4). To this day, some murine models with specific mutations have been developed, which serve as representatives of some particular tumours. ID8 cells, derived from mouse ovarian surface epithelial cells, have been used for the creation of these new cell lines (5). Previous work conducted in the lab consisted of targeting mutations characteristic of HGSOC by CRISPR/Cas9 knockout as a means of creating more representative models. These included tumour suppressor Trp53, known to induce growth arrest or apoptosis; Brca2, another tumour suppressor that maintains the genome’s stability by regulating the homologous recombination (HR) pathway for double-strand DNA (dsDNA) repair; and Nf1 gene, a negative regulator of the RAS signal transduction pathway (6) (uniprot.org). Mutation of the TP53 gene is found in the majority of HGSOC patients (7), whereas both BRCA2 and NF1 mutations are found in around 20% of patients (8)(9). Results showed that Trp53-/-;Brca2-/- double mutant mice have longer survival following treatment with both platinum and PARP inhibitor compared to Trp53-/-;Brca1-/- and Trp53-/- mutants, but that survival of mice bearing mutations in Trp53 and Nf1 genes is reduced after platinum treatment compared to Trp53-/- mice, along with higher intra-tumoural growth due to prolonged activation of the RAS/RAF/MAPK signalling pathway (6). Surprisingly, 50% of HGSOC patients harbouring BRCA2 mutation also present with a mutation in NF1 (8). Here we create a new murine ID8 Trp53-/-;Brca2-/-;Nf1-/- cell line to understand the consequences of Nf1 loss in ID8 Trp53-/-;Brca2-/- cells by CRISPR/Cas9 Nf1 knockout. We want to study the effect of the combined triple loss in cell doubling time in vitro, in the regulation of cell growth and survival pathways, and of response to platinum chemotherapy and PARP inhibition. We hypothesize that lack of Nf1 in ID8 Trp53-/-;Brca2-/- models will counteract to some extent the effect of Brca2 loss in cell growth and response to chemotherapy.

Material And Methods

Cell lines and cell culture

ID8 Trp53-/-;Brca2-/- 2.14 and ID8 Trp53-/-;Nf1-/- 1.20 clones were previously generated (6). ID8 Trp53-/-;Brca2-/-, ID8 Trp53-/-;Nf1-/-, and newly generated ID8 Trp53-/-;Brca2-/-;Nf1-/- — clones were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 4%(v/v) fetal calf serum (FCS; COMPANY), 2mM?? L-glutamine (COMPANY), 100µg/mL penicillin (COMPANY), 100µg/ml streptomycin (COMPANY), and ITS (5µg/ml insulin, 5µg/ml transferrin, and 5ng/ml sodium selenite (COMPANY)) in a 37ºC, 5% CO2 humidified incubator. Cells were detached from flasks by incubation with trypsin 2x when reaching 70-80% confluency and split in new medium at a ratio of 1/10.

FBS with 10% DMSO was used to freeze cells. Cells were transferred to cryovials and stored at -80ºC. For later thawing, cells were washed thoroughly to ensure no freezing media was still present.

CRISPR/Cas9 TECHNIQUE/PROTOCOL/KNOCKOUT

Invitrogen TrueGuide sgRNA for Mus musculus Nf1 5’-GUUGUGCUCGGUGCUGACUU-3’ (ThermoFisher Scientific, gRNA #A35510) was used for CRISPR/Cas9 knockout.

Cells were trypsinized, counted with Cellometer (COMPANY), and seeded in 24-well plates at different concentrations: 5000 cells/ml, 10000 cells/ml, and 20000 cells/ml. After overnight incubation, cell growth was analyzed with the microscope and the 20000 cells/ml plate was chosen for transfection. DMEM medium was replaced with 50µl Cas9nuclease/gRNA solution (5pmol/µl), Cas9 Plus Reagent and Opti-MEM medium mix for CRISPR/Cas9 transfection according to manufactures instructions (PROTOCOL SUP FIGURES?). For the 2 non-targeting controls (NT), Nf1 gRNA was substituted for H2O, and for the 2 negative controls (ctrl -) OptiMEM Medium alone was used for transfection. After 45 minutes, 500µl of OptiMEM Medium alone were added to each well. Following overnight incubation, OptiMEM Medium was replaced with for 500µl of DMEM, and after 48 hours, half of the cells were frozen at -80ºC and the other half were used for subsequent analyses.

Mismatch cleavage assay

GeneArt Genomic Cleavage Detection Kit (Life Technologies, Catalog #A24372) was used for PCR amplification and mismatch cleavage assay. 50µl of Cell Lysis Buffer and Protein Degrader mix was added to ID8 Trp53-/-;Brca2-/- 2.14 cells transfected with Nf1, and incubated in a thermal cycler (BIO-RAD, DNA Engine, Peltier Thermal Cycler) at 68ºC for 15 minutes and 95ºC for 10 minutes.

PCR primers were designed using Primer3 Software (http://primer3.ut.ee) to expand exon 2, where Cas9 is expected to cut (F: 5’-gggacatagctctggttcct-3’; R: 5’-acatgctctccaaacttcca-3’). 1µl of 10µM (IN MOLS!) F/R primer mix was added in PCR tubes with 2µl of cell lysate, 25µl of AmpliTaq Gold 360 Master Mix, and water to a final volume of 50µl. For the PCR control 1µl of Control Template & Primers, 25µl of AmpliTaq Gold 360 Master Mix, and 24µl of water were used. Amplified samples were run in a 2% agarose gel and imaged in GelDoc ultraviolet (UV) light transilluminator (COMPANY). After PCR validation, samples were denatured and re-annealed and 1µl of Detection Enzyme was added. After 1 hour incubation at 37ºC, samples were run on a 2% agarose gel with 10µl of water for 40 minutes at 80V. The gel was imaged in the UV transilluminator, band intensities were calculated using Fiji Software, and cleavage efficiency was calculated using the following equations: Cleavage Efficiency = 1-[(1-Fraction Cleaved)1/2], where Fraction Cleaved = sum of cleaved band intensities/(sum of the cleaved and parental band intensities). Background noise was ———–.

PROTOCOL SUP FIGURES? And less explanation here

Limiting dilution cloning assay

0.5 cells/well from Brca2 2.14 Nf1.1 Mix were seeded in 5 96-well plates and incubated at 37ºC for 7 days.

1. Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
2. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer.
3. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints.
4. Driver mutations in TP53 are ubiquitous in high-grade serous carcinoma of the ovary
5. Development of a syngeneic mouse model for events related to ovarian cancer.
6. CRISPR/Cas9-derived models of ovarian high-grade serous carcinoma targeting Brca1, Pten and Nf1, and correlation with platinum sensitivity
7. Integrated genomic analyses of ovarian carcinoma
8. Copy-number signatures and mutational processes in ovarian carcinoma. MACINTYRE 2018
9. Neurofibromin 1 (NF1) defects are common in human ovarian serous carcinomas and co-occur with TP53 mutations

Introduction

The advancements in NGS technologies and the emergence of Omics field has led to the development of various approaches in studying cancer. The common approaches to identify molecular mechanisms in cancer include scanning the genome for cancer-specific mutations, exploring differential expression of mRNA through transcriptomics or that of protein through proteomics [Chakraborty S et al. (2018)]. However, limiting the analysis to only one type of data is not very efficient because it does not provide a holistic view of the system and the major causal differences between diseased states. Recently, multi-omics approach that looks at integration of omics data using different methods and tools, is believed to give a more comprehensive understanding of biological systems[Ashar Ahmad & Holger Fröhlich (2016), Hasin et al, (2017)].

The incidence of Cancer has been increasing in India, with higher female:male cases being reported in the year 2015[Mathew A et al. (2018)] . Among female malignancies, breast is ranked first, cervical second and ovarian fourth [Masakazu Toi et al, (2010)], although over the years the trend of cervical cancer is decreasing compared to that of ovarian which is emerging at a high pace [Malvia S et al. (2017)]. It was also observed that the onset of breast and ovarian cancer in Indian population occurs at a much earlier age (45-50 years) than in other high-income countries (age>60 years) [Masakazu Toi et al. (2010)]. Several works have focused on identifying genomic alterations, aberrant mRNA and protein expression of breast and ovarian cancers, but there have been minimal efforts towards integration of such data sets for a systemic understanding of disease mechanisms.

This study will be centred towards understanding the molecular mechanisms of ovarian and breast cancer through integration of exome and transcriptome data. The use of exome and transcriptome sequencing for cancer research is rapidly increasing due to its reduced costs. Exome-seq data is used for the identification of variants across the exonic regions of the genome, while RNA-seq is usually used for expression profiling and to study events like splicing and RNA-editing[O’Brien, T. et al. (2015)]. Individually, these two data sets have been applied in various cancer studies, but only a few studies have carried out an integrated analysis. Such an analysis enables one to determine the effects of variants on gene expression, validate common mutations at the gene and transcript level or even explore variants that may no longer be observed at the transcript level due to events like RNA-editing. Moreover, when using only exome seq data, some of the variants may not be functionally relevant and similarly when using only RNA-seq, the underlying cause of differential expression may not be identified. An example would be integrating mutations and expression data to model interactions between groups of different mutated genes and the resulting modifications at the gene expression level.

Literature Review

Data integration in the field of life sciences is not new, many review papers and research articles have explained the various software tools, methods and workflow of how integration of data has been implemented in their study [Pinu F.R et al. (2019), Brunk E et al. (2016), Yizhak K et al. (2010), Zampieri M et al. (2017), Beal D J et al. (2016), Chen R et al. (2012), Gunther O P et al. (2014)]. A review article in 2014, claimed that the number of papers which had the term data integration in their abstract or title had doubled from the years 2006 to 2013 [Gomez-Cabrero D et al. (2014)]. The review paper published in the ‘Briefings in Bioinformatics’ journal has explained in detail about the various tools used in analysis of genomic, transcriptomic and proteome data as well as the various initiatives happening in the field of data integration. [Claudia Manzoni et al. (2018)].

A review article published in 2019, has explained all the strengths and challenges involved in all the omics data individually. They have also represented challenges that are specific and those that are shared by different omic fields. Apart from these, a list of all the tools, their platforms and what they can be used for has also been summarized. Some examples of where these integrations have been implemented has been briefed about in their paper [Misra et al. (2019)].

Chakraborty S et al. (2018), a review article has explained the potentials of how multi-omics can be used to interrogate cancer at molecular, cellular, and systems levels. There have been successful integration of transcriptome and epigenome for identifying methylation that affect gene expression; proteome and transcriptome data to identify concordance between mRNA and protein levels in cancer; proteogenomics which is to understand to identify novel genes and updating the annotation of existing genomes; metabolome and transcriptome data for overall insight into altered metabolic networks that are tightly controlled by the transcriptional network.

Davis J. McCarthy et al. (2018), Fleck, J.L., Pavel, A.B. & Cassandras, C.G. (2016), Xiong, Q et al. (2012), Junfei Zhao et al. (2012), Burkhardt et al. (2015) are some cases where integration of mutation and gene expression data has been done for various reasons . Davis J. McCarthy et al. (2018) has identified a novel approach to integrate DNA-seq and RNA-seq data to identify the clonal cell from which the RNA-seq data was obtained. It was also pointed out that the cell cycle pathways were commonly distorted in the mutated clonal cell populations. This study was mainly focused only for inferring the clone of origin of individual cells that have been assayed using single-cell RNA-seq.

Fleck, J.L., Pavel, A.B. & Cassandras, C.G. (2016) proposed a model to infer the temporal sequence in which mutations occur and lead to changes at the gene expression level during cancer progression. It used an integer linear program to identify the order in which mutations occured in three different phases of cancer progression and also the corresponding gene expression changes were studied. They selected only the genes which had previously been reported as tumor suppressor genes or oncogenes and checked for mutations and gene expression changes associated with these genes and the model was tested on stimulated as well as TCGA obtained breast carcinoma data.

Xiong Q et al. (2012) developed a single statistical framework GSAA (Gene set association analysis) which measures the genetic variations across the genome and gene expression variations simultaneously to identify sets of genes enriched with gene expression changes and/or trait associated genetic markers. They had concluded that the power to detect real associations between the genes is better in the case of integrated analysis rather than individual genomic data analysis. They performed the analysis on two human diseases (Glioblastoma and Crohn’s disease), the study identified abnormalities in pathways that were previously known to be involved with the disease.

Junfei Zhao et al. developed a method for integration that could differentiate between genes having identical mutational profiles and also identified gene sets with an optimal score. Their method focused on identifying driver genes and pathways by combining the two measures coverage and exclusivity. Here, high coverage meant that a mutation occurred at least once in a pathway for many samples and high exclusivity meant that most samples had no more than one mutation in the pathway.

Burkhardt et al. (2015) studied underlying mechanisms associated with metabolic disorders by integrating SNP, expression and metabolite data. They first identified variants associated with metabolite levels through GWAS, correlated the validated variants with expression data and analyzed the relationship between these genes expression level and metabolites. At the end, they integrated the three associations and inferred causal relationships among them.

Shi, Kai et al. (2016) used the network based integration of mutation, expression and gene network to identify the driver mutations from a series of passage mutations. This method was used for breast cancer, ovarian cancer and glioblastoma. Apart from identifying the high frequency mutations in driver genes, the method was efficient to identify the driver genes with rare mutations too. The data sets were obtained from the DriverNet.

Multi-omics analysis though having many advantages has some constraints with respect to various aspects. Multi-omics research is expensive and requires a lot of funding. Multi-omics data have demonstrated their utility mainly within the field of precision medicine and personalized and these are considered to be moderately useful. Until the scale is increased to population studies their utility will not be truly recognized. Even though there have been improvements in the tools used for integrating and visualization of the integrated data, there still needs to be more easier and user friendly tools that have open access, so that it can reach biologists with limited knowledge in the area of mathematics and statistics. Another major problem involved in the integration of data is dispersed data sets and the noninterpretability of the tools used in bioinformatics. Above all these, nature of the omics data is an important parameter that decides if the data can be integrated; and if it can be done, how good the integrated data is and so on [Pinu, F.R (2019)].

Literature Gap and Rationale of work

As mentioned in the previous section various studies were carried out on the integration of exome and transcriptome, with some focused on ovarian and breast, but these works were not specific to an Indian cohort. No work on the integration of transcriptome and exome of breast and ovarian cancer has been reported till date which is specific to Indian patient samples. This study is directed towards Indian patients, hence the exome-seq and RNA-seq will be specific to an Indian cohort. The research carried out on breast and ovarian cancer has not resulted in an effective therapy till date. Hence, looking specifically into indigenous populations might be advantageous.

Though the methods used for integration have been described in many research works, only a few of these methods have been replicated successfully in other studies. Also, some of them have not been used on breast and ovarian cancer data. In this study we aim to identify these gaps and evaluate the best tools which are easy to use, efficient in integrating data and provide a new perspective with a deeper understanding of pathways and mechanisms in cancer.

Many studies have focused on mutations that occur in the coding regions which have an affect on the proteins that are translated. In this study we also aim to look at the mutations in UTR regions and how these mutations may affect gene expression. It is known that most miRNA’s bind to the 3’ UTR regions of mRNA. Identifying mutations in these regions along with the gene expression levels of the respective transcripts can shed some light on miRNA-based post-transcriptional control in cancers.

Objectives

• To identify and compare methods and tools used for integration of exome and transcriptome data
• To integrate SNP and gene expression data in order to understand the underlying molecular mechanisms involved in breast and ovarian cancer
• Comparison of breast and ovarian cancer results

Scope of Work

The integrated analysis of exome and transcriptome can lead to the identification of pathways and mechanisms that would have otherwise been missed when a single data set would have been analysed against databases. Although an integrated data analysis can prove to give more insights, it will surely require appropriate selection of tools and methods based on the purpose of the research. This study will involve the identification and comparison of such methods and tools and their subsequent use in understanding underlying molecular mechanisms of breast and ovarian cancer by integrating mutation and expression data of an Indian cohort. Further, the mutations in the 3’ UTR regions could be used to interpret gene regulation by miRNA. The resulting mechanisms could lead to the identification of novel therapeutic targets that would be highly effective for therapies directed towards Indian patients.

Common facts about the organ and symptoms

Ovaries are an integral organ in the female reproductive system in which ultrasound plays an outstanding role in diagnosing and differentiating many ovarian pathologies. The main function of the ovary is to release a mature oocyte every 28 days and to secrete oestrogen and progesterone for maintaining the reproductive cycle and to support pregnancy. The importance of differentiation between functional to non-functional pathologies can provide significant implications in terms of management to relieving symptoms in women across the age groups.

Possible causes of ovarian pathology can be presented as asymptomatically and symptomatically. Often symptomatic reasons are pain, pressure symptoms, palpable masses, irregular menstrual bleeding, amenorrhea (no bleeding for more than 3 months), infertility or subfertility and postmenopausal bleeding. Some of the ovarian pathologies interpreted using ultrasound are simple cysts (functional cysts arising from the hormonal effect on the follicles or larger cysts from dominant Graafian follicle or corpus luteum cyst) Haemorrhagic cysts (formed when a Graafian or corpus luteum cyst bleeds and become larger), endometriomas ( a cyst that grows when the endometrial tissue grows in the ovary) dermoid cysts (germ cell tumour arising from the ectodermal, endodermal and mesodermal tissues) and other malignant ovarian tumours arising from existing benign tumours such as cystadenoma (a larger tumour that is filled with fluid or mucin).

Patient history

Prior to scanning, it is also important to establish the patient history and clinical presentation. The information about the patient’s age and last day of the menstrual cycle (pre-menopausal or post-menopausal status) can predict the benign and malignancies of some ovarian pathologies. Reproductive women have a low likelihood of malignant ovarian tumours and a high chance of ovarian torsion due to dermoid cysts or endometriomas whilst post-menopausal women have a higher risk of developing ovarian cancer. Any other medications (tamoxifen, HRT) and contraception can further affect the presentation and occurrence rate of these pathologies and the appearance of the normal presentation.

Advantages of U/S. Ultrasound has a high sensitivity in diagnosing ovarian pathology and the quick and painless examination makes it ideal for first-line imaging pathway in suspected cases. It is also an inexpensive, simple imaging modality in the hands of an experienced operator and has no bioeffects when used with recommended safety guidelines. Interpretation of the images are made during scanning and often a conclusive result can be given to the woman for further management.

U/S TAS vs TVs. Ultrasound is highly sensitive in detecting ovarian pathologies and it is a painless, inexpensive, non-invasive procedure without bioeffects of ionising radiation. Interpretations are made during the examination and often a conclusive result can be given. In addition, the adnexal masses are detected through transabdominal (TAS) and transvaginal (TVS) approach in ultrasound. Both methods can be beneficial however TVS is more sensitive in differentiating gynaecological pathology in contrast to TAS. TVS transducer has a higher frequency that is able to provide the best resolution of the uterus and adnexal structures and able to come close proximity to adnexal organs to analyse subtle malignancies. TAS can be utilised prior to TVS to optimise the penetration and to visualise a wider field of view into the bladder, rectum and surrounding peritoneal organs with a full bladder as an acoustic window into the uterus. However, U/S imaging can be limited in some cases where the mass effect of bowel gas and high body mass index degrading the quality of greyscale images. TVS approach can be intrusive and painful for some women presenting with severe symptoms.

Normal appearances of ovaries. The ovaries are normally positioned laterally on either side of the uterus and attached to the broad ligament through its own mesentery called the round ligaments. The ovaries are attached to the uterus by the ovarian ligaments and the upper portions are in close proximity to the fallopian tube fimbriae. Ovaries are oval in shape and made up of a central portion (medulla) and outer cortex (stroma). Sonographically, the medulla has a reflective appearance and the stroma contains many follicles peripherally with appearances of anechoic, small cystic structures. One of the ovarian follicles then matures to form the dominant Graafian follicle that is a physiologically normal variant in the pre-menopausal women (seen in the late proliferative phase), unless the patient is pre-pubertal, postmenopausal, pregnant or the mean diameter exceeds above 3cm.

Normal corpus luteus. Often following menstruation, a corpus luteum cyst is formed from the remains of the dominant Graafian follicle that realised the egg, and this is also considered as a normal variant in pre-menopausal women. Corpus luteum is further seen as a thick-walled cyst with a characteristic ring of fire appearance of the peripheral vessel on colour doppler. During scanning, the position of the ovaries can vary due to the mobile nature of the mesentery and often the follicular appearance is helpful in reproductive women but atrophy of ovaries with the absence of follicles make it difficult to visualise in post-menopausal women. Due to the follicular content, a pre-menopausal ovary can measure 4 x 3 x 2 cm (6-12mls) whereas a post-menopausal ovary can only have a volume of less than 4mls.

Simple cysts. Simple cyst is defined as a larger cyst arising from a smaller follicle (functional cysts) or from a dominant Graafian follicle or a corpus luteum cyst in the ovaries. Commonly identified in pre-menopausal women and usually expectant management or follow up U/S is offered to monitor the regression of the cyst. A large cyst in a post-menopausal woman may mimic the appearance of serous cystadenoma hence appropriate management may be directed more towards blood/hormonal tests such as CA-125 or other imaging. These are usually benign in nature and may give rise to larger (>3cm) functional cysts such as follicular or corpus luteum cysts. Follicular cysts are formed when a dominant Graafian follicle fails to ovulate and becomes larger (3-10cm) with similar appearances. Sonographically, simple cysts appear as anechoic, unilocular and thin-walled with posterior acoustic shadowing. No solid components or septations shouldn’t be seen. No vascularity is also seen with the colour doppler.

Dermoid cysts. Dermoid cysts are benign germ cell tumours arising from the ectodermal, endodermal and mesodermal tissues in the ovary. Commonly seen in adolescents and reproductive women. Germ cell layers contain hair, bone, teeth, skin tissues, sweat glands and pockets of sebum encapsulated in a sebaceous cystic structure. These tend to be located in the ovaries with a less than 10cm diameter and can be bilateral in small cases. Further may be subjected to rupture or torsion of the ovary. U/S appearance can be a unilocular cyst (can be cystic or cystic solid) with mixed echogenicity. If echogenic material is seen on the proximal borders the tip of the iceberg sign gives rise to acoustic shadowing. Dermoid mesh/dot sign is also observed as echogenic particles floating in the hypoechoic medium. Sometimes the appearance of fat/fluid levels is seen within the cysts.

Endometrioma

Endometrioma is a cyst that grows when the endometrial tissue grows in the ovary. In small cases, endometriomas are also associated with adenomyosis and deep infiltrating endometriosis. The causative factors are due to retrograde menstruation and/or abnormal change in the endometrial tissue. Location of endometriomas can be within the ovary or adjacent to it depicting the kissing ovary sign-on u/s. These are mostly observed in the pre-menopausal stage and are benign in nature. The content of the endometrioma can rupture or cause torsion. Sonographically, endometriomas are unilocular lesions with ground glass echogenicity. No internal flow with colour doppler should be seen. However, it is important to assess for any further adhesions in the uterosacral ligament

Cystadenoma

Cystadenoma is another benign ovarian tumour that is filled with mucous or fluid and is more common in post-menopausal women in the 4th or 5th decades of life. Cystadenoma is often presented asymptomatically but when symptoms are observed it is mostly due to the pressure from a palpable mass in the adnexa. It can be subdivided into serous or mucinous cystadenoma where the malignant form is known as cystadenocarcinoma. Serous cystadenoma has a classic appearance to a simple cyst sonographically but appears larger within the ovary. In addition, they can be seen as a thin-walled, unilocular anechoic cyst measuring more than 3cm in diameter. Internally the fluid may appear echo-free or maybe echogenic if a haemorrhage has occurred with thin papillary projections from the walls. If the cystic rupture is observed free fluid can be seen in the pouch of Douglas or in vesico-uterine pouch. Whilst, mucinous cystadenoma appears larger on u/s as a thin-walled, multilocular cyst. Due to the mucin, the internal fluid also appears more echogenic. Internal populations can be seen from the walls but no colour flow with doppler. Often the appearance of serous cystadenoma has a classic appearance to a simple cyst sonographically, hence often demographics and clinical risk factors for ovarian cancer (post-menopausal, history of breast cancer, BRCA-1/2 gene) need to be further examined. In suspected malignant ovarian pathologies, it is also necessary to scan peritoneal organs near to the uterus to rule out early to late-stage ovarian cancer.

Often the appearance of endometriomas within the ovary can mimic an early stage haemorrhagic cyst. To differentiate fully U/S follow up is needed after 8/10 weeks without the specific features of a haemorrhagic cyst. When differentiating a follicular and a corpus luteum cyst, it is crucial to apply colour doppler to observe the ring of a fire sign. Serous cystadenoma has a classic appearance to a simple cyst sonographically, hence often demographics and clinical risk factors for ovarian cancer (post-menopausal, history of breast cancer, BRCA-1/2 gene) need to be further examined. In suspected malignant ovarian pathologies, it is also necessary to scan peritoneal organs near to the uterus to rule out early to late stages of ovarian cancer.

The use of B-mode U/S and doppler imaging is extremely useful in diagnosing and differentiating functional, benign and malignant ovarian pathology in all age groups. The use of risk models can further allow the sonographer to reach a conclusive diagnosis and direct patients for medical and surgical management effectively and in a timely manner.

Ovarian cancer (OC) is one of the important causes of death within gynecological tumors in the western world, with a 5-year survival rate of approximately 30% in advanced-stage disease(152). About 10-15% of all OC patients report a positive family history of the disease and can be included as “familial ovarian cancer (FOC).(11,12) FOC patients were defined as those with a history of ovarian cancer in two or more family members or in combination with common cancer diagnosed at a young age.(11,12)

Most hereditary and familial OCs are associated with germline mutations in BRCA1 or BRCA2. (153-155) Basically, there are three founder mutations in BRCA genes, two in BRCA1 (BRCA185delAG, BRCA15382insC) and one in BRCA2 (BRCA2 6174delT).(156) BRCA1 5382insC mutation are present in different ethnic groups, including Ashkenazi Jews, Icelanders, and Russians. (156) Sequencing is the gold standard technique to detect the previous mutations.(157) One of the sequencing by synthesis techniques is pyrosequencing which has the advantages of being sensitive, specific and cost-effective.(157)

To date, in Egypt, there has not been yet any published study detecting BRCA1 185delAG and BRCA15382 insC mutations among FOC patients.

Therefore, fifty Egyptian FOC patients were included in the present study to investigate the previous mutations using the pyrosequencing technique.

In the current study, BRCA1 5382insC mutation was not identified among controls. However, four out of fifty patients showed heterozygous BRCA1 5382insC mutation; a carrier frequency of 8%, CI(2.2-19.2). That is comparable to the international frequency values;5-15%. (158) Similarly, Agnieszka synoweic et al, who conducted a study on 125 FOC polish patients, found that 12 patients had BRCAI 5382insC mutation (a carrier frequency of 9.6%).(159)Also, Moslehi et al, reported 14 out of 208 FOC patients had BRCA15382insC mutation (carrier frequency of 6.7%).(160)

Moreover, in the present study, there were two main significant differences between BRCAI5382insC carrier and non-carrier FOC patients. The first significant difference was the response to standard chemotherapy during the studying period; platinum taxane combination (Fisher Exact test, p=0.046). Twenty non-carrier patients (43.5%) showed a good response to chemotherapy (platinum-sensitive), while the remaining 26 non-carrier patients (56.5 %) were platinum-resistant. For carriers, all four patients (100%) showed a good response to chemotherapy.

Many studies showed favorable outcomes in BRCA1 mutation carriers than noncarriers. Tamar Safra et al, who analyzed retrospectively 256 FOC patients, reported that 84% of BRCA1 mutation carriers (including 5382insC) were platinum-sensitive while 16% of BRCA1 mutation carriers were platinum-resistant.(161) Also, for non-carriers, most of them (60%) were platinum-sensitive, while 40% of them were platinum-resistant (chi-square test, P=0.001)(160) Additionally, Vencken et al, found that 87% of BRCA1 mutation carriers (including 5382insC) are platinum-sensitive after the first-line of chemotherapy that in comparison to non- mutation carrier patients (Chi-square test, P = 0.002). (162) Similarly, Tan et al, demonstrated that most of BRCA-associated FOC patients showed good response to platinum-based agents than nonmutation carrier patients. It has been postulated that this favorable outcome may be due to increased platinum sensitivity in BRCA mutation carriers. (163) Since BRCA genes are important for repairing DNA break via homologous recombination.(164) Therefore, BRCA mutation carriers are unable to repair damaged DNA which make them hypersensitive to DNA-damaging treatments such as platinum chemotherapy; cisplatin or carboplatin (intravenous). (164,165)

On contrary, few studies such as Tingya shi et al did not find any significant difference between carriers and non-carriers regarding the chemotherapeutic response. (166)

Oral PARP inhibitor e.g. olaparib has been recently received food and drug administration (FDA) approval and European medicine agency (EMEA) for treatment of recurrent OC with mutated BRCA1/2 or maintenance therapy for platinum-sensitive ovarian cancer respectively.(167,168) There are several hypotheses that explain the enhanced responsiveness of BRCA-mutation carriers to PARP inhibitors. (167) The most convincing of these is that PARP inhibitors lead to accumulation of ssDNA damage, which is converted to double-strand breaks (DSBs) during subsequent cellular replication .(169) Accumulation of unrepaired DSBs results in cytotoxicity and cell death, this is known as synthetic lethality.(170)

The second significant difference found between BRCA15382insC carriers and noncarriers was concerning the number of affected family members (Monte carlo test, p=0.009). For nonmutation carriers, 38 patients (82.6%) reported one family member who was affected, while 8 patients (17.4%) reported two family members affected. For carriers, one patient (25%) reported one family member affected with colon cancer, two patients (50%) reported two family members affected with colon, ovarian, and breast cancer and one patient (25%) reported three family members affected with ovarian and breast cancer.

Similarly, Shi T et al, reported that patients with BRCA1 (including BRCA1 5382insC) mutations exhibited significantly higher rates of family history of breast or OC (45.7%) and other cancers (39.1%), compared to those without BRCA1 mutations (PAlso, Moslehi al, demonstrated that BRCA1 mutation carriers were present in 78% of women with two or more affected relatives (breast or ovarian cancers.(160)

On the other hand, for BRCA1185delAG, we did not find either heterozygous or homozygous mutations either among FOC patients or among controls. The frequency of the previous mutation among the different population are variable. For example, Revital B et al, from Murraco found a low carrier frequency of 1.1%.(171) However, Sirisha P et al, from India reported a relatively higher carrier frequency of 16.4%.(172)

Collectively, screening of BRCA1 5382 insC using such an affordable technique; pyrosequencing among FOC patients who have two or more affected family members would assist in predicting the patient outcome. Those carriers may be sensitive to platinum, and even patients who are platinum-resistant would benefit from alternative oral target therapy (PARP-inhibitor.)

It is usually not possible to know the exact reason why one person develops cancer and others do not. However, studies have examined the reasoning for why certain risk factors may increase or decrease a person’s chances of developing cancer. A few important reasons such as a person’s lifestyle behaviors, environmental and dietary factors, and occupational exposure are contributed to the number of cancer cases and deaths (Huether & McCance, 2017). Research conducted by the American Cancer Society states that age is the most dominant risk factor for ovarian cancer (Ovarian cancer risk factors.2018).

A risk factor can be defined as anything or something that changes an individual’s chance of getting a disease such as cancer (Tew & Fleming, 2015). A more complex definition of a risk factor suggests that all cancers have originated from both the environment and genetics of an individual. This means that there are both external factors as well as internal genetic changes that can play a role in the reason humans develop cancer (Huether & McCance, 2017). Since there are so many different types of cancers out there, there is no single reason for why someone gets cancer. However, there is much research stating that there are common key associations with the causes of cancer. Cancer is a term for a disease that means that abnormal cells divide without control and can invade nearby tissues (Huether & McCance, 2017). Furthermore, cancer cells can also spread to other parts of the body through the blood and lymph system. This research is sought to examine one risk factor of ovarian cancer, expose the pathogenesis of age correlation with ovarian cancer, state the risk factors’ effects on epigenetics, as well as to emphasize on methods of detection and prevention.

One key association is age and the correlation with numerous types of cancer, explicitly in regards to ovarian cancer. In 2017, around 22,440 women in the United States have been diagnosed with ovarian cancer and about 14,080, unfortunately, have died from this type of cancer (Tortorella & Vizzielli,). Ovarian cancer is a type of cancer that begins in the ovaries. There are over 30 kinds of ovarian cancer and they are classified by the cell type from which they begin (Tew & Fleming, 2015). The ovaries are made up of three types of cells; epithelial tumors, stromal tumors, and germ cell tumors. The most common types of ovarian cancers come from epithelial tumors. Roughly about 90 percent of ovarian cancers are epithelial tumors that form on the outer layer of the ovaries (Tew & Fleming, 2015).

Statistics have shown that the number of older women with ovarian cancer is rapidly increasing and around half of these patients are over the age of 65 years (Tortorella & Vizzielli). A longitudinal study was conducted on 49, 932 women with ovarian cancer diagnosed from 1975 to 2011, and the results showed that for women with stage III and IV tumors, excess mortality is much greater for older women (Tew & Fleming, 2015). Among all stages, survival decreased with increasing age and with time since diagnosis. The decrease in relative survival was more common for women with advanced-stage tumors (Tortorella & Vizzielli). The reason for the poorer prognosis of older patients is not well explained; a number of factors may influence the outcome. It has been showing that increasing age is associated with a more advanced stage at diagnosis and an increased rate of mortality (Pal & Tyler, 2016).

The number of elderly people diagnosed with cancer and living with cancer is expected to grow in the oncoming decades due to longer life expectancy and increased survival (Tew & Fleming, 2015). Women of the older generation tend to be more commonly undertreated meaning that they receive less chemotherapy and surgery even though this is technically considered to be the optimal treatment for these patients (Tew & Fleming, 2015). This may be predominantly due to minimal amounts of evidence behind this as well as the physician’s assurance about the overall administration of elderly women who have ovarian cancer (Tortorella & Vizzielli). This emphasizes the importance of more research conducted with the elderly population to help further knowledge to create more treatment and management plans for these patients.

When thinking about what the definition of “aging” truly means, it can sometimes be difficult to set a specific definition to it. To sum it all up, aging basically means it is the process of becoming older in which it is a biological process and environmental factors also play a role as well. There are many different consequences that come with aging, especially one’s health (Pal & Tyler, 2016). Epigenetic alterations serve as one extremely important mechanism behind the functions distinguished during aging and in age-related disorders (Pal & Tyler, 2016). Epigenetics serves as the opposite genetic mechanism that occurs without any adjustment of the underlying DNA sequence (Pal & Tyler, 2016). Epigenetic changes are stemmed from a nature influence or by external or internal influences. Many scientists claim that epigenetics may serve as the missing piece when explaining the pattern of aging and the difference genetically between two identical people (e.g., identical twins) (Pashayan, Reisel, & Widschwendter, 2016). Different environmental conditions can cause differential alterations of stored epigenetic information to create vast differences in physical appearance, even though these two individuals have identical DNA content (Pashayan, Reisel, & Widschwendter, 2016). Therefore, examining and comprehending the epigenetic changes that happen during aging is a crucial continuous area of study that may possibly lead the way to the development of therapeutic approaches to slow down the aging process and age-related diseases (Pal & Tyler, 2016).

According to the Surveillance, Epidemiology and End Results (SEER) the National Cancer Institute program, ovarian cancer is the 11th most frequent cancer among women, the fifth leading cause of cancer-related death among women, and is the deadliest of gynecologic cancers. Further statistical research from the American Cancer Society has shown that the mortality rates for Caucasian women are somewhat higher than African-American women (Ovarian cancer risk factors.2018). For women aged 55-64 years have the highest rates of being diagnosed with ovarian cancer (Ovarian cancer risk factors.2018). Furthermore, survival rates for ovarian cancer are much lower than other cancers that affect women (Ovarian cancer risk factors.2018). The survival rates vary enormously depending on the stage of the diagnosis, which incline means women diagnosed at an early stage (before cancer has spread) have a much higher chance of survival rate than those diagnosed at a later stage (Tew & Fleming, 2015).

Unfortunately, there is no specific treatment or pharmaceutical drug that can delay or completely stop the biological aging process (Pal & Tyler, 2016). However, there are many methods for prevention that may help try to decrease one’s chances of getting ovarian cancer from the risk factor of undergoing the inevitable aging process. One vital method for prevention is participating in a healthy lifestyle which consists of consuming the proper nutrition and exercising regularly. According to the National Resource Centre on Nutrition, Physical Activity can help an individual’s body from aging quickly. Many people are unaware of the importance of living a healthy lifestyle. Statistics have shown that 1 in 4 Americans of the older generations have poor nutrition. Malnutrition puts you at risk of becoming overweight or underweight, which needs to be stressed more to people of all ages. (Bloom & Lawerence, 2018) It can weaken your muscles and bones. It also leaves you vulnerable to disease (Clark, blister, & Greene). The Study of Exercise and Nutrition in Older Rhode Islanders the SENIOR project II, was a study that was done to stress the importance of both exercise and healthy eating in older adults (Clark, blister, & Greene). The study found 1277 community members that were older adults to engage in different interventions focused towards behavior that was designed to increase exercise as well as higher consumption of fruits and vegetables(Clark, blister, & Greene, ). The demonstrated with adequate food and exercise intake older adults can lead a healthy and productive life. One-third of the senior participants stated that their joint pain drastically decreased and many of their common medical conditions (e.g., hypertension, high cholesterol), as well as psychosocial variable (e.g., depression, resilience, life satisfaction), decreased tremendously (Clark, blister, & Greene, ). Children are our future, in which they must be educated on these topics so the rates of cancers start to decrease.

Another method for the prevention of ovarian cancer is to restrain from any type of tobacco use. Tobacco smoking causes cancer in more than 15 organ sites and cigarette smoking remains the most important cause of cancer (Huether & McCance, 2017). Even exposure to secondhand smoke and parental smoking causes cancer in other non-smokers (Huether & McCance, 2017). The largest preventable cause of cancer is tobacco use. Tobacco smoking is pandemic and affects more than 1 billion people of all ages (Huether & McCance, 2017). The greatest people at risk are those who begin to smoke when young and continue throughout life (Huether & McCance, 2017). Smoking nearly affects every organ in the body. It is so important that people of all ages are educated on these facts regarding tobacco use because it could help prevent people from getting cancer. Therefore, by restraining smoking of any type a person is dramatically decreasing their chances of being diagnosed with cancer.

Lastly, a remarkably important method for prevention, which also goes with a method of detection is going to one’s primary care physician for regular good examinations. As simple as that sounds, routinely going for good exams is essential to a person’s health. Regular good exams and tests can help find diseases or conditions before they are even in full effect (Bloom & Lawerence, 2018). They can also help find diseases or conditions early, which means a person’s chances for treatment and cure are higher (Huether & McCance, 2017). The Centres for Disease Control and Prevention (CDC) states by staying on track with all the right health services, screenings, and treatments, there is a much greater chance of living a longer and healthier life (Ovarian cancer risk factors.2018).

To conclude, this research sought to determine one risk factor of ovarian cancer. The research exposed the pathogenesis of age correlation with ovarian cancer, as well as stating the risk factors’ effects on epigenetics. Ultimately, leading to the emphasis on methods of detection and prevention. The overall limitation of the risk factor of age is that no specific treatment or pharmaceutical drug can delay or stop the inevitable biological aging process (Pal & Tyler, 2016). However, there has been a ton of research conducted that states there are many ways of significantly decreasing your chances of cancer by following the proper methods for prevention. Possibly in the next ten years, further research will be conducted to educate and help the population decrease their risk factors for cancer even more. It is our goal to make sure that people grow up starting at a young age understanding the importance of a healthy lifestyle.

References

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8. Tew, W., & Fleming, G. (2015). Treatment of ovarian cancer in older woman. Gynecologic Oncology, 136(1), 136-142. Retrieved from https://www.sciencedirect.com/science/article/pii/S0090825814014127
9. Tortorella, L., & Vizzielli, G.Ovarian cancer management in the oldest old: Improving outcomes and tailoring treatments. US National Library of Medicine National Institutes of Health, Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5614329/

Diagnosis of ovarian cancer is impeded by the absence of symptoms in the early stage hence the disease presents with a high mortality rate. Since the discovery of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125) to the development of multivariate assays (Ova1, ROMA, Overa) that have increased specificity and sensitivity, there has been considerable progress in the discovery of biomarkers for the detection of ovarian cancer at an early stage. The use of ultrasound, MRI, PET/CT, PET/MRI, Computer-Aided Detection (CAD) algorithms can help in predicting whether neoplasms are benign or malignant. Since the disease has a higher prevalence in postmenopausal women, annual screening with the use of an all-inclusive biomarker panel and the use of imaging techniques for high-risk populations is expected to make early diagnosis possible. This may in turn help in reducing the mortality rate. This review article briefly describes various biomarkers involved and the use of imaging techniques such as ultrasound, MRI, and CT scan that facilitate the diagnosis of ovarian masses.

Despite being termed ovarian, many high-grade serous tumors are believed to have originated in the fallopian tube as well then metastasize to the ovary. Hence the term ovarian cancer encompasses epithelial cancers having origin in the ovary, fallopian tube, and all other histologically relevant peritoneal cancers. Ovarian cancer is a disease of postmenopausal women, the risk increasing with age and peaking around 70 years1.

Genetic risk factors identified include mutations in BRCA1 and BRCA2 genes and Hereditary Non-Polyposis Colorectal Cancer (HNPCC)/Lynch syndrome which place women at an increased risk of developing ovarian cancer. Nongenetic risk factors that play a role in the development of this disease include age at menarche and menopause, pregnancy, breastfeeding, infertility/fertility drug use, oral contraceptives, menopausal hormones, chronic inflammation, and Non-Steroidal Anti-Inflammatory Drugs (NSAIDs), diet, alcohol, smoking, talc, and asbestos exposure1.2.

According to Globocan 2018 data, in India, ovarian cancer accounts for 3.1% of all cancer cases and 6.2% of female cancers. Among the gynecological malignancies, ovarian cancer is one of the most invasive diseases with an age-standardized mortality rate of 57.5% 3,4. In 2018, 36,170 women were diagnosed with ovarian cancer with 24,015 deaths making it the 9th most common cause of cancer death4.

Commonly associated symptoms with ovarian cancer include abdominal distension, abdominal or pelvic bloating, abdominal mass, loss of appetite, and abdominal or pelvic pain. Other symptoms encountered include diarrhea, isolated abdominal pain, weight loss, change in bowel habits, constipation, urinary frequency or urgency, dyspepsia, and abnormal vaginal bleeding. These symptoms however are quite subjective5. Many women lack the symptomatic phase. The symptoms are of vague nature and are often encountered in healthy women as well. Also, a precancerous lesion in epithelial ovarian cancer has not been detected, and hence examining early changes in the ovary is not possible6.

Cystic solid or solid adnexal masses can be detected by the use of 3D power Doppler angiography vascular indices. This method may be of significance with regards to specificity over gray-scale and 2D power Doppler US. However, the practicability of its use in routine diagnosis is still questioned. Computer-aided detection (CAD) algorithms make use of images by the US and using artificial intelligence grades the lesions as benign or malignant. Specificity and sensitivity revealed were 99.2 and 99.6% respectively. However, the large population must be screened before establishing it as a definitive diagnostic technique for routine practice17.

Ultrasound (US) combined with the transabdominal approach gave poor resolution. However, with the advent of the transvaginal probe, the accuracy was increased and was used as a gold standard for detecting adnexal masses. ‘Pattern Recognition can be used to identify different types of tumors depending on their peculiar appearance on gray-scale imaging. The application of Color Doppler has been limited in distinguishing neoplasms that have vascularity in the center or those that were recognized as malignant in B-mode sonography17.

The sensitivity of US and MRI to detect malignant ovarian neoplasms is 100% and 97% respectively. However, the specificity and accuracy of detection by MRI are higher as compared to the US. The apparent diffusion coefficient (ADC) obtained by diffusion-weighted MRI (DWI) has also been useful to differentiate benign and malignant masses18.

Another perspective technique to diagnose ovarian cancer is PET/CT which can effectively diagnose malignant and borderline tumors. Compared to pelvic US and CT or MRI, PET/CT has a precision of 92% against 83% and 75% respectively. Sensitivity and specificity for detecting malignant lesions were reported to be 87 and 100% respectively. Given the greater resolution of soft tissues and no artifacts by MRI, it is considered superior to CT. DWI/MRI can be used to assess the metastatic spread of the disease. Compared to PET/CT radiation exposure was reduced to 80% in PET/MRI. A study reported that for recurrent pelvic malignancies both PET/CT and PET/MRI are of significant diagnostic value. When it comes to defining primary tumors, PET/MRI is superior to PET/CT.

In the last few years, a wide range of biomarkers has been tested in various combinations with improved specificity and sensitivity. Thus, designing an appropriate biomarker panel that will precisely detect ovarian cancer in the early stage is the immediate need. Combining the results obtained by ultrasonography and using different models suggested by IOTA may help in a preliminary screening of high-risk populations. The use of non-invasive techniques such as determining the miRNA levels or concentration of cfDNA is also an emerging tool for diagnosis. A large cohort study for precision levels obtained by the use of CAD algorithms in detecting pelvic mass would help in confirmatory diagnosis at the early stage possible. PET/CT and PET/MRI have similar outcomes in the diagnosis of pelvic masses. This suggests that the appropriate use of nucleic acid and protein biomarkers along with medical imaging techniques should help provide a clear diagnostic image of the gynecological malignancies at an earlier stage. This would facilitate early medical intervention probably chemotherapeutic sessions and thereby improving cancer survival rates.

References:

1. Webb PM, Jordan SJ, Epidemiology of epithelial ovarian cancer, Best Practice & Research Clinical Obstetrics and Gynaecology (2016), http://dx.doi.org/10.1016/j.bpobgyn.2016.08.006
2. Mclemore, Monica R., et al. “Epidemiological and Genetic Factors Associated With Ovarian Cancer.” Cancer Nursing, vol. 32, no. 4, 2009, pp. 281–288., doi:10.1097/ncc.0b013e31819d30d6
3. Epidemiological and Genetic Factors Associated With Ovarian Cancer. Cancer Nursing, 32(4), 289-290. doi:10.1097/ncc.0b013e3181adb252
4. Globocan 2018: India Factsheet. (2019, May). Retrieved January 7, 2020, from https://gco.iarc.fr/today/data/factsheets/populations/356-india-fact-sheets.pdf
5. Ebell, Mark H, et al. “A Systematic Review of Symptoms for the Diagnosis of Ovarian Cancer.” American Journal of Preventive Medicine, U.S. National Library of Medicine, Mar. 2016, www.ncbi.nlm.nih.gov/pubmed/26541098.
6. Schwartz, P. E. (2001). Nongenetic screening of ovarian malignancies. Obstetrics and Gynecology Clinics of North America, 28(4), 637-651. doi:10.1016/s0889-8545(05)70226-6