An overview of ovarian cancer: the development of biomarkers for early detection
By Dr. Rianne Esquivel and Dr. Francesca De Simone, Fujirebio Diagnostics Inc.
Ovarian cancer and the need for early detection
It has been estimated that in 2020, about 21,750 women in the US will receive a new ovarian cancer diagnosis and about 13,940 women will die from this disease1.
Ovarian cancer ranks 5th in cancer deaths among women. A woman has a 1 in 78 chance of developing this disease during her lifetime and a 1 in 108 chance of dying from the disease1,2.
Ovarian cancer, also known as the ”silent killer”, is difficult to detect, especially during early stages, due to its vague symptoms that are often dismissed by women as being associated with aging, menopause and previous pregnancies3,4.
The combination of vague symptoms, together with lack of awareness, can lead to difficult and/or delayed diagnosis of ovarian cancer3.
The most common reported symptoms of ovarian cancer are abdominal discomfort and swelling, urinary frequency and urgency, malnutrition, pleural effusion and pain on surrounding organs from the pressure caused by the tumor mass3,5. When these symptoms become persistent, the disease is likely already widespread3.
Ovarian cancer is characterized by 4 different stages (stage I through IV), which help indicate whether the cancer is confined to the ovaries (stage I), or if the cancer is in an advanced stage, and, therefore, has metastasized to distant sites and organs.
The staging of ovarian cancer is based on 2 very similar systems, the FIGO (International Federation of Gynecology and Obstetrics) system and the AJCC (American Joint Committee on Cancer) TNM staging system. Both use surgical results to determine the extent of the primary tumor (T), whether the cancer is present in the lymph nodes (N) and whether the cancer has metastasized (M) to other parts of the body (such as liver, bones or brains)6,7.
Ovarian cancers can be classified into 3 large groups: epithelial (EOC), germ cell, and specialized stromal cell tumors8. EOC can be further subdivided into 2 different histological subtypes: Type I and Type II tumors.
CA125 (MUC16), is the most widely used tumor marker in ovarian cancer and is often considered the “gold standard”22. First identified in 1981, and FDA approved in 1997, CA125 has been used clinically to follow women diagnosed with ovarian cancer for prognosis, surveillance and optimization of care.
Moreover, a decline in CA125 expression levels are considered to have positive prognostic value (occurrence) during chemotherapy, thus aiding healthcare providers to monitor therapeutic outcomes, and to assess stabilization of the disease23.
Despite the widespread use of serum CA125, there are significant limitations regarding its utility. Prospective studies carried out over the years have indicated the limited and inadequate sensitivity and specificity of this biomarker in ovarian cancer diagnostics24–27. Specifically, CA125 levels appear to be elevated in only 50% of patients with Stage I ovarian cancer28. CA125 has also been found to strongly correlate with specific subtypes of ovarian cancer, such as serous rather than mucinous tumors29. Notably, serum levels of CA125 are known to be within normal limits in ~20% of women diagnosed with advanced EOC, indicating reduced sensitivity of this biomarker30.
Furthermore, CA125 concentrations are also known to be elevated in common benign gynecological conditions (e.g. endometriosis, follicular cysts, cystadenomas, ovulatory cycle and pregnancy) especially in premenopausal women. This significantly affects CA125 specificity as a useful biomarker for ovarian cancer24,30,31.
Because of these limitations in sensitivity and specificity, CA125 cannot be used as a stand-alone biomarker, but can only be used in combination with patient data, imaging and a tumor marker profile32.
Human epididymis secretory protein E4 (HE4/WFDC2), was first discovered in 1991 and became available on the US market in 2008 as a biomarker for monitoring of disease recurrence or progression33.
HE4 is known to promote migration and adhesion of ovarian cancer cells34, and to induce cancer cell proliferation in vivo and in vitro, leading to tumor progression35.
As with most biomarkers, HE4 is expressed in both normal and malignant tissues. Specifically, its expression has been observed in several normal tissues such as the epithelium of fallopian tubes, endometrium and in the endocervical glands, as well as the epithelia of the respiratory tract, renal convoluted tubules and salivary glands30.
Among malignant tissues, HE4 is highly expressed in ovarian cancer, but also in other cancers, including mesothelioma, lung, endometrial, breast, gastrointestinal, renal and transitional cell carcinomas36.
In healthy women, HE4 levels appear to increase with age (starting from the age of 40), reaching their highest levels in the 8th and 9th decade of life. Noting this aspect is crucial to avoid false-positive results30.
HE4 has been shown to be less frequently elevated than CA125 in benign ovarian tumors, in both pre- and post-menopausal women, indicating that this marker may be a valuable complement to distinguish malignant from benign ovarian tumors30.
Interestingly, overexpression of HE4 is observed in specific subtypes of ovarian cancer such as endometroid (80-100%), serous ovarian cancer (93-100%) and clear-cell carcinomas of the ovary (50-83%) suggesting that HE4 may also aid physicians to differentiate and characterize specific histological ovarian cancer subtypes30,33.
A meta-analysis from 2014 by Zhen et al. provided evidence that HE4 is indeed superior to CA125 for diagnostic performance in ovarian cancer. Specifically, their results revealed a pooled sensitivity for HE4 of 74% and a pooled specificity of 90% compared to CA125’s 74% sensitivity and 83% specificity. This indicates that HE4 appears to be superior to CA125 for diagnostic accuracy in distinguishing ovarian cancer from benign gynecological diseases37 .
Overall, HE4 better differentiates borderline tumors from benign masses than CA125, and has been shown to better identify early-stage EOCs33.
Interestingly, the combination of HE4 and CA125 has been shown to improve overall sensitivity and specificity (76.5% and 100%, respectively). Furthermore, HE4 predicts ovarian cancer recurrence earlier than CA125, and thus might overcome the lack of CA125 expression in some cancers38,39.
Likelihood of malignancy assessment of a pelvic mass
Currently, several algorithms have been developed that combine age, menopausal status, serum biomarker(s) panels and imaging into a single index to help assess the risk that an adnexal mass will be found to be cancerous or benign upon surgery.
In 2009 OVA1 became available on the US market as a new algorithm to help distinguish malignant from benign pelvic masses in patients with an adnexal mass who are scheduled for surgery. OVA1 is a multivariate index that combines data from imaging, menopausal status, and a panel of 5 biomarkers: CA125, ApoA1, TTR, Tf and β2-macroglobulin40.
In patients with ovarian cancer, two of these biomarkers are expected to be upregulated (specifically CA125 and β2-macroglobulin) and three to be downregulated (ApoA1, TTR and Tf)30. During testing, the patient receives a score ranging from 0 to 10. Women are qualified as high-risk when the value is ≥ 5.0 in the pre-menopause group, and ≥4.4 in the post-menopausal group.
In 2016, the FDA cleared a second-generation multivariate index assay called OVERA, which combines CA125, HE4, ApoA1, follicle stimulating hormone (FSH) and Tf. Because FSH is part of the panel, there is no need to determine menopausal status.
Similar to OVA1, the OVERA score also ranges from 0.0 to 10.0, where values below 5.0 indicate low risk of finding malignancies and values higher than 5.0 indicate high risk of finding malignancies upon surgery.
Overall, OVERA presents similar sensitivity and NPV to OVA1 (specifically 91% vs. 94% sensitivity respectively and 97% NPV), though performing better for specificity (69% vs. 54% respectively) and PPV (40% vs. 31%).
Risk of Ovarian Malignancy Algorithm (ROMA)
In 2009, Moore et al. developed a new algorithm called the Risk of Ovarian Malignancy Algorithm (ROMA), based on the encouraging results of HE4 in EOC diagnosis, especially when combined with CA12541. Specifically, this approach consisted of associating serum HE4 and CA125 levels to the menopausal status of the patient (defined by lack of menstruation or clinical signs of menopause for 6 months).
The ROMA assay became available on the US market in 2011 as a qualitative serum test aimed to assess the likelihood of finding malignancies upon surgery in women with an adnexal mass.
The ROMA score corresponds to the Predicted Probability (PP), and is expressed as a percentage rate42.
The ROMA algorithm uses the cutoff values (> 13.1% in pre-menopausal women, and > 27.7% for post-menopausal women), to identify and differentiate women with high risk of developing ovarian cancer from the low-risk group30. In addition to the score, the ROMA test also provides the individual HE4 and CA125 concentration values.
The creators of this algorithm revealed a sensitivity of 93.8% (88.9% for pre-menopausal and 94.6% for post-menopausal women) and a specificity of 75% for the diagnosis of EOC41.
When compared to other algorithms such as the Risk of Malignancy Index (RMI), the ROMA score performed better in the diagnosis of OC, demonstrating higher sensitivity (94.3% vs. 84.6% at 75% specificity). This performance difference was even stronger when diagnosing early stages (stage I and II) of ovarian cancer30,43.
A ROMA score can be used by primary physicians to better evaluate whether to refer a patient to a gynecologist or to a gynecologic oncologist for initial surgery, thereby reducing the risk of complications, and improving the patient’s survival rate.
Additional serum biomarker algorithms for research use
Over the past decade, many efforts have been made to better understand the heterogeneity of ovarian cancer, which has resulted in the investigation of new biomarkers and algorithms for disease prediction and prognosis intended to improve early detection and to help improve treatment of patients.
In 1990, Jacobs et al. developed the Risk of Malignancy Index (RMI) to evaluate the possible malignancy of pelvic masses by considering three pre-surgical evaluations: ultrasound findings, CA125 levels and menopausal status44.
RMI score above a specific threshold of 200, describes a strong association with high risk of malignancy, with a sensitivity of 85.4% and a specificity of 96.9%42.
Since the RMI score is based on a specific threshold (200), the calculation of specificity, sensitivity, positive and negative predicted values is not based on any specific value, but whether such value is higher or lower than a threshold42.
More recently (2015), the Copenhagen Index (CPH-I) was reported as a novel diagnostic score in ovarian cancers, which uses the same mathematical method as the ROMA score, but includes patient’s age as one of the variables45.
The ROC and AUC were similar when comparing CPH-I, ROMA and RMI algorithms (0.96, 0.95 and 0.96, respectively)42. Interestingly though, the introduction of age into the algorithm did not appear to improve diagnosis of ovarian cancer.
The biggest challenge in ovarian cancer is the need for health providers to make the correct diagnosis as early as possible, despite the vagueness of symptoms.
Triage guidelines from the American College of Obstetricians and Gynecologists (ACOG) and the Society of Gynecologic Oncologists (SGO) recommend referral of women with adnexal masses and at high risk of developing ovarian cancer to a gynecologic oncologist. It is widely accepted, in fact, that specialized treatment can improve patients’ outcomes46,47. In order to ensure correct referral, ACOG recommends the use of risk assessment algorithms utilizing serum biomarkers48. Specific diagnostic tools are needed in order to allow early detection of ovarian cancer, cause fewer complications from initial surgery, and consequently improve women’s health and medical outcomes.
The introduction of algorithms (such as ROMA and OVA1) that combine age, menopausal status, serum biomarker(s) panels and imaging into a single index, are clear examples of advancement within pelvic mass risk diagnostics and triage.
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