May 11, 2024

Cancer

Can ovarian cancer be detected by genetic analysis of cervical cancer screening samples?

A proof-of-concept method for adding ovarian cancer screening to existing protocols

Peter Attia

Read Time 7 minutes

The best way to reduce the risk of dying from cancer is early detection and diagnosis. For cancers like colon and breast cancers, screening can often detect disease before perceivable symptoms, which often portend advanced cancer and hence a worse prognosis. However, many cancers do not have reliable screening methods, including ovarian cancer, one of the most lethal gynecologic cancers. Even when ovarian cancer becomes symptomatic, the symptoms are nonspecific, which often causes further delays in diagnosis. Indeed, most ovarian cancer is not diagnosed until stage III (when it has invaded the abdominal cavity) or stage IV (when it has become metastatic and spread to distant organs), at which point the five-year survival rate is less than 30%. In contrast, the five-year survival rate for stage I ovarian cancer is above 90%.

Clinical trials have tested whether modalities typically used to make ovarian cancer diagnoses (e.g., plasma biomarker cancer antigen 125, either alone or combined with transvaginal ultrasounds) could be used for screening, but these tests were not sufficiently sensitive to early-stage disease.1,2 Since earlier detection is crucial for improving ovarian cancer prognosis, a proof-of-principle study recently explored whether high-grade ovarian cancer could be detected years earlier by analyzing the DNA from cervical cancer screening Papanicolaou (Pap) tests.3 (Brace yourselves – we’re about to dive into some technical details of cancer development and screening test performance metrics. For a little more background in these areas, see AMA #56.)

Why might this work?

The vast majority of solid tumors originate from the epithelium, the cells that cover the outer surfaces of your organs and line cavities, like the air sacs in your lungs. Epithelial cells have a high turnover rate, resulting in a higher likelihood of mutations that can cause cancer over time. High-grade serous ovarian carcinoma (HGSOC) is an epithelial cancer that accounts for 75% of all ovarian cancers but does not originate in the ovary. This type of cancer is initiated in the epithelial cells of the fallopian tube as serous tubal intraepithelial carcinoma (STIC), and from there, the cancerous cells can travel into the uterine cavity and eventually the cervical canal, potentially allowing them to be detected in cervical cancer screenings.  

The investigators in this present study used shallow whole-genome sequencing to detect genetic hallmarks of HGSOC. Although most of our cells contain two copies of each gene, occasionally, errors occur during cell division (in non-germline cells) which results in either a gain or loss of large sections of DNA, changing the number of genes, called somatic copy number alterations (SCNAs). One mechanism that can cause SCNAs is a failure to properly segregate duplicated chromosomes into two daughter cells during mitosis. This results in aneuploidy, since the gain or loss changes the total number of chromosomes. Aneuploidy and SCNAs in general are rarely found in normal tissues but are common in cancers, especially ovarian cancer, which is why these genetic signatures might be able to facilitate early detection. 

What the study found

This study was a retrospective analysis of 250 archived Pap tests from 113 presymptomatic women with HGSOC at different time points relative to their ovarian cancer diagnosis (some with up to four repeat tests over time), as well as 77 healthy women. The pre-HGSOC women and healthy women were divided into two groups, 62 pre-HGSOC and 52 healthy women were used to train a model and the remaining 51 pre-HGSOC and 25 healthy women were used for subsequent validation. For the women who ultimately developed ovarian cancer, the investigators used purified DNA from both the ovarian tumor (tDNA) following diagnosis and Pap samples (pDNA) collected at or before the time of diagnosis (up to 13.6 years before). The comparison of tDNA to pDNA facilitated working backward from the genetic alterations of the tumor cells to earlier genetic changes, before the onset of symptoms.

In the pDNA from pre-ovarian cancer samples, approximately 89% had detectable SCNAs involving the gain or loss of broad chromosomal regions and up to 75% were found to be in regions common with their paired ovarian cancer tumor sample. The samples of tDNA had a high number of genetic alterations across all chromosomes, which was not characteristic of the pDNA samples, indicating that HGSOC acquires many of its mutations during the late stages of tumor development. However, a commonality between pre-symptomatic pDNA and tDNA was a loss of DNA on chromosome 16, which was detected early in pDNA and remained present in the tDNA. This is consistent with previous research reporting that the loss of genetic material on chromosome 16 is an early genomic alteration in ovarian cancers.6 

Rather than relying on detecting a common genomic aberration, the investigators used a measure of overall genomic instability, copy number profile abnormality (CPA) score. Based on the training data set, two thresholds were empirically determined using a genome-wide analysis, excluding regions known to have SCNAs in healthy people. A CPA score below the first threshold was considered negative for genomic alterations, and a score above the second threshold was considered positive, reflecting clear detection of aneuploidy. Scores between the two thresholds were regarded as being in a “gray zone” – or an area of uncertainty in which a result would require other follow-up testing to confirm or rule out ovarian cancer. This analysis detected an aneuploid genome (a “positive” result) in the samples of pDNA from up to nine years before cancer diagnosis. 

Test performance

As for all diagnostic tests, it is important to understand what a “positive” or “negative” result actually means. This test had a 75% sensitivity, meaning that, of the pDNA samples from women who will ultimately go on to develop ovarian cancer, 75% were found to have a positive result (above the second threshold). Likewise, this test had a 96% specificity, meaning that 96% of the pDNA samples that had a “negative” result would not go on to develop ovarian cancer. Together, the sensitivity and specificity result in an overall 81% accuracy.

For women of average risk (an estimated rate of 9 new ovarian cancers per 100,000 women each year), the negative predictive value of this test is 99.99%, meaning that this test has very few false positives and that if you receive a negative result, it’s unlikely that you have ovarian cancer. However, the positive predictive value is extremely low at 0.5%, meaning that for the most part, in a population without known risk factors, the vast majority of positive results will be false positives that would require further testing to rule out a cancer diagnosis.

However, in women with a BRCA1 mutation who are considered high risk (a 40 to 60-fold increased risk of ovarian cancer), the predictive nature of the test changes. In these women, the positive predictive value is 19.7% and the negative predictive value is 99.7%, a more reasonable screening performance to implement when risk is high, though follow-up tests would still be required, as only one in five positive tests would correspond to a true positive case of cancer. So although neither the sensitivity nor the specificity of this test is high enough to warrant routine screening for normal-risk populations, it may nonetheless have utility in high-risk populations if it is tracked over time or if it is stacked with other less specific tests, like liquid biopsy, to either rule out or motivate further diagnostics. 

What are the limitations?

This test used shallow whole-genome sequencing, which has the advantage of being a relatively low-cost way to look for generic variants in samples with many cells. However, the “shallow” methods have a “coverage” that ranges from about 0.5 to 1, meaning that any given portion of the DNA is read only up to one time, as compared to “deep” whole-genome sequencing, which reads each portion of the DNA many times (30 is considered typical for clinical genetic tests). Shallow WGS tends to be more cost-effective and can analyze a larger number of cells but has lower sequencing resolution, which may explain why there was only 75% overlap in the SCNA regions detected in pre-HGSOC and tumor samples. Increasing the sequencing resolution by increasing “depth” would also likely increase the sensitivity of this test by differentiating more of the women in the “gray zone” who would go on to develop cancer from those who would not. Since many of the genetic alterations occurred at around twenty genetic loci – a relatively limited number of positions – future studies will need to investigate whether to interrogate these specific genes with more depth or to maintain the shallow sequencing methods covering the whole genome. 

In addition to the type of genetic sequencing, this study needs to be replicated in women of matched age groups. Since this was a retrospective study, the samples were not collected for the specific purpose of this type of testing, and there was a difference in the median age of those with ovarian cancer and the healthy controls (60 versus 43 years of age). It is possible that some of the observed genetic alterations could be associated with aging rather than cancer, a limitation of this study that needs to be addressed before translating these methods from proof-of-concept to screening methods tested on a larger population.

The bottom line

Previous trials that used modalities typically used to make ovarian cancer diagnoses have failed to reduce ovarian cancer mortality because the methods of detection were not sensitive enough to early-stage disease.1,2 By contrast, the genomic analyses employed in this study were able to detect cancerous changes nearly a decade before diagnosis – an enormous advantage in combating a disease for which the five-year survival rate at stage I is over three times higher than the survival rate at stage IV. Since the DNA analysis can be done on samples that are already routinely collected for cervical cancer screening, this may be a feasible way to add ovarian cancer screening to the current standard of care in populations most vulnerable to this type of cancer. While the test, as it currently stands, is far too susceptible to false positives to justify use in the general female population of average ovarian cancer risk, its use as a screening method for women with especially high baseline risk has the potential to improve clinical outcomes associated with this disease. 

 

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References

  1. Menon U, Gentry-Maharaj A, Burnell M, et al. Ovarian cancer population screening and mortality after long-term follow-up in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial. Lancet. 2021;397(10290):2182-2193. doi:10.1016/S0140-6736(21)00731-5
  2. Buys SS, Partridge E, Black A, et al. Effect of screening on ovarian cancer mortality: the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial. JAMA. 2011;305(22):2295-2303. doi:10.1001/jama.2011.766
  3. Paracchini L, Mannarino L, Romualdi C, et al. Genomic instability analysis in DNA from Papanicolaou test provides proof-of-principle early diagnosis of high-grade serous ovarian cancer. Sci Transl Med. 2023;15(725):eadi2556. doi:10.1126/scitranslmed.adi2556
  4. Paracchini L, Pesenti C, Delle Marchette M, et al. Detection of TP53 Clonal Variants in Papanicolaou Test Samples Collected up to 6 Years Prior to High-Grade Serous Epithelial Ovarian Cancer Diagnosis. JAMA Netw Open. 2020;3(7):e207566. doi:10.1001/jamanetworkopen.2020.7566
  5. Salk JJ, Loubet-Senear K, Maritschnegg E, et al. Ultra-Sensitive TP53 Sequencing for Cancer Detection Reveals Progressive Clonal Selection in Normal Tissue over a Century of Human Lifespan. Cell Rep. 2019;28(1):132-144.e3. doi:10.1016/j.celrep.2019.05.109
  6. Gerstung M, Jolly C, Leshchiner I, et al. The evolutionary history of 2,658 cancers. Nature. 2020;578(7793):122-128. doi:10.1038/s41586-019-1907-7
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