AI-based liquid biopsy could detect ovarian cancer earlier

A blood test that uses a combination of artificial intelligence (AI) and protein biomarkers could help screen women for early signs of ovarian cancer, according to a study led by researchers at the Johns Hopkins Kimmel Cancer Center and published in the journal Cancer Discovery.

Ovarian cancer is the fifth most common cause of cancer deaths among women in the United States, with a five-year survival rate of approximately 50%. As noted by co-first author Dr Jamie Medina, “Most women are diagnosed late in the course of the disease, when survival rates are much lower. The lack of specific symptoms early in the course of the disease or effective biomarkers has hindered earlier detection efforts.”

The investigators had previously demonstrated that the AI-powered DELFI (DNA Evaluation of Fragments for early Interception) test method utilises a new approach for liquid biopsies, called fragmentomics, that improves detection of DNA fragments in the blood and effectively detects lung cancer. The technology takes advantage of the fact that DNA, neatly packaged in healthy cells, becomes disorganised in cancer cells. When healthy cells die and break apart, they leave behind a predictable, orderly set of DNA fragments in the blood. However, when cancer cells die and break apart, the DNA fragments left behind are irregular and chaotic.

The team’s latest study used blood samples from 94 women with ovarian cancer, 203 women with benign ovarian tumours and 182 women without any known ovarian growths, all of whom had been treated at hospitals in the Netherlands and Denmark. The researchers used the DELFI-Pro test, which combines AI-powered cell-free DNA analysis with tests for protein biomarkers known as cancer antigen 125 (CA-125) and human epididymis protein 4 (HE4). These were previously identified as ovarian cancer biomarkers but could not on their own reliably detect ovarian cancer.

DELFI-Pro was able to detect substantially more cases of ovarian cancer than tests for either protein alone, and it did so with almost no false positives. In fact, it detected 72%, 69%, 87% and 100% of ovarian cancer cases stages I–IV, respectively, while at the same specificity, CA-125 alone detected 34%, 62%, 63% and 100% of ovarian cancers for stages I–IV.

To confirm the results, the researchers used the test in a second sample of American women that included 40 patients with ovarian cancer, 50 patients with benign ovarian growths and 22 without known ovarian lesions. The test achieved similar success rates, with 73% of all cancers detected and 81% of the high-grade serous ovarian carcinoma, the most aggressive form of the disease, with almost no false positives in women without cancer. The DELFI-Pro test was also able to effectively distinguish between benign growths and cancerous tumours — something ultrasound exams cannot.

“Ovarian cancers have a unique DNA fragmentation signature that is not present in benign lesions,” said co-first author Akshaya Annapragada, an MD/PhD student at the Johns Hopkins University School of Medicine. Being able to distinguish benign from cancerous ovarian growth is important because the next step in cancer screening for women with ovarian growths detected via ultrasound is invasive exploratory surgery.

Senior author Professor Victor E Velculescu, Co-Director of the Cancer Genetics and Epigenetics Program at the Johns Hopkins Kimmel Cancer Center, intends to validate the test’s utility in larger samples from randomised clinical trials but so far finds the results encouraging, even in patients with early-stage disease.

“This AI-enabled approach has the potential to be an affordable, accessible method for widespread screening for ovarian cancer,” he said.

Image credit: Carolyn Hruban, PhD.