Speeding Discoveries in Women’s Health Through Genetic Research
Dr. Maria C. Suciu talked about using genetic research to speed up the diagnosis of endometriosis at the Women in Tech Summit (WITS) Northeast 2023.
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On the second and final day of the Women In Tech Summit (WITS) Northeast 2023, Dr. Maria Suciu presented on how genetic research could be used to get faster endometriosis diagnoses.
Currently, the average time it takes for someone to get diagnosed with endometriosis is seven years, and it can often take surgery to get a definitive answer. Given how common it is, affecting 10% of women, this long diagnosis time and lack of diagnostic tools is surprising.
Dr. Suciu’s research aims to change this.
Suciu is a Senior Manager in Statistical Genetics at Regeneron Genetic Center (RGC).
Before diving into the specifics of her research, Suciu offered some general information about genetics as a primer. She described cells as “Lego building blocks” that hold DNA.
“So DNA is literally made of four letters. If you want, you can put them [on] a string and there’s about six billion letters. So now we’re kind of entering, coding this into data. What we try to do, we try to chop this DNA up, and basically read that sequence. But that is not enough,” explained Suciu.
She continued her explanation, stating that since we share 99.9% of genes with other people, scientists have to look at the 0.1% of genes we don’t share to see any differences.
Suciu and her team are working on finding abnormalities in the DNA sequence that relate to an increased likeliness in developing endometriosis. The hope is that people will be able to be screened for it like they are with the genes that relate to breast cancer, BRCA1 and BRCA2.
To find these abnormalities they score the single-nucleotide polymorphism (SNP) of the DNA. According the to National Cancer Institute, SNP is “a DNA sequence variation that occurs when a single nucleotide (adenine, thymine, cytosine, or guanine) in the genome sequence is altered and the particular alteration is present in at least 1% of the population.”
Suciu explained, “The whole idea is that when you make the plot of the scores, you want your cases to have a higher score than your controls.”
A higher peak indicates a difference in the gene of someone sick vs. someone healthy.
Suciu and her team began to collect data by building their sample.
Suciu explained, “So the first thing is that I know that there are small differences and I need a big sample size. I also know that endometriosis is more prevalent, for example, most women are African American. So can I use this information? Can I use our diverse database?”
The challenge that their research has been under-diagnosing. Suciu explained that if she just went off of DNA that her center has in-house, her team would end up with 27,000 cases of people who have been diagnosed with endometriosis and 600,000.
She noted that these numbers show how underdiagnosed the disease is, since 27,000 is way under 10% of 600,000. As stated earlier, approximately 1 in 10 women have the disease so there should be 60,000 cases.
Her team also weeded out conditions with similar symptoms, but different genes, from the control group using AI.
So far they have found one gene that they are pursuing.
Suciu grew up in Transylvania before going to Jacobs University Bremen in Germany. She originally went to study international politics, but switched to biochemistry and cell biology.
She then received a Master of Science in Prenatal Genetics and Fetal Medicine at University College London and Doctor of Philosophy (DPhil) in Genomic Medicine and Statistics from University of Oxford.
Before working at RGC, Suciu worked as Project Delivery Manager, Board Member, and Admissions Lead at Oxford for Romania.