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Undergraduate team wins 2nd place at global synthetic biology competition

Â鶹´«Ã½ bioengineering student Varun Govil presents the Epinoma platform, along with Ishan Goyal and Marin Cross. Photo taken by iGEM Foundation and Justin Knight.

By Kritin Karkare

San Diego, CA, December 10, 2018 -- Finding an accurate way to detect cancer using a drop of blood instead of the traditional tissue biopsy approach is a difficult technical challenge that billion dollar companies and academic institutions are working to solve. So, too, is a team of 11 undergraduate students from Â鶹´«Ã½. Their approach to creating an accurate liquid biopsy test for cancer earned them a second place win at the International Genetically Engineered Machine (iGem) competition and a tentative $100,000 grant over four years from the Tata Institute of Genetics and Society to develop a prototype.

In addition to placing second out of 250 teams from around the world, the Â鶹´«Ã½ 2018 iGem team, composed of undergraduate students from bioengineering, biochemistry and cell biology, chemistry, and molecular biology, received a slew of other awards to back up their achievement, including Best Poster, Best Diagnostics Project, Best Entrepreneurship and Best Education and Public Engagement.

The International Genetically Engineered Machine competition is a synthetic biology competition—applying engineering principles to biology— that brings together high school, undergraduate and graduate teams from around the world to design a revolutionary biological tool or process that solves an existing issue using synthetic biology.

Over four months starting in May, the Â鶹´«Ã½ team dedicated hundreds of hours to designing their project—a liquid biopsy cancer diagnostic tool called . Their prize winning approach detects abnormal methylation in DNA—a change in the way DNA functions that is a tell-tale sign of cancer— from a sample of blood.  Their approach offers a novel, quick and precise approach to diagnose cancer, unlike the current tissue biopsy method which is expensive, invasive and often inaccurate.

Varun Govil, a third year bioengineering undergraduate at Â鶹´«Ã½, a , and the Epinoma project team lead, explained that the team learned through existing literature that the first step of tumor growth is called DNA promoter methylation. They figured that marking this change in DNA would be an earlier and more effective sign of cancer than waiting to sample tissue from a suspected tumor. The team then used machine learning to classify and learn which genes are methylated for specific cancers using data gathered from hepatocellular carcinoma, a cancer of cells found in the liver.

The Â鶹´«Ã½ iGem team was up against some steep competition. Pictured here are all 250 teams from around the world that competed. Photo taken by iGEM Foundation and Justin Knight.

Using this data, they determined the top 15 biomarkers that would light up when methylated. Their proof of concept looked at liver cancer, but their platform could be used to detect any cancer, or any disease that has characterized methylome data.

“We found that methyl-binding domain proteins (MBD) can specifically bind to DNA,” explained Zhijian Li, a fourth year chemistry and biology double major and Â鶹´«Ã½ iGEM team member. “We can then repurpose DNA to detect and quantify methylation level by adding a fluorescent probe to the MBD, measuring the fluorescence and using that as a diagnostic tool to show the amount of hypermethylation.”

Solving this medical and diagnostic challenge was the team’s goal, but they wanted to make sure they didn’t do so in a vacuum; they sought input from practitioners and stakeholders to ensure Epinoma would be useful and usable. The students talked to stakeholders around the world, from doctoral students to CEOs, venture capitalists, research scientist and startup incubators, and used the feedback to refine their project.

One particularly useful interaction was meeting with the Cancer Cell Map Initiative (CCMI), a joint Â鶹´«Ã½ and UC San Francisco research group the team is partnering with. The series of difficult technical questions CCMI asked affirmed that “we knew we were headed in the right direction and just had to make tweaks,” Govil said.          

The Epinoma team also worked on education engagement for their project, writing a 200-page textbook full of case studies involving synthetic biology. They created a “Periodic Table of Synthetic Biology” that summarizes upwards of 30 of the key principles people interested in synthetic biology should know, such as machine learning, CRISPR-Cas9, and the Zika virus.

For Govil, this was a satisfying victory. This was his second time competing at iGEM, and he said he was glad the team had more people than in previous years—he suspects the way they marketed the project, using words like machine learning and cancer diagnostics, had something to do with the increased student interest.

Looking ahead, the team is taking a small breather before getting back to the grindstone.

 “We have tentatively secured a $100,000 grant from the TATA Institute of Genetics and Society to streamline the implementation of our clinical test in developing countries like India,” Govil said. “There are some strategies that we need to test in the wet lab to improve sensitivity. Our workflow also includes a digital healthcare app that will streamline doctor-patient communication. If this is as game changing an approach as we think, we’ll need to keep validating the tool.” 

To learn more about their iGEM project, check out their iGEM wiki page. It details the description of their project, the research they’ve done, information on their interviews with professionals, and more.

Media Contacts

Katherine Connor
Jacobs School of Engineering
858-534-8374
khconnor@ucsd.edu