In a collaboration between Vanderbilt University Medical Center’s Department of Otolaryngology-Head and Neck Surgery and the Vanderbilt University School of Engineering, investigators have received a $2.5 million grant to develop a head-mounted augmented reality system that can guide surgeons in ensuring complete tumor removal in head and neck cancer surgery and potentially reduce the recurrence rate of tumors. 

The National Institutes of Health grant was awarded to primary investigator Jie Ying Wu, PhD, assistant professor of Computer Science, with secondary appointments in Biomedical Engineering, Electrical and Computer Engineering, and Mechanical Engineering at Vanderbilt University. Wu also has an appointment in the Department of Surgery at Vanderbilt University Medical Center. 

Co-investigators include Michael Miga, PhD, director of the Vanderbilt Institute for Surgery and Engineering and the Harvie Branscomb Professor and chair of the Department of Biomedical Engineering, as well as Michael Topf, MD, associate professor of Otolaryngology-Head and Neck Surgery, and Matthew Weinger, MD, professor of Anesthesiology and Biomedical Informatics. 

“I am delighted to receive this award to transform surgical care for head and neck cancer,” said Wu. “This funding will allow us to build novel deformation models for heterogeneous tissue shrinkage and ensure the augmented reality software design is intuitive for surgeons and fits within the clinical workflow.” 

The development of the technology stems from a deficit Topf noticed in surgical oncology. While three-dimensional scanning has become part of the norm for other aspects of patient care, from same-day dental crowns to prosthetic limbs, Topf was troubled by the lack of application for 3D scanning in oncologic surgery. Topf implemented a protocol to create 3D models of resected cancers for surgeons, pathologists and oncologists to reference. 

“We came up with a way to 3D scan a surgical specimen in real time in less than 10 minutes prior to processing and not interfere with all the other important things that are going on in the pathology lab,” said Topf. “Encouragingly, this is a widely transferable practice and would be applicable to most cancer surgeries, from orthopaedic oncology to breast cancer.” 

Weinger, who is a faculty member of the Center for Research and Innovation in Systems Safety (CRISS) at VUMC, expressed the organization’s eagerness to support the research. 

“CRISS is excited to contribute to this important project, applying advanced engineering to ensure the user interface of this technology guides surgeons to safely and effectively treat cancer patients,” said Weinger, who holds the Norman Ty Smith Chair in Patient Safety and Medical Simulation. 

Safety and effectiveness are at the core of the research. As Miga explained, the 3D mapping technology will allow surgeons to rely less on a fallible mental construction of the resection plane, thereby reducing the risk of human error affecting the procedure. 

“When it comes to cancer surgery, surgeons often say, ‘We think we got it all,’” said Miga. “What many don’t realize is that every operation requires the surgeon to construct a mental spatial map, linking the visible surgical field to their internal understanding of the tumor’s extent. It’s an incredibly complex task, and sometimes, despite best efforts, reoperations are necessary. 

“Now imagine if, while the patient is still on the table, we could detect the margin in real time, and then, using a holographic overlay, highlight the precise region that needs further attention. Through our collaboration, that’s the kind of transformation we’re seeking to make commonplace with this research.” 

Collaboration has been consistent over the last few years between the Medical Center and the University, said Wu. She hopes research into the technology will eventually support a clinical trial, a sentiment shared by Eben Rosenthal, MD, Barry and Amy Baker Professor and chair of the Department of Otolaryngology-Head and Neck Surgery. 

“Improving surgical outcomes is of the utmost importance, especially when it comes to ensuring total tumor removal and reduced risk of recurrence for cancer patients,” said Rosenthal. “The research supported by this grant will help us perfect this technology as we seek practical applications for patient care, including clinical trials and, eventually, everyday use in the operating room.” 

This study is supported by NIH grant R01EB037685. 

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An ambitious project led by Vanderbilt University Medical Center investigators aims to use artificial intelligence technologies to generate antibody therapies against any antigen target of interest. 

VUMC has been awarded up to $30 million from the Advanced Research Projects Agency for Health (ARPA-H) to build a massive antibody-antigen atlas, develop AI-based algorithms to engineer antigen-specific antibodies, and apply the AI technology to identify and develop potential therapeutic antibodies. 

ARPA-H is an agency within the U.S. Department of Health and Human Services that supports transformative high-risk, high-reward research to drive biomedical and health breakthroughs to benefit everyone. 

“Over the last few decades, monoclonal antibodies have started playing an important therapeutic role in a wide range of disease settings, but we’re just scratching the surface. Monoclonal antibody discovery has the potential to impact a lot of different diseases where currently there are no therapeutics,” said Ivelin Georgiev, PhD, professor of Pathology, Microbiology and Immunology, director of the Vanderbilt Center for Computational Microbiology and Immunology, and the project principal investigator. 

Traditional methods for antibody discovery are limited by inefficiency, high costs and fail rates, logistical hurdles, long turnaround times and limited scalability, Georgiev said. 

“What we’re proposing to do is going to address all of these big bottlenecks with the traditional antibody discovery process and make it a more democratized process — where you can figure out what your antigen target is and have a good chance of generating a monoclonal antibody therapeutic against that target in a very effective and efficient way,” said Georgiev, who is also professor of Biomedical Informatics, Computer Science, and Chemical and Biomolecular Engineering. 

Antibodies are part of our immune system. They are proteins produced by white blood cells (B cells) that bind to and inactivate antigens — targets on viruses, bacteria and even our own cells. Antibodies are effective as preventive and therapeutic treatments against viruses, cancers, autoimmune disorders and other diseases. 

To identify a candidate therapeutic antibody, researchers generally screen and test thousands of antibodies against an antigen target, looking for the “needle in the haystack” that binds to and neutralizes the target. The traditional discovery process requires specific types of biological samples. For example, to find antibodies against an infectious disease pathogen, blood samples from people or animal models exposed to the pathogen are required. And then, if the pathogen mutates, a therapeutic antibody may become ineffective. 

“With a computational approach, you’re no longer dependent on access to biological samples or multiple screening cycles,” Georgiev said. “You can simulate variants and generate antibodies ahead of time before the variants arise.” 

Georgiev and his colleagues are engaged in three tasks as they work toward developing computational approaches for antibody discovery: 

1. Generation of an antibody-antigen atlas of unprecedented size and variety 

2. Development of AI-based algorithms for extracting information from the antibody-antigen atlas and engineering antigen-specific antibodies 

3. Proof-of-concept studies to apply the AI technology to identify antibody candidates against antigen targets of biomedical interest 

For the first task, the researchers are using a technology they developed called LIBRA seq (Linking B-cell Receptor to Antigen specificity through sequencing) that enables high-throughput mapping of antibody-antigen interactions for many antigens and B cells at the same time. 

“For computational methods to work, we need to have a lot of data,” Georgiev said. “The scale of data that’s available for antibodies and antigens is lower than in other fields, which has been one of the limiting factors when it comes to developing AI approaches. 

“If we train algorithms on the data that exists currently — much of it is for SARS-CoV-2, flu and HIV — the algorithms may be accurate for these targets, but they are less likely to be successful in extrapolating to a new target. We need to train them with a more diverse set of antigen targets, which is where LIBRA-seq comes into play.” 

The investigators aim for the atlas to include hundreds of thousands — and potentially over 1 million — antibody-antigen pairs, compared to approximately 15,000 pairs currently available from published data, providing an unparalleled resource for researchers worldwide. 

The team is already moving forward on the second task of building computational models, which they will improve as they populate the antibody-antigen atlas. For the third task, they will apply the AI technology to develop antibodies against cancer antigens and bacterial, viral and autoimmune targets. They will select one candidate antibody for preclinical development up to and including IND (investigational new drug) application. 

“Our project will be providing a platform that can be used for a variety of different diseases, not just the specific targets we’re interested in,” Georgiev said. “Our team has spent many years trying to discover antibodies against a variety of indications, and it’s such an inefficient process with a lot of failure. If we can help change that, that’s going to be huge — not just for us, but for the entire field and for people with diseases where antibody therapies can make a difference. 

“It’s going to be hard. It’s not an easy problem, but I think we have a good foundation for it, and we’ll do the best we can to make it work.” 

Collaborators on the project are: Ben Ho Park, MD, PhD, Sarah Croessmann, PhD, Eric Skaar, PhD, MPH, Maria Hadjifrangiskou, PhD, and Jeremy Goettel, PhD, at VUMC; Tedd Ross, PhD, and Giuseppe Sautto, PhD, at Cleveland Clinic; and Maria del Pilar Quintana Varon, PhD, and Lars Hviid, PhD, at the University of Copenhagen. The Brock Family Center for Applied Innovation, a catalyst for advancing translational research to market, has engaged with and supported the Georgiev team. 

Vanderbilt University and VUMC shared resources that are critical to the project are: VANTAGE (Vanderbilt Technologies for Advanced Genomics), ACCRE (Advanced Computing Center for Research and Education), and FCSR (Flow Cytometry Shared Resource). Wheeler Bio will participate in IND-enabling studies, cell line development and manufacturing activities.

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