Vanderbilt Health and Bertis, an artificial intelligence-driven proteomics-based precision medicine company, have announced a joint research and co-development collaboration. The endeavor marks a significant milestone in oncology by advancing the convergence of AI, spatial biology and translational cancer research.

By integrating Vanderbilt Health’s Molecular AI Initiative capabilities with Bertis’ proprietary deep proteomics and AI-enabled target discovery technologies, the collaboration will build an advanced, spatially resolved dataset to identify novel therapeutic targets and predictive biomarkers.

Traditional target discovery often relies on bulk tissue analysis, which loses the critical context of how cells are organized within a tumor. Vanderbilt Health’s Molecular AI approach changes this paradigm by employing sophisticated computational spatial analysis to generate high-resolution spatial molecular maps. This AI-driven spatial biology allows researchers to visualize and decode the complex architecture of the tumor microenvironment, specifically identifying how tumor, immune and stromal (connective tissue) cells interact in biologically and therapeutically relevant regions. By mapping the precise locations and spatial relationships of these cells, the Molecular AI platform can isolate the key cell populations responsible for treatment response or resistance.

These advanced spatial insights are then integrated with Bertis’ cutting-edge proteomics capabilities. While Vanderbilt Health maps the critical spatial context, Bertis will conduct deep proteomic and metabolomic profiling, applying its proprietary AI-enabled computational models to prioritize the most viable, druggable targets.

The initial focus of this joint research will be on HER2-low tumors (cancers that express low levels of the growth-promoting protein HER2), a historically challenging clinical area, with the potential to expand into additional tumor types based on data outcomes and joint scientific discussions. By layering spatial context over proteome-level data, the teams aim to pinpoint cell surface proteins that are uniquely positioned for emerging therapeutic modalities, including antibody-drug conjugates and cell-based therapies.

This sophisticated AI-driven spatial multimodal and deep proteomics pipeline is spearheaded by Tae Hyun Hwang, PhD, professor of Surgery, founding director of Molecular AI Initiative and director of AI Research in the Section of Surgical Sciences at Vanderbilt Health. Hwang also co-leads gastric cancer atlas efforts within the National Cancer Institute-funded Human Tumor Atlas Network (HTAN) and is spearheading international HTAN collaborations with South Korea’s National Cancer Center.

Highlighting the clinical necessity of this integrated approach, Hwang said, “Identifying therapeutic targets and understanding treatment response require a precise view of proteins, spatial context and tumor biology. By combining Vanderbilt Health’s Molecular AI and spatial analysis capabilities with Bertis’ proteomics and AI-enabled target discovery platform, this collaboration is designed to generate high-confidence therapeutic targets and predictive biomarkers that can support future translational research and therapeutic development.”

Bertis is led by co-CEOs Dong-young Noh and Seung-man Han, who emphasized the collaboration accelerates the global reach of their platform.

“Collaborating with Vanderbilt Health, a leading U.S. academic medical center with strong expertise in Molecular AI, spatial biology and cancer research, is highly meaningful and reflects the growing global recognition of Bertis’ technological capabilities,” Han said. “Through this collaboration, we aim to expand the role of AI-driven proteomics in drug discovery and identify therapeutic targets that may open new possibilities in oncology.”

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Researchers at Vanderbilt University Medical Center using artificial intelligence have helped develop two technologies for improving cancer care. 

One technology called MSI-SEER, described in a study published in npj Digital Medicine, better predicts microsatellite instability-high status from standard pathology slides and provides clinicians with specific data, including any uncertainties with predictions. The other technology, a breakthrough three-dimensional imaging tool described in a study published in Nature Communications, has transformative potential beyond cancer diagnostics. 

These new technologies showcase how VUMC researchers are using the power of AI to meet a wide range of medical needs, said Tae Hyun Hwang, PhD, professor of Surgery, founding director of the Molecular AI Initiative, and director of AI Research for the Vanderbilt Section of Surgical Sciences. He noted that the 3D imaging could significantly advance development of therapeutic drugs, provide more detailed assessments of organ transplant rejections, assist with personalized medicine, and aid with tissue analysis for biopharmaceutical development. 

“This technology fundamentally redefines how we visualize and analyze tissue architecture, moving from traditional two-dimensional views to full 3D microenvironment mapping at the subcellular level,” said Hwang, a corresponding author of the study, who provided senior leadership in the development, validation and translational development of the technology. 

The 3D study published in Nature Communications introduced an innovative framework that integrates holotomography with deep learning to generate hematoxylin- and eosin-stained images directly from thick tissue samples. This noninvasive, AI-driven approach preserves tissue integrity, overcomes the traditional 4- to 5-micron thickness limit of routine histology, and enables volumetric visualization of biological structures up to 50 microns thick. 

By preserving tissue samples and avoiding chemical alteration, this method also ensures compatibility with downstream molecular assays, such as spatial transcriptomics, proteomics and genomic profiling — enhancing the breadth and depth of diagnostic and research capabilities.  

“This is not just a digital copy of hematoxylin- and eosin-staining,” Hwang said. “It is a foundational platform for AI-driven volumetric tissue analysis that accelerates discoveries in oncology, immunology, regenerative medicine and therapeutic development.” 

The multi-institutional effort also included researchers from KAIST, Tomocube Inc., Yonsei University College of Medicine and Mayo Clinic. Hwang received funding support from the National Cancer Institute (grants R01CA276690, R37CA265967, U01CA294518). 

VUMC researchers developed the MSI-SEER predictor technology in collaboration with Mayo Clinic, Yonsei Severance Hospital and Seoul St. Mary’s Hospital in South Korea. This technology identifies patients who will benefit from an immunotherapy that might otherwise be missed with existing prediction models. 

Microsatellite instability-high (MSI-H) status is a well-established biomarker used to identify patients likely to respond to immune checkpoint inhibitors, especially patients with gastrointestinal cancers. However, traditional testing methods — including immunohistochemistry and PCR-based assays — offer only a binary result and often miss focal or heterogeneous MSI-H regions within tumors.  

MSI-SEER overcomes this limitation by dividing each pathology slide into thousands of image tiles and generating region-by-region predictions of MSI-H probability. This enables visualization of the tumor’s spatial heterogeneity and quantification of the MSI-H fraction across the tumor. In multiple cases, MSI-SEER identified MSI-H regions in tumors previously classified as microsatellite stability, and those patients subsequently responded to immunotherapy. 

“This is analogous to what we say in HER2-low gastric cancer, where patients previously not eligible for targeted therapy are now being treated with agents like trastuzumab deruxtecan,” Hwang said. “Likewise, patients with low or heterogeneous MSI-features may now be reconsidered for immunotherapy if spatially resolved analysis like MSI-SEER is used.” 

A key innovation of MSI-SEER is its ability to report not only predictions but the confidence level for each result.  

“AI should not dictate clinical decisions; it should support them,” Hwang said. “MSI-SEER gives clinicians both the answer and a measure of how reliable the answer is. It’s not about replacing human expertise but about combining the best of AI computation with physician judgment to drive safe, precise decisions.” 

Hwang, who conceptualized the study and is the paper’s senior author, received research support from the National Cancer Institute and the Department of Defense. He also received support from the Eric and Wendy Schmidt Fund for AI Research and Innovation and the American Association for Cancer Research Innovation and Discovery Grant.  

Other VUMC researchers who authored the study are Sunho Park, PhD, Minji Kim, MS, Jean Clemenceau, PhD, and Inyeop Jang, PhD. 

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Researchers from Vanderbilt University Medical Center are set to play a pivotal role at the American Association for Cancer Research (AACR) Annual Meeting 2025, co-organizing a methods workshop that highlights the integration of computational pathology, artificial intelligence (AI) and spatial multiomics to advance cancer research and precision oncology. 

The workshop, “Integrating Computational Pathology, AI, and Spatial Multi-Omics in 2D and 3D,” will take place April 26 from 8 a.m. to 9:30 a.m.It will be co-chaired by Tae Hyun Hwang, PhD (VUMC), Linghua Wang, MD, PhD (University of Texas MD Anderson Cancer Center), and Mingyao Li, PhD (University of Pennsylvania). This session will provide a deep dive into how AI-driven 3D spatial molecular and multimodal approaches are transforming the landscape of oncology research and clinical applications. 

Hwang, a national leader in AI-driven oncology research and director of AI Research in the Section of Surgical Sciences at VUMC, is the founding director of VUMC’s Molecular AI Initiative. He will present a talk titled “AI-Driven 3D Spatial Mapping of the Tumor Immune Microenvironment for Precision Oncology,” based on novel technologies his lab is utilizing and developing, integrating advanced holotomography with AI-driven spatial sorting and molecular profiling techniques. 

Hwang co-leads the National Cancer Institute Pre-Gastric Cancer Human Tumor Atlas Network and serves as an executive committee member of the Center for Computational Systems Biology at Vanderbilt University. His research focuses on leveraging AI and machine learning coupled with innovative experimental approaches to analyze 3D and 4D tumor ecosystems at single-cell and subcellular resolutions, integrating spatial molecular data to reveal key mechanisms of cancer progression, immune interactions and therapeutic response. This cutting-edge approach aims to enhance early detection, refine treatment strategies, advance therapeutic development and propel next-generation precision medicine. 

As part of Vanderbilt’s Molecular AI Initiative, Hwang and his team are pioneering holotomography-based 3D reconstructions of tumor tissue samples, integrating AI-driven spatial molecular profiling for advanced characterization of cancer biology. This work is at the forefront of predicting disease progression and therapeutic response, ultimately informing the future of cancer treatment. 

Through this workshop, VUMC continues to assert itself as a global leader in AI-driven precision oncology, fostering collaborations with leading cancer research institutions and pushing the boundaries of AI-powered cancer diagnostics and therapeutic innovations.  For more information, please visit the AACR Annual Meeting Website or contact Hwang at taehyun.hwang@vumc.org. 

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