Cell Publication Highlights Ionpath MIBI Spatial Proteomics Technology in Foundational Study Predicting Breast Cancer Progression

Innovative study deployed multiplexed ion beam imaging (MIBI™) technology revealing a high-multiplex spatial proteomic signature as a predictive biomarker for breast cancer treatment

Excerpt from the Press Release:

MENLO PARK, Calif.–(BUSINESS WIRE)–Ionpath, Inc., the leader in high-definition spatial proteomics, today announced that a peer-reviewed research study led by scientists at Stanford University, Washington University, Duke University, and Arizona State University has been published in the journal Cell. The study, which focused on investigating potential indicators in the progression of ductal carcinoma in situ (DCIS) into invasive breast cancer (IBC), discovered a highly predictive, high-multiplex spatial proteomic signature (greater than 20 markers) that may pave the way for future development of new targeted therapies and prognostic tests.

“This is an important study that for the first time offers a spatial atlas of preinvasive breast cancer that illustrates how tissue structure and function change with progression to invasive disease,” said senior author Mike Angelo, MD, PhD, Assistant Professor of Pathology at Stanford University and co-founder of Ionpath. “Being able to image dozens of proteins at subcellular resolution was essential for being able to identify what aspects of the tumor microenvironment were predictive of clinical outcome. We are optimistic that this work will further the development of high-dimensional imaging tools that can be used to guide clinical care for more than 60,000 women diagnosed with DCIS each year in the U.S. alone.”

DCIS is diagnosed based on the specific organization of tumor, stromal, and myoepithelial cells. In this study, the research team deployed Ionpath’s MIBI spatial proteomics technology to resolve precise cell locations, compositions, and functions using 37 proteins of interest. With this approach, they characterized 79 archival samples from normal breast, DCIS, or IBC. Then they implemented machine learning tools to identify and map 16 different cell populations and their varied spatial parameters across the samples. Overall, they measured more than 400 features in each sample and incorporated these into a classifier to determine which ones could be clearly linked to disease progression patterns.

“This study is a powerful demonstration of how spatial proteomics enabled by Ionpath’s MIBI technology can illuminate high-definition details of the tumor microenvironment and pave the way for the development of new high-multiplex spatial signatures that can be used to develop new diagnostic tests and therapies that deliver on the promise of precision medicine,” said Sander Gubbens, CEO at Ionpath. “Ionpath is honored to have played a role in this foundational study and congratulates the entire research team on their work to improve outcomes for women with breast cancer.”

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