Scientists at the Icahn School of Medicine at Mount Sinai have developed a powerful new computational tool that could transform how cancer tissues are analysed and help pave the way for more personalised treatments.

The study, published in Nature Biomedical Engineering, introduces MARQO, a next-generation image analysis process that extracts detailed cellular and spatial information from tumour tissue slides with unprecedented accuracy and scalability.

Developed by a team led by Sacha Gnjatic, PhD, Professor of Immunology and Immunotherapy at the Icahn School of Medicine, MARQO streamlines the complex task of analysing immunohistochemistry (IHC) and immunofluorescence (IF) images, which are produced via staining methods commonly used to detect immune cells and other biomarkers in cancerous tissues.

When someone has cancer, pathologists inspect stained tissue sections under the microscope to see which cells are present and how they are arranged.

Doing this by hand is labour‑intensive and usually limited to small areas of the sample.

MARQO tackles this challenge in three key ways: First, while other tools can process entire images, they often require users to chop slides into patches or rely on costly computing clusters.

MARQO keeps slides intact and finishes the job in minutes rather than hours, even on standard graphics processing units.

Second, MARQO works with a range of common IHC and IF staining technologies, making study‑to‑study comparisons easier and boosting reproducibility.

Third, MARQO automatically flags likely positive cells and assigns coordinates and marker intensities, then hands off the final validation to the pathologist, keeping human expertise at the centre of the workflow.

“We designed MARQO to fill a major gap in the field: turning complex whole‑slide images into usable, structured data quickly and consistently,” said Dr. Gnjatic.

“By automating the heavy lifting, we let experts focus on interpretation and discovery.”

While MARQO is currently designed for research use and has not been validated for clinical diagnostics, its compatibility with standard clinical staining methods could enable future applications in pathology labs.

The research team plans to continue developing MARQO by improving its user interface, adding advanced spatial and neighbourhood analysis tools, and expanding its use in high-performance computing environments to support large-scale projects involving millions of digitised tissue slides.

“This platform could accelerate biomarker discovery, improve how we predict which patients will benefit from specific treatments, and ultimately support the development of more precise cancer diagnostics,” said Dr. Gnjatic.

Source: The Mount Sinai Hospital / Mount Sinai School of Medicine