Collaborative Nature Communications study with The University of Queensland, Yale School of Medicine, and Nucleai examines how tumor cell positioning and glucose use predict which NSCLC patients benefit from immunotherapy
TEL AVIV, Israel–(BUSINESS WIRE)–Nucleai, a leader in AI-powered multimodal spatial biology, today announced its contribution to a collaborative international study published in Nature Communications that explores how spatial organization and metabolic characteristics of tumor cells are associated with response and resistance to immunotherapy in non-small cell lung cancer (NSCLC).
The study, led by academic researchers at The University of Queensland and Yale School of Medicine, applied multiplex immunofluorescence (mIF) and computational approaches to analyze tumor tissue at single-cell resolution. By examining how different cell populations are organized within the tumor microenvironment and how they metabolize glucose, the researchers identified distinct spatial and metabolic patterns associated with immunotherapy outcomes.
As part of the collaboration, Nucleai’s AI-powered multiplex immunofluorescence (mIF) analysis pipeline enabled accurate identification and classification of tumor and immune cell populations at scale, providing a consistent and reproducible foundation for downstream spatial and metabolic analyses conducted by the academic research teams.
“Understanding response to lung cancer treatment requires insight into the different cell states and cell-cell interactions within the tumor, not just which cells and markers are present,” said Ettai Markovits, Director of Biomedical Research at Nucleai. “This study highlights the importance of spatial context in cancer biology, and we are pleased to have supported this work by enabling robust, AI-based spatial analysis applied to multiplex imaging data.”
Immunotherapy has transformed the treatment landscape for lung cancer, yet only a subset of patients experience durable benefit. Findings from this study suggest that spatially defined metabolic features within tumors may help explain variability in treatment response, reinforcing the need for more nuanced approaches to characterizing tumor biology beyond traditional single-marker assessments.
This work builds on Nucleai’s broader multimodal spatial AI platform, which is designed to support scalable and rapid spatial profiling across large research cohorts. By transforming complex multiplex imaging data into structured, quantitative spatial insights, the platform supports collaborative efforts to advance precision oncology research.
“This study demonstrates the power of multiplex imaging data to shed light on nuanced spatial interactions linked to treatment response to immunotherapy,” said Associate Professor Arutha Kulasinghe from UQ’s Frazer Institute. “However, translating this spatial complexity into clinical insights requires sophisticated computational analysis. Nucleai’s contributions helped connect high-dimensional spatial imaging with clinical outcomes more efficiently.”
The research was conducted in collaboration with The University of Queensland’s Frazer Institute, Yale School of Medicine, Wesley Research Institute, Quanterix, and Nucleai, and is published in Nature Communications.
About Nucleai
Nucleai is an AI-powered multimodal spatial biology company, transforming tissue imaging into actionable insights for drug development and diagnostics. Nucleai’s multimodal spatial operating system integrates high-plex spatial proteomics, histopathology, and clinical data to identify predictive spatial biomarkers and power the development of next-generation precision medicine products. Nucleai partners with leading pharmaceutical, diagnostic, and academic institutions globally. Learn more at https://nucleai.ai/.
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