TEL AVIV, Israel–(BUSINESS WIRE)–Nucleai, an AI-powered multimodal spatial biology company, has contributed to a groundbreaking study published in Nature Communications, which investigates how the positioning of tumor cells and their glucose utilization can predict the efficacy of immunotherapy in patients with non-small cell lung cancer (NSCLC). The research was conducted in collaboration with The University of Queensland and Yale School of Medicine, highlighting the critical role of tumor microenvironments in treatment responses.
The study employed multiplex immunofluorescence (mIF) and advanced computational techniques to analyze tumor tissues at a single-cell resolution. By focusing on the spatial arrangement and metabolic characteristics of different cell populations within the tumor microenvironment, researchers were able to discern distinct patterns that correlate with patient responses to immunotherapy.
Nucleai’s AI-driven mIF analysis pipeline played a significant role in the study, allowing for the precise identification and classification of tumor and immune cell populations on a large scale. This technological contribution provided a robust foundation for the subsequent spatial and metabolic analyses conducted by the academic teams involved.
“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 significantly changed the treatment paradigm for lung cancer; however, a limited number of patients achieve sustained benefits. The findings from this study indicate that specific spatially defined metabolic features within tumors may help explain the variability in treatment responses, underscoring the need for more complex approaches to characterizing tumor biology beyond traditional single-marker assessments.
This research aligns with Nucleai’s mission, leveraging its multimodal spatial AI platform designed to facilitate scalable and rapid spatial profiling across extensive research cohorts. The platform transforms complex multiplex imaging data into structured, quantitative spatial insights, aiding collaborative efforts aimed at advancing 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,” stated 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 collaborative research involved various institutions, including 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 at the forefront of AI-powered multimodal spatial biology, turning tissue imaging into actionable insights for drug development and diagnostics. The company’s multimodal spatial operating system integrates high-plex spatial proteomics, histopathology, and clinical data to identify predictive spatial biomarkers and drive the development of next-generation precision medicine products. Nucleai collaborates with prominent pharmaceutical, diagnostic, and academic institutions worldwide. For more information, visit nucleai.ai.
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