Nucleai, a spatial AI biomarker company that deciphers cellular conversations and maps cellular interactions within tissue samples to predict therapeutic outcomes, will showcase its developments in cancer research and diagnostics at the American Association for Cancer Research (AACR) Annual Meeting in San Diego, California from April 5-10, 2024. Nucleai’s posters will highlight novel AI spatial biomarkers and enhanced histopathology workflows that promise to revolutionize cancer treatment and diagnostics.
Nucleai’s first poster, in collaboration with Arutha Kulasinghe, PhD, senior research fellow, Frazer Institute, University of Queensland, Australia, where he also leads the Clinical-oMx Lab, unveils the identification of a novel signature linked to immunotherapy treatment outcomes in lung cancer. Leveraging Nucleai’s unique AI spatial biology solution, the research uncovered the correlation between the metabolic state and spatial distribution of tumor and immune cells in the tumor microenvironment, shedding new light on treatment outcomes and potential resistance mechanisms. This discovery holds the promise of improving immunotherapy response rates by providing crucial insights into disease progression, treatment response, resistance mechanisms, and the development of more effective therapeutics and diagnostics. Nucleai is driving innovation in cancer research and collaborating with leading scientists to translate these findings into tangible benefits for patients and healthcare providers worldwide.
Nucleai’s second poster introduces the H&E 2.0 algorithm, a significant advancement in cancer pathology. This advanced algorithm, trained on spatial biology technologies such as multiplex immunofluorescence, sets a new standard for precision and depth in disease management and treatment selection. Unlike traditional AI solutions, H&E 2.0 augments manual pathologist annotations, enabling the automated identification of rare and hard-to-detect cell types in patient tissue biopsies. By implementing the H&E 2.0 algorithm, pathologists can automate challenging tasks, improve accuracy and scalability, and extract deeper insights from existing biobanks of H&E-stained clinical samples. This solution marks a leap toward more precise and efficient cancer diagnosis and treatment selection.
Oscar Puig, PhD, Vice President, Translational Medicine & Diagnostics of Nucleai, said of the significance of the AACR posters: “Our collaborations and innovation are pushing the boundaries of spatial biology; they are redefining the standards in cancer research and diagnostics. The breakthroughs we’re unveiling at AACR epitomize the potency of merging innovative machine learning algorithms with pioneering spatial biology techniques to ensure cancer care is more resolute than ever so we can improve patient outcomes and have a positive impact on the lives of patients with cancer.”
Dr. Kulasinghe said, “Our study has unveiled a unique metabolic signature in tumor cells that is closely linked to immunotherapy resistance and overall survival in lung cancer patients. This discovery, rooted in the century-old Warburg effect, sheds light on how the functional and metabolic activity of tumor cells causes them to proliferate aggressively and develop resistance to immunotherapy. By identifying patients with this tissue signature, we could personalize treatment strategies, for example, giving them combination therapies, to improve outcomes. This research represents a significant leap forward in our understanding of cancer metabolism and its implications for personalized medicine.”
Nucleai’s advanced spatial biology and AI-driven platform enhance pathology by extracting deeper insights from pathology slides. This approach enhances the precision of medicine, accelerates drug development, and improves patient care. By integrating spatial biology with AI, Nucleai creates actionable insights from complex pathology data, setting a new standard for diagnosis and treatment, and advancing precision medicine.
SOURCE: Businesswire
