NEW YORK –Nucleai researchers investigated whether, by using spatial biology to analyze interactions between the immune system and tumor microenvironment, it is possible to find biomarkers predictive of sensitivity to immuno-oncology drugs.
In a retrospective multi-center study conducted in the US and Israel, where the firm is based, Nucleai analyzed biopsy slides and clinical outcomes data from about 200 patients treated with first-line, single-agent Keytruda. They then trained their machine learning algorithm using 100 slides from patients who responded and 100 slides from those who didn’t and generated a spatial signature from the images that distinguished between the two groups. Factoring in features such as the interactions between the cells and the densities of different cell populations, they built a classifier to score patients positive or negative for response to immunotherapy.
The classifier was able to separate responders and nonresponders independent of other approved biomarkers such as PD-L1 expression. Nucleai is now expanding the study to include more patients and hopes to publish the results.