True precision medicine through single-cell science - Nature

In 2021, ScaiVision has been used to find better predictive biomarkers for patients with metastatic non-small cell lung cancer requiring treatment with one of two common checkpoint inhibitors: a PD-L1 inhibitor or a PD-1 inhibitor. These therapies only work in about 30% of patients, and the sole biomarker available to guide therapy choice—tumor PD-L1 expression level scoring—is often unreliable and inaccurate.

Using easily accessible peripheral blood mononuclear cell samples, ScaiVision was trained to predict treatment response in patients receiving either a PD-L1 inhibitor or a PD-1 inhibitor. In the PD-L1 inhibitor-treated patients, ScaiVision identified cell populations and an associated novel single-cell gene signature predicting response to PD-L1 therapy with 87% accuracy. The predictive performance of this signature was then evaluated in the PD-1 inhibitor-treated patients. “Interestingly, this signature performed even better, reaching 100% accuracy in predicting therapy response,” said Benjamin Essigman, data scientist. “Additionally, our algorithm prediction significantly outperformed PD-L1 scoring.”

Based on the identified cell population and gene signature genes, Scailyte is currently planning an expanded validation in a new cohort, as well as testing its gene signature prototype in bulk RNA sequencing data to translate it into a prototype assay for clinical implementation.

Scailyte is open to partnering with companies interested in accelerating development of their products and improving clinical outcomes. “Using multi-omics single-cell data in combination with AI to discover and develop biomarkers fully exploits the power of these cutting-edge technologies in translational research,” said Martijn van Attekum, technical lead. “With our game-changing platform we are enabling the development of next-generation therapeutics for the benefit of patients with complex diseases.”