Precision Diagnostics for Predicting Therapy Response to Bispecific Antibodies

Immunotherapies have revolutionized cancer treatment by utilizing the patient's immune system, offering hope for treating a wide range of diseases. However, challenges such as therapy resistance, poor response rates, and high costs remain.

Precision diagnostics that accurately identify patients likely to benefit from specific immunotherapies could optimize treatment efficacy, reduce unnecessary treatments, and significantly decrease healthcare expenses.

A major limitation of current predictive biomarkers is their focus on the abundance of individual immune components, such as T cell numbers or specific surface markers, without considering the crucial interactions among immune cells or between immune and cancer cells.

Such cellular interactions are particularly relevant for the efficacy of the immunotherapeutic class of bispecific antibodies, which target cancer by facilitating direct interactions between cancer and immune cells.

Traditional methods for predicting response to immunotherapies fail to account for the complexity of these cellular interactions.

Our ERC-Starting Grant project introduces an innovative framework for simultaneously mapping single-cell immune landscapes and cellular interactions at a low cost and ultra-high scale.

Applying this approach to pre-treatment samples from children with acute lymphoblastic leukemia undergoing bispecific antibody therapy has allowed us to identify specific cellular interactions associated with therapy response or failure.

Here we suggest to refine, validate and prepare the clinical implementation of our approach with the goal to develop a novel precision diagnostic tool that accurately predicts response to bispecific antibodies.