Open Access

Dean Lee, Ross Fulton, Monika Manne, Liang Schweizer, Andreas Raue

• The identification of predictive biomarkers for patient treatment response is urgently needed to increase the probability of success of existing and novel experimental therapies. Single-cell profiling has provided novel biological insights into drug responses in the tumor microenvironment, but its potential for biomarker discovery has not been fully explored for therapeutic purposes. We describe a novel approach to discover predictive response biomarkers from single-cell data from a small patient cohort using the T cell receptor sequence intrinsic to each T cell to match clonotypes between pre- and post-treatment tumor samples. As a result, we have identified a predictive gene expression signature for immune checkpoint blockade and validated its predictive performance using data from three larger clinical studies. Our results demonstrated that applying clonotyping with single-cell genomic profiling is a promising novel approach for biomarker identification that does not require dataThe identification of predictive biomarkers for patient treatment response is urgently needed to increase the probability of success of existing and novel experimental therapies. Single-cell profiling has provided novel biological insights into drug responses in the tumor microenvironment, but its potential for biomarker discovery has not been fully explored for therapeutic purposes. We describe a novel approach to discover predictive response biomarkers from single-cell data from a small patient cohort using the T cell receptor sequence intrinsic to each T cell to match clonotypes between pre- and post-treatment tumor samples. As a result, we have identified a predictive gene expression signature for immune checkpoint blockade and validated its predictive performance using data from three larger clinical studies. Our results demonstrated that applying clonotyping with single-cell genomic profiling is a promising novel approach for biomarker identification that does not require data collected from large patient cohorts. This could increase success rates, reduce clinical trial size, and significantly impact future clinical developments of immunomodulatory therapeutics.…