Empowering target discovery with our proprietary cfChIP-seq gene expression database spanning tumors, tissues, and cell types. The database is designed for seamless integration with other transcriptomic and multi-omics datasets. The platform enables precise target selection and validation. We actively leverage this resource to analyze external omics data and scientific literature, accelerating the identification of high-potential therapeutic targets and advancing early-stage drug discovery.

Boosting trial design and probability of technical success (PTS) through improved patient selection and stratification using chromatin liquid biopsy expression markers. For example, Senseera’s sensitive MASH expression signature enables precise prescreening to: (a) identify high-fibrosis patients for inclusion in clinical trials, or (b) prioritize clinical trial candidates for confirmatory liver biopsy, significantly improving recruitment accuracy and efficiency.

cfChIP-seq reveals cell-type-specific gene expression from blood, providing non-invasive insights into drug biology and cell state. Partners can monitor gene activity changes across tissues and immune compartments to understand mechanism of action, off-target effects, and toxicity. These insights help uncover resistance mechanisms, identify new targets, and support data-driven decisions from preclinical research to clinical development.

Accurate, cell-free chromatin-based models from Senseera infer the expression of drug-relevant genes across both tumors and immune cells, all from a simple blood draw. These non-invasive insights eliminate the need for tissue biopsies, enabling partners to assess target expression dynamics, validate drug mechanisms, and optimize treatment strategies with greater speed, precision, and clinical relevance.

Senseera enables longitudinal monitoring of disease progression and treatment response across tumors, tissues, and immune cells, with single-gene resolution from blood. In a collaboration on an adoptive T cell therapy clinical study, we tracked thousands of genes in treated subjects, capturing dynamic changes throughout treatment. The data revealed shifts in gene expression and cfDNA contributions from tumor cells, immune cells, and liver tissue, providing critical insights into therapeutic impact and safety.