Validation of FGI in Multi-Therapies Prediction

  • Immunotherapy has achieved remarkable success for cancer treatment. Currently, the FDA has approved several monoclonal antibodies targeting immune checkpoint molecules (such as PD-1 and CTLA4) for the treatment of patients with melanoma, non-small cell lung cancer, and several other cancer types. However, only a small proportion of patients could benefit from immune checkpoint inhibitors (ICIs), and no definitive biomarkers have been identified and validated for the prediction. LIWEN’s FGI analysis focus on the scoring and imaging of cancer immunity and immunosuppressive microenvironment, to improve the prediction of responsiveness to ICIs.

Immunotherapy
K-Cell RNA-seq FGI display the result of one patient, whose immune function was highly activated (top left), while immunosuppressive TME was mild down-regulated (down left). FGI predict the patient maybe a responder of ICIs, and clinically the patient was treated with anti-PD1 ICI and has achieved partial response.
 

  • MET amplification and over-expression have been associated with response to MET inhibition. However no cut-offs have been determined for continuous readout of these predictive biomarkers. LIWEN’s FGI evaluate individual gene expression with normalized values based on RNA-seq data.MET amplification and over-expression

    RNA-seq FGI analysis discovered several PDX (Patient-derived xenograft) models with cMet signal activation (over-expression), but not high-level gene copy number (< 5). The example displays one of these PDX models (#LD1-0025-200662), whose anti‐tumor efficacy was verified to be sensitive to cMet inhibitor.