RE: Shares Tipped8 Jan 2024 20:12
Introduction: Unprecedented advantages in cancer treatments with immune checkpoint
inhibitors (ICI) remain limited to a subset of patients, with high demands for development of
better biomarkers to access likely clinical response in advance of ICI therapeutic
interventions. 3D genomics has emerged as a novel molecular biomarker modality with
strong functional correlation to clinical outcomes. The last few years have seen 3D
genomics applications in patient stratifications, offered solutions in symptomatic and
pre-symptomatic diagnosi and prediction of response to therapeutic interventions.
EpiSwitch® is a 3D genomic platform for the discovery of 3D genomic blood-based
biomarkers in a variety of immune-related and oncological diseases. Objectives: To
develop and validate blood-based predictive 3D genomic biomarkers for response to
PD-(L)-1 checkpoint inhibitors. Methodology: To conduct a genome-wide analyses of the
regulatory 3D genome architecture linked to epigenetics and immunogenetic controls
associated with tumour immune evasion. Several independent cancer patient cohorts
treated with Pembrolizumab, Atezolizumab, Durvalumab, in over 15 diverse oncological
indications were analyzed using EpiSwitch®. Results: A clinical blood assay based on 8
markers - Checkpoint Inhibitor Response Test (CiRT) - has been developed to predict
response to ICI from over 30 million data points. The predictive 8 biomarker set is based on
an observational clinical trial and several retrospective cohorts, representing all together
229 treatments of diverse oncological indications, i.e. melanoma, lung, urethral,
hepatocellular, bladder, prostate, head and neck, colon, breast, bone, brain, lymphoma,
larynx. CiRT has demonstrated high accuracy up to 85%, sensitivity of 93% and specificity
of 82%. Conclusion: This study demonstrates that a 3D genomic approach could be
successfully utilized for development of a non-invasive predictive clinical assay for
response to ICI in cancer patients. CiRT can assist in treatment decisions, help improve
patient selection for optimized treatment, utilize alternative effective treatments, minimize
unnecessary toxicity, and efficiently manage the costs.