But why are these biomarkers so effective for patient stratification and improved treatment outcomes compared to the majority of biomarkers that are tested for immuno-oncology companion diagnostics?
What Makes Biomarkers Work So Well
It turns out that companies like Merck that test their biomarker-based drug development strategies had luck on their side when they zeroed in on NSCLC.
Recent oncology reviews have uncovered that the two major predictors of being able to identify good quality biomarkers for a given tumor type are a high mutational burden and high immune cell infiltration of the tumor tissue. NSCLC happens to have both of these.
New Biomarker Strategies for Cancer Treatment
Part 1: High Mutational Burden
A high mutational burden in cancer cells makes the cells stand out more to the surveilling immune system. Mutations often produce novel antigens that are readily recognized by the immune system and cause an increased immune response to that tumor tissue.
A high mutation rate can also be due to a failure of the mismatch repair pathway for maintaining DNA integrity. Mismatch repair-deficient tumors have responded well to immune checkpoint therapy in clinical trials, and a mismatch repair biomarker called MSI-high is an approved companion diagnostic for pembrolizumab in multiple cancer types.
Part 2: Increased Immune Cell Infiltration
However, none of these immune therapies can be effective without high immune cell infiltration of the tumor. High tumor infiltration of CD8+ lymphocytes is associated with an improved prognosis for cancer patients, and allows tumors to be more easily targeted with immune checkpoint drugs.
Clearly none of this could be done without bioinformatics, a key resource that helps mine, process and analyze genomic data in order to gain biomarker insight. These insights will allow even more robust biomarkers for cancer immunotherapy treatment to be developed, improving specificity and patient outcomes.
Jane Cook, Journalist & Content Writer, Bridge Informatics
Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email daniel.dacey@old.bridgeinformatics.com or dan.ryder@old.bridgeinformatics.com.
Sources:
https://www.lumakrashcp.com/advanced-non-small-cell-lung-cancer-treatment-efficacy
https://pubmed.ncbi.nlm.nih.gov/27924752/
https://www.science.org/doi/10.1126/science.aan6733
