A study published in the Journal of Translational Medicine discusses to a new class of biomarkers (anti-FSN antibodies) that are sufficiently comprehensive to predict immune checkpoint inhibitors outcomes in lung cancer.
For developing the first, most heterogeneous predictive model of ICI response, sera from the 66 patients who were treated for at least 6 weeks were analyzed. Differential anti-FSP antibody activities were identified on the FSP arrays that correlated with future outcomes. We demonstrated that 226 FSPs preferentially bound by an outcome group could predict response with 97.8% accuracy in the patient samples showing cumulative non-zero model scores .
In a third model, patients who had received ICI in combination with chemotherapy were excluded. Accuracy was only marginally reduced, even though this was the smallest sample cohort size of the models tested. Outcome-associated model-peptides increased more than twofold and the proportion of predicted samples increased to 80%. The larger number of outcome-associated FSPs likely contributed to the reduction in indeterminates.
A variety of molecular biomarkers have been studied toward developing a test for predicting patient responses to ICI therapeutics. Some recent approaches have shown encouraging predictive value; however, biomarker extraction or testing is elaborate, and sometimes not possible. By contrast, antibodies are immune effectors and therefore they directly read out immune activity. Antibody biomarkers are massively amplified by B cells that have been activated because of their recognition of tumors.
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