Conventional detection of cancer involves highly invasive and expensive diagnostic procedures, often leading to non-compliance from patients. Therefore, there is a strong requirement for the development of non-invasive techniques that can facilitate rapid and timely diagnosis of the disease. The tumor-immune interaction often leads to anomalous expression of different soluble immune signaling molecules like cytokines and chemokines, thus making them promising candidates for sensing disease development and progression. Furthermore, differential expression of soluble isoforms of several immune-checkpoint molecules like PD-L1, CTLA-4 etc., has been found to have strong correlation with tissue-specific tumor development, disease progression and in many cases, disease prognosis. Therefore, development of biosensors, to rapidly detect and analyze the levels of these soluble immune molecules in different body fluids, requiring minimal sample volume, has the potential to be a game-changer in the field of cancer diagnosis. In addition, real time monitoring of these soluble immune checkpoint molecules in patient-derived biofluids may serve as decision support tools for patient selection for immunotherapeutic interventions. Majority of the biosensors designed to detect the soluble immune biomarkers, have used a two-antibody based sandwich system to capture the target analyte. However, new technologies using bioreceptors like the aptamers or nano-yeast scFv antibody fragments have made possible multiplexed detection of several analytes simultaneously. The use of gold nanoparticles or carbon nanotubes on the electrode surface serves to increase the sensitivity of detection, due to their high electrical conductivity. Further, fabrication of the biosensors on microfluidic platforms enable the detection of these analytes at ultra-low levels. This review discusses the recent advances made in the development of biosensors for specific and selective detection of these immune-markers that can be successfully translated to the clinics as a new paradigm in disease diagnosis and monitoring.
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