Frontiers in Oncology (Jul 2021)
Beyond Colonoscopy: Exploring New Cell Surface Biomarkers for Detection of Early, Heterogenous Colorectal Lesions
Abstract
Colorectal cancer (CRC) is the third leading cause of cancer-related deaths among both men and women in the United States. Early detection and surgical removal of high-risk lesions in the colon can prevent disease from developing and spreading. Despite implementation of programs aimed at early detection, screening colonoscopies fail to detect a fraction of potentially aggressive colorectal lesions because of their location or nonobvious morphology. Optical colonoscopies, while highly effective, rely on direct visualization to detect changes on the surface mucosa that are consistent with dysplasia. Recent advances in endoscopy techniques and molecular imaging permit microscale visualization of the colonic mucosa. These technologies can be combined with various molecular probes that recognize and target heterogenous lesion surfaces to achieve early, real-time, and potentially non-invasive, detection of pre-cancerous lesions. The primary goal of this review is to contextualize existing and emergent CRC surface biomarkers and assess each’s potential as a candidate marker for early marker-based detection of CRC lesions. CRC markers that we include were stratified by the level of support gleaned from peer-reviewed publications, abstracts, and databases of both CRC and other cancers. The selected biomarkers, accessible on the cell surface and preferably on the luminal surface of the colon tissue, are organized into three categories: (1) established biomarkers (those with considerable data and high confidence), (2) emerging biomarkers (those with increasing research interest but with less supporting data), and (3) novel candidates (those with very recent data, and/or supportive evidence from other tissue systems). We also present an overview of recent advances in imaging techniques useful for visual detection of surface biomarkers, and discuss the ease with which these methods can be combined with microscopic visualization.
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