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Thousands of candidate cancer biomarkers have been proposed, but so far, few are used in cancer screening. Failure to implement these biomarkers is attributed to technical and design flaws in the discovery and validation phases, but a major obstacle stems from cancer biology itself. Oncogenomics has revealed broad genetic heterogeneity among tumors of the same histology and same tissue (or organ) from different patients, while tumors of different tissue origins also share common genetic mutations. Moreover, there is wide intratumor genetic heterogeneity among cells within any single neoplasm. These findings seriously limit the prospects of finding a single biomarker with high specificity for early cancer detection. Current research focuses on developing biomarker panels, with data assessment by machine‐learning algorithms. Whether such approaches will overcome the inherent limitations posed by tumor biology and lead to tests with true clinical value remains to be seen. [ABSTRACT FROM AUTHOR]

Enormous efforts and resources are being invested in the search for biomarkers for early cancer detection. However, the biology and genetics of cancer, an extremely heterogeneous disease from the molecular viewpoint, thwart the clinical application of laboratory discoveries, and the sought-for pan-cancer biomarkers for population screening remain out of reach. Notwithstanding advances made using artificial intelligence to overcome current limitations in cancer biomarker research, in article number 1900122, Tommaso A. Dragani et al. speculate that there is still a long way to go before this important public health goal is achieved. [Extracted from the article]