A cancer biomarker is a biological molecule found in blood, other body fluids, or tissues that indicates the presence of cancer in the body. These markers can be produced either by the cancer cells themselves or by the body in response to cancer. Biomarkers play a crucial role in cancer detection, diagnosis, prognosis, treatment selection, and monitoring disease progression or recurrence.
Cancer biomarkers can be classified into several categories based on their function and application. Some serve as diagnostic biomarkers, helping detect cancer at an early stage (e.g., prostate-specific antigen [PSA] for prostate cancer). Others function as prognostic biomarkers, providing information about the likely course of the disease, such as HER2 in breast cancer, which is associated with a more aggressive form of the disease. Predictive biomarkers help determine how likely a patient is to respond to a specific therapy. For example, tumors that express PD-L1 may respond better to immunotherapy drugs that target the PD-1/PD-L1 pathway.
Modern cancer biomarker research focuses heavily on genomic and molecular markers, including mutations, gene expression profiles, and epigenetic changes. Technologies such as next-generation sequencing (NGS) and liquid biopsies have revolutionized biomarker discovery by enabling non-invasive detection of tumor DNA in blood samples. This advancement allows for real-time monitoring of tumor evolution and resistance mechanisms.
Despite their promise, not all biomarkers are suitable for clinical use. An ideal cancer biomarker should be highly specific (accurately identifying cancer without false positives), sensitive (able to detect cancer at early stages), and reproducible. Many currently available biomarkers, such as CA-125 for ovarian cancer or CEA for colorectal cancer, can be elevated in non-cancerous conditions, which limits their diagnostic accuracy when used alone.
Biomarkers are also central to personalized medicine, where treatment is tailored to the individual’s specific tumor biology. For instance, the presence of EGFR mutations in lung cancer patients can predict responsiveness to EGFR inhibitors, leading to more effective and targeted treatment with fewer side effects.