Biomarkers: Transforming Medicine
When Angela Haupt, a health and wellness editor at Time Magazine, had nearly two dozen vials of her blood drawn for a health startup, she received eight pages of clinical notes analyzing 105 personalized biomarkers. Did she receive valuable insights predictive of her future health, or an overload of indecipherable data? The answer is debatable. It may depend on who you ask, who is reading the information, and for what purpose. What is certain is that the use of biomarkers in the healthcare industry is expected to grow exponentially. The global market for personalized medicine biomarkers is projected to reach $72.7 billion by 2033. For a growing number of reasons, biomarkers are finding useful research, diagnostic, and treatment applications and continuing to revolutionize the healthcare field.
What are Biomarkers?
In simple terms, biomarkers are indicators that may predict a patient’s future health or the effectiveness of a particular treatment. They include biomolecules like carbohydrates, proteins, lipids, DNA, RNA, platelets, enzymes, hormones, or any characteristic biological structure or process. Biomarkers have diverse applications and can be classified as diagnostic, prognostic, pharmacodynamic, and surrogate endpoints.
Diagnostic biomarkers confirm specific conditions. Elevated prostate-specific antigen (PSA) levels, for example, may indicate prostate cancer. Although healthcare providers must interpret the results alongside other clinical data for diagnostic accuracy.
Predictive biomarkers help identify patients likely to benefit from a specific treatment. For instance, erlotinib maintenance treatment for advanced non-small-cell lung cancer is more effective in patients with a tumor mutation called “EGFR.” In other words, the presence of an EGFR mutation is a predictive biomarker for response to erlotinib treatment.
Pharmacodynamic biomarkers can help monitor how drugs interact with the body and evaluate treatments. Physicians can use phosphorylated AKT (pAKT) levels, for example, to assess the effectiveness of Pi3k inhibitors in cancer treatments. When the pAKT levels decrease, physicians know the inhibitor is functioning as intended.
The fourth category of biomarkers is known as the surrogate endpoints and is not so straightforward. These biomarkers correlate with clinical endpoints but may not have a guaranteed relationship. Elevated cholesterol levels and high blood pressure are common examples; they can suggest heart disease risk but are not always accurate predictors. To be useful for selecting a treatment, biomarkers should help predict clinical outcomes. However, pharmacological therapeutics that affect a surrogate endpoint may not always treat the disease, limiting the biomarker’s usefulness. Lowering cholesterol or blood pressure, for example, may not always prevent heart disease.
The Rise of Biomarkers
While biomarkers in medicine are not new, the emergence of artificial intelligence (AI) and liquid biopsy technologies has revolutionized their use in medical research, diagnostics, and treatment. This is particularly true in oncology.
Scientists are using advanced AI processes to decode vast datasets, such as tumor biopsy samples, blood tests, and medical images, identifying biomarkers that traditional statistical analysis might miss. AI combines insights from radiology, pathology, and other medical imaging with molecular data to provide a comprehensive view of cancer, improving diagnostic accuracy and prognosis. This combination helps tailor therapies to individual patients, enhancing efficacy and reducing side effects.
Liquid biopsies, which analyze circulating tumor DNA without tissue samples, are also game-changing. They allow clinicians to collect important data less invasively, expanding the scope of data available for interpretation. Incorporating this data into AI analysis helps develop more accurate biomarkers.
Looking Ahead
Despite the promising future of biomarkers, challenges remain. A lack of standardization across laboratories and institutions, and variability in sample collection, data processing and interpretation can lead to inconsistencies affecting biomarker accuracy. The FDA recently published regulatory guidance on bioanalytical method validation for biomarkers (Guidance for Industry, issued January 2025), sparking discussion on balancing innovation with regulatory oversight.
Cost is another issue. Developing and validating new biomarkers, especially those dependent on AI, involves significant resources, potentially affecting healthcare accessibility. Ethical considerations surrounding data privacy and bias are also major concerns necessitating further discussion and, potentially, further regulatory guidance.
Despite these challenges, the discovery and use of sophisticated biomarkers are changing medicine. As one physician explained: “We’re backwards in the way that we approach healthcare—we wait for disease to show up ... And I think there is very much a role for preventive screening and getting on top of these things before they cause disease.”
The expanding role of biomarkers in preventive medicine and treatment monitoring represents a fundamental shift in healthcare delivery. Biomarkers hold the promise of early intervention and treatment, helping people live healthier, longer, better lives.
Authored by Lisa Krist, Berkley Life Sciences, VP, Chief Customer Focus Officer