Biomarkers

Definition

The NIH defines a biomarker as:

"A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention."

In practical terms, a biomarker is any measurable biological parameter — a molecule, gene, protein, imaging finding, or physiological measurement — that signals something meaningful about health or disease.

Classification by Clinical Purpose

Biomarkers are best classified by how they are used clinically:

Purpose	What It Answers	Examples
Diagnostic	Is this disease present?	AFP in hepatocellular carcinoma, troponin in MI
Prognostic	How will the disease behave?	NT-proBNP in heart failure predicts outcomes
Predictive	Will this patient respond to treatment?	EGFR mutation → benefit from EGFR inhibitor
Screening	Is preclinical disease present?	Fecal occult blood for colorectal cancer, hCG for choriocarcinoma
Monitoring	Is treatment working / is disease recurring?	CA-125 serial measurement after ovarian cancer treatment
Pharmacodynamic	Is the drug hitting its target?	Plasma drug levels, downstream pathway markers

— Tietz Textbook of Laboratory Medicine, 7th Edition

Major Categories by Biomarker Type

1. Serum / Plasma Proteins

The most widely used class. Secreted into the bloodstream, easily sampled.

Cardiac: Troponin I/T (myocardial injury), BNP / NT-proBNP (heart failure), CK-MB
Oncology: PSA (prostate), CEA (colorectal), AFP (liver/testicular), CA-125 (ovarian), CA15-3 (breast), CA19-9 (pancreatic), hCG (gestational trophoblastic disease)
Hepatic: ALT, AST, GGT, bilirubin
Renal: Creatinine, cystatin C, eGFR

2. Cellular / Tissue Markers

Measured on tumor biopsies or blood cells:

Hormone receptors: ER, PR (breast cancer — guides endocrine therapy)
Oncoproteins: HER2/neu (breast/gastric — guides trastuzumab therapy)
Immune markers: PD-L1 (predicts response to checkpoint inhibitors)

3. Molecular / Genetic Markers

DNA/RNA-level characteristics:

Somatic mutations: EGFR, KRAS, BRAF, ALK, KIT — guide targeted therapy in lung, colorectal, melanoma, GI tumors
Germline mutations: BRCA1/2 — cancer risk and PARP inhibitor eligibility
Chromosomal alterations: BCR-ABL in CML (monitored by PCR to assess treatment response)

4. Metabolites & Small Molecules

Lysosphingolipids: lyso-Gb3 in Fabry disease, lyso-Gb1 (glucosylsphingosine) in Gaucher disease — used for diagnosis, severity, and treatment monitoring
HbA1c: reflects average glycemia over 3 months in diabetes

5. Imaging Biomarkers

Quantitative MRI parameters (T1 mapping, ECV) in cardiac amyloidosis
Global longitudinal strain (echocardiographic marker of systolic dysfunction)
PET SUV measurements in oncology

Historical Evolution

Timeline of tumor marker discovery and testing methods from pre-1970s through 2010+, showing progression from Bence-Jones protein/LDH/CEA through immunoassay techniques to modern genetic profiling of EGFR/BRAF/KRAS

From Tietz Textbook of Laboratory Medicine, 7th Ed — Evolution of biomarker testing from enzymatic/immunochemical methods (pre-1970s) through radioimmunoassay and monoclonal antibody technology (1970s–90s) to modern genetic profiling (2010+)

Key Principles for Clinical Use

No biomarker is perfectly sensitive or specific. False positives (e.g., BNP elevated in pulmonary embolism, obesity lowering BNP) and false negatives must be anticipated.
Negative predictive value often exceeds positive predictive value — particularly useful for ruling out disease (e.g., natriuretic peptides ruling out heart failure as a cause of dyspnea). — Braunwald's Heart Disease
Baseline measurement is essential for monitoring. Only markers elevated at diagnosis reliably track recurrence.
Context always matters. Troponin elevation in acute heart failure does not equal Type II MI. CA-125 elevation occurs in endometriosis and peritonitis, not only ovarian cancer.
Standardization and inter-assay variation are critical issues, particularly for cancer markers — results from different platforms may not be interchangeable. — Tietz Textbook of Laboratory Medicine, 7th Ed

Emerging Frontiers

Liquid biopsy: Circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) — non-invasive cancer monitoring and early detection
Multi-omics panels: Combining proteomics, metabolomics, and genomics for multiparametric cancer diagnosis
AI/machine learning: Neural networks applied to biomarker discovery and multivariate classification
Point-of-care testing: Rapid bedside biomarker measurement (troponin, BNP) improving emergency triage

Let me know if you'd like a deep dive into any specific area — cardiac biomarkers, oncology tumor markers, genetic/molecular markers, a specific disease, or biomarkers in drug development.

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