Biomarkers for targeted therapy and treatment decision making

Over the past few years, targeted therapy has become the backbone of precision oncology, with personalized treatment plans shaped by the individual tumor's molecular and genetic profile. Underpinning this treatment approach, biomarkers enable identification of patient subgroups in whom specific agents are likely to be most beneficial, assist with monitoring response to treatment, and enable anticipation or prevention of drug resistance. This chapter gives an in-depth overview of biomarkers in targeted therapy and their central role in treatment decision-making across different types of cancer. It outlines established biomarkers including EGFR mutations in non-small cell lung cancer, HER2 amplification in breast cancer, BRAF mutations in melanoma, ALK rearrangement, and PD-L1 expression as integral in the direction of immunotherapy. This chapter also takes into account the growing impact of liquid biopsy techniques, such as circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), exosomes, and microRNAs, as minimally invasive approaches to dynamic monitoring, early diagnosis and therapeutic direction. It also discusses tumor mutational burden (TMB), microsatellite instability (MSI), and gene expression signatures as more general indicators of responsiveness to treatment. The integration of multi-omics approaches, artificial intelligence, and machine learning is also enabling new horizons in biomarker discovery and validation. However, there are obstacles to clinical translation, such as tumor heterogeneity, adaptive resistance mechanisms, and regulatory and ethical considerations for biomarker-directed therapies. By integrating current advances and clinical applications, this chapter emphasizes the central role of biomarkers in optimizing treatment approaches, reducing adverse effects, and ultimately enhancing patient outcomes. The discussion emphasizes the potential capabilities of precision medicine to revolutionize cancer care, where biomarker-directed treatment decisions are predictive, prognostic, and dynamically responsive to tumor evolution.