Identification of efficacy biomarkers associated with treatment response and survival in cancer patients to assist with personalized treatment

Cancer is a complex disease that affects millions of people worldwide. It can vary significantly from person to person, with different types of cancer responding differently to treatments. While medical advancements have led to improved therapies—including chemotherapy, targeted therapy, endocrine therapy, and immunotherapy—some treatments work better for certain patients than others. Traditional chemotherapy attacks rapidly dividing cells, which may be healthy or cancerous, whereas targeted therapy is more precise, focusing on cancer cells and reducing harm to healthy parts of the body. Endocrine therapy, also known as hormone therapy, blocks hormones such as estrogen or androgens that may be driving cancer growth, thereby slowing tumor progression. Immunotherapy helps the body's own immune system recognize and eliminate cancer cells. However, the variation in how effective treatments are for different patients makes it challenging for doctors to select the best therapy for an individual.

Currently, many cancer treatments follow a "one-size-fits-all" approach, meaning patients with similar diagnoses may receive the same therapy, even though their responses to treatment may differ. This research aims to improve cancer treatment by identifying key biological markers, known as biomarkers, that can predict how well a patient will respond to a specific therapy. Biomarkers are measurable characteristics in the body—such as proteins, gene mutations, or immune markers—that provide important information about a disease. By studying these biomarkers, doctors may be able to personalize treatment plans, ensuring that each patient receives the most effective therapy for their specific cancer type, ultimately improving survival rates.

To achieve this goal, researchers will analyze data from several clinical trials that have studied cancer patients and their treatment responses in order to identify specific biomarkers linked to better treatment outcomes. By refining biomarker-driven patient stratification, this study seeks to move away from generalized treatment approaches toward more personalized, precision-based cancer care.