Personalized Medicine and the Limitations of Molecular Biomarkers

Cancer is a complex disease caused by mutations in DNA. Over the years, significant progress has been made in understanding its molecular basis, which has led to the development of targeted therapies. Immunotherapy is one such approach that uses the power of the patient’s immune system to fight cancer cells. Molecular biomarkers can help identify specific tumor characteristics and predict the likelihood of response to immunotherapy. However, they are not without their limitations, highlighting the need for personalized medicine.

Molecular biomarkers are biological molecules found in the blood, other body fluids or samples, or tumor tissues which can be used to predict the risk, progression, or response to therapy of a disease. In cancer, biomarkers are used to identify the molecular subtypes of cancer, predict prognosis, and guide treatment decisions.

Immunotherapy is a type of cancer treatment that works by stimulating the patient’s immune system to recognize and attack cancer cells. While it has been a game-changer in the fight against cancer, it is not always effective for all patients, and some may experience severe side effects. In this scenario, molecular biomarkers help identify patients who are most likely to respond to immunotherapy and predict the risk of side effects.

One example of a biomarker is the programmed Death-Ligand 1 (PD-L1), a protein present on the surface of certain normal cells and at higher levels in cancer cells. PD-L1 engages with PD-1, a receptor located on T cells, a critical component of the immune system. This interaction suppresses the immune response and allows cancer cells to evade destruction by the immune system.

While molecular biomarkers can be helpful, they have certain limitations. One of the challenges with biomarkers is that they can be specific to certain types of cancer and, even within the same type of cancer, can vary between patients. For example, the PD-L1 biomarker is used to predict response to immunotherapy in lung cancer, but not all patients who express PD-L1 respond to immunotherapy.

Another limitation is that they may not provide a complete picture of the patient’s cancer, not capturing the full complexity of the cancer. This is particularly true in cases where the tumor has multiple mutations or alterations.

Personalized medicine offers a more comprehensive approach to cancer treatment by considering the unique genetic makeup of a patient’s tumor.

The I3LUNG project serves serves as an example of a personalized medicine initiative. By gathering data from over 2000 patients with metastatic non-small cell lung cancer (mNSCLC) and integrating this information with advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML), the project aims to forecast each patient’s unique response to immunotherapy. With this tool, it will be possible to design an individualized treatment plan for each patient, thereby shifting the emphasis of lung cancer care from a one-size-fits-all approach to a tailored and personalized treatment strategy, ultimately enhancing the quality of life of patients.

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