Treatment resistance to cancer therapy can be a major stumbling block in a patient''s clinical journey, but it may be addressed with combination therapy or personalized medicine.
Personalized medicine uses patient-specific information to tailor treatment strategies to each individual, reducing the likelihood of treatment resistance occurring. OncoHost uses proteomic analyses and AI-driven technology to examine how a patient will respond to treatment, equipping the patient with the information to take them on the driver seat of their care journey.
Technology Networks talked with CEO of OncoHost, Dr. Ofer Sharon, to discuss how OncoHost allows patient-specific treatment plans based on personalized medicine approaches.
Katie Brighton (KB): Can you explain how personalized oncology therapies have developed in the last decade? How has this transformed the patient experience?
Over the years, drug-based anti-cancer therapy has evolved from a non-specific carpet bombing approach to targeted therapies biologic therapies that target specific cancer cells with driver mutations. Over the past decade, there has been an increase in immunotherapies used in the clinic.
Immunotherapy is a new type of anti-cancer therapy that stimulates the patient''s immune response towards the cancer. Targeted therapies provide improved response rates with a different range of adverse events, in that patients respond to treatment longer periods of time, and are more likely to respond well. While this approach to cancer management improves survival, immunotherapy also greatly improves clinical outcomes.
Oftentimes, patients may experience a cancer resistance breakthrough while they continue to respond to their given treatment. This issue stems from the failure to pre-emptively determine which patient will respond and which will not. This frustration, fear, and isolation are all associated with targeted therapies.
How does treatment resistance affect cancer patients? Is there some cancer types that are more likely to become treatment-resistant? How can we help with this?
OS: Resistance to treatment is a multifactorial process. When cancer cells have a particular molecular trait that causes them to be resistant to a specific medication due to biological mechanisms related to the body''s response to the anti-cancer therapy, resistance is often demonstrated today. This phenomenon, known as host response, is one of the most significant reasons for treatment resistance.
Resistance mechanisms are generally diverse and non-specific to certain cancer types, and may be considered as primary resistance, intrinsic resistance, or acquired resistance. Personalized medicine is a specific approach that involves modifying the treatment plan to suit the patient''s specific requirements rather than adopting a one-size-fits-all therapy.
What benefits do proteomics analysis and artificial intelligence (AI) offer to understanding which treatment is best for the patient?
Proteins are the foundations and forces of biological processes in our bodies. Our analyses enable us to investigate the complex interactions of the tumor, the therapy, and the host (patient). This complex biological interaction involves intrinsic cancer cell traits with the body''s host response to treatment and is the basis for treatment resistance.
Thousands of proteins are involved in the body, and making clinical sense of these protein levels and dynamics requires sophisticated mathematical and bioinformatic tools. Using plasma protein analysis with machine learning tools allows us to respond to three clinical questions:
1.Will the patient respond?
2.Why is there a need for resistance?
3.What might be the next stage of therapy?
This combination analysis, therefore, is completely personalized, specific and appropriate for each individual patient.
How does OncoHost assist patients in making decisions about their treatment regimen?
OS: We provide oncologists with an insight on the response probability for individual patients for the first year of treatment, analysis of the biological pathways involved in resistance, and the identification of potential resistance-associated proteins. We also provide insight on the therapies that are targeting those resistance-associated proteins. This analysis allows us to intervene early and support the clinical decision-making process with a significantly improved clarity.
KB: Are there any issues to consider when it comes to managing patient data? As personalized treatments become more widespread in the clinic?
OS: As with any other type of personal health information, we must comply with the guidelines established by legislation and patient privacy standards and best practices. HIPAA and GDPR are excellent examples of these measures.
Kopi: Where can you see personalized therapy headed in the future?
OS: Diagnostics will be based on several evaluations across the disease''s continuum in the future. Early detection, treatment guidance, identification of intrinsic resistance, acquired resistance, and diverse treatment strategies are all examples of the future course of diagnostics. I believe that the results to identify these factors and guide clinical decisions will be based on multiomic analysis at different points in time. This will allow for the guidance of clinical decisions specifically matched for each individual patient.
Dr. Ofer Sharon spoke with Katie Brighton, a scientific copywriter for technology networks.