Personalization for Cancer Therapy is stepping up

Personalization for Cancer Therapy is stepping up ...

Treatment resistance to cancer therapy may be a serious obstacle in a patients'' clinical journey, but it may be addressed with combination therapy or personalized medicine.

Personalized medicine employs patient-specific information to tailor treatment strategies to each individual, reducing the probability of treatment resistance occurring. OncoHost uses proteomic analyses and AI-driven technology to provide insights into how a patient will respond to treatment, ensuring that the patient gets the information necessary to take them on the safe seat of their care journey.

Katie Brighton (KB): Can you tell us how personalized oncology treatments have evolved in the last decade? How has this altered the patient experience?

Over the years, drug-based anti-cancer therapy has evolved from a non-specific carpet bombing approach to targeted therapeutic therapies biologic therapies that target specific cancer cells that harbor driver mutations. In addition, there has been a surge in immunotherapies used in the clinic.

Immunotherapy is a fresh type of anti-cancer therapy that activates the patient''s immune response to the cancer. Targeted therapies offer improved response rates with a different sample of adverse events, in that patients are longer-term likely to respond well to treatment. Despite this approach to cancer management, immunotherapy also significantly improves clinical outcomes.

A key issue with targeted therapies is that they are only applicable to a minority of patients for whom driver mutations can be identified. At some point during treatment, patients may experience a cancer resistance breakthrough, and they eventually stop responding to their given treatment. This issue stems from the inability to pre-emptively determine which patient will respond and who will not. This risk arises from the inability to anticipate patient response.

How does cancer patients develop treatment resistance? Are certain cancer types that are more likely to become treatment-resistant? How do we do this?

OS: Resistance to treatment is a multifactorial process. Resiliency occurs when cancer cells have a certain molecular component that makes them resist to a specific medication because of biological mechanisms linked to the body''s response to the anti-cancer treatment. This phenomenon, known as host response, is today one of the main reasons for resistance to treatment.

Resistance mechanisms are generally different and may be identified as primary resistance, intrinsic resistance, or acquired resistance. Personalized medicine is a variety of approaches that involve adapting the treatment plan to patients'' specific needs rather than adopting a one-size-fits-all treatment program.

What benefits do proteomics analysis and artificial intelligence (AI) provide to understanding which treatment is best for the patient?

Proteins are the foundations and components of biological processes in our body. We can further understand the complex interaction between the tumor, the therapy, and the host (patient). This complex biological interaction involves intrinsic cancer cell characteristics with the body''s host response to treatment and is the foundation for treatment resistance.

Thousands of proteins are found in the body, and making clinical sense of these protein levels and dynamics requires advanced mathematical and bioinformatic tools. We can re-examine plasma protein analysis with machine learning tools to answer three clinical questions:

1.Will the patient respond?

2.Why does resistance occur?

3.What might be the next phase of therapy?

This combination analysis is a great deal about how it works, particularly when it comes to each individual patient.

How Does OncoHost assist patients in making informed choices on their treatment plan?

OS: We provide oncologists with an insight on the response probability for each patient for the first year of treatment, the analysis of the biological pathways involved in resistance and the identification of potential resistance-associated proteins. We also provide an analysis of the therapeutic options that target those resistance-associated proteins. This study allows us to intervene early and support the clinical decision-making process with a significant improved clarity.

KB: Are there any questions that should be addressed about managing patient data as personalized therapies are becoming more prevalent in the clinic?

OS: As with any other type of personal health information, we must comply with legislation, patient privacy standards, and best practices. HIPAA and GDPR are excellent examples of those measures.

What are the paths you may pursue in the near future?

OS: Diagnostics will be based on several assessments across the continuum of the disease. I believe that early detection, treatment guidance, and the identification of intrinsic resistance, acquired resistance, and other treatment strategies are all examples of the future course of diagnostics. I believe that the data to identify these factors and guide clinical decisions will be based on multiomic analysis at different points in time. This will allow for clinical decisions to be specific matched for each individual patient.

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