Developing Personalized Strategies To Maximize Cancer Therapy Success

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The number of cancer cases and fatalities across the globe continue to grow. Advances in our understanding of cancer pathophysiology, coupled with the ability to harness the human immune system, has enabled the development of precision therapies such as immunotherapy.

However, a patient's response to such therapies isn't always guaranteed. OncoHost combines life-science research, advanced machine learning and bioinformatics approaches to develop personalized strategies to maximize the success of cancer therapy.

Technology Networks recently spoke with Ofer Sharon, CEO of OncoHost, to learn more about ways to overcome patient resistance to immunotherapy and how proteomic profiling in oncology can aid diagnosis, prognosis and treatment of this incredibly complex disease.

Molly Campbell (MC): For our readers that may be unfamiliar, please can you explain why some patients may be resistant to certain cancer therapies? Why does this support a "personalized" approach?

Ofer Sharon (OS): There are many reasons for treatment resistance - some may be tumor-related, and others may be linked to the patient’s body response mechanisms. An example of tumor-related resistance to treatment is when the tumor doesn’t respond because its cells do not express the specific target of the drug. An example of host (patient) related treatment resistance is a case in which the body, in response to treatment, creates new blood vessels for the tumor, supplying it with more oxygen and nutrients and thereby allowing the tumor to overcome the treatment.

Today, the vast majority of treatments are based on a “one-size-fits-all” approach, whereby we look for a very specific biomarker and all patients with the same biomarker  receive the same treatment protocol (based on that  biomarker). This approach has led to significant progress in cancer care and research, but we know that the human body is a complex biological system - every patient, and how their body responds to treatment, is different.

The “one-size-fits-all” approach doesn’t take these differences into consideration, which is why we see many cases where two patients who have the same tumor clinically (stage, grade and type), and the same clinical background, do not benefit from the same treatment in the same way. Only by improving the profiling of our patients we will be able to match the right treatment with the right patient.

Laura Lansdowne (LL): How does the Host Response Profiling Platform work?

OS: Blood samples are drawn from the patient right before and immediately after their first treatment and The proteins found in the blood sample are measured and analyzed for any treatment-related changes. The analysis is via a proprietary host response profiling platform, PROphet, which uses sophisticated bioinformatic and machine learning algorithms to predict response and identify the key biological process that lead to treatment resistance. The results of the analysis are then sent to the patient’s physician, who can use the host response analysis to personalize the treatment strategy for their patient.

PROphet also identifies potential drug targets, advancing the development of novel therapeutic strategies.The technology enables a more accurate and repeatable quantitative measurement of proteins.

LL: Why is proteomic analysis preferable to genomic?

OS: We don’t claim superiority over genomic testing. These are two technologies that can work together. Having said that, it is important to understand that protein-level analysis, while more complicated than genomic testing, provides us with a deeper understanding of the processes that are taking place in the patient’s body.

Genes represent the potential for a protein and for a process. A protein means that there is a process. It’s almost like genes are a "recipe" that represent potential for a good meal. Proteins are the "meal" itself.

LL: What specific benefits does proteomic profiling offer the following “groups” – patients, pharmaceutical companies, treating oncologists?

OS:

Patients: Host response profiling offers the improved ability to know early in the treatment course whether the patient will respond or not. Because our profiling platform is highly predictive of individual patient outcomes, personalized treatment planning is enabled, improving the chances of the treatment’s success and decreasing unnecessary side effects for the patient.

Pharmaceutical companies: The platform enables targeted selection of patients in clinical trials, which is a great benefit for pharma. Proteomic profiling increases the odds of trial participants benefiting from their treatment, reduces the risk associated with the clinical program and increases the odds of successful trials in advanced phases. Essentially, proteomic profiling improves clinical trial success rate, lowers cost and enables faster and easier regulatory approval.

Treating oncologists: Proteomic profiling offers clinical decision support systems that can improve patient outcome and reduce unnecessary side effects. It also adds information on what might be done in order to increase the odds of success, namely treatment combination strategies to improve outcomes.

MC: What challenges exist in conducting proteomic analysis in the clinical space?

OS: Choosing the right proteins for measurement, creating a platform that enables repeatable, accurate measurements and, most importantly, knowing how to make sense of those measurements.