The Impact of Proteomics on Precision Oncology

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The molecular landscape of cancer is complex. To gain a more thorough understanding of the pathways involved in cancer dynamics and drug resistance, researchers are combining information from multiomics disciplines including proteomics, genomics, single-cell analysis and more. This approach enables treatments – or combinations of treatments – to be tailored for the patient, reducing the likelihood of adverse side effects or tumor recurrence.

To find out more about how proteomics is providing new insights into cancer dynamics and resistance mechanisms, Technology Networks spoke to Dr. Ofer Sharon, CEO of OncoHost, a precision diagnostics company centered on predictive biomarker development for improved patient care. 

Katie Brighton (KB): Can you tell us a little bit about the impact precision medicine could have – or is having – on cancer patients?

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Ofer Sharon (OS): Precision medicine can have a significant impact on cancer patients by providing more personalized care that is tailored to their unique biological profile. With the increasing utilization of genetic and proteomic testing for diagnosis and therapy guidance, precision oncology is rapidly becoming an integral component of cancer care. But we are far from a personalized approach to cancer management.  

New drugs, new combinations and new modality combinations (for example, radiotherapy combined with immunotherapy) are constantly being developed, but biomarker development is lagging.  

When examining the genetic, transcriptomic and proteomic differences between patients, the true blueprints to the biological system, one can finally understand why so many patients are not responding to their treatment, and why one-size-fits-all trials are failing. Patients are simply not one size! We need different lenses to notice these differences.

While there are many precision oncology tools out there, here at OncoHost we use proteomic pattern recognition, which is playing an important role in precision oncology and the fight against cancer. Proteomic pattern recognition allows us to analyze the proteins present in a patient's plasma, which can provide valuable insights into the specific biological pathways that are driving the growth and spread of their cancer. By understanding these pathways, we can develop more targeted and effective therapies that are better suited to the individual needs of each patient.

In addition to improving treatment outcomes, precision medicine can also help to drive down costs over the long term. By identifying the most effective treatments for each patient, we can reduce the likelihood of them receiving unnecessary treatments or treatments with adverse side effects, which can ultimately result in significant cost savings for patients.

In summary, true precision medicine in oncology can greatly shift the landscape of cancer care as we know it and we have only just begun to scratch the surface.

KB: Why might some patients become resistant to cancer therapies? How can it be overcome?

OS: Cancer therapy resistance is a major challenge facing researchers and patients today. Resistance can be primary or acquired. With primary resistance, cancer cells are insensitive to a specific cancer drug, or a class of cancer drugs. Acquired resistance occurs when cancer cells become insensitive to the effects of drugs used for treatment, resulting in tumor recurrence or relapse. Resistance can happen due to intrinsic or acquired changes in the cancer cells, including molecular alterations that affect the drug's target, the tumor microenvironment and broader cellular changes.  

To better understand and overcome resistance, OncoHost utilizes proteomic pattern recognition to identify predictive patterns that provide clinically meaningful insights into the active tumor resistance pathways. Proteins offer a holistic view of what is taking place inside the patient's body and therefore a deeper understanding of the disease dynamics and the therapy–tumor–patient interaction.

By analyzing thousands of protein features using artificial intelligence and machine learning technologies, proteomic pattern recognition enables clinicians to make educated decisions and improve each patient's overall survival by providing personalized treatment plans based on the patient’s unique biology. This can vastly improve the odds of treatment response and overall outcomes, a game changer for cancer patients.

Furthermore, our use of proteomic pattern recognition allows clinicians to identify whether a patient should be treated with immunotherapy alone or in combination with chemotherapy, leading to more precise decision-making and reducing the administration of unnecessary therapies, particularly chemotherapy, which can result in increased adverse events.

With the continued development of these types of technologies, we are one step closer to overcoming cancer therapy resistance and improving the future for cancer patients.

KB: What is the PROphet^® platform? How does it work and how does it compare to current methods of measuring cancer prognosis?

OS: PROphet^® is a proprietary plasma-based proteomic pattern recognition tool that combines system biology, bioinformatics and machine learning to support clinical decision making. Requiring just one pre-treatment blood test for analysis, the platform identifies expression patterns in a panel of approximately 7,000 proteins and assigns a PROphet^® score, a measure of the predicted clinical benefit from anticancer treatment correlated to the patient’s overall survival.

The PROphet^® algorithm is trained and validated on our large-scale clinical trial,  PROPHETIC. To date, the trial has over 1700 patients recruited across 40 sites worldwide, making it one of the largest prospective cohorts in the precision oncology field.  

The first cancer indication for the test is non-small cell lung cancer (NSCLC), which is now available across the United States.

It is important to note that PROphet^® is not a prognostic test, but rather a predictive test. The PROphet^® report predicts a patient’s clinical benefit (progression-free survival > 12 months) from anti-PD-1/PD-L1 immunotherapy-based treatment plans. Combining these findings with a patient’s PD-L1 level allows for a clear distinction between patients who will benefit from immunotherapy alone versus immunotherapy combined with chemotherapy. In addition, it may improve the patient’s overall response rate. PROphet^® thereby addresses one of the most common daily dilemmas of the oncologist with a higher level of accuracy and resolution.

KB: OncoHost is collaborating with Baylor, Scott and White Research Institute and the Translational Genomics Research Institute in a new research study. What are the aims of the collaboration, and how will it advance our understanding of resistance mechanisms and cancer dynamics?

OS: The aim of this study is to identify early resistance to treatment using multiomics technologies, including proteomics, single cell analysis, circulating tumor DNA and microbiome analysis combined with bioinformatics and machine learning tools, which are further revolutionizing biomedical research.  

A comprehensive analysis will be conducted on lung cancer patients at various stages of the disease, analyzing elements such as the host response, the patient's microbiome, tumor DNA and immune system activity. Testing will look at the patient's urine, blood, stool, tissue, cells, DNA, RNA and proteins. The study will include some 350 patients who will receive follow-ups for up to five years.

This long-term study will provide an extensive assessment of cancer dynamics and the development of resistance. We are working towards gaining a better understanding of patient response and, ultimately, improved overall survival for patients with advanced cancer.

The five-year time frame will provide information that is not available with just a single analysis. Cancer is not a one-time event. Even when a blood test or tissue assessment of the tumor is conducted today, it's usually just a snapshot of the full picture. We are trying to figure out what the cancer resistance mechanism dynamics look like across time by looking at multiple factors and combining them with our powerful algorithm.

KB: How will the PROphet^® platform be used within this research?

OS: Our PROphet^® platform will perform the proteomics analysis for all recruited patients in the study.  The OncoHost scientific team with extensive expertise in machine learning, computational biology and systems biology will develop the technology to combine the multiomics tools into one powerful algorithm to analyze, characterize and identify treatment strategies to mitigate cancer resistance.

KB: PROphet^® has been validated for use in NSCLC patients and will soon be available for melanoma patients. Which other cancer types will you be looking to apply the PROphet^® platform to?  

OS: The PROphet^® platform is indication–agnostic in nature, allowing us almost limitless opportunities in scaling it up for more indications, as well as expanding its technological capabilities.

Following NSCLC and melanoma, we will be focusing on colorectal cancer, head and neck cancer and urogenital cancer. We also plan to expand the PROphet^® platform to cover more treatment modalities and at earlier disease stages i.e., targeted therapies, chemotherapy, radiation and combinations of these modalities.  

Dr. Ofer Sharon was speaking to Katie Brighton, Scientific Copywriter for Technology Networks.