• AI models of genomic data can predict cancer outcomes, which can then help personalize ovarian cancer treatments
• Results come from Helomics’ work on the 100,000 Genomes Project in Genomics England’s National Genomic Research Library
• Model learns patterns in genetic mutations of patients’ tumors, uses that knowledge to predict patient survival

In an historic announcement in the space where artificial intelligence and precision medicine intersect, Predictive Oncology (NASDAQ: POAI) has released initial results for its AI-driven models of ovarian cancer (https://ibn.fm/H3As3). According to Predictive Oncology, the initial results demonstrate that AI models of genomic data can predict cancer outcomes which has the potential to help personalize ovarian cancer treatments and drive the discovery of new therapies.

“We are excited to be able to show the impact of using our AI and machine learning approach that leverages complex genomic data to deliver improved, more personalized therapy for ovarian cancer that, worldwide, affects over 300,000 women,” said Predictive Oncology CEO J. Melville Engle. “We are continuing to refine these AI models with the goal of providing highly accurate predictive models of ovarian cancer to help oncologists and drive the development of the next generation precision ovarian cancer therapies.”

The results come from POAI subsidiary Helomics’ work on the 100,000 Genomes Project in Genomics England’s National Genomic Research Library (“NGRL”). Helomics has created a new AI-driven model that predicts post-treatment survival time for ovarian cancer patients. According to the company, these AI models have the potential to not only improve treatment paths for ovarian cancer but also to drive the development of new therapies. POAI noted that official study results will be available as a preprint on Biorxiv in the coming weeks.

To build the model, Helomics used a machine learning approach to extract key genomic features from nearly 500 ovarian cancer participants in the 100,000 Genomes Project. The AI model learned patterns in the genetic mutations of patients’ tumors and then used that knowledge to predict survival time. The model predicted rates with almost 70% accuracy. With that success as a backdrop, Helomics is now focused on refining its AI models to achieve an even greater accuracy for predictions.

The news is notable in the world of oncology because no biomarkers for prognosis and treatment responses in ovarian cancer currently exist, making it difficult for health-care providers to tailor treatments for individual patients. Instead, doctors are forced to choose from a set “menu” of drugs and therapies, observed POAI, a menu that has seen little progress in more than 20 years, even with the significant amount of research devoted to cancer. 

Using the predictions of patients’ responses to certain therapies, Helomics’ AI model could assist health-care providers in narrowing down the ovarian cancer treatment options, thereby improving patients’ prognosis and offering clinicians a more efficient and cost-effective precision medicine approach to treatment. These models also give doctors and scientists better insights into which genes are involved in response to treatment, which could lead to the development of new precision medicines.

“We’re delighted that our multiyear partnership with Helomics has resulted in this important research into ovarian cancer — a disease with significant unmet need,” said Parker Moss, chief commercial and partnership officer at Genomics England. “We are incredibly grateful to the around 900 participants in the 100,000 Genomes Project who suffer from ovarian cancer and have made their data available for this ground-breaking research. Genomics England is pleased to have contributed to Helomics’ work through our ovarian cancer dataset, as this has allowed them to validate their discoveries and create predictive models that will advance drug discovery and support ovarian cancer patients and their doctors.”

POAI is bringing precision medicine, or tailored medical treatment using the individual characteristics of each patient, to the treatment of cancer. Through its Helomics division, the Company leverages its unique, clinically validated patient derived (“PDx”) smart tumor profiling platform to provide oncologists with a roadmap to help individualize therapy. In addition, the Company is leveraging artificial intelligence and its proprietary database of more than 150,000 cancer cases tumors to build AI-driven models of tumor drug response to improve outcomes for the patients of today and tomorrow.  

For more information, visit the company’s website at www.Predictive-Oncology.com.

NOTE TO INVESTORS: The latest news and updates relating to POAI are available in the company’s newsroom at http://ibn.fm/POAI

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