According to the American Cancer Society, an estimated 21,750 women received a new diagnosis of ovarian cancer this year, and almost 14,000 women will die from the disease (https://ibn.fm/4yfJn). Those numbers make ovarian cancer the fifth-leading cause of cancer deaths in women; ovarian cancer causes more deaths than any other cancer of the female reproductive system. These are the numbers that Predictive Oncology (NASDAQ: POAI) is working to change.
For more than a year, Helomics, a wholly owned subsidiary of POAI, has been working with the University of Pittsburgh School of Medicine (“UPMC”) to study the use of artificial intelligence (“AI”) to improve clinical decision making for ovarian cancer patients (https://ibn.fm/88drd). The project, a joint collaborative agreement with UPMC called CancerQuest2020, kicked off in summer 2019.
The collaboration entails using Helomics’ AI platform and proprietary data to analyze the genomic and drug response profiles of women with ovarian cancer to evaluate predictive value in terms of response and non-response to different treatments.
“Helomics has pioneered the testing of drugs on the patient’s own tumor to help oncologists individualize treatment options — what we call functional precision medicine,” said Helomics chief innovation officer, Dr. Mark Collins. “We believe that by linking both the drug response profile and the genomic profile of the patient’s tumor using a machine learning approach, we can provide ‘multi-omic’ predictive models that will have greater decision-making impact than just genomics alone, which in turn will positively benefit oncologists and their patients.”
According to POAI, the predictive models “embody the knowledge from these profiles to create a ‘computational expert’ or a ‘virtual patient’ that can be queried. Predictive models such as these are also of high value to pharmaceutical companies as they can be used to quickly select patients for clinical trials and assess potential new drugs or biomarkers computationally (‘in silico’) before initiating expensive laboratory experiments. In short, Helomics’ predictive models will save time and money in the search for new targeted therapies” (https://ibn.fm/pFLn0).
Helomics brings to the project data generated from more than 150,000 tumor cases obtained from over 15 years of clinical testing on living patient tumors. That date includes drug response, genomic (mutations), transcriptomic (gene expression) and tissue-omic (tumor pathology) profiles that help predict drug response and outcome.
According to the company, “We have developed a unique technology that combines our clinically validated primary tumor cell assay and drug response data . . . together with proven artificial intelligence (‘AI’) to allow us to test potential drugs against patient tumor cells, much earlier in the drug discovery process. We believe this patient-centric approach will significantly improve the success of translating compounds into the clinic, saving time, cost, and most importantly getting therapies to patients more quickly” (https://ibn.fm/iqjra).
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 over 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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