Predictive Oncology’s (NASDAQ: POAI) Helomics Exec Talks AI, Big Data and Biology During Podcast Appearance

  • Convergence of AI, big data and biology allows Helomics to test drugs on living tumor tissue rather than artificial systems
  • Helomics model evaluates multiple measurements in powerful multi-omics approach to cancer research
  • Knowledge encapsulated by Helomics AI is “gold dust,” says chief innovation officer

Data, AI and biology come together at Helomics in a way that promises exciting things for the future, according to  Mark Collins, PhD and chief technical officer at the company, which is a subsidiary of Predictive Oncology (NASDAQ: POAI) ( Collins was a guest speaker on a DojoLIVE! podcast titled “Can We Cure Cancer with Artificial Intelligence?” where he discussed what Helomics is bringing to the table in the field of cancer research.

“At Helomics, we have a nice convergence of data that we’ve gathered from testing live tumors, tumors outside the patient body, on drugs, standard of care drugs, normal drugs that you might get if you were diagnosed with cancer,” he said. “We have a massive collection of data that goes along with that drug response — over 150,000 tumors, which is a huge data set. And then again we have AI. And it’s that convergence of AI, the big data if you will, and the biology that means we’re able to test drugs on living tumor tissue rather than some artificial system that is really what’s driving what we do.”

During the podcast, Collins explained what makes Helomics distinctive.

“We don’t just look at one aspect of the tumor,” he said. “We look at multiple, as it were, measurements of that tumor. How it responds to drugs, what it looks like, what mutations it has in its DNA, how different genes are expressed, so it’s what we call a multi-omics approach. . . . Cancer is so complex, the human body is so complex, it’s not going to be just down to one thing that’s driving how the tumor or the patient responds to drugs.”

Collins pointed out that Helomics currently specializes in ovarian cancer. “We developed and have a validated clinical assay where we take a sample from the patient following surgery to remove the tumor,” he explained. “We grow that tumor in the lab, we test drugs on it, we look at different mutations in the tumor, and then we compare that with the data that we have in our database, and we feed that back to the oncologist to guide treatment.”

The company doesn’t make a recommendation or prescribe medication, but the information Helomics provides helps the clinician determine drug treatment. “This drug response profile that we can generate really moves the needle for patients,” Collins said. “And in our clinical validation studies we can show that if the clinician chooses the drug that was indicated by this drug response assay, that we can extend the disease-free period for about two and half times longer than if they didn’t follow that recommendation.”

While AI isn’t a part of this particular process at the moment, Collins says the expectation is that it soon will be. “We’re doing a study with a local hospital in ovarian cancer where we expect to be able to produce a predictive model that will predict what drug [the patient] should be on and what outcome you’ll have. Now we have to clinically validate that before we can use it to individualize treatment,” he continues, “so it will be a couple of years before the AI is making those kinds of clinical decisions. But because the AI has encapsulated all this knowledge about the tumor, that is gold dust. Pharma companies are becoming very interested in these kind of models that use real world data and can help them discover new therapies in a much more knowledge-driven way than they perhaps do at the moment.

“AI will also help us discover drugs in the not-too-distant future,” Collins concludes. “Faster because we do less experimentation, smarter because we use the machine learning. Coupling adaptive learning (a special kind of machine learning) together with our data allows us to decide which experiments to do, and then we do those experiments using the right biology which is our live tumor testing platform. Certainly, pharma companies that we’ve already talked to are beginning to see that this kind of union between simulation, AI, big data and relevant biology is really going to make a difference to our ability to make new medicines for cancer.”

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

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

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