NetworkNewsWire Editorial Coverage: Despite the enormous progress made in the war against cancers, ovarian cancer has proved to be a tenacious foe. Progress is being made, however, as AI predictive models are being used to better target deadly ovarian cancers.
Ovarian cancer is difficult to treat and lethal, with survival rates much lower than other cancers that affect women. Recently, the battle against ovarian cancer has shifted strategy, and new optimism has come to the fore. Predictive models using artificial intelligence on large data sets of patient drug-treatment protocols and historical outcomes are now providing actionable intelligence for pharma to develop targeted therapeutics and for oncologists to prescribe the best course of treatment to improve individual patient outcomes. Predictive Oncology (NASDAQ: POAI) (POAI Profile) is laser focused on providing the molecular information critically needed to improve the lives of women stricken with ovarian cancer. The company has begun sequencing ovarian cancers as part of its CancerQuest2020 project and is building the largest ovarian multi-omic database in the world, designed to speed the development of new drugs and provide better therapeutic choices. The rest of pharma is also racing to find solutions. Roche Holdings AG (OTCQX: RHHBY) acquired a molecular information company, is researching new ovarian specific drugs and is finding ways to identify the patients who will benefit most from detailed molecular information. GlaxoSmithKline PLC (NYSE: GSK) has developed a long-term approach to finding new cancer treatments by focusing attention in four key areas. AstraZeneca (NYSE: AZN) recently announced a new first-line maintenance treatment for advanced ovarian cancer. And Bristol-Myers Squibb (NYSE: BMY) has entered into clinical collaborations to evaluate drug combination therapeutic regimens for ovarian cancers.
- Ovarian cancer has only a 46% survival rate.
- Understanding the complexities of ovarian cancers through multi-omic sequencing delivers insights and actionable intelligence.
- New insights speed drug development and deliver better targeted treatments.
Ovarian Cancer Kills
Cancer is one of the most significant human challenges in our history. It’s a worldwide scourge, responsible for one in six deaths each year. Major progress has been achieved on multiple fronts, but the war is far from over. Although no simple solution to the complexities of the disease exists, with new insights and intelligence, the battle against unyielding cancers can be won. Ovarian cancer is a formidable assailant. The disease is the 11th most common cancer among women but is the fifth leading cause of cancer-related death and is the deadliest of gynecologic cancers. These numbers are nearly meaningless unless the diseases takes someone close, such as a mother, sister, wife, daughter or close friend. Thankfully, the dismal prognosis looks like it may be about to change.
New Therapeutic Paradigm
There’s a paradigm shift occurring in cancer therapeutics, and ovarian cancer patients look to be the beneficiaries. Treatment protocols for ovarian cancer have historically consisted of a combination of surgery and chemotherapy. However, the scientific community has come to the realization that, given the vagaries and complexities of a patient’s cancer, targeted therapeutics are imperative to increase survival rates. To administer exactly the right drug or drug combinations that give a patient the best chance of survival requires comprehensive molecular information.
That’s why the work of Predictive Oncology (NASDAQ: POAI) is so important. Predictive Oncology’s subsidiary, Helomics, currently has about 150,000 cases on its molecular information platform, 38,000 of which are specific to ovarian cancer. This unique and valuable scientific asset places Predictive Oncology among the leaders in providing the critical molecular information needed for more effective patient treatments and new drug discovery.
Leveraging this asset, Predictive Oncology, through Helomics, entered a collaborative agreement with UPMC-Magee to establish a data- and artificial-intelligence-driven approach to treating ovarian cancer. This collaboration is expected to validate the enormous value of using AI-powered decision-making for identifying specific treatments on specific genotypes to predict clinical outcomes for ovarian cancer patients. Better information leads to better therapeutics and better outcomes.
Marking another company milestone in its CancerQuest 2020 project, Predictive Oncology announced just last week that Helomics has begun sequencing retrospective ovarian cancer cases from the UPMC-Magee collaboration. Helomics is looking at both the mutations in the tumor (genome) as well as the expression of genes (transcriptome), in order to build a comprehensive multi-omic picture of the tumor that can then be brought together with Helomics’ data set of drug-response profiles to build an AI-driven predictive model of ovarian cancer. This is enormously important, as it will give clinicians, oncologists and researchers unparalleled insights into exactly which drug or drug combinations to use in ovarian cancer treatments and provide the actionable intelligence required by pharma for new drug development.
“We believe the combination of the rich multi-omic profile of the tumor and clinical outcome data will allow us to build an AI-driven model of ovarian cancer capable of predicting the tumor drug response and patient outcome,” said Helomics CTO Dr. Mark Collins.
The value of these clinically validated, AI-driven predictive models can’t be overstated. They may be used to predict the relationship between a genomic profile of the patient’s tumor, the drug response and the eventual outcome for that patient. This is expected to provide clear, actionable insights for the clinician to truly personalize each patient’s therapy. In the short term, these models will be used together with Helomics’ PDx tumor-profiling platform in revenue-generating partnerships with pharma companies to develop new targeted therapies and to select patents for clinical trials and in translational research projects. In translational research projects, the model is used to dramatically reduce the amount of experimental work by performing experiments “in-silico,” or inside a computer. The model will then make recommendations that Helomics can test in its CLIA laboratory.
Today Helomics has drug-response profiles and samples of over 38,000 ovarian tumors that, when sequenced, would be the largest ovarian multi-omic database in the world. This allows Helomics to build AI-driven predictive models of considerable scientific and commercial value. With the company’s first predictive model targeted for completion in early 2020, Helomics plans to have an initial pilot with pharma using its AI predictive models to look for new drugs/biomarkers for ovarian cancer.
In addition, Helomics has another 120,000 tumors with drug-response data across 137 cancer types that include lung, breast, pancreatic, colon and head and neck. The company intends to continue moving forward in sequencing all 120,000 tumors and build out predictive models in these additional disease categories. Once all tumors are sequenced, Helomics will have the largest pan-cancer, multi-omic database with drug responses in the market. Helomics has initiated its outreach for lung-cancer outcome data and looks to ramp up its efforts in the first quarter 2020 to develop AI models to predict clinical outcomes for lung cancer from genomic, drug response and outcome data. The company plans on integrating that data along with its drug responses to have an initial pilot in lung cancer with pharma in late-second quarter 2020.
Market comparables are difficult to find since potential competitors are privately held. The nearest comparable is Foundation Medicine, which has been acquired by Swiss biopharma giant, Roche. Roche paid $1 billion to acquire a 57% stake in Foundation Medicine in January 2015. At the time of the acquisition, Foundation Medicine reported 68,000 cases on its molecular information platform. Underscoring the importance of precision medicine and the value of molecular information on cancers, Roche acquired the remainder of Foundation Medicine last year for $2.4 billion, valuing the company at over $5.3 billion.
By comparison, Predictive Oncology’s molecular information platform currently has 150,000 cases with 38,000 of these specific to ovarian cancer. POAI is rapidly building the world’s largest ovarian multi-omic database and expects the initial model developed with UPMC-Magee to be commercialized with pharma early next year. The company then intends to sequence the remaining 120,000 cases to create what will become the largest pan-cancer, multi-omic database with drug response in the market.
Strong industry demand and large unmet medical need indicate that Predictive Oncology’s assets may be grossly undervalued. As PAOI continues to execute on its strategy of validation and commercialization, it wouldn’t be surprising to see the company soar in value.
Predictive Oncology isn’t the only company savvy enough to see the opportunity.
One of the first companies to offer targeted treatments based on comprehensive molecular information was Roche Holdings AG (OTCQX: RHHBY). The world’s largest biotech company, Roche has put huge efforts into cancer treatment, making it a global leader in this field. Those efforts have paid off in more effective therapies. A large part of this comes in the form of new drugs, an area where Roche looks to make breakthroughs. The company has invested substantial resources into finding ways to target its treatments for the people who will most benefit from them.
British drug giant GlaxoSmithKline PLC (NYSE: GSK) has made cancer treatment one of its main areas of focus. It takes years to develop new treatments, so the company has developed a long-term strategic approach to R&D in which it identifies novel targets, mechanisms and potential treatments into which it can channel resources. This concerted effort has resulted in four areas of focus for battling cancer: using the immune system, engineering human T-cells, affecting how DNA is directed by the epigenome, and treatments that work by targeting the cancer in two ways at once.
AstraZeneca (NYSE: AZN) has used its proximity to some of the world’s finest minds to become a cancer-fighting powerhouse. The company recently announced successful trials using Lynparza to battle ovarian cancer, with indications of better results than chemotherapy in specific advanced forms of the cancer. The same drug has shown signs of effectiveness in prostate cancer.
Bristol-Myers Squibb (NYSE: BMY) has also seen success in recent clinical trials. These often explore not just a single drug but a combination of treatments, as in the company’s use of Opdivo and Yervoy together with chemotherapy to tackle non-small cell lung cancer, a treatment which has proven more effective than chemotherapy alone. The European Commission has recently approved the use of Opdivo in treating specific melanomas, allowing another pool of patients access to more effective treatment.
As companies dedicate resources and work to win the war against ovarian cancer, companies wielding powerful data combined with AI-driven analytics appear well positioned to lead the pack.
For more information on Predictive Oncology, visit Predictive Oncology (NASDAQ: POAI)
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