Richard Gabriel didn’t expect to work in cancer research, but now, after 30 years studying one of the most complex diseases known to humankind, the Senior Vice President of Research and Development for Predictive Oncology explains his work as if he were born to do it.
“All the cells in our body like different signals, different foods, and different environments,” he said. “Some like hard structures, some like soft structures, and some are more fluid. So, you take all that and say ‘how can you ever make sense of all this?’”
But that’s exactly what Gabriel and the team at Predictive Oncology are setting out to do.
A winding path from pharma to capturing serial killers and back
Launching his first company, Gabriel Consulting, in 1984, Gabriel’s journey toward cancer research had officially begun. His chemistry background was a natural fit, he said, and small scale drug manufacturing seemed to be a space replete with opportunities. When his company acquired a business called Pharm-Eco Laboratories, however, he secured several government contracts along with i t— including a cancer contract with the National Cancer Institute (NCI).
“It was really in 1990 that I launched my full-fledged career in pharmaceuticals,” he said. We built that from just me, an engineer, a pipe fitter, and a couple of employees to being a 165 person company.”
Now as Pharm-Eco Laboratories, also constructed a large Good Manufacturing Practices (GMP) manufacturing facility. Partnerships and collaborations were struck with multiple government agencies and research organizations. Eventually Gabriel’s business was acquired and, due to a non-compete agreement, Gabriel was forced to step away from the industry.
Using genetics to capture serial killers
His initial transition away from pharmaceuticals would end up being a pivotal moment in his career. Gabriel went to work for a company in Florida that was focused on genetics research. During his time there, Gabriel and his team found themselves using genetics to capture serial killers. The first murdered at least seven women in Louisiana before police apprehended him, and they needed Gabriel and his team’s help to do it. They tested a sample of what the police believed to be the killer’s DNA. Knowing the description of the suspect police were searching for, Gabriel and team tested the sample. They came up with some serious news:
“The good news was we could help them identify the person of interest in these murders, but the bad news was they were looking at the wrong population,” Gabriel said.
Law enforcement was skeptical of the results, though, so they put Gabriel and his colleagues to the test first.
“They sent us 12 samples from people where they knew something about their heritage but we did not,” Gabriel said. “We turned it around in 48 hours. We called them up again and, in a room of about 30 people including the state attorney general and assistant director of the FBI, we told them we were able to identify 11 of the 12 samples.”
For a moment the room grumbled and groaned with disappointment, Gabriel said, when Dr. Tony Frudakis, then founder and Chief Science Officer of the company and now a Senior Scientist at TumorGenesis Inc., a Predictive Oncology company, chimed in: “We can’t identify the twelfth sample because it barks!”
Having successfully identified the red herring placed by law enforcement as dog DNA, the police were convinced the company and team could help them capture the culprit. And indeed they did: his name was Derrick Todd Lee, the Baton Rouge Serial Killer. He was identified and arrested after volunteers of the same genetic background offered their photos for a composite sketch of the suspect.
Gabriel and team would go on to help law enforcement apprehend seven serial killers in this way until the hedge fund capitalizing the company went under during the financial crisis of 2008. So, with serial killer hunting under his belt, Gabriel found himself gravitating towards cancer research.
Stepping into the world of cancer research
Like many people working in cancer research, Gabriel had a personal experience with cancer. At 29 years old, more than 40 years before he would begin working in the cancer industry, his wife passed away, leaving him with a 3 year old daughter.
According to Gabriel, now remarried, she was one of the first patients ever to receive a combination therapy for leukemia developed by the NCI. Initially, Gabriel said, the treatment saved her life. Sadly, though, her cancer eventually returned and she ultimately passed away.
“It changed me, but it wasn’t a moment where now I’m gonna go into cancer research. It didn’t happen that way,” he said. “It sort of progressed, in the back of my mind always.”
Now would be his chance to make an impact and help to advance critical research in cancer treatment. Gabriel leveraged his experience in genetics to launch a new company called GLG Pharma with two partners. GLG Pharma would focus on the cancer mechanism known as STAT3, which is present in about 30 different cancers, Gabriel said.
“When STAT3 goes crazy, it’s the cause of metastasis, chemo resistance, and other bad things,” Gabriel said. The company would find success in licensing some of its technology and partnering with the NCI to advance research on STAT3.
From there, things picked up pace rapidly. Gabriel met Mel Engle, current CEO of Predictive Oncology, while serving on the boards of multiple companies in the life sciences domain, including Skyline Medical’s board of directors that became Predictive Oncology. The two began working together to integrate some of the work GLG Pharma in 3D cell culturing was doing into Predictive Oncology’s operations.
A company called TumorGenesis, today part of Predictive Oncology, was born.
The heterogeneity of tumors and how to map them
As Gabriel describes it, tumors are heterogeneous masses of cells that contain multiple different types of cells, all of which need to be destroyed. There’s the “drivers,” the most active and deadly cells in the tumor. These receive most of the attention from cancer researchers and make up the majority of the 60 cancer cell lines immortalized by the National Cancer Institute (NCI) for cancer research.
In addition to these drivers, Gabriel explained, there are also “passengers” and “sleepers,” which can sometimes activate even after a patient appears to enter remission. To truly cure cancer, healthcare providers need to destroy all three of these cancerous cells.
“You have to know the driver, the passengers, and the sleepers,” Gabriel said. “If you don’t kill all three, cancer is gonna come back.”
Unfortunately, killing all three is easier said than done. According to Gabriel, existing efforts like proteomics, transcriptomics, and DNA analysis only go so far in identifying tumors — to truly understand them, he says, you need to work at the cellular level.
That’s what TumorGenesis is working on. The team there is studying tumors in a new way: by removing them from patients and preserving them in unique growing mediums that mimic the conditions in the patient’s body.
By keeping these tumors alive in the laboratory, Gabriel said, researchers can leverage artificial intelligence to run countless treatment scenarios and predict patient outcomes. Then, it can provide a list of optimal drug combinations based on factors like the patient’s genetic background, family history, dietary habits, tumor type, and more.
“What we’re able to do is work at the cellular level,” Gabriel said. “We separate tumors, identify the cells, and put them back together again as a colony. Then you use the colony to figure out what combination of drugs is going to kill all of the drivers, passengers, and sleepers.”
Bringing the patient into the cancer discovery process
The biggest remaining problem after mapping the tumor, Gabriel said, is the combination of patients and their tumor heterogeneity is a massively complicating factor. The exponential combinations of various genetic and environmental factors that apply to an individual patient coupled with the unique complexity of their cancer makes identifying optimal treatments extremely challenging, slowing the drug discovery process and wasting billions of dollars on the development of ineffective drugs that fail in U.S. Food and Drug Administration (FDA) Stage 3 clinical trials.
Theoretically, though, if you could take all those factors into account, you would be able to optimize cancer treatments on a patient by patient, cancer by cancer basis.
“To understand all that you need AI, because it’s just way too complex,” Gabriel said. “What we do is use a database of 150,000 patients, 131 tumor types, and 30 different cancers. It includes the height, weight, age, diabetes status, do they smoke, drink, and so on. In addition, we have screening information from cancers we’ve isolated on what drugs worked and didn’t work.”
That AI used is an iterative one and uses three components, known as CoRE, PeDAL, and TruTumor. The system is managed by Helomics, a Predictive Oncology company. That data can then be used to support other efforts at companies working toward Predictive Oncology’s larger mission of eliminating cancer once and for all, Gabriel said.
“We’re bringing the patient into the discovery process,” he added. “It was too complex before we had computers and AI, but now we do and so we’re bringing that patient data back into the discovery process early on. And we’re bringing in tumor heterogeneity as well.”
Gabriel’s hope is to deploy this method of tumor mapping and treatment optimization to expedite drug discovery and increase the number of effective cancer treatments that make it to market quickly. Further, it’s about identifying which patient populations respond best to which drugs, so as to match the genetic background of the patient and their type of cancer to a specialized treatment regimen.
Toward a cancer-free future
Predictive Oncology has brought together the team to recognize the mission that Gabriel and so many others share. With the CoRE, PeDAL, and TruTumor AI algorithms from Helomics, the insights from TumorGenesis research, and the ability of Soluble Biotech to improve the efficacy of vaccines and biological medications, the complete puzzle of cutting edge cancer research needed to one day eliminate cancer altogether is finally coming together.
Richard Gabriel is the President of TumorGenesis a wholly owned subsidiary of Predictive Oncology and senior vice president of research and development for Predictive Oncology, an artificial intelligence powered cancer research company dedicated to eliminating cancer, has spent his career using chemistry, genetics, and big data to do amazing things.