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Evidence-Based Oncology Patient-Centered Oncology Care 2015

Personalized Medicine Enters the "Postgenome Revolution"

Mary Caffrey
Emanuel F. Petricoin III, PhD, said patients have much to gain from "democratization" as clinical trials move beyond academic centers.
Patients have much to gain from the “democratization” of personalized medicine, as scientific advances and new clinical trials improve treatment for those far from major academic centers, according to Emanuel F. Petricoin III, PhD, a co-director of the Center for Applied Proteomics and Molecular Medicine at George Mason University.
 
Petricoin opened the 4th annual meeting of Patient-Centered Oncology Care in Baltimore, Maryland, with his talk, Democratization of Precision Therapy: Omics 101 for Payers.” He discussed how the whole way of thinking about cancer is shifting, from looking at how cancer affects organs to how it is regulated by signaling pathways. It’s not just about finding the mutated gene in question, he said, but understanding how it regulated at the molecular level.
 
“We’re now entering the postgenome revolution, where we’re talking not just about whether or not genomic alterations exist, but are they transcribed into RNA and ultimately into proteins, and phosphoproteins, and metabolites,” he said. “These are the business end of the cell.”
 
Trials getting under way, such as the TAPUR (Targeted Agent and Profiling Utilization Registry) study and NCI-MATCH (the National Cancer Institute Molecular Analysis for Therapy Choice study), both aim to use molecular analysis of tumor specimens in patients. NCI-MATCH will add or drop treatments in multiple arms over time, while TAPUR will test drugs that are already commercially available, reaching patients in community settings.
 
With these developments, Petricoin said, genomics has become a commodity. The good news is that cancer patients in remote locations can have tissue collected and tests done elsewhere—there are companies springing up to speed the process, help make sure the right tests are ordered, and ensure that results get back to oncologists in a timely manner. But a common problem is one of “insufficient material,” which demands technology that can examine hundreds of proteins, phosphoproteins, and signaling proteins without much tissue. The National Institutes of Health has developed such a technology, which has been commercialized, he said.
 
Petricoin described a 2014 article in Nature that reported an effort by a bioinformatics team looking at cell lines and multiple inhibitors—transcriptomic, genomic, exome, proteomic. “It was the RPPA (reverse phase protein array) data, the protein data, which showed the best prediction for drug sensitivity, all beyond the genome,” he said. “And so we see this manifest in clinical trials where we can look at signaling pathways at the proteomic level.”
 
Clinical trials today are showing that the proteins are the drug targets, and our understanding about alternations at this level is essential, Petricoin said. He discussed several examples of trials in which protein differences were the distinguishing feature for whether a subgroup of patients would respond to a cancer therapy. Going forward, he said, “We have to prospectively validate this, of course, and this is what we’re doing with some of the pharmaceutical companies.” If a company would normally enroll 1000 patients in a phase 3 clinical trial, using proteomics to identify those likely to respond to a therapy will allow enrollment of a much smaller cohort. “Enrolling 400 is a lot faster than enrolling 1000,” and requires less time and money.
 
“This is extremely important,” Petricoin said. He predicted that in the next year or so, some clinical trials will fail because they did not enroll patients this way and “they have not been able to accrue enough patients into the arms that have been genomically categorized. And that’s because the incidence rate for these mutations is very small on a population basis.”
 
What’s more, Petricoin said, patients who have metastatic breast cancer were shown to have greatly increased progression-free survival when their treatment decisions were based on multi-omic analysis. This kind of analysis can be used in larger trials, and the turnaround for physicians to make finely tuned treatment decisions can be as short as 7 days, he said.
 
“We’re going to be in a position where we’re not talking about classification by location (of the cancer tumor), but classification by multi-omic analysis,” Petricoin said. “And we’re seeing this in trials like TAPUR, which are starting (in spring 2016), where any oncologist in the community can get tumor profiling and get access now to the drugs. The NCI-MATCH trial is doing the same thing.”
 
“The problem will be the incidence of the cancer genome alterations that are going to drive whether or not these trials are successful," he said.
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