For tens of thousands of patients, precision medicine is rewriting their cancer stories.
Linda Boyed, for example, an energetic 52-year-old occupational therapist, was thrilled to be on vacation with her family in Hawaii, hitting the beaches and taking long walks. But she couldn’t shake a constant feeling of fatigue. By the time she returned home, near Columbus, Ohio, her skin had yellowed. Her doctor passed her to an oncologist, who delivered the bad news: Cancer of the bile ducts in her liver had already spread too far for chemotherapy or surgery to do any good. He offered to help keep her comfortable for her final few months.
Boyed’s husband refused to accept that prognosis. He found a doctor at Ohio State’s cancer center who was running studies of experimental drugs for gastrointestinal cancers. Boyed signed herself up. Genetic tests on her tumors revealed a mutation in a gene called FGFR (short for “fibroblast growth factor receptor”), which was likely spurring the cancer’s growth. The doctor gave her an experimental drug, called BGJ398, to inhibit the action of the FGFR mutation. Boyed’s symptoms cleared up, the tumors stopped growing, and she regained the weight she had lost.
That was three years ago.
These days Boyed gets downright bubbly when she tells the story. “I basically lead a normal life now,” she says. “I just watched my son graduate from high school. I think I actually did more in the past year than I did before the cancer.”
Stories like Boyed’s are playing out across the U.S., as new cancer drugs emerge from labs and enter trials. The days when cancer patients received one-size-fits-all regimens of chemotherapy and radiation may soon be a thing of the past. Instead, doctors are taking a far more nuanced view of what drugs and treatments will work on which patients and on what different kinds of cancers. The idea of this so-called precision medicine, or personalized medicine, is that ultimately doctors will use genetic tests—of both the patient and the cancer tumor—to determine the exact drugs or treatments that have the best chance of working.
Although precision-medicine techniques are now being trained on many diseases, their impact is being felt most strongly in cancer treatment. Researchers are building a growing list of genes and genetic mutations that show up in tumors and matching them to drugs that can stop them. The cancer genes that drugs can target now number in the dozens, and researchers are hot on the trail of hundreds more. For some cancers once considered virtual death sentences, the outlook is already much improved: About half of lung-cancer patients respond well to one of the new gene-matched therapies, and in half of those cases, the cancer doesn’t come back. FGFR inhibitors, the drug that saved Boyed, have shown promise not only in bile duct cancer but also for some types of bladder, lung, breast and uterine cancers. “We have six trials open now for FGFR inhibitor drugs alone,” says Sameek Roychowdhury, the oncologist who saved Boyed’s life. “By the end of this year there should be 20.”
After decades of fits and starts in the field of cancer research, the progress made in precision medicine is welcome news indeed. But make no mistake: There is no “cure.” Medicine is not even close to bringing cancer to its knees. For patients diagnosed with advanced cancers—those that have already metastasized, or spread—only one in 10 turn out to have genes currently known to make the cancer susceptible to a new drug. “Our goal is to give 100 percent of patients a new therapy based on genomic testing,” says Roychowdhury. “But today we don’t know how to provide a special treatment for the results of nine of 10 genomic tests we do.”
Most patients don’t even get that one-in-ten chance. Many doctors still lack expertise in the area and fail to administer the genetic tests that could open the door to a precision medicine treatment. Expense is also an obstacle: Insurance companies don’t reimburse adequately for the tests. For these reasons, only 10 percent of cancer patients undergo genetic testing. Precision medicine is helping, at best, only a few percent of the nearly 2 million people who are diagnosed with cancer in the U.S. each year, and the fraction is much smaller among the 17 million cancer patients worldwide.
To increase the number of patients eligible for treatment, doctors are turning to artificial intelligence for help. Genetic testing is churning out so much data that even an army of Ph.Ds couldn’t make sense of it all. Artificial intelligence turns that volume of data from a liability to an advantage. Scientists are now delegating the task of finding the weaknesses in cancer tumors to “deep learning” software that can churn through millions of genetic test results and patient outcomes to find new relationships between tumor genes, cancer growth and specific drugs.
Teasing Out Patterns
To increase the odds that a cancer patient who walks through their doors is given a treatment option, City of Hope National Medical Center outside of Los Angeles plans within two years to be the first major hospital in the U.S. to do genomic testing on the tumors of every single one of its 9,000 cancer patients a year. “Tumors that look identical under the microscope look vastly different under from a genomic point of view,” says Michael Caligiuri, a physician and president of City of Hope National Medical Center outside of Los Angeles. “They need to be treated differently.”
As other hospitals follow suit, they will generate a vast volume of data—grist for the AI mill. The 20,000 genes of a typical human genome include three billion DNA nucleotides, or bits of information, any of which can be mutated, repeated or moved in any number of ways to cause cancer. Each of the human body’s billions of cells has its own copy of the genome, subject to its own mutations.
But DNA is only part of the picture: Whereas DNA is a blueprint, the real work in our cells is carried out by proteins—complex molecules that control almost everything in our biology. Proteins govern both the growth of a cancer tumor and the work of the immune system in fighting it. There are as many as 6 million basic proteins and variations on them, and researchers are now measuring thousands of them directly in cancer-tissue samples and feeding that information to the deep-learning programs.
“Drugs don’t target genes, they target proteins,” says David Spetzler, chief scientific officer of Caris Life Sciences in Irving, Texas. “That’s where we’re seeing the most progress in understanding cancer, and it’s what’s going to be the most useful information we gather in the next five years.” Says Jeffrey Balser, a physician who heads the Vanderbilt University Medical Center: “That’s a lot of incredibly deep knowledge coming to the table.”
Deep-learning algorithms don’t work the way scientists do—they never “understand” the biology behind the cancer they’re analyzing. Instead, they digest reams of information from tissue samples of patients that had certain kinds of cancer, and correlate that information with the ultimate fate of those patients—who responded to which treatments and who didn’t. It’s a kind of hit-or-miss association exercise, but one that’s conducted thousands of times, using vast amounts of data. Computers can tease out patterns in the data that a human could never see—linking, say, the presence of the FGFR gene to a particular cancer of the bile duct.
Spetzler’s company, for instance, is working to crunch protein-fortified data with deep-learning software. To wring useful insights out of the data from 170,000 cancer patients that Caris has access to, the company enlists hundreds of different deep-learning algorithms. The programs essentially compete with one another to find patterns in the data that indicate which drugs will work best with which patients. “Different algorithms will miss different patients, but together they can do a better job,” says Spetzler.
AI is helping provide yet another critical set of clues to how to match patients to new drugs by learning to read slides of tissue samples taken in biopsies. Those slides have always been read under a microscope by pathologists, who come up with a cancer diagnosis based on the cells’ appearance. So-called “machine learning” programs are starting to step in. An Israeli company called Nucleai has trained its software with 20 million digitized biopsy slides to recognize cancer, and it already performs with 97 percent accuracy.
Diagnosing cancer is just the start, says Nucleai CEO Avi Veidman. The goal now is to use AI to extract more information from slides than pathologists can—information that can help match patients to new drugs. “Most of the information in that tissue isn’t being used when doctors or software are trying to predict the treatments that will work,” says Veidman, who spent two decades with Israel’s intelligence forces developing AI software to recognize missile bases and terrorist activity in satellite images before turning his attention to cancer three years ago. “AI can analyze the different types of features in the image much more efficiently and find hidden patterns.” He notes, for example, that subtle signs of the battle between the patient’s cancer cells and immune-system cells can be spotted by the software, and those signs can provide essential clues to whether or not the cancer might be vulnerable to one of several new immunotherapy drugs—that is, drugs that work not by fighting the cancer directly, but by boosting a patient’s immune system so it can attack the tumor.
South-Korean firm Lunit has developed AI software that can analyze pathology slides to predict, for example, which patients will respond to a relatively new type of cancer drug called checkpoint inhibitors, which can prevent cancer cells from blocking a patient’s immune cells. Lunit claims that the software is 50 percent more accurate than tests that use genetic data alone. “That’s going way beyond what human eyes can do,” says CEO and physician Beomseok Brandon Suh. “The software is finding patterns that are too complex for people to recognize, but that have biological meaning.”
Similar advances are taking place with AI-based systems that are reading X-rays, MRIs and other image data. “There are already algorithms that are as good at reading a mammogram as a highly trained radiologist, or at recognizing skin cancer as a dermatologist,” says Chi Young Ok, a pathologist at the MD Anderson Cancer Center in Houston. “The progress is astounding.” Eventually those images, too, are likely to help AI systems go beyond diagnosing cancer to spotting hints of the vulnerability of a patient’s unique cancer.
Deep-learning algorithms look at more data and analyze it more thoroughly than machine learning programs do. They are a bit like Seymour, the ravenous plant in Little Shop of Horrors, whose appetite never stopped growing. Although researchers and clinicians now have access to databases that contain information from as many as 250,000 cancer patients, it’s not nearly enough.
Thousands of different mutations in a patient’s genome can shape the development of cancers and determine which treatments are effective. Each cancer cell is a moving target, continually developing new mutations that can help it evade immune cells and survive powerful cancer drugs. Since AI software needs thousands of examples of a particular pattern before it can begin to recognize it, and since a particular pattern of mutations may come up in only a few thousand patients altogether, the software may well need access to the data of millions of patients to make faster progress. “We can make predictions now about how tumors will evolve and what treatments will work, but right now a significant fraction of those predictions are wrong,” says UCLA’s Paul Boutros, a physician who heads up cancer data science for the UCLA Jonsson Comprehensive Cancer Center.
A number of collaborations—with names like the International Cancer Genome Consortium, the Oncology Research Information Exchange Network, and the Actionable Genome Consortium—have sprung up among research centers and hospitals to share patient data. Gathered with patients’ permission and with personally identifiable information stripped out, that data could eventually help researchers reach the needed critical mass of information. “We need to get to the point where all these different data networks are tied together into a network of networks,” says City of Hope’s Caligiuri. Clinicians need access to that data, too, to find patients like the ones they’re treating to see what might work. “We should be able to go to a computer, type in information about a patient’s cancer, and up will pop 50 cases around the world that are similar at the molecular level,” he says.
Easing the Bottleneck
Medicine is of no use if patients don’t have access to it. To get new drugs out faster, researchers are using AI to speed the process of drug development. One of the biggest causes of delay in testing new drugs is recruiting enough patients for a trial. Researchers not only need a group to try the new drug, but another “control” group to get the standard treatment, for purposes of comparison. Even when a new precision drug is promising, it can take years to run the trials that demonstrate the drug actually works for an identifiable group of patients.
To speed things along, researchers are starting to use high-powered statistics and computer models to avoid having to recruit a control group at all. Instead, they use a mashup of data from past studies to predict how a real control group would fare. “The results you get from a synthetic control arm are as reliable as if you had actually enrolled control-group patients in the trial with the same physicians and protocols,” says Glen de Vries, president of Medidata Solutions, which has designed the statistical tools.
That won’t be enough to ease the trial bottleneck for clinicians and researchers hoping to come up with precision treatments for the deadliest, most aggressive cancers. For instance, glioblastoma, the brain cancer, has the lowest median survival time from diagnosis—15 months—of any major cancer. It’s challenging enough to design a drug that can make it through the blood-brain barrier to get at a glioblastoma tumor. The disease works so quickly that there’s barely time to give an experimental drug a chance to show whether or not it is effective.
To give more experimental precision drugs a better shot at glioblastomas, the newly created Ivy Brain Tumor Center at the Barrow Neurological Institute in Phoenix has developed “accelerated trials” for its brain-cancer patients. A newly diagnosed patient is first given a dose of an experimental precision drug. The dose is too small to harm the patient (in case it turns out to be toxic, always a risk with new drugs) but big enough to reach the tumor. After surgery, doctors test the tumor to see if the drug had any effect. If it did, the patient continues with an increased dose. If not, the patient and doctor find out in time to take another course of treatment. “Speed is the key to finding drugs that work,” says Ivy director Nader Sanai. The approach has already turned up a personalized treatment that in one patient’s case beat back a form of brain cancer called malignant meningioma.
While all these approaches together are likely to bring us closer to the day when most cancers succumb to precision treatments, no one thinks that day will be here soon. Still, the move to personalized treatments is benefitting almost all cancer patients by sparing them the ordeal of a treatment that has little chance of working. “If you can look at a genomic or other test and know ahead of time whether or not a patient’s tumor will respond to a treatment, then even if only one out of 100 patients responds you’ve saved 99 patients from unnecessary complications and expense,” says Stanley Robboy, vice-chair for diagnostic pathology at the Duke University Cancer Center. “These drugs can cost $100,000, and can bankrupt families.”
Even that modest benefit, however, is being denied to most advanced cancer patients today. Health insurance companies frequently balk at paying for the genetic tests, which can cost as much as $10,000. “Medicare and some companies are starting to provide some coverage,” says Roychowdhury. “But it’s an arduous process to get reimbursed for the testing, and it’s hard to get the cutting-edge tests covered at all.” That’s one reason most of the top cancer centers in the country don’t routinely provide the testing to all their patients, even though virtually all experts agree that should be the standard of care everywhere for cancer.
When a patient does get a tumor tested and the test shows a match to a promising precision drug, insurers often refuse to pay for the drug too, says Roychowdhury. The insurers cover only drugs that have already gotten FDA approval as a standard treatment, after a long period of trials. FGFR inhibitors of the sort that rescued Linda Boyed and many others are still usually not reimbursable. Patients who become part of formal drug trial, as Boyed did, usually get the drug for free. But in some cases patients with the most advanced cancers—the ones who need experimental drugs the most—are excluded from trials. Drug companies and even academic researchers often want to avoid including very sick patients out of fear they’ll skew the results toward failure.
Payment isn’t the only obstacle to treatment. About 85 percent of U.S. cancer patients get treated at a community hospital, where they see an oncologist who treats many different types of cancers. Those generalists are typically not up on the latest tests and treatments, says Caligiuri. The hospitals who employ them don’t expect them to go through the time and expense of figuring it out. While highly regarded cancer centers place as many as a quarter of their patients on newer precision drugs, the percentage at most community hospitals is nearly zero.
What should patients do? “The first and most important thing I would say to anyone who has just received a diagnosis of cancer is that you need to get a second opinion from an oncologist who is a specialist in your type of cancer before you start any treatment,” says Caligiuri. “If your first treatment isn’t the optimal one, the tumor develops multiple resistances not only to that treatment but to other treatments that might have worked if you got them first.” When asked about other treatment options, community oncologists often insist that patients are best off starting treatment first. Some play on patients’ fears that even a short delay might hurt their chances of recovery—when in fact, it might save their lives.
Vanderbilt’s cancer center is trying to fix this problem by boosting the participation of community-hospital oncologists in precision-medicine initiatives. Its My Cancer Genome website helps doctors and patients find out what new treatments and trials might be available for any particular cancer—the site lists 4,000 trials. “It pains me when patients come to us and they’ve already been given a treatment that wasn’t going to help them,” says Vanderbilt’s Balser. “At that point the patient is behind the eight ball, and all we can do is try to pick up the pieces.” Like many other top cancer centers, Vanderbilt is also creating affiliations with community hospitals in its region to support those hospitals in gaining access to precision-medicine expertise, genetic testing and trials of the newest drugs. Vanderbilt already has forged such ties to nearly 70 hospitals in five states.
A growing roster of precision-medicine approaches will also help in preventing cancers from taking hold in the first place. Some imaging techniques, such as PET scans, are approaching the needed sensitivity and resolution to pick up a cluster of newly formed cancer cells so tiny that it can be blasted away on the spot with a beam of focused radiation. Such treatments would be convenient and come with fewer complications.
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