At the annual American Society of Clinical Oncology meeting last June, Bristol-Myers Squibb (BMS) researchers presented data on a cohort of patients not responding to the company’s approved checkpoint inhibitor nivolumab (Opdivo). Layering on a novel immunotherapy antibody was effective in half of patients tested, the team reported, with no major increase in side effects compared to nivolumab alone.1 Each of the therapies aims to unleash an immune cell–fueled tumor attack by targeting a molecule that normally suppresses T-cell activation—programmed death 1 (PD-1) in the case of nivolumab, and lymphocyte-activation gene 3 (LAG-3) in the case of the new, investigational antibody. The combination worked particularly well in patients whose T cells displayed LAG-3 on their surface. “We now have a population that we can sensitize to immunotherapy that was resistant to anti-PD-1 treatment,” says Nils Lonberg, who heads the immune oncology and targeted drug discovery efforts at BMS in Redwood City, CA.
BMS has several newer checkpoint inhibitors, targeting other immune pathways, that trigger T cells to home in on tumors, and company researchers are accumulating data on combining each with nivolumab. “We focus on both innate and acquired immunity pathways to treat more patients with tumors we know can respond to immunotherapy, and also to open up other cancer types to immunotherapy,” says Lonberg. “From basic-science principles, what we look for first in a combination are drugs with nonredundant mechanisms.”
Other companies, including those with their own FDA-approved checkpoint inhibitors, such as AstraZeneca, Merck, and Roche, are taking similar approaches. The US Food and Drug Administration (FDA) approved the first checkpoint inhibitor antibody—ipilimumab (Yervoy), which targets cytotoxic T-lymphocyte antigen 4 (CTLA-4)—for advanced melanoma in 2011. Five other checkpoint inhibitor antibodies followed—six in total—targeting the PD-1 pathway for numerous cancer types. In 2015, the first and thus far only FDA-approved combination of two immunotherapies hit the US market: nivolumab plus ipilimumab for metastatic melanoma patients.
Combining multiple treatments for patients with recalcitrant cancers is not a new concept. Among the first pairings of anticancer drugs were two or more different chemotherapies. As drug companies developed additional types of cancer drugs, combinations of different modalities—including chemotherapy, radiation, targeted small molecules, and eventually immunotherapies—followed. (See illustration below.) “There has long been a feeling that drug combinations will be needed to have the type of impact in cancer patient care that we would like to see,” says David Hyman, a medical oncologist who specializes in early drug development at the Memorial Sloan Kettering Cancer Center in New York City.
But only a handful of cancer drug combos have so far been approved by the FDA, in part because many of the tested combinations were conceived largely at random—an inefficient approach given the dizzying number of approved and investigational therapies that could be combined. In fact, of the hundreds or even thousands of novel combos currently in clinical trials, many, if not most, were designed based on little more than convenience, depending on what drugs a company owns, says Charles Swanton, a cancer geneticist at The Francis Crick Institute in London. “My view is that there are too many trials, especially immunotherapy ones, being conducted in a serendipitous manner,” he says. “It’s more about, ‘We’ve got these two cancer drugs, so let’s put them together and see what happens.’”
Only recently have researchers adopted more-systematic approaches. One method that’s growing in popularity is the use of high-throughput screens that allow researchers to quickly evaluate interactions between different cancer therapies to predict which might form a successful combo. Alternatively or in addition, some researchers are relying on knowledge of the underlying biology to determine which therapies are likely to make the strongest pairings, as is the case for BMS’s checkpoint inhibitor combos. “We have to start from fundamental principles of tumor biology,” says Swanton. “Once we know this information, then we can start to come up with rational combo approaches.”
Ross Camidge, a thoracic oncologist at the University of Colorado Denver, agrees. “Our chances of successful combination therapies are only as good as the science going into the selection of the combinations.”
Casting a wide net
In vitro screening of large numbers of drug combinations is one of the approaches to sort through a vast ocean of drug-pairing possibilities. In silico screening methods typically rely on compiling data generated by in vitro experiments and animal studies, then using the data as a basis for computer algorithms to predict promising interactions. But these methods are labor intensive and in vitro drug combination screening is also expensive, which is why they have not been widely adopted. “There are not many academic labs with the capability to do [large-scale] combination screens, and not many pharmaceutical companies are doing it either, for that matter,” says Marc Ferrer, a researcher at the Chemical Genomics Center within the National Center for Advancing Translational Sciences.
To bypass the need for having libraries of drug compounds to physically pair, researchers have been taking advantage of genetic methods, including novel gene-editing techniques, to identify potential drug pairings that kill cancer cells. These approaches can be easier and less expensive than traditional cell-based drug combination screens using multiwall plates. Using guide RNAs to knock out pairs of genes using CRISPR, for example, Stanford University’s Michael Bassik identified pairs of genetic targets that might encourage cancer cell death.2 Researchers can then use databases to search for drugs that bind to and inhibit the proteins encoded by those gene pairs. Possible combos identified through such screening methods require validation in cell culture and animal experiments. With the CRISPR screen, “we’re using a genetic proxy for a drug effect: pairs of genes versus pairs of drugs, which require extensive robotics, plates, lots of time and money,” says Bassik. “We are making assumptions that there are specific drugs for those gene targets, which is often, but not always true.”
In addition, some researchers are going directly to cell culture–based screens to identify promising combos. In 2009, Georgetown University pediatric oncologist and researcher Jeffrey Toretsky identified a novel small molecule that targets an oncogenic fusion protein, EWS-FLI1, found exclusively in Ewing sarcoma, a type of bone cancer. His lab pulled out the molecule from a biophysical screen that tested the ability of thousands of compounds to bind a recombinant EWS-FLI1 protein. Then, using cell culture, Toretsky’s lab tested pairwise combinations of the small-molecule inhibitor with 69 generic cancer drugs. This second screen uncovered a synergy with the chemotherapy drug vincristine (Marqibo, Vincasar PFS),3 a finding that Toretsky and his colleagues confirmed with in vivo data last year, showing that the combination thwarted tumor growth in two Ewing sarcoma xenograft mouse models.4 The human version of the EWS-FLI1 inhibitor, called TK216, is now in a Phase 1 clinical trial for Ewing sarcoma, and the combination will also be tested, says Toretsky.
Such cell culture–based screens are able to relatively quickly parse through large numbers of potential combinations, says Toretsky. Traditionally, however, only chemotherapies, targeted small molecules, and certain targeted antibodies—not immunotherapies—could be screened using cell culture–based screens. “There are many cellular interactions that are not captured in a 2-D monolayer of cells,” says Ferrer.
Because cancer drug combinations are showing promise in clinical trials, Ferrer and his colleagues are trying to devise more-dynamic in vivo screens that better mimic the tumor and its microenvironment. But this is no easy feat. In 2012, his team developed a way to systematically screen many cancer drugs using three-dimensional sphere cultures of tumor cells,5 an approach that identified drug-combination effects that were drastically different than those measured in 2-D cultures.6 Ferrer has used the high-throughput 3-D assay to test dose ranges of drug combinations, and he’s now working to increase the complexity of the cultures by mixing tumor cells with cells from the tumor microenvironment, hoping to eventually include immune cells.
To further narrow the search, many researchers urge forethought on the front-end and reasoning on the backend, examining what is known about how certain drugs work and thinking about mechanisms that might pair well together. “The permutations of potential combinations are endless,” says Samir Khleif of Augusta University’s Georgia Cancer Center. “The best thing that we have in our hands is biology and logic.” Khleif, for his part, is testing currently available immunotherapy drugs in various combinations in animal models based on hypotheses of what pathways might work well together to fight tumors….
