In the early 2000s, back when biologist Olivia Rossanese worked as an investigator at GlaxoSmithKline, fighting cancer was an exercise in brute force. Researchers at the company had set their sights on developing inhibitors of B-Raf, a protein kinase involved in cell signaling that becomes dysfunctional in many cancers, and “what we were thinking was that we needed to hit this . . . protein so hard,” says Rossanese. “You had to inhibit it 99.999 percent to shut down the signaling pathway.”
In 2008, Rossanese and her GSK colleagues discovered just the sort of compound they were after: a small molecule, dabrafenib, that potently inhibited B-Raf and showed striking effects in melanoma patients with certain mutations in the BRAF gene. With dabrafenib, says Rossanese, “we see really amazing responses, and melanomas go away.” The drug was approved by the US Food and Drug Administration (FDA) in 2013.
But in a majority of patients, the effect doesn’t last long. The cancer usually comes back within just six months or so—and when it does, it’s resistant to dabrafenib. GSK’s data showed that less than half of metastatic melanoma patients treated with dabrafenib alone survived more than two years.
Just assume resistance from the start. If you do that, and you change your mindset that way, then how would you design drugs?—Olivia Rossanese, Centre for Cancer Drug Discovery, Institute of Cancer Research
It’s a familiar refrain. The vast majority of cancer deaths in the US come about not because of a lack of treatment, but because the treatments themselves stop working in the patients receiving them. Accordingly, the emergence of drug resistance is now widely regarded by oncologists as the biggest challenge in cancer therapy. Insensitivity to drugs may arise due to changes in gene expression that allow a cancer cell to rewire its metabolism to circumvent the targeted pathway. Resistance also arises through genetic mutations, which, provided they offer a survival or growth advantage, can come to dominate a population of replicating cancer cells much as they would a population of organisms undergoing adaptive evolution in a new environment.
Growing appreciation of cancer’s capacity to evolve drug resistance is revealing fatal weaknesses in the drug-it-until-it-dies mentality that dominates cancer treatment and drug discovery efforts, says Mel Greaves, director of the Centre for Evolution and Cancer at the Institute of Cancer Research (ICR) in the UK. During traditional drug screening, for example, oncologists “take a drug, put it in a test tube with a cell culture or cell line of cancer, and ask if it kills the cells,” says Greaves. At the ICR, which launched its Centre for Cancer Drug Discovery last summer, “we’re saying, that’s just wrong.”
Instead, several groups of cancer biologists are looking for therapies and treatment strategies that target cancer evolution itself, says Rossanese, now head of biology at the new center, which claims to have the “world’s first ‘Darwinian’ drug discovery program” specifically designed to tackle drug resistance. This can take the form of manipulating the course of cancer evolution to clinicians’ advantage, or putting the brakes on the processes that drive it in order to limit a tumor’s capacity to adapt. In taking this approach, researchers “just assume resistance from the start,” Rossanese says. “If you do that, and you change your mindset that way, then how would you design drugs? How would you design trials?”
Exploiting collateral sensitivity
The emergence of resistance to potent inhibitors such as dabrafenib isn’t surprising, says Rossanese. By the time of diagnosis, a typical tumor might already comprise more than 1 billion cells, each of which has the entire human genome at its disposal. During the tumor’s development up to that point, the accumulation of mutations in replicating cells will have led to heterogeneity, the substrate for evolution, among different subpopulations of cancer cells.
When a clinician administers high doses of a drug that blocks an important cellular pathway, “the pressure on the cells to come up with a resistance mechanism is quite strong,” Rossanese says. Any mutation conferring an advantage in that scenario, even if it’s present in just a few cells, offers an escape route, and can quickly sweep through the population to produce a drug-resistant cancer that thwarts further treatment.
One way to try to block cancer’s evolutionary escape routes is to use drug combinations that target multiple oncogenic pathways at once. For example, the combination of dabrafenib and trametinib—a drug that targets another central protein in cellular signaling, MEK—was approved in 2014 for certain types of melanoma and later for other cancers after showing improvement in survival rates compared with dabrafenib treatment alone. However, many cancers eventually go on to evolve multidrug resistance. There’s also the issue of toxicity: generally, the more drugs a clinician administers, the higher a patient’s risk of side effects.
An alternative strategy is to set a sort of evolutionary trap by administering a combination of drugs in a particular order. The aim is to select for resistance to the first therapy before hitting surviving cancer cells with a second therapy designed to target a vulnerability created by the very mutations that conferred resistance to drug 1. Known as evolutionary herding, the method exploits the fact that any biological adaptation often involves trade-offs; being better at surviving in one environment may mean being worse at surviving in another. As part of an announcement last year about the ICR’s new drug discovery center, computational biologist Andrea Sottoriva likened the approach to sending cancer “down dead ends and to its own destruction.”
Playing Tricks on Tumors
Treating cancers with high doses of tumor-targeting drugs often triggers the evolution of drug resistance, which leads to tumor progression. Researchers are consequently exploring alternative treatment strategies that manipulate tumor evolution to a patient’s advantage—by exploiting drug resistance instead of trying to avoid it.
Evolutionary Dead End
Clinicians administer a drug and thus select for cells with resistance-conferring mutations. Then, having narrowed the population down to just those resistant cells, they administer a second drug designed to target a weakness, what researchers refer to as a “collateral sensitivity,” in those same cells.
© LUCY READING-IKKANDA
Researchers administer low levels of a drug, enough to kill most, but not all, of the vulnerable cells in the tumor population while favoring the survival of drug-resistant lineages. Once the tumor has shrunk, clinicians stop administering the drug. The drug-sensitive cells, which tend to have a competitive edge over cells that have invested in a costly drug-resistance mechanism, can now begin to grow back. Competition between drug-sensitive and drug-resistant cells for resources in the tumor microenvironment keeps the tumor size in check.
© LUCY READING-IKKANDA
To turn the idea of evolutionary herding into practical cancer therapies, oncologists are using computational and experimental techniques to predict which combinations of drugs, and in what order, are most likely to work. In one 2016 study, MIT researchers exploited the evolution of drug resistance in a murine model of an aggressive type of acute lymphoblastic leukemia (ALL) called Philadelphia chromosome–positive ALL. This cancer is characterized by the fusion of two genes, BCR and ABL1, and is often treated with high doses of small-molecule drugs that inhibit the resulting BCR-ABL1 oncoprotein.
Blasting ALL cells in vitro with dasatinib or bosutinib, two common BCR-ABL1 inhibitors, resulted in the emergence of resistance to both drugs, regardless of which compound the cells were exposed to, the researchers found. But drug screening revealed that these resistant cells showed increased vulnerability, or “collateral sensitivity,” to a selection of other drugs. Sequencing assays revealed a single base mutation, a guanine-to-cytosine substitution, in ABL1 that appeared to be responsible both for protection against dasatinib and bosutinib and for sensitivity to at least four other small-molecule drugs—a weakness that, the researchers write in their paper, should be “therapeutically exploitable.”
More recently, Sottoriva and colleagues applied a similar approach to manipulate the evolution of non-small cell lung cancer (NSCLC) cells in vitro. The researchers first bombarded their cell lines with trametinib, which, as expected, caused major cell death followed by the growth of drug-resistant cells a few weeks later. Sequencing revealed that these trametinib-resistant cell lines had all lost functional copies of the gene coding for CDKN2A, a protein that helps regulate cyclin-dependent kinases (CDK) and, consequently, cell division. Because the loss of CDKN2A is known to lead to increased production of certain CDKs, the team predicted that the cells would be particularly sensitive to CDK inhibitors. Sure enough, such drugs proved twice as lethal in the trametinib-evolved lines as in control NSCLC cells.
A cancer cell with a resistance mechanism would have an advantage over other cancer cells competing for space and resources—but only when the relevant drug was present.
Efforts to target collateral sensitivity bring their own challenges, however. For starters, collateral sensitivity may be relatively rare, or at least difficult to identify. In their study, Sottoriva and colleagues described a second set of NSCLC lines, this time treated with gefitinib, which targets another protein involved in cell signaling called epidermal growth factor receptor (EGFR). The cells duly gained gefitinib resistance, but they didn’t show collateral sensitivity to any of the nearly 500 other drugs the team hit them with, including several that the researchers identified as good candidates based on genetic sequencing of resistant cell lines.
Even when researchers can identify collateral sensitivity, work by several groups suggests that it often arises unpredictably and may be temporary. Tumors continue to diversify as cells replicate and accumulate mutations, so cancers may eventually evolve resistance to both the original treatment and the therapy designed to take advantage of resulting collateral sensitivities. Charles Swanton, a clinician scientist at the Francis Crick Institute and University College London, notes that in lung cancer, for example, tumor sequencing data suggest that evolution becomes “less constrained, not more constrained” as cancer progresses. “In terms of forcing tumors down cul-de-sacs, I think perhaps in very early stages of disease that might be a fruitful approach,” he says. “But I think in later stages of disease, the tumor is too diverse for that to be possible.”
Forcing cancer to compete with itself
About 15 years ago, while perusing internet news, Robert Gatenby came across an article about the diamondback moth, and the damage that this pest species had been wreaking on cabbages and other cruciferous plants around the world for much of the last century. Ecologists had realized, the article explained, that by smothering their crops in chemicals, farmers were merely encouraging this fast-reproducing species to evolve insecticide resistance, while killing off any competing insects in the ecosystem that might have helped keep moth populations in check. To Gatenby, a radiologist and codirector of the Center of Excellence for Evolutionary Therapy at the Moffitt Cancer Center, the parallels to cancer were obvious. High doses of cancer therapy are “the same as high-dose insecticide,” he says. “You’re selecting for resistance, and you’re taking away competitors.”
He says the story made him wonder whether oncologists might harness competition within the cancer ecosystem—that is, among the various clonal subpopulations making up a tumor—to stave off the evolution of resistance. A cancer cell with a resistance mechanism would have an advantage over other cancer cells competing for space and resources, but only when the relevant drug was present, Gatenby reasoned in theoretical papers during the 2000s and 2010s. In the drug’s absence, it would have no such edge—indeed, the resistance mechanism might even come with a cost, if the cell directs resources toward resistance instead of growth and replication, for example.
This idea has been bolstered by years of clinical data. For example, one small 2015 study of patients with colorectal cancer who were receiving EGFR-targeting drugs found that, soon after the study started, several patients’ tumors were taken over by cells with mutations in KRAS, a well-known oncogene that helps cancer cells bypass normal metabolism and confers drug resistance. Yet when clinicians stopped administering the drugs, that same KRAS-mutated subpopulation took a hit, with liquid biopsies showing declining numbers of these cells with respect to other subpopulations. The drug-resistant cells appeared to be drug-dependent.
To turn the idea into a therapeutic strategy, Gatenby proposed cycling between providing a treatment and withholding it, thus growing and shrinking different cancer cell subpopulations in a way that would limit the overall size of the tumor and block drug-resistant cells from taking over completely. A team at Novartis tested this strategy in the early 2010s using patient-derived melanoma xenografts in immunocompromised mice. The researchers found that while a continuous high-dose treatment of melanoma tumors with the B-Raf inhibitor vemurafenib led to lethal drug-resistant disease in mice within a few months, intermittent dosing on a four-weeks-on, two-weeks-off schedule staved off resistance for the full 200 days of the study, and drastically slowed overall tumor growth.
We don’t try to remove the cancer, we try to keep it at a low level that doesn’t kill you or impact your life.—Benjamin Roche, Center for Ecological and Evolutionary Cancer Research
More recently, Gatenby’s group has used the method in patients with metastatic castration-resistant prostate cancer (mCRPC). Patients treated with the hormone therapy abiraterone usually progress to a drug-resistant and thus more lethal form of the disease after about 16 months. But instead of dosing continuously, the Moffitt trial’s clinicians monitored 11 patients’ blood levels of PSA, a biomarker for prostate cancer, and administered abiraterone only until PSA had dropped to 50 percent of its pretreatment level. Then, they suspended treatment, waited a few weeks or months until a patient’s PSA had risen back to pretreatment levels, and started over.
Interim findings published in 2017 indicated that, in patients exposed to intermittent dosing, the average time to progression to the more aggressive form of the cancer was at least 27 months. Last year, after expanding the group to 15 men, the team published an update: six had progressed, but the rest had not, boosting the median time to progression to at least 30 months. With the trial now wrapping up after having accrued more than 20 patients in total, “those patients, it looks like, are going to have about a twenty-month increase in their median time to progression,” says Gatenby. Plus, “they’re getting less than half the dose of drugs that they would have received otherwise”—a factor that could help reduce costs and potential side effects. The team is now testing this approach in patients with castration-sensitive prostate cancer, thyroid cancer, melanoma, and, as of later this year, metastatic pediatric sarcoma.
Theoretical work by the Moffitt researchers suggests that the approach could be more effective if clinicians use multiple drugs to control the proportions of cell subpopulations—taking advantage, for example, of any collateral sensitivities. Simulations the team published last year indicate that repeatedly alternating between abiraterone and the chemotherapy drug docetaxel could have significantly increased time-to-progression in the mCRPC trial, although effective implementation of such a regime would require precise techniques for monitoring the proportion of various tumor subpopulations. (See “Tracking Changes” on page 30.)
One implication of strategies that exploit competition among tumor subpopulations is that they shift the goalposts for treatment, notes Benjamin Roche, codirector of the Center for Ecological and Evolutionary Cancer Research in Montpellier, France. By trying to maintain “competition between resistant and sensitive cancer cells . . . we are completely changing our priorities about the fight against cancer,” says Roche, who has collaborated with Gatenby on papers in the past. “We don’t try to remove the cancer, we try to keep it at a low level that doesn’t kill you . . . or impact your life.”
This mentality has taken a while to percolate through the oncology community, says Gatenby. A decade ago, the idea that clinicians might chose to manage, rather than obliterate, a cancer “was a difficult sell.” Understandably, he says, “physicians as a group did not like it. [At conferences] the usual comment was, ‘This is bullshit. Just give me better drugs.’” Now, with clinical trials underway, Gatenby says, there’s more interest in these sorts of approaches for cancers that are all but untreatable.
Greaves says he agrees that controlling, rather than curing, aggressive cancers may be a sensible goal. But there’s still “a bit of a gap between the modeling and the clinical practice,” he notes. Current theory can’t produce clear treatment recommendations for individual patients, he continues. “We aren’t there yet.”
New Technologies for Monitoring Tumor Subpopulations
To effectively manipulate a tumor’s evolution, researchers need a way of monitoring the various subpopulations of cancer cells within that tumor. Standard tissue biopsies are impractical for many cancer types and tend to provide poor measures of tumor heterogeneity: one study of renal carcinoma patients found that a single biopsy identified on average just a little more than half of the mutations in each tumor (NEJM, 366:883–92, 2012). Many researchers have consequently switched their attention to liquid biopsies, which pick up cancer-related biomarkers circulating in the blood and may prove to be cheaper, less-invasive, and more-effective ways of monitoring within-tumor changes over time.
A proof-of-concept study by a team at Institut Curie in France a couple of years ago used whole-exome sequencing analyses of circulating DNA to detect the rise of tumor subpopulations resistant to chemotherapy in 19 patients with neuroblastoma tumors (Clin Cancer Res, 24:939–49, 2018), while a team at Asahikawa Medical University in Japan did the same with non-small cell lung cancer patients receiving tyrosine kinase inhibitors (BMC Cancer, 18:1136, 2018). One analysis published last year found that, in nearly 80 percent of cases, this approach identified clinically relevant, resistance-inducing mutations that had been missed by tissue biopsies, raising researchers’ hopes that the technique could be effective for monitoring tumor evolution (Nat Med, 25:1415–21, 2019). Other types of liquid biopsies include analyses of circulating tumor cells and of genetic material (usually RNAs) in tumor-derived extracellular vesicles.
These approaches are being evaluated in multiple clinical trials as a way to monitor patient responses to cancer therapies, but it will be a while before they’re ready for use in long-term, evolution-focused treatments, says Robert Gatenby of the Moffitt Cancer Center. Researchers don’t know, for example, if the proportions of various types of circulating DNA directly mirror the proportions of each tumor subpopulation, or whether they “represent disproportionately the populations that are losing—or the populations that are winning—the evolutionary battles,” he says. “We’re hoping [these techniques] will help us, but there’s a lot of work that has to be done.”
Hobbling cancer’s evolutionary mechanisms
Efforts to herd or otherwise manipulate cancer evolution assume that the emergence of drug resistance is inevitable, and is thus best directed in order to achieve clinical benefit. But some researchers are interested in how a cancer gets to be so adaptable in the first place, and whether that process itself might be blocked or slowed down. The key, Rossanese explains, is the heterogeneity of the cancer cell population. “When you increase heterogeneity, you’re giving evolution a bigger substrate to act upon,” she explains. “So what if we could reduce some of the ways cancers generate heterogeneity?”
Although research shows that some cancer-related mutations arise as a result of treatment itself, most DNA errors are generated spontaneously as cells in the tumor multiply. Data on when and where these mutations usually arise are pouring in from research projects, such as the Pan-Cancer Analysis of Whole Genomes (PCAWG), that sequence and analyze tumor DNA. For example, researchers working on the TRAcking Cancer Evolution through therapy (TRACERx) project, which Swanton leads, have sequenced tumors from hundreds of NSCLC patients in the UK to explore how mutation patterns change as cancer progresses. (Swanton also cofounded a company, Achilles Therapeutics, to develop personalized T cell therapies targeting antigens identified by mutations in patient sequencing data.) Among other things, such data can help identify particular types of mutations in a tumor that are associated with elevated genetic diversity overall, and thus identify potential targets for evolution-stalling therapies.
One such target generating interest among oncologists is the apolipoprotein B mRNA-editing catalytic polypeptide-like (APOBEC) protein family, a group of enzymes that modify nucleic acids by changing cytosine bases to uracil and are thought to be involved in the innate immune response. A 2019 study using TRACERx and other datasets containing genomic information on various lung and thoracic cancers found a strong correlation between APOBEC-driven mutagenesis and overall tumor heterogeneity, suggesting that APOBEC activity may be a significant contributor to the diversification of cancer cell subpopulations.7 Other research has linked the proteins to tumor diversity and disease progression in head and neck, breast, and bladder cancer, among others. “We know it’s an active process that’s driving heterogeneity in cancer,” says Rossanese.
APOBEC3B in particular appears to be upregulated in at least half of all known cancers, and ICR researchers are currently in the early stages of developing small-molecule inhibitors of the enzyme, Rossanese tells The Scientist. “The idea is to test the hypothesis that reducing mutational load and heterogeneity will in fact delay drug resistance,” she explains. Researchers in New Zealand, meanwhile, are targeting the protein using oligonucleotides. In work published last fall, a team at Massey University described an oligonucleotide drug that selectively inhibited APOBEC3B in vitro.8
Like other evolution-based approaches to therapy, the strategy has limitations. At diagnosis, most cancer patients have already accumulated substantial within-tumor heterogeneity. While better cancer screening could help tackle that problem from a public health perspective through earlier diagnosis, therapies targeting APOBEC or as-yet-undiscovered evolution-driving proteins might have little effect on late-stage disease, notes Gatenby. “I think once the horse is out of the barn, it’s going to be very hard to suppress evolution,” he says, adding that cancer cells may well find new escape routes that researchers can’t predict. According to the axiom attributed to evolutionary biologist Leslie Orgel, “Evolution is cleverer than you are,” Gatenby says. “I take that to heart.”
Rossanese isn’t dissuaded. She notes that evolution-stalling therapies would probably be used in conjunction with more-traditional approaches. Even for a patient who already has high tumor heterogeneity, “a lot of times, a primary therapy is going to take out 99 percent of the cancer cells, and that 1 percent that’s left is going to have to adapt its new condition,” she says. “We’re trying to hobble those remaining cells as much as possible.”
Catherine Offord is an associate editor at The Scientist. Email her at firstname.lastname@example.org.