Evasive Action

Features

Could interrupting the evolutionary process of mutating cells hold the key to vanquishing cancer? Researchers led by Dr. Dan Landau are on the case. 

By Sue De Pasquale

Just as species evolve through natural selection, individual cancer cells find ways to adapt to treatment and then evade it through an evolutionary process, notes Dr. Dan Landau, associate professor of medicine and a member of the Meyer Cancer Center at Weill Cornell Medicine, whose team is at the vanguard of this new frontier of cancer research. Their strategy: By identifying how individual cells evolve to mutate and proliferate, the scientists aim to develop tools to interrupt the evolutionary process — work key to detecting and treating cancer and avoiding unnecessary treatment.

The New Frontier: Single-Cell Multiomics

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We are all born with 30 trillion or so cells that evolve throughout our lives. Even when we are healthy, these cells accrue mutations in their DNA that can then grow into “clones” or clusters of genetically distinct cells, Dr. Landau explains. “What is it that allows that single mutated cell, that is not yet cancer, to outcompete and outgrow its neighbors?” he asks. Since malignancy represents a “runaway process of evolution,” he says, it’s crucial to understand the behavior of that single misbehaving cell as an early harbinger of the malignancy process.

Dr. Landau and colleagues at Weill Cornell Medicine and the New York Genome Center are doing just that, using a set of advanced new techniques — called single-cell multi-omics — that allow them to profile thousands of individual cells in unprecedented detail.

In recent work reported in Nature Genetics, the scientists sampled individual cells from patients with glioma brain tumors. In addition to mapping distinct cell behaviors in gliomas, and identifying key programming marks on DNA (called “methylations”) that appear to shift glioma cells from one cellular behavior to another, the team devised techniques to reconstruct an “ancestral tree” for each cell.

“It’s like having a time machine,” says Dr. Landau. “We can take a sample from a patient’s tumor and infer many details of how that tumor has been developing” — going back to the very origin of the tumor.

The lineage trees revealed that glioblastoma cells had a higher degree of plasticity when compared to cells from lower-grade gliomas, Dr. Landau notes. This made it easier for them to switch back and forth between stem cell states and more mature states — potentially allowing the cells to survive stem cell-killing treatments.

Taken together, the findings have offered crucial new insights into the dynamics of gliomas — information that should be helpful, Dr. Landau says, in developing better methods for detecting, staging, monitoring and treating brain cancer. And in principle, he says, “This approach could be used to study the development of many types of cancer.”

Transformative Technology

As a patient’s cancer cells evolve, clinicians need a way to detect and monitor those changes in order to better optimize treatment. In the case of many tumors, “the chance of recurrence after initial treatment is quite high, but up to now we haven’t always had a good way to detect these recurrences early, when they are most treatable,” says Dr. Landau.

That’s because existing methods for detecting DNA fragments from tumor cells — known as “liquid biopsy” because they are detected through a blood test — target a handful of specific genes or “hotspots” in the genome where cancer-associated mutations are expected. This “targeted sequencing” approach only works when DNA in blood samples includes sufficient fragments containing targeted mutations. But often following cancer surgery, the tumor burden is much too low to be detected through standard targeted-sequencing techniques.

So Dr. Landau and his team have turned to “whole genome sequencing” — allowing them to sequence all of the circulating DNA in a patient’s blood sample, which boosts the effective sensitivity by orders of magnitude. “We are able to scan the entire genome for the tens of thousands of mutations that are found in human cancer,” he explains.

In work reported in Nature Medicine, Dr. Landau and his collaborators demonstrated the value of this whole genome DNA-sequencing strategy, powered by machine learning, with blood tests on patients who had melanomas, colorectal cancers, and lung cancers. The team showed their method could detect tumor DNA in some lung cancer and colorectal cancer patients following surgery when the tumor burden is very low. “And in patients with metastatic melanoma who were being treated with immunotherapy, we could track their responses in real time,” he says.

“When we first started pursuing this strategy in 2016, everyone thought we were crazy because whole genome sequencing was prohibitively expensive,” recalls Dr. Landau. “But we developed our approach anticipating that the technology would advance and costs would come down dramatically — and they have,” he says. To speed clinical application of the new liquid biopsy platform, Dr. Landau has co-founded and is a member of the scientific advisory board of a company, C2i Genomics (see Biography). 

Next Steps

Whole genome DNA sequencing, supported by machine learning, represents a “tectonic shift” in cancer research and treatment, Dr. Landau believes. Currently, without knowing whether residual cancer remains after surgery or other treatment, the paradigm is often to prescribe aggressive follow-up treatment that can carry significant side effects — and in most cases isn’t needed. And for some patients with residual cancer, the cells can adapt to treatment and evolve to become more aggressive — without doctors knowing.

“Imagine instead if you could have a simple blood test after surgery and find out yes, you do have residual disease and we can begin treatment and follow up to make sure it is working, or no, you are clear and don’t need aggressive treatment,” says Dr. Landau. His team will continue to develop their technique to be even more sensitive and specific and aim to confirm its effectiveness through clinical trials.

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