Tumor cells tend to cluster together in the earliest stages of cancer development. As the tumor progresses, however, some of these cells slip free and spread to distant parts of the body. This process, known as metastasis, is how most cancers turn deadly (see Targeting metastasis to halt cancer’s spread). But if there were a simple, quick way to spot these restive cells early on, patients and doctors would have more time to optimize treatment plans.
In a recent study in Physical Review X, physicists from Leipzig University in Germany potentially took the first steps toward this feat by finding a simple set of criteria to recognize mobilizing breast cancer cells under a microscope, an early step in the onset of metastasis. The cells have elongated nuclei and are less densely packed against their neighbors, compared to immobile tumor cells. Identifying mobile cells in tumor biopsies could one day help oncologists flag high-risk cases before the cancer becomes widespread, says senior author and soft matter physicist Josef Käs.
When most doctors diagnose cancer, they say it has a high risk of spreading when it metastasizes from a local tumor to nearby lymph nodes. But the invasion of lymph nodes is an imperfect diagnostic marker, Käs says. As the study notes, among women who will develop distant tumors, some 30% never get cancer in their lymph tissue. And among women who do see their cancers invade the lymph, about a third will never develop distant tumors in other parts of their body. This latest work sought a more reliable biomechanical marker for cell motility.
Among physicists who study cancer, researchers often describe clusters of cells as being “jammed,” meaning that particles are so tightly packed side-by-side that individuals can’t move, or “unjammed,” meaning that cells become collectively mobile, exchanging positions by slipping past one another. Cell unjamming has been documented by Käs’ group and other physicists studying asthma and cancer over the last decade. In this latest work, Käs and collaborators wanted to clinically document how unjamming becomes part of the metastatic cascade. “This is the first time where soft matter physics may enter the clinic,” Käs says.
The work began with digitized scans of paper-thin slices of tumors, collected from 1,380 breast cancer patients. Each slice was 3 to 5 micrometers thick, capturing a single layer of cells. Lead author Pablo Gottheil, a doctoral student in Käs’ group, measured cell and nucleus shapes, and cell sizes, using both machine learning and computer vision algorithms. He found that the average distance between adjacent cells increased significantly among patients whose cancer had spread.
Today, when doctors want to assess the prognosis of a patient, they consider tumor size, tumor grade, and whether cancer has spread to the lymph nodes. When Gottheil added his biomarkers to the existing prognosis model, he found a 26% increase in the ability to predict disease outcomes for the 1,380 patients. To visualize this, he says, imagine a doctor has two patients, and wants to predict which one is at higher risk. Using known risk factors, the doctor would make the correct prediction a certain percentage of the time. Now, if the doctor also considered biomarkers of cell mobility, he or she would succeed 26% more often Gottheil says. Follow-up clinical trials will be necessary to determine whether the researchers’ conclusions hold up across more samples, and in more types of cancer. If they do, Käs hopes that cell-to-cell density and nuclear elongation could someday be adopted as simple diagnostic tools in pathology labs worldwide.
Tumor biologist Michael Rogers at Harvard Medical School and Boston Children’s Hospital in Massachusetts calls this study provocative. Cancer is typically framed in terms of chemical and biological signaling, he says; it’s useful to consider it in terms of mechanical forces. It’s also prudent to identify a simple set of cellular characteristics associated with a higher risk of metastasis and death, he says.
But Rogers, for one, has some reluctance about applying the term “cellular unjamming” to this case. The phenomenon that the authors observed, he says, shouldn’t be described in terms of fluid dynamics “when a developmental description is better supported in the literature.”
Rogers suspects that interactions between neighboring breast cancer cells hold them together. He notes that breast cancer cells move as a sheet in the early stages of tumor development, with tight junctions between neighbors. As the cancer progresses, however, the cells are known to lose some of their ability to communicate and become disorganized, in a process called epithelial-mesenchymal transition, or “EMT.” Rogers suspects that the increase in cellular mobility over time is just an intermediate step in the breakdown of epithelial cell junctions. “In other words, what they describe as unjamming looks like dedifferentiation or EMT to me and the latter processes are much better understood in the cancer context,” Rogers notes.
Käs, for his part, will continue to study these dynamics with a view toward clinical applications. And he will continue to scrutinize the “underlying subcellular physics” of how these cancer cells metastasize, which, he says, “is still up for grabs.”
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