Skip to main content
Premium Trial:

Request an Annual Quote

Model of Tumor Heterogeneity Can Help Predict How Disease Responds to Treatment

NEW YORK (GenomeWeb Daily News) – Based on their characterization of heterogeneity in breast cancer tumors, a team of researchers developed a stochastic computational model to predict how tumors develop and evolve.

As the group led by Kornelia Polyak, a principal investigator at Dana-­Farber Cancer Institute and Harvard Medical School, detailed in Cell Reports this week, they examined the genetic heterogeneity within tumors as well as how that diversity changes in response to chemotherapy treatment.

"By analyzing tumors before and during treatment, we can build models that predict how the tumor will evolve," Polyak said in a statement. "Better understanding of tumor evolution is key to improving the design of cancer therapies and for truly individualized cancer treatment."

Tumors are not uniform throughout as they contain pockets of cells harboring different traits, including differences in response to treatment and in metastatic ability.

Using immunofluorescence in situ hybridization with BAC probes for certain commonly amplified chromosomal regions and centromeres, Polyak and her colleagues surveyed the heterogeneity of pre- and post-treatment tumor biopsies from 47 patients with breast cancer. At the same time, they evaluated the tumors' phenotypic heterogeneity by staining for CD44 and CD24, two markers that track with certain molecular and biological properties of cancer cells.

By focusing on the 8q24 BAC probe signals and a corresponding centromere probe, the researchers found that there wasn't a significant change in overall genetic diversity in any of the tumors they examined before or after chemotherapy. Genetic diversity, the researchers noted, appears to be intrinsic to tumors and stable over time, an effect that they then noted was replicated when examining other commonly amplified loci.

Treatment, though, affected the phenotypic heterogeneity of tumors, the researchers added. For instance, after treatment, they noted an increase in the number of CD44-CD24+ cells in luminal A, luminal B, and TNBC tumors. Residual TNBC tumors were also enriched for CD44-CD24- cells. At the same time, luminal A and triple-negative tumors had lower levels of CD44+CD24- cells, while the cell subpopulations in Her2+ tumors did not change much.

The researchers speculated that such changes in diversity could be linked to differences in cellular proliferation as cancer therapeutics are thought to target fast-growing cells. And by examining the proliferation maker Ki67+, they found that the fraction of Ki67+ cells declined in all cell types in all tumors after treatment.

Microenvironment differences — like the extent of vascularization or changes to the extracellular matrix — could also affect tumor diversity. To assess those effects, the investigators developed a topology map based on the distribution of subpopulations with distinct genotypic and phenotypic features in various tumoral regions.

Based on those maps, Polyak and her colleagues found that, in most cases, indices of genetic diversity in those regions were not significantly different before and after treatment. However, when they examined differences in copy number in those regions pre- and post-treatment, they noted that that there was cell-to-cell variability in 8q24 BAC and chromosome 8 CEP counts, with some cells having higher numbers and others lower after treatment.

From this, the investigators noted that tumor cells appear to cluster more based on their phenotype than on their genotype.

"Because phenotypic diversity in combination with selection pressure by local microenvironmental signals is the driver of tumor evolution, our results highlight the importance of using an integrated approach," the researchers added.

To understand these patterns, Polyak and her colleagues develop a stochastic computational model of cell proliferation and death based on the topology and Ki67 data they generated. They then applied this model to data from three patients. Through this, they uncovered that the clustering they observed was lower than what would be expected. By including cellular motility and phenotypic switching, they then could recapitulate the post-treatment data.

This model, they said, provides a proof-of principle that therapy-induced phenotypic changes can be predicted based on the characterization of tissue samples.

"Based on this knowledge, we could predict which tumor cells will likely be eliminated or slowed down by treatment and how this may change the tumor overall," Polyak said. "This knowledge could aid the design of subsequent therapies for those who do not respond to the first line of treatment."