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New Mathematical Model Helps Predict Tumor Evolution and Treatment Effectiveness

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Key Points

  • Researchers analyzed breast cancer tumors before and after treatment for their distinct genetic and phenotypic heterogeneity and used the data to develop computational models to predict how each tumor is likely to change during treatment.
  • Tumors with less genetic diversity were more likely to completely respond to treatment.
  • Computational models built on the patient-specific characterization of tumors at the single-cell level in situ prior to and after chemotherapy illustrate the feasibility of predicting the evolution of tumors during treatment and could be used for the design of more effective treatment strategies.

A study by Almendro et al analyzed breast cancer tumors before and after treatment for important characteristics, including chromosome copy number, the presence or absence of certain protein markers, and their proliferative capacity. The scientists then used the data to develop computational models to predict how each tumor is likely to change during treatment. The study is published in Cell Reports.

Scientists from Dana-Farber Cancer Institute in Boston analyzed the pre- and post-treatment tumor biopsies of 47 patients with breast cancer undergoing neoadjuvant chemotherapy. The tumor samples included about equal numbers of the four major breast cancer subtypes: luminal A, luminal B, HER2 positive, and triple-negative.

The researchers then assessed the genetic heterogeneity of the tumors based on immunofluorescence in situ hybridization in intact tissues. The tumors were also assessed for phenotypic heterogeneity by staining for CD44 and CD24. The researchers then integrated the data on the genotypes and phenotypes of single cells in intact tissues to generate their computational predictive model and were able to anticipate how an individual tumor would likely respond to therapy.

Study Results

The researchers found that genetic diversity within a tumor, such as differences in how many copies of a DNA segment are present, did not change much in cancers that had no response or only a partial response to treatment. In addition, tumors with less genetic diversity among their cells are more likely to completely respond to treatment than are tumors with more genetic complexity. While the genetic diversity of tumor cells was not strongly affected by chemotherapy in patients with partial or no response to treatment, the study showed that certain types of cells—those more likely to grow rapidly—were more likely to be eliminated, and the locations of cell populations changed.

Computational models built on the patient-specific characterization of tumors at the single-cell level in situ prior to and after chemotherapy illustrate the feasibility of predicting the evolution of tumors during treatment, wrote the study authors. This knowledge could be used for the design of more effective treatment strategies.

The Next Step

“In the future, we would like to take the patient’s [tissue] sample and look at the details of the phenotypes and genotypes of the cells and then run a computational model of different types of treatments that could be applied to that tumor and then predict which one would be best,” said Kornelia Polyak, MD, PhD, a breast cancer researcher in the Susan F. Smith Center for Women’s Cancers and Professor of Medicine at Dana-Farber Cancer Institute; Professor of Medicine at Harvard Medical School; and a coauthor of the study.

Dr. Polyak is also studying the feasibility of a computational model that could predict how the timing and administration of therapy might improve outcome. “The timing of treatment and how you do it is not very rationally designed, it’s just tradition,” said Dr. Polyak. “We would like to explore that detail in terms of looking at the tumors and different timing of the therapy, different combinations of therapy, and different ways of administering therapy to see if it could potentially improve outcome. If you understand the dynamics of tumors and how they change, how cells interact, proliferate, and die, then we would be able to design treatments in a much more rational way.”

This study was supported by the National Cancer Institute Physical Sciences Oncology Center, the Susan G. Komen Foundation, Cellex Foundation, Redes Temáticas de Investigación en Cáncer, and the Breast Cancer Research Foundation.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.


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