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Revisiting the Mode of Cancer Evolution

September 24, 2018

genomic DNANew Brunswick, N.J. – Analysis by investigators at Rutgers Cancer Institute of New Jersey further examines tumor evolution through genomic data captured from thousands of cancer tumor samples using a mathematical model. Senior author Subhajyoti De, PhD, a researcher in the Genomic Instability and Cancer Genetics Research Program at Rutgers Cancer Institute and assistant professor of pathology and laboratory medicine at Rutgers Robert Wood Johnson Medical School, along with lead author Abdul Balaparya, Rutgers School of Arts and Sciences, share more about the findings published in the September 24 online edition of Nature Genetics (doi: 10.1038/s41588-018-0219-4).

Q: Why is this work important to explore?

A: By the time of detection a typical tumor comprises of billions of cells, which trace their root back to a single renegade cell in the body. This process of tumor evolution takes years or even decades, and without major symptoms go unnoticed until late. Over this time tumor cells continue to divide, mutate, and compete for resources such that tumor cells become increasingly different from their renegade ancestor; and under natural selection the clones of more aggressive tumor cells dominate the malignant mass. It is important to study the footprints of cancer progression to understand how the tumor came to be its current self and how it might behave during treatment. Cancer cell lines grown outside the human body in the laboratories and animal models (e.g. genetically engineered mice) provide some important insights, but they do not capture all aspects of human cancers. For instance, mice have a body size a fraction of that in a human. They do not live as long, and do not smoke, drink, or experience stresses comparable to us.

As a complementary approach, here we used a mathematical framework to study modes of tumor evolution in humans using genomic data from thousands of tumor samples from multiple different cancer types. This is of fundamental interest, because an understanding of the mode of tumor evolution can potentially indicate how it might respond during treatment. At present further testing is needed, but one day this information could guide clinical management and decision making for personalized care of the patients.     

Subhajyoti De 
Subhajyoti De, PhD

Q: What did you find?

A: We used mathematical models of tumor evolution to study mode of tumor progression using genomic data from thousands of human tumor samples from multiple different cancer types, and our works converged with works of several other research groups. We were particularly interested to infer the pressure and tempo of natural selection in individual tumors from genomic data. While initially it appeared that natural selection among clones of tumor cells within a malignant mass is generally weak for a majority of the tumors, we and others showed that one needs to be cautious because of the complexity of genomic data. Particularly noisy data could misleadingly suggest absence of selection, while competition for resources and natural selection could indeed be fierce within a resource-hungry, starving tumor.

Q: What are the implications for informing cancer treatment decisions?

A: It is crucial to identify the mode of cancer evolution to appreciate how the tumor developed and how it might respond to certain types of treatment. Our work suggests that it can be possible to predict the patterns of evolutionary dynamics in individual tumors or in types of tumors using genomic data, but in the light of complexity of genomic data caution is urged to avoid incorrect inference. It is still premature, but one day this information alongside mutation profiles from tumors could guide clinical decision making for individualized care of cancer patients.     

Dr. De acknowledges support from the National Institutes of Health Cancer Center Support Grant (P30CA072720) and Shared Instrument S10 Grant (1S10OD012346-01A1).
 

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