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January 19, 2016

Cancer Evolution Deemed Explicable Chain of Cause and Effect

  • Scientific determinism holds that if measurements about the present are available in sufficient number and detail, then past and future values should be calculable, given that natural processes are invariably subject to a small number of immutable laws. While the advocates of scientific determinism have even said that a single formula could predict the fates of the greatest bodies of the universe and those of the tiniest atoms, they have not been so bold to say the same of cancer cells. Until now.

    Scientists at the Institute of Cancer Research and Queen Mary University of London (QMUL) have developed a model that assumes that in many tumors, all important cancer genes are already present at the beginning of tumor growth, and that new mutations inside the tumors are essentially “passengers” with no additional effect. Despite this simplifying assumption, the model worked well enough that the scientists are confident that in the future they will be able to predict how a cancer will grow and develop by applying natural laws to single genetic snapshots taken from a tumor.

    The scientists presented their work January 18 in the journal Nature Genetics, in an article entitled “Identification of neutral tumor evolution across cancer types.” The article notes that “despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging.” It also suggests that the mechanics of cancer evolution could become tractable through the application of a tried-and-true scientific approach: simplification, approximation, and refinement.

    “[We] demonstrate that neutral tumor evolution results in a power-law distribution of the mutant allele frequencies reported by next-generation sequencing of tumor bulk samples,” wrote the authors. “We find that the neutral power law fits with high precision 323 of 904 cancers from 14 types and from different cohorts.”

    Essentially, many cancers evolve in specific patterns that can be predicted. In particular, many cancer types, such as bowel, stomach, and some lung cancers, closely followed the paths set out by the theoretical model, which was built to describe the accumulation and spread of genetic mutations during a single rapid expansion.

    “In malignancies identified as evolving neutrally, all clonal selection seemingly occurred before the onset of cancer growth and not in later-arising subclones, resulting in numerous passenger mutations that are responsible for intratumoral heterogeneity,” the authors continued. “Reanalyzing cancer sequencing data within the neutral framework allowed the measurement, in each patient, of both the in vivo mutation rate and the order and timing of mutations.”

    Yet the model was less successful at predicting the path of some other cancers, such as brain and pancreatic cancers. In these cases, the problem of passenger mutations could not be so readily overlooked. These perturbations will have to be accommodated in more elaborate mathematical models.

    In the meantime, the scientists plan to determine how the new predictive features they can measure—such as the speed of emergence of aggressive or drug-resistant mutations—map to outcomes for patients over time.

    Study co-leader Andrea Sottoriva, Ph.D., Chris Rokos Fellow in Evolution and Cancer at The Institute of Cancer Research, London, said, “Our study shows that the spread of mutations through a cancer follows natural laws—and is therefore theoretically predictable, just as we can predict the movement of celestial bodies or the weather.

    “This predictability means that the vast amount of genetic data we can generate from tumor biopsies could tell us how a given cancer will develop over time—which mutations will come to drive it into more aggressive disease, when they will emerge, and which drugs are best to treat them. Like in a game of chess, the aim is anticipating the next move of the adversary, to ultimately win the game.”

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