By combining mathematical modeling, statistical methods, and machine learning, with experimental, epidemiological, and DNA sequencing data, we have provided the first quantitative evidence for the large role in cancer causation played by the normal, i.e. endogenous, accumulation of somatic mutations in the cells of the human body. These results highlight the necessity of combining cancer's early detection with primary prevention in order to dramatically decrease cancer mortality. They also point to the number of cells in an organ, and their division rate, as key variables when assessing cancer risk.

    Tomasetti et al. Science 2017Tomasetti C. Mutated clones are the new normal. Science 2019

    Dr. Tomasetti's lab has led the effort to develop algorithms for the early detection of cancer, using machine learning and statistical analysis in combination with cell-free DNA sequencing and protein data. The resulting tests are among the very first ever produced, and are currently being further developed and tested to make them available to the general public.

    Cohen et al. Science 2018Lennon et al. Science 2020

    Our lab develops mathematical models of tumorigenesis, that in combination with experimental and epidemiological data, provide important insights on cancer evolution. For example, we have provided: 1) the first in vivo tissue-specific genome-wide pre-tumor mutation rates estimates in humans, and the first evidence for the relatively large number of somatic mutations found in our normal tissues (Tomasetti et al. PNAS 2013); 2) evidence that up to 3 driver mutations may be all that is required for cancer to occur (Tomasetti et al. PNAS 2015), 3) a new methodology for the estimation of the fitness advantages caused by different types of driver mutations, with unexpected results indicating how large these effects can be (Lahouel et al. PNAS 2020). Our most recent model has been considered "the most comprehensive mathematical model of tumorigenesis" currently available.

    Lahouel et al. PNAS 2020Tomasetti et al. PNAS 2013


Division of Biostatistics and Bioinformatics, Department of Oncology

Johns Hopkins Medicine, Sidney Kimmel Comprehensive Cancer Center

Department of Biostatistics

Johns Hopkins Bloomberg School of Public Health

Cancer Etiology, Evolution, Early Detection and Risk Prediction Mathematical Modeling – Biostatistics

Two factors are widely recognized as causing cancer: environment (E) and heredity (H).

A paper published in Science in 2015 provided evidence for a third factor: the random mistakes made when normal stem cells divide. The results in this paper showed that there was a strong correlation between the lifetime number of normal stem cell divisions in an organ and its lifetime cancer incidence. This contributes to explain why certain cancer types have a much higher incidence than others….