Cristian Tomasetti, Ph.D.

EARLY DETECTION OF CANCER VIA CELL-FREE DNA TESTING
Dr. Tomasetti's lab has led the technologies and effort to develop algorithms for the early detection of cancer, using machine learning and mathematical 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 and accessible to the general public.

CANCER EVOLUTION
Dr. Tomasetti’s lab develops mathematical models of tumorigenesis, that in combination with experimental and epidemiological data, provide important insights on cancer evolution. For example, he has 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). His most recent model has been considered "the most comprehensive mathematical model of tumorigenesis" currently available.
 
CANCER ETIOLOGY: THE BAD LUCK OF CANCER
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, their division rate and their mutational load, as key variables when assessing cancer risk.