• EARLY DETECTION OF CANCER
    VIA CELL-FREE DNA TESTING

    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.

  • CANCER EVOLUTION

    Our lab develops mathematical models of tumorigenesis, that in combination with experimental and epidemiological data, provide important insights on cancer evolution. 

  • 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. 

Research in Cancer

Tomasetti’s Lab is focused on using mathematical modeling, statistics, machine learning, A.I. and bioinformatics methods to address:

Early Cancer Detection and Monitoring

Noninvasive cell-free DNA (cfDNA) blood tests are showing the potential to detect cancers years before conventional diagnostic methods.

Cancer Evolution

Cancer is driven by the sequential accumulation of genetic and epigenetic change. we provide mathematical models of the full process of tumor evolution. Those models are able to recapitulate a substantial proportion of the observed cancer incidence in several cancer types.

You can think of Cancer as a mathematical model. The better we understand that model, the better we understand cancer and the better we can fight it.

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CRISTiAN TOMASETTI

Director, C​enter for Cancer Prevention and Early Detection, City of Hope

Professor and Director, Division of Mathematics for Cancer Evolution and Early Detection, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope 

Professor and Director, Division of Integrated Cancer Genomics, Translational Genomics Research Institute

Cancer Etiology, Evolution, Risk Prediction, Early Detection and Monitoring.

By combining mathematical modeling, statistical analysis,  machine learning, A.I. and bioinformatics with experimental, epidemiological, and DNA sequencing data, Dr. Tomasetti has 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.

As an applied mathematician, he currently leads the effort to develop classification algorithms for the early detection of cancer via a simple blood test.

Meet the People

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The Division of Mathematics for Cancer Evolution and Early Detection draws upon a talented group of accomplished experts in mathematical modeling, statistics, machine learning, A.I. and bioinformatics at City of Hope and TGen.

Latest Publications

Circulating tumor DNA guides chemotherapy use in stage II Colon Cancer.

The role of adjuvant chemotherapy in stage II colon cancer continues to be debated. The presence of circulating tumor DNA (ctDNA) after surgery predicts very poor recurrence-free survival, whereas its absence predicts ...

Evaluating the Impact of Multi-Cancer Early Detection Testing on Health and Economic Outcomes: Toward a Decision Modeling Strategy.

Emerging data provide initial support for the concept that a single, minimally invasive liquid biopsy test, performed in conjunction ...

Circulating tumor DNA dynamics and recurrence risk in patients undergoing curative intent resection of colorectal cancer liver metastases: a prospective cohort study. Special Issue on Early detection and Minimal Residual Disease.

In patients with resectable colorectal liver metastases (CRLM), the role of pre- and postoperative systemic ...

The Bad Luck of Random Mutations

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Cancer Etiology, Evolution, Risk Prediction, Early Detection and monitoring.

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….

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Division of Mathematics for Cancer Evolution and Early Detection, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope

Address

1500 E Duarte Rd, Duarte, CA 91010, USA

Mailing Adress

ctomasetti@coh.org