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Activity Number: 596 - Statistical and Mathematical Methods in Cancer Etiology and Cancer Early Detection
Type: Invited
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #300563
Title: Statistical and Mathematical Approaches to Cancer Etiology
Author(s): Cristian Tomasetti and Lu Li*
Companies: Johsn Hopkins University and Johns Hopkins University
Keywords: Mathematical Modeling; Bayesian Methods; Cancer Etiology; Mutation Rates; Artificial Intelligence; Deep Learning

Recent findings have shown that the normal accumulation of mutations in our tissues, simply due to cell division, plays a large role in cancer etiology. On average, these replicative mutations contribute substantially to the number of driver mutations required for cancers to occur. An open question is, however, what is the overall contribution of mutations to cancer. While driver mutations appear to be a necessary condition for cancer, it is not currently known how large is their contribution when compared to that one of all other factors (e.g. immune system, microenvironment, diet, ... ). By a novel approach, combining mathematical modeling and statistical Bayesian methods we provide for the first time evidence that mutations take the lion's share in explaining cancer risk.

This talk will serve also as an introduction to the two following talks in this session, whose focus is on novel methodologies to predict the causes of cancer using mutational signatures and mutation rates, as well as to predict the presence of cancer via a simple blood test.

Authors who are presenting talks have a * after their name.

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