Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-16T18:05:33.801Z Has data issue: false hasContentIssue false

4 - Assessing molecular variability in cancer genomes

Published online by Cambridge University Press:  07 September 2011

A. D. Barbour
Affiliation:
University of Zürich
S. Tavaré
Affiliation:
University of Cambridge
N. H. Bingham
Affiliation:
Imperial College, London
C. M. Goldie
Affiliation:
University of Sussex
Get access

Summary

Abstract

The dynamics of tumour evolution are not well understood. In this paper we provide a statistical framework for evaluating the molecular variation observed in different parts of a colorectal tumour. A multi-sample version of the Ewens Sampling Formula forms the basis for our modelling of the data, and we provide a simulation procedure for use in obtaining reference distributions for the statistics of interest. We also describe the large-sample asymptotics of the joint distributions of the variation observed in different parts of the tumour. While actual data should be evaluated with reference to the simulation procedure, the asymptotics serve to provide theoretical guidelines, for instance with reference to the choice of possible statistics.

AMS subject classification (MSC2010) 92D20; 92D15, 92C50, 60C05, 62E17

Introduction

Cancers are thought to develop as clonal expansions from a single transformed, ancestral cell. Large-scale sequencing studies have shown that cancer genomes contain somatic mutations occurring in many genes; cf. Greenman et al., Sjöblom et al., Shah et al. Many of these mutations are thought to be passenger mutations (those that are not driving the behaviour of the tumour), and some are pathogenic driver mutations that influence the growth of the tumour. The dynamics of tumour evolution are not well understood, in part because serial observation of tumour growth in humans is not possible.

In an attempt to better understand tumour growth and structure, a number of evolutionary approaches have been described.

Type
Chapter
Information
Probability and Mathematical Genetics
Papers in Honour of Sir John Kingman
, pp. 91 - 112
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×