Evidence map›Paper›PMID 20051132›Full record

ArticleBMC cancer2010

A simple algebraic cancer equation: calculating how cancers may arise with normal mutation rates.

Peter Calabrese, Darryl Shibata

Abstract read
In one paragraph

Article in BMC cancer, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Article
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  14. Bioenergetics of life, disease and death phenomena.Theory in biosciences = Theorie in den Biowissenschaften · 2018
    Article
  15. Article
  16. Article
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  18. Review
  19. Size, longevity and cancer: age structure.Proceedings. Biological sciences · 2016
    Article
  20. Sporadic colorectal cancer: Studying ways to an end.United European gastroenterology journal · 2016
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Peter CalabreseProgram in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
Darryl Shibata

Funding

Tumor Diversity As A Biomarker For Colorectal CancerR21CA149990 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI SHIBATA, DARRYL K · 2010 to 2011
$383k
NCI NIH HHS R21 CA149990
6 · The paper itself

Abstract

backgroundThe purpose of this article is to present a relatively easy to understand cancer model where transformation occurs when the first cell, among many at risk within a colon, accumulates a set of driver mutations. The analysis of this model yields a simple algebraic equation, which takes as inputs the number of stem cells, mutation and division rates, and the number of driver mutations, and makes predictions about cancer epidemiology.

methodsThe equation [p = 1 - (1 - (1 - (1 - u)d)k)Nm ] calculates the probability of cancer (p) and contains five parameters: the number of divisions (d), the number of stem cells (N x m), the number of critical rate-limiting pathway driver mutations (k), and the mutation rate (u). In this model progression to cancer "starts" at conception and mutations accumulate with cell division. Transformation occurs when a critical number of rate-limiting pathway mutations first accumulates within a single stem cell.

resultsWhen applied to several colorectal cancer data sets, parameter values consistent with crypt stem cell biology and normal mutation rates were able to match the increase in cancer with aging, and the mutation frequencies found in cancer genomes. The equation can help explain how cancer risks may vary with age, height, germline mutations, and aspirin use. APC mutations may shorten pathways to cancer by effectively increasing the numbers of stem cells at risk.

conclusionsThe equation illustrates that age-related increases in cancer frequencies may result from relatively normal division and mutation rates. Although this equation does not encompass all of the known complexity of cancer, it may be useful, especially in a teaching setting, to help illustrate relationships between small and large cancer features.

Indexed as

MutationAgedCell DivisionCell Transformation, NeoplasticDisease ProgressionHumansModels, GeneticModels, TheoreticalNeoplasm MetastasisNeoplasmsProbabilityRiskStem Cells

Identifiers

PMID20051132
PMCPMC2829925

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.