Evidence map›Paper›PMID 28196079›Full record

ArticlePLoS computational biology2017

Mathematical models of tissue stem and transit target cell divisions and the risk of radiation- or smoking-associated cancer.

Mark P Little, Jolyon H Hendry

Abstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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.

Mark P LittleRadiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Rockville, MD, United States of America.ORCID 0000-0003-0980-7567
Jolyon H HendryChristie Medical Physics and Engineering, Christie Hospital and University of Manchester, Wilmslow Road, Manchester M20 4BX, United Kingdom.

Funding

Studies of Populations Exposed to Environmental Sources of RadiationZIACP010132 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI CAHOON, ELIZABETH · 2009 to 2025
$35.2M
6 · The paper itself

Abstract

There is compelling biological data to suggest that cancer arises from a series of mutations in single target cells, resulting in defects in cell renewal and differentiation processes which lead to malignancy. Because much mutagenic damage is expressed following cell division, more-rapidly renewing tissues could be at higher risk because of the larger number of cell replications. Cairns suggested that renewing tissues may reduce cancer risk by partitioning the dividing cell populations into lineages comprising infrequently-dividing long-lived stem cells and frequently-dividing short-lived daughter transit cells. We develop generalizations of three recent cancer-induction models that account for the joint maintenance and renewal of stem and transit cells, also competing processes of partially transformed cell proliferation and differentiation/apoptosis. We are particularly interested in using these models to separately assess the probabilities of mutation and development of cancer associated with "spontaneous" processes and with those linked to a specific environmental mutagen, specifically ionizing radiation or cigarette smoking. All three models demonstrate substantial variation in cancer risks, by at least 20 orders of magnitude, depending on the assumed number of critical mutations required for cancer, and the stem-cell and transition-cell mutation rates. However, in most cases the conditional probabilities of cancer being mutagen-induced range between 7-96%. The relative risks associated with mutagen exposure compared to background rates are also stable, ranging from 1.0-16.0. Very few cancers, generally <0.5%, arise from mutations occurring solely in stem cells rather than in a combination of stem and transit cells. However, for cancers with 2 or 3 critical mutations, a substantial proportion of cancers, in some cases 100%, have at least one mutation derived from a mutated stem cell. Little difference is made to relative risks if competing processes of proliferation and differentiation in the partially transformed stem and transit cell population are allowed for, nor is any difference made if one assumes that transit cells require an extra mutation to confer malignancy from the number required by stem cells. The probability of a cancer being mutagen-induced correlates across cancer sites with the estimated cumulative number of stem cell divisions in the associated tissue (p<0.05), although in some cases there is sensitivity of findings to removal of high-leverage outliers and in some cases only modest variation in probability, but these issues do not affect the validity of the findings. There are no significant correlations (p>0.3) between lifetime cancer-site specific radiation risk and the probability of that cancer being mutagen-induced. These results do not depend on the assumed critical number of mutations leading to cancer, or on the assumed mutagen-associated mutation rate, within the generally-accepted ranges tested. However, there are borderline significant negative correlations (p = 0.08) between the smoking-associated mortality rate difference (current vs former smokers) and the probability of cancer being mutagen-induced. This is only the case where values of the critical number of mutations leading to cancer, k, is 3 or 4 and not for smaller values (1 or 2), but does not strongly depend on the assumed mutagen-associated mutation rate.

Indexed as

Models, GeneticSmokeSmokingAnimalsCell DivisionCell Transformation, NeoplasticComputer SimulationEpigenesis, GeneticHumansMutationNeoplasms, Radiation-InducedNeoplastic Stem CellsNicotianaSmoke

Identifiers

PMID28196079
PMCPMC5347390

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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.