Literature DB >> 11166023

Genetic epidemiology of multistage carcinogenesis.

K Hemminki1, P Mutanen.   

Abstract

It is commonly believed that cancer is a multistage, polygenic disease. Even though conceptually appealing, the evidence supporting the multistage theory remains limited. Most known tumor suppresser genes are associated with monogenic dominant cancers following a two-hit pathway. We review results from a recent twin study on 90000 individuals that give support to the multistage theory. Statistically significant heritability estimates were shown for cancers of the colorectum (35%), breast (27%), and prostate (42%). These estimates are much higher than those obtained from family studies in which parents and offspring, or sibs are compared. The difference can be accounted for by the involvement of many genes. A polygenic cancer would show small effects in family studies but large effects in twin studies. We present calculations on the decrease in familial risks when the number of genes involved increases or when the penetrance decreases. We test the apparent number of stages involved in the main cancers from the Swedish Family-Cancer Database. The logarithms of the slopes suggest large differences in the apparent numbers of mutations involved in different cancers. The number of mutations required appears to be less in familial breast cancer compared to sporadic breast cancer. Study designs for gene identification should be revised to accommodate polygenic cancers.

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Year:  2001        PMID: 11166023     DOI: 10.1016/s0027-5107(00)00162-7

Source DB:  PubMed          Journal:  Mutat Res        ISSN: 0027-5107            Impact factor:   2.433


  9 in total

Review 1.  Genetic architecture of cancer and other complex diseases: lessons learned and future directions.

Authors:  Lucia A Hindorff; Elizabeth M Gillanders; Teri A Manolio
Journal:  Carcinogenesis       Date:  2011-03-31       Impact factor: 4.944

2.  A mathematical model for predicting malignancy of solitary pulmonary nodules.

Authors:  Yun Li; Jun Wang
Journal:  World J Surg       Date:  2012-04       Impact factor: 3.352

3.  Genetic Programming Neural Networks: A Powerful Bioinformatics Tool for Human Genetics.

Authors:  Marylyn D Ritchie; Alison A Motsinger; William S Bush; Christopher S Coffey; Jason H Moore
Journal:  Appl Soft Comput       Date:  2007-01       Impact factor: 6.725

Review 4.  Molecular origins of cancer: Molecular basis of colorectal cancer.

Authors:  Sanford D Markowitz; Monica M Bertagnolli
Journal:  N Engl J Med       Date:  2009-12-17       Impact factor: 91.245

5.  A subset of familial colorectal neoplasia kindreds linked to chromosome 9q22.2-31.2.

Authors:  Georgia L Wiesner; Denise Daley; Susan Lewis; Christine Ticknor; Petra Platzer; James Lutterbaugh; Melissa MacMillen; Boris Baliner; Joseph Willis; Robert C Elston; Sanford D Markowitz
Journal:  Proc Natl Acad Sci U S A       Date:  2003-10-17       Impact factor: 11.205

6.  Familial risk of urological cancers: data for clinical counseling.

Authors:  Kari Hemminki; Xinjun Li; Kamila Czene
Journal:  World J Urol       Date:  2003-11-13       Impact factor: 4.226

7.  GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.

Authors:  Alison A Motsinger; Stephen L Lee; George Mellick; Marylyn D Ritchie
Journal:  BMC Bioinformatics       Date:  2006-01-25       Impact factor: 3.169

8.  Genome wide in silico SNP-tumor association analysis.

Authors:  Ping Qiu; Luquan Wang; Mitch Kostich; Wei Ding; Jason S Simon; Jonathan R Greene
Journal:  BMC Cancer       Date:  2004-01-29       Impact factor: 4.430

9.  A Rb1 promoter variant with reduced activity contributes to osteosarcoma susceptibility in irradiated mice.

Authors:  Michael Rosemann; Iria Gonzalez-Vasconcellos; Tanja Domke; Virginija Kuosaite; Ralf Schneider; Markus Kremer; Jack Favor; Michaela Nathrath; Michael J Atkinson
Journal:  Mol Cancer       Date:  2014-08-04       Impact factor: 27.401

  9 in total

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