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Explaining the High Mutation Rates of Cancer Cells to Drug and Multidrug Resistance by Chromosome Reassortments That Are Catalyzed by Aneuploidy

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Specialty Science
Date 2000 Dec 20
PMID 11121035
Citations 54
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Abstract

The mutation rates of cancer cells to drug and multidrug resistance are paradoxically high, i.e., 10(-3) to 10(-6), compared with those altering phenotypes of recessive genes in normal diploid cells of about 10(-12). Here the hypothesis was investigated that these mutations are due to chromosome reassortments that are catalyzed by aneuploidy. Aneuploidy, an abnormal number of chromosomes, is the most common genetic abnormality of cancer cells and is known to change phenotypes (e.g., Down's syndrome). Moreover, we have shown recently that aneuploidy autocatalyzes reassortments of up to 2% per chromosome per mitosis because it unbalances spindle proteins, even centrosome numbers, via gene dosage. The hypothesis predicts that a selected phenotype is associated with multiple unselected ones, because chromosome reassortments unbalance simultaneously thousands of regulatory and structural genes. It also predicts variants of a selected phenotype based on variant reassortments. To test our hypothesis we have investigated in parallel the mutation rates of highly aneuploid and of normal diploid Chinese hamster cells to resistance against puromycin, cytosine arabinoside, colcemid, and methotrexate. The mutation rates of aneuploid cells ranged from 10(-4) to 10(-6), but no drug-resistant mutants were obtained from diploid cells in our conditions. Further selection increased drug resistance at similar mutation rates. Mutants selected from cloned cells for resistance against one drug displayed different unselected phenotypes, e.g., polygonal or fusiform cellular morphology, flat or three-dimensional colonies, and resistances against other unrelated drugs. Thus our hypothesis offers a unifying explanation for the high mutation rates of aneuploid cancer cells and for the association of selected with unselected phenotypes, e.g., multidrug resistance. It also predicts drug-specific chromosome combinations that could become a basis for selecting alternative chemotherapy against drug-resistant cancer.

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