Evolution of Resistance During Clonal Expansion
Acquired drug resistance is a major limitation for cancer therapy. Often, one genetic alteration suffices to confer resistance to an otherwise successful therapy. However, little is known about the dynamics of the emergence of resistant tumor cells. In this article, we consider an exponentially grow...
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| Veröffentlicht in: | Genetics (Austin) 2006-04, Vol.172 (4), p.2557-2566 |
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| creator | Iwasa, Yoh Nowak, Martin A Michor, Franziska |
| description | Acquired drug resistance is a major limitation for cancer therapy. Often, one genetic alteration suffices to confer resistance to an otherwise successful therapy. However, little is known about the dynamics of the emergence of resistant tumor cells. In this article, we consider an exponentially growing population starting from one cancer cell that is sensitive to therapy. Sensitive cancer cells can mutate into resistant ones, which have relative fitness alpha prior to therapy. In the special case of no cell death, our model converges to the one investigated by Luria and Delbrück. We calculate the probability of resistance and the mean number of resistant cells once the cancer has reached detection size M. The probability of resistance is an increasing function of the detection size M times the mutation rate u. If Mu 1, to l nM for neutral mutants (alpha = 1), but converges to an upper limit for deleterious mutants (alpha |
| doi_str_mv | 10.1534/genetics.105.049791 |
| format | Article |
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Often, one genetic alteration suffices to confer resistance to an otherwise successful therapy. However, little is known about the dynamics of the emergence of resistant tumor cells. In this article, we consider an exponentially growing population starting from one cancer cell that is sensitive to therapy. Sensitive cancer cells can mutate into resistant ones, which have relative fitness alpha prior to therapy. In the special case of no cell death, our model converges to the one investigated by Luria and Delbrück. We calculate the probability of resistance and the mean number of resistant cells once the cancer has reached detection size M. The probability of resistance is an increasing function of the detection size M times the mutation rate u. If Mu << 1, then the expected number of resistant cells in cancers with resistance is independent of the mutation rate u and increases with M in proportion to M(1-1/alpha) for advantageous mutants with relative fitness alpha>1, to l nM for neutral mutants (alpha = 1), but converges to an upper limit for deleterious mutants (alpha<1). Further, the probability of resistance and the average number of resistant cells increase with the number of cell divisions in the history of the tumor. Hence a tumor subject to high rates of apoptosis will show a higher incidence of resistance than expected on its detection size only.</description><identifier>ISSN: 0016-6731</identifier><identifier>ISSN: 1943-2631</identifier><identifier>EISSN: 1943-2631</identifier><identifier>DOI: 10.1534/genetics.105.049791</identifier><identifier>PMID: 16636113</identifier><identifier>CODEN: GENTAE</identifier><language>eng</language><publisher>United States: Genetics Soc America</publisher><subject>Antineoplastic Agents - pharmacology ; Cancer ; Cell Line, Tumor ; Cells ; Cloning, Molecular ; Computer Simulation ; Drug resistance ; Drug Resistance, Neoplasm ; Evolution, Molecular ; Humans ; Investigations ; Models, Genetic ; Models, Statistical ; Mutation ; Neoplasms - drug therapy ; Neoplasms - genetics ; Probability ; Stochastic Processes ; Tumors</subject><ispartof>Genetics (Austin), 2006-04, Vol.172 (4), p.2557-2566</ispartof><rights>Copyright Genetics Society of America Apr 2006</rights><rights>Copyright © 2006 by the Genetics Society of America 2006</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c590t-f3c54689366a4b6034b163078f00b13231b703e6827491deee5e2936bcd1b293</citedby><cites>FETCH-LOGICAL-c590t-f3c54689366a4b6034b163078f00b13231b703e6827491deee5e2936bcd1b293</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,777,781,882,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16636113$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Iwasa, Yoh</creatorcontrib><creatorcontrib>Nowak, Martin A</creatorcontrib><creatorcontrib>Michor, Franziska</creatorcontrib><title>Evolution of Resistance During Clonal Expansion</title><title>Genetics (Austin)</title><addtitle>Genetics</addtitle><description>Acquired drug resistance is a major limitation for cancer therapy. Often, one genetic alteration suffices to confer resistance to an otherwise successful therapy. However, little is known about the dynamics of the emergence of resistant tumor cells. In this article, we consider an exponentially growing population starting from one cancer cell that is sensitive to therapy. Sensitive cancer cells can mutate into resistant ones, which have relative fitness alpha prior to therapy. In the special case of no cell death, our model converges to the one investigated by Luria and Delbrück. We calculate the probability of resistance and the mean number of resistant cells once the cancer has reached detection size M. The probability of resistance is an increasing function of the detection size M times the mutation rate u. If Mu << 1, then the expected number of resistant cells in cancers with resistance is independent of the mutation rate u and increases with M in proportion to M(1-1/alpha) for advantageous mutants with relative fitness alpha>1, to l nM for neutral mutants (alpha = 1), but converges to an upper limit for deleterious mutants (alpha<1). Further, the probability of resistance and the average number of resistant cells increase with the number of cell divisions in the history of the tumor. Hence a tumor subject to high rates of apoptosis will show a higher incidence of resistance than expected on its detection size only.</description><subject>Antineoplastic Agents - pharmacology</subject><subject>Cancer</subject><subject>Cell Line, Tumor</subject><subject>Cells</subject><subject>Cloning, Molecular</subject><subject>Computer Simulation</subject><subject>Drug resistance</subject><subject>Drug Resistance, Neoplasm</subject><subject>Evolution, Molecular</subject><subject>Humans</subject><subject>Investigations</subject><subject>Models, Genetic</subject><subject>Models, Statistical</subject><subject>Mutation</subject><subject>Neoplasms - drug therapy</subject><subject>Neoplasms - genetics</subject><subject>Probability</subject><subject>Stochastic Processes</subject><subject>Tumors</subject><issn>0016-6731</issn><issn>1943-2631</issn><issn>1943-2631</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqN0UuLFDEQB_AgijuufgJBGg966tmqPDsXQcbxAQuC7D2ke9IzWTLJmHTv6Lc3MuPzoqeE5FcFVX9CniIsUTB-tXXRTX4oSwSxBK6VxntkgZqzlkqG98kCAGUrFcML8qiUWwCQWnQPyQVKySQiW5Cr9V0K8-RTbNLYfHLFl8nGwTVv5uzjtlmFFG1o1l8ONpaqHpMHow3FPTmfl-Tm7fpm9b69_vjuw-r1dTsIDVM7skFw2WkmpeW9BMZ7lAxUNwL0yCjDXgFzsqOKa9w454SjVffDBvt6uSSvTm0Pc793m8HFKdtgDtnvbf5qkvXmz5_od2ab7gxyIVlHa4MX5wY5fZ5dmczel8GFYKNLczFSaQCF3T8haqEFAvsPSOvqKa_w-V_wNs25brEYihyr0VgRO6Ehp1KyG3_OhmC-x2t-xFsfhDnFW6ue_b6WXzXnPCt4eQI7v90dfXam7G0IlaM5Ho-oqOGGCqHYN_fLroQ</recordid><startdate>20060401</startdate><enddate>20060401</enddate><creator>Iwasa, Yoh</creator><creator>Nowak, Martin A</creator><creator>Michor, Franziska</creator><general>Genetics Soc America</general><general>Genetics Society of America</general><general>Copyright © 2006 by the Genetics Society of America</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>4T-</scope><scope>4U-</scope><scope>7QP</scope><scope>7SS</scope><scope>7TK</scope><scope>7TM</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0K</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7N</scope><scope>M7P</scope><scope>MBDVC</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20060401</creationdate><title>Evolution of Resistance During Clonal Expansion</title><author>Iwasa, Yoh ; Nowak, Martin A ; Michor, Franziska</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c590t-f3c54689366a4b6034b163078f00b13231b703e6827491deee5e2936bcd1b293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Antineoplastic Agents - pharmacology</topic><topic>Cancer</topic><topic>Cell Line, Tumor</topic><topic>Cells</topic><topic>Cloning, Molecular</topic><topic>Computer Simulation</topic><topic>Drug resistance</topic><topic>Drug Resistance, Neoplasm</topic><topic>Evolution, Molecular</topic><topic>Humans</topic><topic>Investigations</topic><topic>Models, Genetic</topic><topic>Models, Statistical</topic><topic>Mutation</topic><topic>Neoplasms - drug therapy</topic><topic>Neoplasms - genetics</topic><topic>Probability</topic><topic>Stochastic Processes</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Iwasa, Yoh</creatorcontrib><creatorcontrib>Nowak, Martin A</creatorcontrib><creatorcontrib>Michor, Franziska</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>University Readers</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Genetics (Austin)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Iwasa, Yoh</au><au>Nowak, Martin A</au><au>Michor, Franziska</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evolution of Resistance During Clonal Expansion</atitle><jtitle>Genetics (Austin)</jtitle><addtitle>Genetics</addtitle><date>2006-04-01</date><risdate>2006</risdate><volume>172</volume><issue>4</issue><spage>2557</spage><epage>2566</epage><pages>2557-2566</pages><issn>0016-6731</issn><issn>1943-2631</issn><eissn>1943-2631</eissn><coden>GENTAE</coden><abstract>Acquired drug resistance is a major limitation for cancer therapy. 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If Mu << 1, then the expected number of resistant cells in cancers with resistance is independent of the mutation rate u and increases with M in proportion to M(1-1/alpha) for advantageous mutants with relative fitness alpha>1, to l nM for neutral mutants (alpha = 1), but converges to an upper limit for deleterious mutants (alpha<1). Further, the probability of resistance and the average number of resistant cells increase with the number of cell divisions in the history of the tumor. Hence a tumor subject to high rates of apoptosis will show a higher incidence of resistance than expected on its detection size only.</abstract><cop>United States</cop><pub>Genetics Soc America</pub><pmid>16636113</pmid><doi>10.1534/genetics.105.049791</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Genetics (Austin), 2006-04, Vol.172 (4), p.2557-2566 |
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| language | eng |
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| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford University Press Journals All Titles (1996-Current); Alma/SFX Local Collection |
| subjects | Antineoplastic Agents - pharmacology Cancer Cell Line, Tumor Cells Cloning, Molecular Computer Simulation Drug resistance Drug Resistance, Neoplasm Evolution, Molecular Humans Investigations Models, Genetic Models, Statistical Mutation Neoplasms - drug therapy Neoplasms - genetics Probability Stochastic Processes Tumors |
| title | Evolution of Resistance During Clonal Expansion |
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