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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">In Vivo Response to Methotrexate Forecasts Outcome of Acute Lymphoblastic Leukemia and Has a Distinct Gene Expression Profile</title><author><name sortKey="Sorich, Michael J" sort="Sorich, Michael J" uniqKey="Sorich M" first="Michael J" last="Sorich">Michael J. Sorich</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff2"> Sansom Institute, University of South Australia, Adelaide, Australia</nlm:aff></affiliation></author><author><name sortKey="Pottier, Nicolas" sort="Pottier, Nicolas" uniqKey="Pottier N" first="Nicolas" last="Pottier">Nicolas Pottier</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff3"> EA2679, Faculté de Médecine de Lille, Pole Recherche, Lille, France</nlm:aff></affiliation></author><author><name sortKey="Pei, Deqing" sort="Pei, Deqing" uniqKey="Pei D" first="Deqing" last="Pei">Deqing Pei</name><affiliation><nlm:aff id="aff4"> Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Yang, Wenjian" sort="Yang, Wenjian" uniqKey="Yang W" first="Wenjian" last="Yang">Wenjian Yang</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Kager, Leo" sort="Kager, Leo" uniqKey="Kager L" first="Leo" last="Kager">Leo Kager</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff5"> Department of Hematology-Oncology, St. Anna Children's Hospital, Vienna, Austria</nlm:aff></affiliation></author><author><name sortKey="Stocco, Gabriele" sort="Stocco, Gabriele" uniqKey="Stocco G" first="Gabriele" last="Stocco">Gabriele Stocco</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff6"> Instituto di Ricovero e Cura a Carattere Scientifico, Burlo Garofolo and University of Trieste, Trieste, Italy</nlm:aff></affiliation></author><author><name sortKey="Cheng, Cheng" sort="Cheng, Cheng" uniqKey="Cheng C" first="Cheng" last="Cheng">Cheng Cheng</name><affiliation><nlm:aff id="aff4"> Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Panetta, John C" sort="Panetta, John C" uniqKey="Panetta J" first="John C" last="Panetta">John C. Panetta</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Pui, Ching Hon" sort="Pui, Ching Hon" uniqKey="Pui C" first="Ching-Hon" last="Pui">Ching-Hon Pui</name><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff8"> Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Relling, Mary V" sort="Relling, Mary V" uniqKey="Relling M" first="Mary V" last="Relling">Mary V. Relling</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff9"> Pharmacogenetics of Anticancer Agents Research Group, National Institutes of Health Pharmacogenetics Research Network, Memphis, Tennessee, United States of America, and Chicago, Illinois, United States of America</nlm:aff></affiliation></author><author><name sortKey="Cheok, Meyling H" sort="Cheok, Meyling H" uniqKey="Cheok M" first="Meyling H" last="Cheok">Meyling H. Cheok</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Evans, William E" sort="Evans, William E" uniqKey="Evans W" first="William E" last="Evans">William E. Evans</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff9"> Pharmacogenetics of Anticancer Agents Research Group, National Institutes of Health Pharmacogenetics Research Network, Memphis, Tennessee, United States of America, and Chicago, Illinois, United States of America</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">18416598</idno><idno type="pmc">2292747</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2292747</idno><idno type="RBID">PMC:2292747</idno><idno type="doi">10.1371/journal.pmed.0050083</idno><date when="2008">2008</date><idno type="wicri:Area/Pmc/Corpus">001513</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001513</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">In Vivo Response to Methotrexate Forecasts Outcome of Acute Lymphoblastic Leukemia and Has a Distinct Gene Expression Profile</title><author><name sortKey="Sorich, Michael J" sort="Sorich, Michael J" uniqKey="Sorich M" first="Michael J" last="Sorich">Michael J. Sorich</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff2"> Sansom Institute, University of South Australia, Adelaide, Australia</nlm:aff></affiliation></author><author><name sortKey="Pottier, Nicolas" sort="Pottier, Nicolas" uniqKey="Pottier N" first="Nicolas" last="Pottier">Nicolas Pottier</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff3"> EA2679, Faculté de Médecine de Lille, Pole Recherche, Lille, France</nlm:aff></affiliation></author><author><name sortKey="Pei, Deqing" sort="Pei, Deqing" uniqKey="Pei D" first="Deqing" last="Pei">Deqing Pei</name><affiliation><nlm:aff id="aff4"> Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Yang, Wenjian" sort="Yang, Wenjian" uniqKey="Yang W" first="Wenjian" last="Yang">Wenjian Yang</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Kager, Leo" sort="Kager, Leo" uniqKey="Kager L" first="Leo" last="Kager">Leo Kager</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff5"> Department of Hematology-Oncology, St. Anna Children's Hospital, Vienna, Austria</nlm:aff></affiliation></author><author><name sortKey="Stocco, Gabriele" sort="Stocco, Gabriele" uniqKey="Stocco G" first="Gabriele" last="Stocco">Gabriele Stocco</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff6"> Instituto di Ricovero e Cura a Carattere Scientifico, Burlo Garofolo and University of Trieste, Trieste, Italy</nlm:aff></affiliation></author><author><name sortKey="Cheng, Cheng" sort="Cheng, Cheng" uniqKey="Cheng C" first="Cheng" last="Cheng">Cheng Cheng</name><affiliation><nlm:aff id="aff4"> Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Panetta, John C" sort="Panetta, John C" uniqKey="Panetta J" first="John C" last="Panetta">John C. Panetta</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Pui, Ching Hon" sort="Pui, Ching Hon" uniqKey="Pui C" first="Ching-Hon" last="Pui">Ching-Hon Pui</name><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff8"> Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Relling, Mary V" sort="Relling, Mary V" uniqKey="Relling M" first="Mary V" last="Relling">Mary V. Relling</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff9"> Pharmacogenetics of Anticancer Agents Research Group, National Institutes of Health Pharmacogenetics Research Network, Memphis, Tennessee, United States of America, and Chicago, Illinois, United States of America</nlm:aff></affiliation></author><author><name sortKey="Cheok, Meyling H" sort="Cheok, Meyling H" uniqKey="Cheok M" first="Meyling H" last="Cheok">Meyling H. Cheok</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation></author><author><name sortKey="Evans, William E" sort="Evans, William E" uniqKey="Evans W" first="William E" last="Evans">William E. Evans</name><affiliation><nlm:aff id="aff1"> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff7"> University of Tennessee, Memphis, Tennessee, United States of America</nlm:aff></affiliation><affiliation><nlm:aff id="aff9"> Pharmacogenetics of Anticancer Agents Research Group, National Institutes of Health Pharmacogenetics Research Network, Memphis, Tennessee, United States of America, and Chicago, Illinois, United States of America</nlm:aff></affiliation></author></analytic><series><title level="j">PLoS Medicine</title><idno type="ISSN">1549-1277</idno><idno type="eISSN">1549-1676</idno><imprint><date when="2008">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="st1"><title>Background</title><p>Childhood acute lymphoblastic leukemia (ALL) is the most common cancer in children, and can now be cured in approximately 80% of patients. Nevertheless, drug resistance is the major cause of treatment failure in children with ALL. The drug methotrexate (MTX), which is widely used to treat many human cancers, is used in essentially all treatment protocols worldwide for newly diagnosed ALL. Although MTX has been extensively studied for many years, relatively little is known about mechanisms of de novo resistance in primary cancer cells, including leukemia cells. This lack of knowledge is due in part to the fact that existing in vitro methods are not sufficiently reliable to permit assessment of MTX resistance in primary ALL cells. Therefore, we measured the in vivo antileukemic effects of MTX and identified genes whose expression differed significantly in patients with a good versus poor response to MTX.</p></sec><sec id="st2"><title>Methods and Findings</title><p>We utilized measures of decreased circulating leukemia cells of 293 newly diagnosed children after initial “up-front” in vivo MTX treatment (1 g/m<sup>2</sup>) to elucidate interpatient differences in the antileukemic effects of MTX. To identify genomic determinants of these effects, we performed a genome-wide assessment of gene expression in primary ALL cells from 161 of these newly diagnosed children (1–18 y). We identified 48 genes and two cDNA clones whose expression was significantly related to the reduction of circulating leukemia cells after initial in vivo treatment with MTX. This finding was validated in an independent cohort of children with ALL. Furthermore, this measure of initial MTX in vivo response and the associated gene expression pattern were predictive of long-term disease-free survival (<italic>p</italic> < 0.001, <italic>p</italic> = 0.02).</p></sec><sec id="st3"><title>Conclusions</title><p>Together, these data provide new insights into the genomic basis of MTX resistance and interpatient differences in MTX response, pointing to new strategies to overcome MTX resistance in childhood ALL.</p><p><bold>Trial registrations</bold>: Total XV, Therapy for Newly Diagnosed Patients With Acute Lymphoblastic Leukemia, <ext-link ext-link-type="uri" xlink:href="http://www.ClinicalTrials.gov">http://www.ClinicalTrials.gov</ext-link> (<ext-link ext-link-type="uri" xlink:href="http://clinicaltrials.gov/ct/show/NCT00137111?order=1">NCT00137111</ext-link>); Total XIIIBH, Phase III Randomized Study of Antimetabolite-Based Induction plus High-Dose MTX Consolidation for Newly Diagnosed Pediatric Acute Lymphocytic Leukemia at Intermediate or High Risk of Treatment Failure (<ext-link ext-link-type="uri" xlink:href="http://www.cancer.gov/search/ViewClinicalTrials.aspx?cdrid=78483&version=HealthProfessional&protocolsearchid=4334254">NCI-T93-0101D</ext-link>); Total XIIIBL, Phase III Randomized Study of Antimetabolite-Based Induction plus High-Dose MTX Consolidation for Newly Diagnosed Pediatric Acute Lymphocytic Leukemia at Lower Risk of Treatment Failure (<ext-link ext-link-type="uri" xlink:href="http://www.cancer.gov/search/ViewClinicalTrials.aspx?cdrid=78484&version=HealthProfessional&protocolsearchid=4334254">NCI-T93-0103D</ext-link>).</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article" xml:lang="EN"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS Med</journal-id><journal-id journal-id-type="publisher-id">pmed</journal-id><journal-id journal-id-type="publisher-id">plme</journal-id><journal-id journal-id-type="pmc">plosmed</journal-id><journal-title>PLoS Medicine</journal-title><issn pub-type="ppub">1549-1277</issn><issn pub-type="epub">1549-1676</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">18416598</article-id><article-id pub-id-type="pmc">2292747</article-id><article-id pub-id-type="doi">10.1371/journal.pmed.0050083</article-id><article-id pub-id-type="publisher-id">07-PLME-RA-1761R2</article-id><article-id pub-id-type="sici">plme-05-04-14</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline"><subject>Genetics and Genomics</subject><subject>Hematology</subject><subject>Pharmacology</subject></subj-group><subj-group subj-group-type="System Taxonomy"><subject>Hematology (including Blood Transfusion)</subject><subject>Oncology</subject><subject>Pharmacology and Toxicology</subject></subj-group></article-categories><title-group><article-title>In Vivo Response to Methotrexate Forecasts Outcome of Acute Lymphoblastic Leukemia and Has a Distinct Gene Expression Profile</article-title><alt-title alt-title-type="running-head">Antileukemic MTX Genomics and Outcome</alt-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Sorich</surname><given-names>Michael J</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author" equal-contrib="yes"><name><surname>Pottier</surname><given-names>Nicolas</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Pei</surname><given-names>Deqing</given-names></name><xref ref-type="aff" rid="aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Yang</surname><given-names>Wenjian</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Kager</surname><given-names>Leo</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff5">5</xref></contrib><contrib contrib-type="author"><name><surname>Stocco</surname><given-names>Gabriele</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff6">6</xref></contrib><contrib contrib-type="author"><name><surname>Cheng</surname><given-names>Cheng</given-names></name><xref ref-type="aff" rid="aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Panetta</surname><given-names>John C</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff7">7</xref></contrib><contrib contrib-type="author"><name><surname>Pui</surname><given-names>Ching-Hon</given-names></name><xref ref-type="aff" rid="aff7">7</xref><xref ref-type="aff" rid="aff8">8</xref></contrib><contrib contrib-type="author"><name><surname>Relling</surname><given-names>Mary V</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff7">7</xref><xref ref-type="aff" rid="aff9">9</xref></contrib><contrib contrib-type="author"><name><surname>Cheok</surname><given-names>Meyling H</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff7">7</xref></contrib><contrib contrib-type="author"><name><surname>Evans</surname><given-names>William E</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff7">7</xref><xref ref-type="aff" rid="aff9">9</xref><xref ref-type="corresp" rid="cor1">*</xref></contrib></contrib-group><aff id="aff1"><label>1</label> Hematological Malignancies Program and the Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</aff><aff id="aff2"><label>2</label> Sansom Institute, University of South Australia, Adelaide, Australia</aff><aff id="aff3"><label>3</label> EA2679, Faculté de Médecine de Lille, Pole Recherche, Lille, France</aff><aff id="aff4"><label>4</label> Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</aff><aff id="aff5"><label>5</label> Department of Hematology-Oncology, St. Anna Children's Hospital, Vienna, Austria</aff><aff id="aff6"><label>6</label> Instituto di Ricovero e Cura a Carattere Scientifico, Burlo Garofolo and University of Trieste, Trieste, Italy</aff><aff id="aff7"><label>7</label> University of Tennessee, Memphis, Tennessee, United States of America</aff><aff id="aff8"><label>8</label> Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America</aff><aff id="aff9"><label>9</label> Pharmacogenetics of Anticancer Agents Research Group, National Institutes of Health Pharmacogenetics Research Network, Memphis, Tennessee, United States of America, and Chicago, Illinois, United States of America</aff><contrib-group><contrib contrib-type="editor"><name><surname>Fischer</surname><given-names>Alain</given-names></name><role>Academic Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1">Hôpital Necker-Enfants Malades, France</aff><author-notes><corresp id="cor1">* To whom correspondence should be addressed. E-mail: <email>william.evans@stjude.org</email></corresp></author-notes><pub-date pub-type="ppub"><month>4</month><year>2008</year></pub-date><pub-date pub-type="epub"><day>15</day><month>4</month><year>2008</year></pub-date><volume>5</volume><issue>4</issue><elocation-id>e83</elocation-id><history><date date-type="received"><day>15</day><month>10</month><year>2007</year></date><date date-type="accepted"><day>3</day><month>3</month><year>2008</year></date></history><copyright-statement><bold>Copyright</bold>: © 2008 Sorich et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</copyright-statement><copyright-year>2008</copyright-year><abstract><sec id="st1"><title>Background</title><p>Childhood acute lymphoblastic leukemia (ALL) is the most common cancer in children, and can now be cured in approximately 80% of patients. Nevertheless, drug resistance is the major cause of treatment failure in children with ALL. The drug methotrexate (MTX), which is widely used to treat many human cancers, is used in essentially all treatment protocols worldwide for newly diagnosed ALL. Although MTX has been extensively studied for many years, relatively little is known about mechanisms of de novo resistance in primary cancer cells, including leukemia cells. This lack of knowledge is due in part to the fact that existing in vitro methods are not sufficiently reliable to permit assessment of MTX resistance in primary ALL cells. Therefore, we measured the in vivo antileukemic effects of MTX and identified genes whose expression differed significantly in patients with a good versus poor response to MTX.</p></sec><sec id="st2"><title>Methods and Findings</title><p>We utilized measures of decreased circulating leukemia cells of 293 newly diagnosed children after initial “up-front” in vivo MTX treatment (1 g/m<sup>2</sup>) to elucidate interpatient differences in the antileukemic effects of MTX. To identify genomic determinants of these effects, we performed a genome-wide assessment of gene expression in primary ALL cells from 161 of these newly diagnosed children (1–18 y). We identified 48 genes and two cDNA clones whose expression was significantly related to the reduction of circulating leukemia cells after initial in vivo treatment with MTX. This finding was validated in an independent cohort of children with ALL. Furthermore, this measure of initial MTX in vivo response and the associated gene expression pattern were predictive of long-term disease-free survival (<italic>p</italic> < 0.001, <italic>p</italic> = 0.02).</p></sec><sec id="st3"><title>Conclusions</title><p>Together, these data provide new insights into the genomic basis of MTX resistance and interpatient differences in MTX response, pointing to new strategies to overcome MTX resistance in childhood ALL.</p><p><bold>Trial registrations</bold>: Total XV, Therapy for Newly Diagnosed Patients With Acute Lymphoblastic Leukemia, <ext-link ext-link-type="uri" xlink:href="http://www.ClinicalTrials.gov">http://www.ClinicalTrials.gov</ext-link> (<ext-link ext-link-type="uri" xlink:href="http://clinicaltrials.gov/ct/show/NCT00137111?order=1">NCT00137111</ext-link>); Total XIIIBH, Phase III Randomized Study of Antimetabolite-Based Induction plus High-Dose MTX Consolidation for Newly Diagnosed Pediatric Acute Lymphocytic Leukemia at Intermediate or High Risk of Treatment Failure (<ext-link ext-link-type="uri" xlink:href="http://www.cancer.gov/search/ViewClinicalTrials.aspx?cdrid=78483&version=HealthProfessional&protocolsearchid=4334254">NCI-T93-0101D</ext-link>); Total XIIIBL, Phase III Randomized Study of Antimetabolite-Based Induction plus High-Dose MTX Consolidation for Newly Diagnosed Pediatric Acute Lymphocytic Leukemia at Lower Risk of Treatment Failure (<ext-link ext-link-type="uri" xlink:href="http://www.cancer.gov/search/ViewClinicalTrials.aspx?cdrid=78484&version=HealthProfessional&protocolsearchid=4334254">NCI-T93-0103D</ext-link>).</p></sec></abstract><abstract abstract-type="toc"><p>William Evans and colleagues investigate the genomic determinants of methotrexate resistance and interpatient differences in methotrexate response in patients newly diagnosed with childhood acute lymphoblastic leukemia.</p></abstract><abstract abstract-type="editor"><title>Editors' Summary</title><sec id="sb1a"><title>Background.</title><p>Every year about 10,000 children develop cancer in the US. Acute lymphoblastic leukemia (ALL), a rapidly progressing blood cancer, accounts for a quarter of these childhood cancers. Normally, cells in the bone marrow (the spongy material inside bones) develop into lymphocytes (white blood cells that fight infections), red blood cells (which carry oxygen round the body), platelets (which prevent excessive bleeding), and granulocytes (another type of white blood cell). However, in ALL, genetic changes in immature lymphocytes (lymphoblasts) mean that these cells divide uncontrollably and fail to mature. Eventually, the bone marrow fills up with these abnormal cells and can no longer make healthy blood cells. As a result, children with ALL cannot fight infections. They also bruise and bleed easily and, because they do not have enough red blood cells, they often complain of tiredness and weakness. With modern chemotherapy protocols (combinations of drugs that kill the fast-dividing cancer cells but leave the normal, nondividing cells in the body largely unscathed), more than 80% of children with ALL live for at least 5 years.</p></sec><sec id="sb1b"><title>Why Was This Study Done?</title><p>Although this survival rate is good, some patients still die because their cancer cells are resistant to one or more chemotherapy drugs. For some drugs, the genetic characteristics of the ALL cells that make them resistant are known. Unfortunately, little is known about why some ALL cells are resistant to methotrexate, a component of most treatment protocols for newly diagnosed ALL. Methotrexate kills dividing cells by interfering with DNA synthesis and repair. Cancer cells can be resistant to methotrexate for many reasons—they may have acquired genetic changes that stop the drug from entering them, for example. These resistance mechanisms need to be understood better before new strategies can be developed for the treatment of methotrexate-resistant ALL. In this study, the researchers have determined the response of newly diagnosed patients to methotrexate and have investigated the gene expression patterns in ALL cells that correlate with good and bad responses to methotrexate.</p></sec><sec id="sb1c"><title>What Did the Researchers Do and Find?</title><p>The researchers measured the reduction in circulating leukemia cells that followed the first treatment with methotrexate of nearly 300 patients with newly diagnosed ALL. They also used “microarray” analysis to investigate the gene expression patterns in lymphoblast samples taken from the bone marrow of 161 patients before treatment. They found that the expression of 50 genes was significantly related to the reduction in circulating leukemia cells after methotrexate treatment (a result confirmed in an independent group of patients). Of these genes, the expression of 29 was higher in patients who responded poorly to methotrexate than in patients who responded well. A “global analysis test,” which examined the gene expression profile of different cellular pathways in relation to the methotrexate response, found a significant association between the nucleotide biosynthesis pathway (which is needed for DNA synthesis and cellular proliferation) and the methotrexate response. Finally, patients with the best methotrexate response and the 50-gene expression profile indicative of a good response were more likely to be alive after 5 years than patients with the worst methotrexate response and the poor-response gene expression profile.</p></sec><sec id="sb1d"><title>What Do These Findings Mean?</title><p>These findings provide important new insights into the genetic basis of methotrexate resistance in newly diagnosed childhood ALL and begin to explain why some patients fail to respond to this drug. They also show that the reduction in circulating leukemic cells shortly after the first methotrexate dose and a specific gene expression profile both predict the long-term survival of patients. These findings also suggest new ways to modulate sensitivity to methotrexate. Down-regulation of the expression of the genes that are expressed more highly in poor responders than in good responders might improve patient responses to methotrexate. Alternatively, it might be possible to find ways to increase the expression of the genes that are underexpressed in methotrexate poor responders and so improve the outlook for at least some of the children with ALL who fail to respond to current chemotherapy protocols.</p></sec><sec id="sb1e"><title>Additional Information.</title><p>Please access these Web sites via the online version of this summary at <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1371/journal.pmed.0050083">http://dx.doi.org/10.1371/journal.pmed.0050083</ext-link>.</p><p>• The US National Cancer Institute provides a fact sheet for patients and caregivers about <ext-link ext-link-type="uri" xlink:href="http://www.cancer.gov/cancertopics/factsheet/ALLinchildren">ALL in children</ext-link> and information about its <ext-link ext-link-type="uri" xlink:href="http://www.cancer.gov/cancertopics/pdq/treatment/childALL/Patient/page5">treatment</ext-link>(in English and Spanish)</p><p>• The UK charity Cancerbackup provides information for patients and caregivers on <ext-link ext-link-type="uri" xlink:href="http://www.cancerbackup.org.uk/Cancertype/Childrenscancers/Typesofchildrenscancers/Acutelymphoblasticleukaemia">ALL in children</ext-link> and on <ext-link ext-link-type="uri" xlink:href="http://www.cancerbackup.org.uk/Treatments/Chemotherapy/Individualdrugs/Methotrexate">methotrexate</ext-link></p><p>• The US Leukemia and Lymphoma Society also provides information for patients and caregivers about <ext-link ext-link-type="uri" xlink:href="http://www.leukemia-lymphoma.org/all_page.adp?item_id=7049">ALL</ext-link></p><p>• The <ext-link ext-link-type="uri" xlink:href="http://www.ukccsg.org.uk/">Children's Cancer and Leukaemia Group</ext-link> (a UK charity) provides information for children with cancer and their families</p><p>• MedlinePlus provides additional information about <ext-link ext-link-type="uri" xlink:href="http://www.nlm.nih.gov/medlineplus/druginfo/medmaster/a682019.html">methotrexate</ext-link> (in English and Spanish)</p></sec></abstract><counts><page-count count="11"></page-count></counts><custom-meta-wrap><custom-meta><meta-name>citation</meta-name><meta-value>Sorich MJ, Pottier N, Pei D, Yang W, Kager L, et al. (2008) In vivo response to methotrexate forecasts outcome of acute lymphoblastic leukemia and has a distinct gene expression profile. PLoS Med 5(4): e83. doi:<ext-link ext-link-type="doi" xlink:href="10.1371/journal.pmed.0050083">10.1371/journal.pmed.0050083</ext-link></meta-value></custom-meta></custom-meta-wrap></article-meta></front><body><sec id="s1"><title>Introduction</title><p>Childhood acute lymphoblastic leukemia (ALL), the most common cancer in children, can now be cured in approximately 80% of patients [<xref ref-type="bibr" rid="pmed-0050083-b001">1</xref>,<xref ref-type="bibr" rid="pmed-0050083-b002">2</xref>]. Pharmacogenomics aims to elucidate the genomic determinants of treatment response and why treatment fails to cure the remaining 20% of patients, many of whom have favorable prognostic features [<xref ref-type="bibr" rid="pmed-0050083-b003">3</xref>,<xref ref-type="bibr" rid="pmed-0050083-b004">4</xref>]. Prior studies have provided insight into the genomic determinants of resistance to several antileukemic agents [<xref ref-type="bibr" rid="pmed-0050083-b005">5</xref>], but methodological constraints have precluded such genome-wide studies of in vitro methotrexate (MTX) resistance. This research gap is unfortunate because MTX is widely used in the treatment of many human cancers, including essentially all treatment protocols for newly diagnosed ALL [<xref ref-type="bibr" rid="pmed-0050083-b001">1</xref>].</p><p>The pharmacokinetics and pharmacodynamics of MTX in ALL cells are well understood, whereas the genomic determinants of the antileukemic effects of MTX remain to be elucidated [<xref ref-type="bibr" rid="pmed-0050083-b006">6</xref>,<xref ref-type="bibr" rid="pmed-0050083-b007">7</xref>]. Cellular uptake of MTX is mediated by the protein reduced folate carrier [<xref ref-type="bibr" rid="pmed-0050083-b008">8</xref>], whereas its efflux is mediated by ATP-binding cassette (ABC), subfamily C 1 (ABCC1) and ABCC4 [<xref ref-type="bibr" rid="pmed-0050083-b009">9</xref>–<xref ref-type="bibr" rid="pmed-0050083-b011">11</xref>]. MTX is a tight-binding inhibitor of its primary target, enzyme dihydrofolate reductase (DHFR), which disrupts cellular folate metabolism [<xref ref-type="bibr" rid="pmed-0050083-b012">12</xref>]. Within leukemic cells, MTX is metabolized into poly(γ-glutamate) forms (MTXPGs) by an adenosine triphosphate (ATP)-dependent reaction catalyzed by folylpolyglutamate synthetase [<xref ref-type="bibr" rid="pmed-0050083-b013">13</xref>]. Compared to their monoglutamate form, MTXPGs are retained longer in cells because they are not readily effluxed by ABC transporters [<xref ref-type="bibr" rid="pmed-0050083-b014">14</xref>,<xref ref-type="bibr" rid="pmed-0050083-b015">15</xref>]. MTXPGs are more potent inhibitors of other target enzymes such as thymidylate synthetase, glycinamide ribonucleotide transformylase, and aminoimidazole carboxamide transformylase. These enzymes are involved in biosynthetic pathways that are critical for DNA synthesis, DNA repair, and cell replication [<xref ref-type="bibr" rid="pmed-0050083-b014">14</xref>,<xref ref-type="bibr" rid="pmed-0050083-b015">15</xref>]. Furthermore, accumulation of MTXPG has also been shown to differ among major ALL subtypes [<xref ref-type="bibr" rid="pmed-0050083-b016">16</xref>] and to influence treatment response and outcome in childhood ALL [<xref ref-type="bibr" rid="pmed-0050083-b017">17</xref>–<xref ref-type="bibr" rid="pmed-0050083-b019">19</xref>].</p><p>A more complete understanding of the mechanisms of MTX resistance in ALL cells is needed if new treatment strategies are to be developed for patients whose leukemia is resistant to this important component of ALL chemotherapy [<xref ref-type="bibr" rid="pmed-0050083-b006">6</xref>]. Prior genomic studies of ALL chemotherapy resistance have not focused on MTX [<xref ref-type="bibr" rid="pmed-0050083-b005">5</xref>,<xref ref-type="bibr" rid="pmed-0050083-b020">20</xref>,<xref ref-type="bibr" rid="pmed-0050083-b021">21</xref>], because the resistance of primary ALL to MTX cannot be accurately measured by in vitro methods such as the MTT assay [<xref ref-type="bibr" rid="pmed-0050083-b022">22</xref>]. For this reason, we used the in vivo response of newly diagnosed patients to initial single-agent MTX treatment, measured as an initial decrease in circulating ALL cells, to quantitate the antileukemic effects of MTX. We then aimed to identify genes whose expression in primary ALL cells is significantly related to the in vivo antileukemic effects of MTX.</p></sec><sec sec-type="methods" id="s2"><title>Methods</title><sec id="s2a"><title>Patients and Genetic Characterization of Leukemia Cells</title><p>A total of 293 children aged 18 y or younger with newly diagnosed ALL, enrolled on the St. Jude Total Therapy XIII and XV protocols, were included in this study (<xref ref-type="fig" rid="pmed-0050083-g001">Figures 1</xref> and <xref ref-type="supplementary-material" rid="pmed-0050083-sg001">S1</xref>). The investigation was approved by the Institutional Review Board at St. Jude Children's Research Hospital, and signed informed consent was obtained from parents or legal guardians before enrollment. Patient characteristics (race, sex, age, pretreatment white blood cell count [WBC<sub>PRE</sub>], ALL subtype) were assigned by investigators at St. Jude Children's Research Hospital. Race, sex, and age were determined by questionnaire; WBC<sub>PRE</sub>, ALL subtype were determined according to the clinical protocol. The diagnosis of ALL was based on morphology, cytochemical staining, and immunophenotyping of blast cells for classification as B cell lineage or T cell lineage, as previously described [<xref ref-type="bibr" rid="pmed-0050083-b023">23</xref>–<xref ref-type="bibr" rid="pmed-0050083-b028">28</xref>]. The only patients excluded were those who did not have a diagnosis of ALL, or were aged < 1 y or > 18 y, or were given ALL treatment prior to referral to SJCRH.</p><fig id="pmed-0050083-g001" position="float"><label>Figure 1</label><caption><title>CONSORT Flow Chart Describing Patients Enrolled in Randomized Clinical Trials at St. Jude Children's Research Hospital, from Which the Current Study Population Was Derived</title><p>The flow chart includes study relevant protocol information for the St. Jude Children's Research Hospital Total Therapy Protocols XIIIA, XIIIB, and XV. Specifically, from the population that received ALL treatment according to one of these three protocols, the current study included only patients who received HDMTX as initial therapy. These protocols included a randomization to determine whether patients received HDMTX or not as initial treatment, the infusion time of HDMTX, and whether MP was given after MTX (LDMTX, low-dose methotrexate). Patients with an insufficient number of ALL cells for gene expression analysis were excluded, as were patients with insufficient data on circulating ALL cells to assess response over 3 d.</p></caption><graphic xlink:href="pmed.0050083.g001"></graphic></fig><p>After stratification for age, WBC<sub>PRE</sub>, immunophenotype, and sex, patients were randomized to receive initial intravenous treatments of high-dose MTX (HDMTX: 1 g/m<sup>2</sup>) either as HDMTX4H (HDMTX by infusion over 4 h; <italic>n</italic> = 108), as HDMTX24H (HDMTX by infusion over 24 h; <italic>n</italic> = 125), or as HDMTX24H+MP (HDMTX by infusion over 24 h plus mercaptopurine [MP] 1 g/m<sup>2</sup> by intravenous injection; <italic>n</italic> = 60). All patients who received allopurinol less than 72 h prior to HDMTX were excluded from the analyses because of potential effects on de novo purine synthesis.</p><p>Circulating leukemia cells were measured before therapy (WBC<sub>PRE</sub>) and at day 3 following start of HDMTX treatment (WBC<sub>Day3</sub>), prior to the administration of other antileukemic agents. Leukocyte counts were determined with a Coulter counter (model F_STKR; Coulter, Hialeah, Florida, United States).</p></sec><sec id="s2b"><title>Isolation of ALL Blasts from Bone Marrow Aspirate</title><p>ALL blasts were obtained from bone marrow aspirates at diagnosis and 42–44 h following treatment. Samples consisted of 5–10 ml of bone marrow collected in syringes containing 800 units of heparin and kept on ice until processed. Leukemic cells were obtained by density separation over a Ficoll-Hypaque gradient and washed three times with a solution of HEPES, Hanks buffered solution, and heparin, as previously described in detail [<xref ref-type="bibr" rid="pmed-0050083-b018">18</xref>].</p></sec><sec id="s2c"><title>RNA Extraction and Gene Expression Profiling of Diagnostic Bone Marrow ALL Cells</title><p>Of the 293 patients treated with up-front HDMTX, 161 had sufficient diagnostic ALL cells for gene expression analysis (i.e., had sufficient leukemia cells in their diagnostic bone marrow aspirates to permit RNA isolation from 5 × 10<sup>6</sup> to 1 × 10<sup>7</sup> ALL cells). High-quality total RNA was extracted with TriReagent (MRC, Cincinnati, Ohio, United States) from cryopreserved mononuclear cell suspensions from bone marrows at diagnosis. Total RNA was processed and hybridized to the HG-U133A oligonucleotide microarray (Affymetrix, Santa Clara, California, United States). This array contains 22,215 gene probe sets, representing approximately 12,357 human genes, plus approximately 3,820 expressed sequence tag clones with unknown function [<xref ref-type="bibr" rid="pmed-0050083-b029">29</xref>]. Following removal of probe sets with >95% absent calls, 13,488 probe sets remained. Scaled expression values of all probe sets were logarithmically transformed to stabilize variance.</p><p>Additional information on the microarray methods and results can be found at <ext-link ext-link-type="uri" xlink:href="http://www.stjuderesearch.org/data/">http://www.stjuderesearch.org/data/</ext-link>.</p></sec><sec id="s2d"><title>Determination of MTX Polyglutamylated Metabolites in Bone Marrow ALL Cells 42–44 Hours Post-treatment</title><p>Intracellular MTXPGs were extracted from 42- to 44-h post-treatment bone marrow ALL cells kept in a buffered solution (Tris, EDTA, and 2-mercaptoethanol [pH 7.8]) by first boiling (100 °C for 10 min), then stored frozen at −80 °C until analysis. The HPLC separation and the radioenzymatic quantitation of MTX and six polyglutamylated metabolites (MTXPG<sub>2–7</sub>) were performed as previously described [<xref ref-type="bibr" rid="pmed-0050083-b030">30</xref>,<xref ref-type="bibr" rid="pmed-0050083-b031">31</xref>]. These MTXPG measurements were available for 230 patients. All results were expressed as picomoles of MTX or MTXPG per 10<sup>9</sup> cells.</p></sec><sec id="s2e"><title>Statistical Analysis and Bioinformatics</title><p>MTX responses as measured by WBC<sub>Day3</sub>, WBC<sub>PRE</sub>, and MTXPG values were logarithmically transformed to normalize their respective distributions. The Pearson correlation test was applied in order to determine the association between WBC<sub>Day3</sub> and ALL subtype, MTXPG, and WBC<sub>PRE</sub>. The difference between WBC<sub>PRE</sub> and WBC<sub>Day3</sub>, WBC<sub>ΔDay3</sub> (the WBC residual based on the linear regression of log[WBC<sub>PRE</sub>] change to log[WBC<sub>Day3</sub>] ) was determined by taking the residuals of the linear regression model of WBC<sub>Day3</sub> versus WBC<sub>PRE</sub>, which was available for 293 patients. Specifically, MTX response is defined as:
<disp-formula id="pmed-0050083-e001"><graphic xlink:href="pmed.0050083.e001.jpg" position="anchor" mimetype="image"></graphic></disp-formula></p><p>We indicated “MTX poor response” and “MTX good response” in <xref ref-type="fig" rid="pmed-0050083-g002">Figures 2</xref> and <xref ref-type="fig" rid="pmed-0050083-g003">3</xref> according to the cutoff for good responders (WBC<sub>ΔDay3</sub> < −0.14) and poor responders (WBC<sub>ΔDay3</sub> > 0.14), based on the bottom and top quartile of 293 patients.</p><fig id="pmed-0050083-g002" position="float"><label>Figure 2</label><caption><title>Scatterplot of WBC<sub>Day3</sub> versus WBC<sub>PRE</sub></title><p>This plot illustrates the leukemia cell count on day 3 (WBC<sub>Day3</sub>) after initial HDMTX treatment versus the pretreatment leukemia cell count (WBC<sub>PRE</sub>) at diagnosis in 293 patients. The solid line indicates the linear regression, and the dotted line the 95% confidence interval with <italic>p</italic> < 0.0001, <italic>r</italic> = 0.76, Spearman rank correlation, and <italic>p</italic> < 0.0001, <italic>r</italic> = 0.79, Pearson correlation.</p></caption><graphic xlink:href="pmed.0050083.g002"></graphic></fig><fig id="pmed-0050083-g003" position="float"><label>Figure 3</label><caption><title>Hierarchical Clustering of Genes Discriminating MTX Response (WBC<sub>ΔDay3</sub>)</title><p>Hierarchical clustering using the top 50 most discriminant gene probe sets (<xref ref-type="supplementary-material" rid="pmed-0050083-st003">Table S3</xref>) discriminating MTX response in 161 patients. Each column represents an ALL sample labeled with red circles for MTX poor responders (<italic>n</italic> = 40, top quartile of WBC<sub>ΔDay3</sub>) and with green circles for MTX good responders (<italic>n</italic> = 40, bottom quartile of WBC<sub>ΔDay3</sub>). Unlabeled patients are intermediate MTX responders. Each row represents a probe set labeled with the gene symbol. The “heat map” indicates high (red) or low (green) level of expression according to the scale shown.</p></caption><graphic xlink:href="pmed.0050083.g003"></graphic></fig><p>Data were available for 161 patients on both WBC<sub>ΔDay3</sub> and gene expression in diagnostic bone marrow leukemia cells (<xref ref-type="supplementary-material" rid="pmed-0050083-sg001">Figure S1</xref>). The association between each individual probe set and WBC<sub>ΔDay3</sub> was determined using the Pearson correlation test. Gene probe sets were rank-ordered by their <italic>p</italic>-values. Final gene probe sets were selected based on a false discovery rate (FDR) cutoff of 1.5%.</p><p>For each patient, we computed a gene expression profile using the weighted average of the expression signals of top selected genes. The Pearson correlation coefficient between each gene's expression and WBC<sub>ΔDay3</sub> was used as the weight. This weighted average of expression signals was used as the summary of the top gene expression profile for each patient. Specifically, the gene expression profile was computed according to the following formula:
<disp-formula id="pmed-0050083-e002"><graphic xlink:href="pmed.0050083.e002.jpg" position="anchor" mimetype="image"></graphic></disp-formula></p><p>Where <italic>j</italic> = 1 … 50 top selected gene probe sets as listed in <xref ref-type="supplementary-material" rid="pmed-0050083-st003">Table S3</xref>, and weights were determined as the correlation coefficients as listed in the same table.</p><p>We compared the top 50 gene profile with the top 100 gene profile; the correlation coefficient was 0.989 (<italic>p</italic> < 0.001, Pearson correlation test). These tests were performed using standard statistical functions in R software.</p><p>We tested 37 GenMAPP pathways and 319 Gene Ontology–biological process (GO–BP) gene groupings for association with WBC<sub>ΔDay3</sub> using the “globaltest” method [<xref ref-type="bibr" rid="pmed-0050083-b032">32</xref>] implemented in the R Bioconductor package [<xref ref-type="bibr" rid="pmed-0050083-b033">33</xref>]. This test was used to infer over-representation of specific biological pathways, and the “geneplot” function was applied to plot the importance of selected genes with default parameters. Multiple testing was adjusted using the Bonferroni method and the FDR according to Storey and Tibshirani [<xref ref-type="bibr" rid="pmed-0050083-b034">34</xref>].</p></sec><sec id="s2f"><title>Cell Cycle Distribution</title><p>The percentage of ALL cells in S-phase was determined in diagnostic bone marrow aspirates from patients for whom an adequate number of cells were available (<italic>n</italic> = 154). Propidium iodide–stained DNA content was measured by flow cytometry using the Coulter EPICS V flow cytometer (Coulter Electronics, Hialeah, Florida, United States), and the computer program ModFit (Verity Software House, Verity, Topsham, Maine, United States) was used to calculate the percentages of cells in G<sub>0</sub>/G<sub>1</sub>, S, and G<sub>2</sub>/M phase.</p></sec><sec id="s2g"><title>Treatment Outcome Analysis</title><p>The duration of disease-free survival (DFS) was defined as the time from diagnosis until the date of leukemia relapse (event), or the last follow-up (censored). Second malignancies and death due to other reasons were censored at the time of occurrence. Treatment outcome was available in 136 patients of the 293 patients treated with HDMTX with WBC<sub>PRE</sub> and WBC<sub>Day3</sub> measured. Of note, patients treated on protocol T15 were excluded because of short follow-up. Time was also censored at the last follow-up date if no failure was observed. Single-variable analysis using Cox proportional hazards regression, as modified by Fine and Gray [<xref ref-type="bibr" rid="pmed-0050083-b035">35</xref>] was used to estimate the relative risk of an event. Significant associations from the single variable analyses were further evaluated in a multiple variable analysis, which included risk classification, age, lineage, and ALL subtype in addition to WBC<sub>ΔDay3</sub> and the top 50 gene expression profile. DFS curves were calculated by reversing the cumulative incidence curve, where MTX poor responders represent the top quartile, intermediate responders the middle two quartiles, and good responders the bottom quartile.</p></sec></sec><sec id="s3"><title>Results</title><sec id="s3a"><title>Relation among WBC<sub>Day3,</sub> WBC<sub>PRE</sub>, Treatment, ALL Subtype, and MTX Metabolism</title><p>Patient characteristics (race, sex, age, WBC<sub>PRE</sub>, ALL subtype) were similar among patients randomly assigned to receive HDMTX4H, HDMTX24H, or HDMTX24H+MP (<italic>p</italic> > 0.13, <xref ref-type="supplementary-material" rid="pmed-0050083-sg002">Figure S2</xref>). Furthermore, there was no difference in WBC<sub>Day3</sub> among the three randomized treatment groups (<xref ref-type="table" rid="pmed-0050083-t001">Table 1</xref>). The lack of differences among treatment groups coupled with our previous findings of minimal de novo purine synthesis (DNPS) inhibition and antileukemic effects of a single dose of intravenous MP [<xref ref-type="bibr" rid="pmed-0050083-b036">36</xref>], allowed us to analyze patients treated with HDMTX4H, HDMTX24H, and HDMTX24H+MP as a single group, to enhance the statistical power of our analyses.</p><table-wrap id="pmed-0050083-t001" content-type="2col" position="float"><label>Table 1</label><caption><p>Patient Characteristics Were Not Different among the HDMTX Treatment Groups</p></caption><graphic xlink:href="pmed.0050083.t001"></graphic></table-wrap><p>WBC<sub>Day3</sub> was significantly lower than WBC<sub>PRE</sub> (<italic>n</italic> = 293, <italic>p</italic> < 0.0001) and was highly correlated with WBC<sub>PRE</sub> (<italic>p</italic> < 0.0001, <italic>r</italic> = 0.79, <xref ref-type="fig" rid="pmed-0050083-g002">Figure 2</xref>). Therefore, to remove the effect of the pretreatment leukemia burden (WBC<sub>PRE</sub>) we used the WBC<sub>ΔDay3</sub> corresponding to the residuals from the linear regression model of WBC<sub>Day3</sub> versus WBC<sub>PRE</sub> (<xref ref-type="fig" rid="pmed-0050083-g002">Figure 2</xref>). As indicated in <xref ref-type="supplementary-material" rid="pmed-0050083-sg003">Figure S3</xref>, the histogram of the residuals approximates a normal distribution, in contrast to the skewed distribution of percentage drop in WBC count from diagnosis to day 3 (WBC<sub>%drop</sub>; <italic>p</italic> = 0.1 versus <italic>p</italic> < 0.001, Kolmogorov-Smirnov test). There was a statistically significant association between WBC<sub>ΔDay3</sub> and polyglutamylated MTX levels (MTXPG<sub>2–7</sub>) in ALL cells (<italic>n</italic> = 230, <italic>p</italic> = 0.0001, <italic>r</italic> = −0.25, <xref ref-type="fig" rid="pmed-0050083-g004">Figure 4</xref>), with higher MTXPG<sub>2–7</sub> associated with greater antileukemic effect. There was not a significant relation between WBC<sub>ΔDay3</sub> and ALL subtype (<italic>n</italic> = 293, <italic>p</italic> = 0.07).</p><fig id="pmed-0050083-g004" position="float"><label>Figure 4</label><caption><title>Scatterplot of WBC<sub>ΔDay3</sub> Versus Level of Total MTXPGs</title><p>There is a significant correlation of WBC<sub>ΔDay3</sub> with the total MTXPG level in ALL cells from 230 patients (i.e., a higher total MTXPG concentration is associated with a better in vivo MTX response) (<italic>p</italic> = 0.0001, <italic>r</italic> = −0.25, Pearson correlation).</p></caption><graphic xlink:href="pmed.0050083.g004"></graphic></fig></sec><sec id="s3b"><title>Relation among WBC<sub>ΔDay3</sub>, Gene Expression, and Pathway Analysis</title><p>Our analyses of antileukemic effects after in vivo MTX treatment and gene expression in pretreatment ALL cells identified the 50 most significant gene probe sets that were associated with antileukemic effect of MTX (WBC<sub>ΔDay3</sub>, <xref ref-type="fig" rid="pmed-0050083-g003">Figure 3</xref>). The FDR was less than 1.5% for these gene probe sets, and each gene had a Pearson correlation coefficient higher than 0.3 or lower than −0.3 and a <italic>p</italic>-value less than 0.001. Among these genes, the expression patterns for 21 were positively and 29 were negatively related to MTX response. Genes significantly associated with MTX response included those involved in nucleotide metabolism (<italic>thymidylate synthetase</italic> [<italic>TYMS</italic>] and <italic>CTP synthase</italic> [<italic>CTPS</italic>]), cell proliferation and apoptosis (<italic>B-cell CLL/lymphoma 3</italic> [<italic>BCL3</italic>], <italic>centromere protein F</italic> [<italic>CENPF</italic>], <italic>cell division cycle 20</italic> [<italic>CDC20</italic>], <italic>abnormal spindle–like</italic> [<italic>ASPM</italic>], <italic>transforming, acidic coiled-coil containing protein 1</italic> [<italic>TACC1</italic>], and <italic>Fas apoptotic inhibitory molecule 3</italic> [<italic>FAIM3</italic>]), and several genes implicated in DNA replication and repair (<italic>DNA polymerase delta, subunit 3</italic> [<italic>POLD3</italic>], <italic>replication protein A3</italic> [<italic>RPA3</italic>], <italic>ribonuclease H2, subunit A</italic> [<italic>RNASEH2A</italic>], <italic>GINS complex subunit 2</italic> [<italic>GINS2</italic>], <italic>ribonucleotide reductase M1</italic> [<italic>RRM1</italic>], <italic>H2A histone family, member X</italic> [<italic>H2AFX</italic>], and <italic>flap structure-specific endonuclease 1</italic> [<italic>FEN1</italic>]).</p><p>To gain more insight into the molecular and cellular pathways related to MTX response, the global test analysis was used to determine whether the gene expression profile of different pathways retrieved from the GO–BP or GenMAPP database, were significantly associated with the antileukemic effect of MTX. As listed in <xref ref-type="supplementary-material" rid="pmed-0050083-st001">Table S1</xref>, a significant association was found between WBC<sub>ΔDay3</sub> and various biological pathways including those involved in cell cycle regulation, DNA repair and replication, or nucleotide metabolism. To further illustrate the influence of individual genes on the antileukemic effects of MTX within the nucleotide biosynthesis pathway, the “gene plot” output was used. As depicted in <xref ref-type="supplementary-material" rid="pmed-0050083-sg004">Figure S4</xref>, three (<italic>TYMS, DHFR,</italic> and <italic>CTPS</italic>) of the ten genes belonging to the nucleotide biosynthesis pathway were most strongly negatively associated with the MTX antileukemic effect (WBC<sub>ΔDay3</sub>).</p></sec><sec id="s3c"><title>Cellular Proliferation and MTX In Vivo Response</title><p>We were able to determine both the percentage of cells in S-phase of the cell cycle and gene expression in 154 patients (these are by ALL subtype: B-lineage hyperdiploid, <italic>n</italic> = 40; B-lineage other, <italic>n</italic> = 47; <italic>E2A-PBX1</italic>, <italic>n</italic> = 14; T-ALL, <italic>n</italic> = 21; <italic>TEL-AML1</italic>, <italic>n</italic> = 32; <italic>BCR-ABL</italic>, <italic>n</italic> = 4; <italic>MLL-AF4</italic>, <italic>n</italic> = 2). There were no significant differences in the percentage of cells in S-phase among different ALL subtypes (<italic>p</italic> = 0.10, Kruskal-Wallis test). The percentage of cells in S-phase of the cell cycle was positively correlated with expression of genes involved in nucleotide biosynthesis <italic>TYMS</italic> (<italic>r</italic> = 0.59, <italic>p</italic> < 0.001), <italic>DHFR</italic> (<italic>r</italic> = 0.39, <italic>p</italic> < 0.001), and <italic>CTPS</italic> (<italic>r</italic> = 0.21, <italic>p</italic> = 0.009) (<xref ref-type="supplementary-material" rid="pmed-0050083-sg005">Figure S5</xref>A, <xref ref-type="table" rid="pmed-0050083-t002">Table 2</xref>), with expression of <italic>TYMS</italic> being the best marker of cell proliferation. The percentage of cells in S-phase was significantly correlated with MTX response measured as WBC<sub>ΔDay3</sub>, with the higher percentage in S-phase associated with a better response (WBC<sub>ΔDay3</sub>, <italic>r</italic> = −0.20, <italic>p</italic> = 0.013; <xref ref-type="supplementary-material" rid="pmed-0050083-sg005">Figure S5</xref>B, <xref ref-type="table" rid="pmed-0050083-t002">Table 2</xref>). The association with percentage S-phase was similar to the top 50 gene expression profile (<italic>r</italic> = −0.57 <italic>p</italic> < 0.001; <xref ref-type="supplementary-material" rid="pmed-0050083-sg005">Figure S5</xref>C, <xref ref-type="table" rid="pmed-0050083-t002">Table 2</xref>). In contrast, the percent drop in WBC count was not significantly related to percentage of cells in S-phase (WBC<sub>%drop</sub>, <italic>r</italic> = 0.034, <italic>p</italic> = 0.67; <xref ref-type="table" rid="pmed-0050083-t002">Table 2</xref>).</p><table-wrap id="pmed-0050083-t002" content-type="1col" position="float"><label>Table 2</label><caption><p>Pearson Correlation of Selected Biological and Response Parameters with Percentage of Cells in S-Phase</p></caption><graphic xlink:href="pmed.0050083.t002"></graphic></table-wrap></sec><sec id="s3d"><title>Relation between Disease-Free Survival and Expression of <italic>CTPS, TYMS</italic>, <italic>DHFR,</italic> MTX Response (WBC<sub>%drop</sub> and WBC<sub>ΔDay3</sub>), and Top 50 Gene Expression Profile</title><p>The median follow-up of patients for this analysis was 9.1 y from diagnosis, comprising patients enrolled in St. Jude Total Therapy XIII protocol (<italic>n</italic> = 136). WBC<sub>ΔDay3</sub>, and <italic>TYMS</italic> and <italic>DHFR</italic> expression, were related to DFS according to Cox proportional hazards regression analyses that compared patients who remained in continuous complete remission with those who relapsed during the follow-up period. The univariable analysis of variables potentially related to DFS revealed significance for expression of <italic>TYMS</italic> (hazard ratio [HR] = 0.6; <italic>p</italic> = 0.008), expression of <italic>DHFR</italic> (HR = 0.41; <italic>p</italic> = 0.015); MTX response (WBC<sub>ΔDay3</sub>, HR = 21.5; <italic>p</italic> < 0.001), and the top 50 gene expression profile (HR = 1.09; <italic>p</italic> = 0.02; <xref ref-type="table" rid="pmed-0050083-t003">Table 3</xref>), but not for the expression of <italic>CTPS</italic> or the WBC<sub>%drop</sub>.</p><table-wrap id="pmed-0050083-t003" content-type="1col" position="float"><label>Table 3</label><caption><p>Univariable Hazard Analysis of the Risk of Relapse with Variables Related to Initial In Vivo MTX Response and Multivariable Cox Proportional Hazard Analyses Each Including Known Prognostic Factors (i.e., ALL Subtype, Age at Diagnosis, Risk Group)</p></caption><graphic xlink:href="pmed.0050083.t003"></graphic></table-wrap><p>Patients with the best MTX response (i.e., bottom quartile of the residual WBC<sub>ΔDay3</sub>) had significantly better 5-y DFS compared to patients with the worst response (top quartile) (DFS ± SE 96.9% ± 3.1% versus 81.4% ± 7%, <italic>p</italic> = 0.033 for quartiles compared; <xref ref-type="fig" rid="pmed-0050083-g005">Figure 5</xref>A). Likewise, patients with a gene expression profile indicative of a good MTX response (bottom quartile of the gene expression profile) had significantly better 5-y DFS compared to patients with a gene expression profile indicative of poor MTX response (top quartile of the gene expression profile) (5-y DFS ± variance: 87.5% ± 6.9% versus 72% ± 9.2%, <italic>p</italic> = 0.019 for quartiles compared; <xref ref-type="fig" rid="pmed-0050083-g005">Figure 5</xref>B). Patients with higher <italic>TYMS</italic> or <italic>DHFR</italic> expression (i.e., top quartile of the expression level) had significantly better 5-y DFS compared to patients with lower <italic>TYMS</italic> or <italic>DHFR</italic> expression (i.e., bottom quartile of the expression level) (5-y DFS ± variance: 87.9% ± 5.8% versus 72.7% ± 7.9%, <italic>p</italic> = 0.024 for <italic>TYMS</italic> and 93.5% ± 4.5% versus 69% ± 8.1%, <italic>p</italic> = 0.041 for <italic>DHFR</italic> for quartiles compared; <xref ref-type="fig" rid="pmed-0050083-g005">Figure 5</xref>C).</p><fig id="pmed-0050083-g005" position="float"><label>Figure 5</label><caption><title>Kaplan-Meier Plots of Disease Relapse Categorized by WBC<sub>ΔDay3</sub>, <italic>TYMS</italic>, and Top 50 Gene Profile</title><p>(A) MTX response is categorized by WBC<sub>ΔDay3</sub> MTX good responders (i.e., bottom quartile, <italic>n</italic> = 28), intermediate (<italic>n</italic> = 67), and poor (i.e., top quartile, <italic>n</italic> = 41) among 293 patients.</p><p>(B) Top 50 gene expression profile is categorized by top 25% (gene profile for good responder, <italic>n</italic> = 20), intermediate (<italic>n</italic> = 53), and bottom 25% (gene profile for poor responder, <italic>n</italic> = 19).</p><p>(C) Proliferation index is categorized by <italic>TYMS</italic> expression top 25% (high proliferation index, <italic>n</italic> = 27), intermediate (<italic>n</italic> = 47) and bottom 25% (low proliferation index, <italic>n</italic> = 18). For <italic>TYMS</italic> and top 50 gene profile, categorization was done among the 161 patients who had ALL cell gene expression data available.</p></caption><graphic xlink:href="pmed.0050083.g005"></graphic></fig><p>Furthermore, multivariable Cox regression analysis (<xref ref-type="table" rid="pmed-0050083-t003">Tables 3</xref> and <xref ref-type="supplementary-material" rid="pmed-0050083-sg002">S2</xref>) that also included the conventional National Cancer Institute ALL risk criteria (i.e., ALL subtype, age, and WBC at diagnosis) revealed significance for MTX response (WBC<sub>ΔDay3</sub>, HR = 22.6; <italic>p</italic> = 0.0046), and the expression of <italic>TYMS</italic> (HR = 0.58; <italic>p</italic> = 0.044) and <italic>DHFR</italic> (HR = 0.31; <italic>p</italic> = 0.019). The top 50 gene expression profile did not reach statistical significance in predicting relapse in the overall population when other known risk factors were included, although the trend remained evident (<italic>p</italic> = 0.08, <xref ref-type="table" rid="pmed-0050083-t003">Tables 3</xref> and <xref ref-type="supplementary-material" rid="pmed-0050083-sg002">S2</xref>).</p></sec><sec id="s3e"><title>Discrimination of MTX Response Using the Top 50 Gene Expression Profile and Assessment in an Independent Validation Cohort</title><p>In an independent test set of 18 additional patients who received initial HDMTX according to the St. Jude Total Therapy XV protocol, we performed gene expression analysis at diagnosis and determined WBC (ALL cell) count at diagnosis and on day 3. The gene expression profile of the top 50 genes was significantly related to the residual WBC<sub>ΔDay3</sub> in this patient cohort (top 50 gene profile, <italic>p</italic> = 0.0065, <italic>r</italic> = 0.62, Pearson correlation; <xref ref-type="fig" rid="pmed-0050083-g006">Figure 6</xref>A), thus validating the gene expression profile as predictive of the MTX response in an independent cohort of patients.</p><fig id="pmed-0050083-g006" position="float"><label>Figure 6</label><caption><title>Model Performance in an Independent Test Set of Patients</title><p>Relation between the in vivo MTX response (WBC<sub>ΔDay3</sub>) and top 50 gene expression profile (<italic>p</italic> = 0.0065, <italic>r</italic> = 0.62, Pearson correlation) for the independent validation cohort (<italic>n</italic> = 18). Relation between (B) the predicted log(WBC<sub>Day3</sub>) using either the linear model function or (C) the median percentage drop determined in 293 patients (mean difference = 0.1812946. <italic>p</italic> = 0.0025, paired <italic>t</italic>-test). The regression lines in graphs (B) and (C) are based on intercept equal to zero and slope equal to one.</p></caption><graphic xlink:href="pmed.0050083.g006"></graphic></fig><p>Additionally, we predicted the WBC<sub>Day3</sub> after initial MTX treatment based on the known WBC<sub>PRE</sub> in these 18 newly enrolled patients. For that, we used either the WBC<sub>ΔDay3</sub> linear regression function or the median WBC<sub>%drop</sub> developed in the original test cohort of 293 patients. The sum of the differences between the observed and the predicted WBC<sub>Day3</sub> squared was 1.042 using the WBC<sub>ΔDay3</sub> linear regression model and 3.35 using the median WBC<sub>%drop</sub>. The observed WBC<sub>Day3</sub> values are significantly closer to the predicted values using WBC<sub>ΔDay3</sub> (<xref ref-type="fig" rid="pmed-0050083-g006">Figure 6</xref>B) than those based on WBC<sub>%drop</sub> (<xref ref-type="fig" rid="pmed-0050083-g006">Figure 6</xref>C; <italic>p</italic> = 0.0025, paired <italic>t</italic>-test), thereby further indicating that WBC<sub>ΔDay3</sub> is a more accurate measure of in vivo MTX response than WBC<sub>%drop</sub>.</p></sec></sec><sec id="s4"><title>Discussion</title><p>The current studies have identified genes that are expressed at a significantly different level in acute lymphoblastic leukemia cells of patients who exhibit a poor in vivo response to HDMTX. High-throughput genomic approaches to assess the expression levels of RNA transcripts in cancer cells are providing new insights into pathogenesis, classification, diagnosis, stratification, and prognosis of many human cancers [<xref ref-type="bibr" rid="pmed-0050083-b023">23</xref>,<xref ref-type="bibr" rid="pmed-0050083-b037">37</xref>–<xref ref-type="bibr" rid="pmed-0050083-b039">39</xref>]. The drug resistance and gene expression profiles of leukemia cells have also been used to identify genes related to the sensitivity of ALL cells to several antileukemic agents and to forecast differences in treatment response [<xref ref-type="bibr" rid="pmed-0050083-b005">5</xref>,<xref ref-type="bibr" rid="pmed-0050083-b020">20</xref>,<xref ref-type="bibr" rid="pmed-0050083-b021">21</xref>]. These findings have also revealed novel targets for the discovery of new agents to reverse drug resistance, such as our prior discovery of <italic>MCL1</italic> overexpression in glucocorticoid-resistant ALL [<xref ref-type="bibr" rid="pmed-0050083-b005">5</xref>], and the subsequent discovery that rapamycin can down-regulate <italic>MCL1</italic> expression and increase sensitivity of leukemia cells to dexamethasone [<xref ref-type="bibr" rid="pmed-0050083-b037">37</xref>,<xref ref-type="bibr" rid="pmed-0050083-b040">40</xref>]. However, prior to the current study, there has not been a comprehensive analysis of genes related to the antileukemic effects of MTX in primary leukemia cells.</p><p>We therefore evaluated MTX response in vivo after initial therapy, because this is the only possible time to assess the antileukemic effects of MTX as a single agent in patients and because there are no reliable in vitro methods. Thus, our study focused on treatment-naive ALL, and assessed de novo resistance. This revealed that WBC<sub>ΔDay3</sub> is a superior measure of in vivo MTX response when compared to the percentage drop in leukemia cells (i.e., WBC<sub>%drop</sub>), and that WBC<sub>ΔDay3</sub> was predictive of long-term DFS. Furthermore, the difference in survival cannot simply be explained by differences in MTX systemic exposure (<xref ref-type="supplementary-material" rid="pmed-0050083-sg006">Figure S6</xref>).</p><p>To better understand the biological basis underlying MTX response in ALL cells, we used an unbiased genome-wide approach to identify genes whose expression in primary leukemia cells in vivo was significantly related to WBC<sub>ΔDay3</sub>. This process revealed 48 genes and two cDNA clones that are highly related to the in vivo MTX response (WBC<sub>ΔDay3</sub>), even after adjusting for MTXPG accumulation (<italic>n</italic> = 230) (<xref ref-type="supplementary-material" rid="pmed-0050083-st003">Table S3</xref>). Among those genes significantly associated with MTX response were genes involved in nucleotide metabolism (<italic>TYMS</italic> and <italic>CTPS</italic>), cell proliferation and apoptosis (<italic>BCL3, CDC20, CENPF,</italic> and <italic>FAIM3</italic>), and DNA replication or repair (<italic>POLD3, RPA3, RNASEH2A, RPM1,</italic> and <italic>H2AFX</italic>). The antileukemic effects of MTX involve inhibition of purine and pyrimidine synthesis, and the current findings indicate that interindividual differences in nucleotide synthesis influence the in vivo antileukemic effects of MTX. This finding was confirmed by a global test analysis that identified the nucleotide biosynthesis pathway as one of the most discriminating biological pathways related to MTX response. Significance of the global test was largely explained by three key genes (<italic>TYMS</italic>, <italic>DHFR</italic>, and <italic>CTPS</italic>) belonging to the nucleotide biosynthesis pathway.</p><p>Our analysis also showed that low expression of <italic>DHFR, TYMS,</italic> and <italic>CTPS</italic> was significantly correlated with poor in vivo MTX response [<xref ref-type="bibr" rid="pmed-0050083-b006">6</xref>,<xref ref-type="bibr" rid="pmed-0050083-b041">41</xref>,<xref ref-type="bibr" rid="pmed-0050083-b042">42</xref>]. It has been shown that <italic>DHFR, TYMS,</italic> and <italic>CTPS</italic> expression is associated with critical biological processes such as DNA synthesis and cell proliferation [<xref ref-type="bibr" rid="pmed-0050083-b043">43</xref>,<xref ref-type="bibr" rid="pmed-0050083-b044">44</xref>], a finding consistent with low expression of these genes reflecting a decrease in the number of ALL cells in S-phase. As MTX selectively affects cells in the S-phase of the cell cycle [<xref ref-type="bibr" rid="pmed-0050083-b007">7</xref>,<xref ref-type="bibr" rid="pmed-0050083-b044">44</xref>,<xref ref-type="bibr" rid="pmed-0050083-b045">45</xref>], it is likely that low expression of these genes explains the observed association with MTX response. To support this hypothesis, we showed that the percentage of leukemia cells in the S-phase was strongly correlated with <italic>DHFR</italic>, <italic>TYMS</italic>, and <italic>CTPS</italic> expression and with the MTX in vivo response (<xref ref-type="supplementary-material" rid="pmed-0050083-sg005">Figure S5</xref>, <xref ref-type="table" rid="pmed-0050083-t002">Table 2</xref>). This finding does not preclude the possibility that genetically determined high <italic>TYMS</italic> expression in ALL cells is associated with a worse prognosis in ALL, as we have previously reported [<xref ref-type="bibr" rid="pmed-0050083-b046">46</xref>]. High <italic>TYMS</italic> expression in the current study was related to higher cell proliferation, whereas higher constitutive <italic>TYMS</italic> expression is due to a genetic polymorphism in the <italic>TYMS</italic> promoter region. After remission is achieved, higher <italic>TYMS</italic> expression due to the promoter polymorphism would connote a worse prognosis due to higher levels of the MTX target, thymidylate synthase, independent of cellular proliferation rates.</p><p>Our current data showed that low cell proliferation levels, in addition to our measure of in vivo MTX response, is an important ALL cell characteristic related to worse outcome. This result is in agreement with those of a previous study that found treatment-naïve blasts with a low proliferation rate are more resistant to several anticancer drugs in vitro [<xref ref-type="bibr" rid="pmed-0050083-b047">47</xref>]. In the current study, the gene expression profile predicting MTX response was not associated with overall disease outcome after adjusting for other known prognostic variables in the entire study population (<italic>p</italic> = 0.08), but was significantly related to DFS within high-risk patients (<italic>p</italic> = 0.014). Leukemia cells of patients with high-risk ALL may intrinsically have a higher potential for poor MTX response (e.g., because of oncogenic gene fusions), in contrast to lower-risk patients whose ALL cells may acquire resistance mechanisms during the 2–3 y of therapy. Further, it is possible that patients with high-risk leukemia may be more prone to acquire resistance during therapy for various reasons (e.g., greater genetic instability in their ALL cells).</p><p>Interestingly, other known folate metabolism genes were not among the top genes, suggesting that expression of the known folate metabolism genes in pretreatment ALL cells is less important in causing de novo MTX resistance than previously thought. It may well be that these folate pathway genes are important for the acquired drug resistance that emerges during MTX treatment. It is also plausible that expression or function of these proteins is not reflected by the level of their mRNA expression in ALL cells. These possibilities merit further investigation, which is beyond the scope of the current work.</p><p>Defining the genomic determinants of ALL resistance to individual antileukemic agents is essential if the pharmacogenomics of drug resistance are to be elucidated, because the current and prior studies have shown that genes discriminating drug resistance in ALL are drug specific [<xref ref-type="bibr" rid="pmed-0050083-b005">5</xref>,<xref ref-type="bibr" rid="pmed-0050083-b048">48</xref>]. To assess whether the genes we identified as related to de novo MTX resistance reflect a global resistance phenotype versus a MTX-specific effect, we compared the previously reported gene expression profiles for ALL resistance to PVAD (prednisone, vincristine, asparaginase, and daunorubicin), with the top 50 genes discriminating MTX response in the current study. This comparison revealed no overlap in the genes related to MTX resistance and the 124 genes related to prednisone, vincristine, asparaginase or daunorubicin PVAD resistance [<xref ref-type="bibr" rid="pmed-0050083-b005">5</xref>]. This result indicates that genes identified in the current study are not a marker of general drug resistance or a global predictor of survival, rather they are specific to MTX (or perhaps other antifolates, but not all ALL chemotherapy). Furthermore, we applied our MTX gene expression profile to the publicly available German/Dutch dataset [<xref ref-type="bibr" rid="pmed-0050083-b005">5</xref>], and documented that the MTX gene expression profile is not related to prednisolone sensitivity in this independent patient cohort (unpublished data).</p><p>Among the 50 genes that were expressed at a significantly different level in leukemia cells of MTX good responders versus poor responders, 29 were overexpressed in the MTX poor- responders. It is plausible that these overexpressed genes would be candidate targets for small molecules or other strategies to down-regulate their function, as a means to modify MTX response. Such a strategy has already proven successful in finding agents to modify the sensitivity of ALL cells to steroids [<xref ref-type="bibr" rid="pmed-0050083-b040">40</xref>], and it is plausible that specific inhibitors of genes overexpressed in leukemia cells resistant to MTX could be viable targets for modulating the antileukemic effects of MTX. Likewise, strategies to invoke the expression of genes that are underexpressed in MTX poor responders could be tested for their ability to modulate MTX sensitivity.</p><p>The current study is the first, to our knowledge, to identify genes whose expression is related to in vivo MTX response in patients with newly diagnosed ALL. Our data provide new insights into the genomic basis of interpatient differences in MTX response and point to new strategies for overcoming de novo MTX resistance in childhood ALL. In addition, our data indicate that early treatment response to MTX is a significant prognostic indicator for long term DFS in children with ALL.</p></sec><sec sec-type="supplementary-material" id="s5"><title>Supporting Information</title><supplementary-material content-type="local-data" id="pmed-0050083-sg001"><label>Figure S1</label><caption><title>Flow Diagram of Data and Methods Used</title><p>Gray boxes indicate data used for the analyses, white boxes intermediate data, shaded boxes data analysis method used, and <italic>n</italic> the number of patients.</p><p>(657 KB PDF)</p></caption><media xlink:href="pmed.0050083.sg001.pdf" mimetype="application" mime-subtype="pdf"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-sg002"><label>Figure S2</label><caption><title>Box Plot of WBCDay3 Versus MTX Treatment Group</title><p>The three initial treatment groups with HDMTX were not different in their WBCDay3 (HDMTX24H, <italic>n</italic> = 125; HDMTX24H+MP, <italic>n</italic> = 60; HDMTX4H, <italic>n</italic> = 108, Welch two-sample <italic>t</italic>-test).</p><p>(16 KB PDF)</p></caption><media xlink:href="pmed.0050083.sg002.pdf" mimetype="application" mime-subtype="pdf"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-sg003"><label>Figure S3</label><caption><title>Histogram of WBC Change and WBC Residuals</title><p>Shown are the distributions of 293 patients for (A) WBC<sub>LogChange</sub> that is defined as log(WBC<sub>PRE</sub>) minus log(WBC<sub>Day3</sub>), <italic>p</italic> = 0.04, i.e., is significantly different from a normal distribution; and (B) WBC<sub>ΔDay3</sub> that is the residuals of the (log[WBC<sub>Day3</sub>] with log[WBC<sub>PRE</sub>]) linear regression, <italic>p</italic> = 0.10, i.e., is not significantly different from a normal distribution.</p><p>(653 KB PDF)</p></caption><media xlink:href="pmed.0050083.sg003.pdf" mimetype="application" mime-subtype="pdf"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-sg004"><label>Figure S4</label><caption><title>Gene Plot of the Gene Ontology Nucleotide Biosynthesis Pathway</title><p>The gene plot gives a bar and a reference line for each gene probe set categorized for this pathway. The bar indicates the influence of each probe set on the correlation with MTX response (WBC<sub>ΔDay3</sub>). If the height of the bar exceeds the reference line the probe set is significantly related to MTX response. Marks indicate the standard deviations by which the bar exceeds the reference line. Red indicates gene probe sets with a positive correlation and green indicates gene probe sets with a negative correlation with MTX response.</p><p>(340 KB PDF)</p></caption><media xlink:href="pmed.0050083.sg004.pdf" mimetype="application" mime-subtype="pdf"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-sg005"><label>Figure S5</label><caption><title>Scatterplot of <italic>TYMS</italic> and <italic>DHFR</italic> Expression, WBC<sub>%drop</sub>, WBC<sub>ΔDay3</sub>, and Top 50 Gene Profile Versus Percentage of Leukemia Cells in S-Phase</title><p>Shown are the scatterplots for 154 patients correlating percentage of leukemia cells in S-phase with (A) the expression of <italic>TYMS</italic> and <italic>DHFR</italic> (respectively, <italic>r</italic> = 0.59; <italic>p</italic> < 0.001; <italic>r</italic> = 0.39, <italic>p</italic> < 0.001, Pearson correlation); and (B) for WBC<sub>%drop</sub> and the residual WBC<sub>ΔDay3</sub> of the WBC change (respectively, <italic>r</italic> = 0.034, <italic>p</italic> = 0.67; <italic>r</italic> = −0.20, <italic>p</italic> = 0.013, Pearson correlation); and (C) the top 50 gene expression profile (<italic>r</italic> = −0.57, <italic>p</italic> < 0.001, Pearson correlation).</p><p>(163 KB PDF)</p></caption><media xlink:href="pmed.0050083.sg005.ppt" mimetype="application" mime-subtype="vnd.ms-powerpoint"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-sg006"><label>Figure S6</label><caption><title>MTX Systemic Exposure Was Not Different among Responder Groups</title><p>There was no difference (<italic>p</italic> = 0.82) in MTX<sub>AUC</sub> (methotrexate area under the curve, representing MTX systemic exposure) in the groups (MTX good responder [GR] versus MTX intermediate [IR] versus MTX poor responder [PR]). We used the same cutoff for MTX response (WBC<sub>ΔDay3</sub>) as in <xref ref-type="fig" rid="pmed-0050083-g003">Figure 3</xref>. Therefore, the difference in survival cannot be explained by the difference in MTX<sub>AUC</sub>.</p><p>(59 KB PDF)</p></caption><media xlink:href="pmed.0050083.sg006.ppt" mimetype="application" mime-subtype="vnd.ms-powerpoint"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-st001"><label>Table S1</label><caption><title>The Top Ten GenMAPP (of 37) (A) and GO–BP (of 319) (B) Pathways Associated with WBC<sub>ΔDay3</sub></title><p>The column titled “probe-set correlations with WBC<sub>ΔDay3</sub>” indicates whether most probe sets in the pathway have a positive correlation, a negative correlation or a mixture of positive and negative correlations with WBC<sub>ΔDay3</sub>.</p><p>(56 KB DOC)</p></caption><media xlink:href="pmed.0050083.st001.doc" mimetype="application" mime-subtype="msword"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-st002"><label>Table S2</label><caption><title>Multivariable Cox Proportional Hazard Analysis of the Risk of Relapse</title><p>Individual factors related to MTX response are highlighted in gray (i.e., WBC<sub>ΔDay3</sub>, TYMS, DHFR, top 50 gene profile), each including known prognostic factors (i.e., ALL subtype, age at diagnosis, risk group).</p><p>(57 KB DOC)</p></caption><media xlink:href="pmed.0050083.st002.doc" mimetype="application" mime-subtype="msword"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-st003"><label>Table S3</label><caption><title>The Top 50 Probe Sets Associated with WBC<sub>ΔDay3</sub></title><p>(105 KB DOC)</p></caption><media xlink:href="pmed.0050083.st003.doc" mimetype="application" mime-subtype="msword"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pmed-0050083-se001"><label>Text S1</label><caption><title>CONSORT Checklist</title><p>(64 KB DOC)</p></caption><media xlink:href="pmed.0050083.sd001.doc" mimetype="application" mime-subtype="msword"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><sec id="s5a"><title>Accession Numbers</title><p>MIAME-compliant primary microarray data are available through the Gene Expression Omnibus (NCBI) at <ext-link ext-link-type="uri" xlink:href="http://www.ncbi.nlm.nih.gov/geo/">http://www.ncbi.nlm.nih.gov/geo/</ext-link> under GSE10255 and GSM258912–GSM259072.</p></sec></sec></body><back><ack><p>We thank the patients and their parents for their participation in this study; our clinical staff for facilitating protocol-based patient care; and our research nurses, Sheri Ring, Lisa Walters, Terri Kuehner, Margaret Edwards, Vickey Simmons, and Paula Condy. We also thank Yaqin Chu, May Chung, Emily Melton, and Siamac Salehy for outstanding technical assistance; and Nancy Kornegay and Mark Wilkinson for computer and database expertise.</p></ack><glossary><title>Abbreviations</title><def-list><def-item><term>ABC</term><def><p>ATP-binding cassette</p></def></def-item><def-item><term>ALL</term><def><p>childhood acute lymphoblastic leukemia</p></def></def-item><def-item><term>DFS</term><def><p>disease-free survival</p></def></def-item><def-item><term>FDR</term><def><p>false discovery rate</p></def></def-item><def-item><term>GO–BP</term><def><p>Gene Ontology–biological process</p></def></def-item><def-item><term>HDMTX</term><def><p>high-dose methotrexate</p></def></def-item><def-item><term>HR</term><def><p>hazard ratio</p></def></def-item><def-item><term>MTX</term><def><p>methotrexate</p></def></def-item><def-item><term>MTXPG</term><def><p>methotrexate polyglutamate</p></def></def-item><def-item><term>WBC%<sub>drop</sub></term><def><p>percent WBC change</p></def></def-item><def-item><term>WBC</term><def><p>white blood cell count</p></def></def-item><def-item><term>WBCΔ<sub>Day3</sub></term><def><p>MTX response as defined by the WBC residual based on the linear regression of log(WBC<sub>PRE</sub>) change to log(WBC<sub>Day3</sub>)</p></def></def-item><def-item><term>WBC<sub>Day3</sub></term><def><p>peripheral WBC count on day 3 following start of HDMTX treatment</p></def></def-item><def-item><term>WBC<sub>PRE</sub></term><def><p>peripheral WBC count pretreatment</p></def></def-item></def-list></glossary><ref-list><title>References</title><ref id="pmed-0050083-b001"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Evans</surname><given-names>WE</given-names></name></person-group><year>2006</year><article-title>Treatment of acute lymphoblastic leukemia</article-title><source>N Engl J Med</source><volume>354</volume><fpage>166</fpage><lpage>178</lpage><pub-id pub-id-type="pmid">16407512</pub-id></citation></ref><ref id="pmed-0050083-b002"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Relling</surname><given-names>MV</given-names></name><name><surname>Downing</surname><given-names>JR</given-names></name></person-group><year>2004</year><article-title>Acute lymphoblastic leukemia</article-title><source>N Engl J Med</source><volume>350</volume><fpage>1535</fpage><lpage>1548</lpage><pub-id pub-id-type="pmid">15071128</pub-id></citation></ref><ref id="pmed-0050083-b003"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Evans</surname><given-names>WE</given-names></name><name><surname>Relling</surname><given-names>MV</given-names></name></person-group><year>2004</year><article-title>Moving towards individualized medicine with pharmacogenomics</article-title><source>Nature</source><volume>429</volume><fpage>464</fpage><lpage>468</lpage><pub-id pub-id-type="pmid">15164072</pub-id></citation></ref><ref id="pmed-0050083-b004"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Cheok</surname><given-names>MH</given-names></name><name><surname>Evans</surname><given-names>WE</given-names></name></person-group><year>2006</year><article-title>Acute lymphoblastic leukaemia: a model for the pharmacogenomics of cancer therapy</article-title><source>Nat Rev Cancer</source><volume>6</volume><fpage>117</fpage><lpage>129</lpage><pub-id pub-id-type="pmid">16491071</pub-id></citation></ref><ref id="pmed-0050083-b005"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Holleman</surname><given-names>A</given-names></name><name><surname>Cheok</surname><given-names>MH</given-names></name><name><surname>den Boer</surname><given-names>ML</given-names></name><name><surname>Yang</surname><given-names>W</given-names></name><name><surname>Veerman</surname><given-names>AJ</given-names></name><etal></etal></person-group><year>2004</year><article-title>Gene-expression patterns in drug-resistant acute lymphoblastic leukemia cells and response to treatment</article-title><source>N Engl J Med</source><volume>351</volume><fpage>533</fpage><lpage>542</lpage><pub-id pub-id-type="pmid">15295046</pub-id></citation></ref><ref id="pmed-0050083-b006"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Gorlick</surname><given-names>R</given-names></name><name><surname>Goker</surname><given-names>E</given-names></name><name><surname>Trippett</surname><given-names>T</given-names></name><name><surname>Waltham</surname><given-names>M</given-names></name><name><surname>Banerjee</surname><given-names>D</given-names></name><etal></etal></person-group><year>1996</year><article-title>Intrinsic and acquired resistance to methotrexate in acute leukemia</article-title><source>N Engl J Med</source><volume>335</volume><fpage>1041</fpage><lpage>1048</lpage><pub-id pub-id-type="pmid">8793930</pub-id></citation></ref><ref id="pmed-0050083-b007"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Longo-Sorbello</surname><given-names>GS</given-names></name><name><surname>Bertino</surname><given-names>JR</given-names></name></person-group><year>2001</year><article-title>Current understanding of methotrexate pharmacology and efficacy in acute leukemias. Use of newer antifolates in clinical trials</article-title><source>Haematologica</source><volume>86</volume><fpage>121</fpage><lpage>127</lpage><pub-id pub-id-type="pmid">11224479</pub-id></citation></ref><ref id="pmed-0050083-b008"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Matherly</surname><given-names>LH</given-names></name><name><surname>Goldman</surname><given-names>DI</given-names></name></person-group><year>2003</year><article-title>Membrane transport of folates</article-title><source>Vitam Horm</source><volume>66</volume><fpage>403</fpage><lpage>456</lpage><pub-id pub-id-type="pmid">12852262</pub-id></citation></ref><ref id="pmed-0050083-b009"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Chen</surname><given-names>ZS</given-names></name><name><surname>Lee</surname><given-names>K</given-names></name><name><surname>Walther</surname><given-names>S</given-names></name><name><surname>Raftogianis</surname><given-names>RB</given-names></name><name><surname>Kuwano</surname><given-names>M</given-names></name><etal></etal></person-group><year>2002</year><article-title>Analysis of methotrexate and folate transport by multidrug resistance protein 4 (ABCC4): MRP4 is a component of the methotrexate efflux system</article-title><source>Cancer Res</source><volume>62</volume><fpage>3144</fpage><lpage>3150</lpage><pub-id pub-id-type="pmid">12036927</pub-id></citation></ref><ref id="pmed-0050083-b010"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hooijberg</surname><given-names>JH</given-names></name><name><surname>Broxterman</surname><given-names>HJ</given-names></name><name><surname>Kool</surname><given-names>M</given-names></name><name><surname>Assaraf</surname><given-names>YG</given-names></name><name><surname>Peters</surname><given-names>GJ</given-names></name><etal></etal></person-group><year>1999</year><article-title>Antifolate resistance mediated by the multidrug resistance proteins MRP1 and MRP2</article-title><source>Cancer Res</source><volume>59</volume><fpage>2532</fpage><lpage>2535</lpage><pub-id pub-id-type="pmid">10363967</pub-id></citation></ref><ref id="pmed-0050083-b011"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zeng</surname><given-names>H</given-names></name><name><surname>Chen</surname><given-names>ZS</given-names></name><name><surname>Belinsky</surname><given-names>MG</given-names></name><name><surname>Rea</surname><given-names>PA</given-names></name><name><surname>Kruh</surname><given-names>GD</given-names></name></person-group><year>2001</year><article-title>Transport of methotrexate (MTX) and folates by multidrug resistance protein (MRP) 3 and MRP1: effect of polyglutamylation on MTX transport</article-title><source>Cancer Res</source><volume>61</volume><fpage>7225</fpage><lpage>7232</lpage><pub-id pub-id-type="pmid">11585759</pub-id></citation></ref><ref id="pmed-0050083-b012"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Uga</surname><given-names>H</given-names></name><name><surname>Kuramori</surname><given-names>C</given-names></name><name><surname>Ohta</surname><given-names>A</given-names></name><name><surname>Tsuboi</surname><given-names>Y</given-names></name><name><surname>Tanaka</surname><given-names>H</given-names></name><etal></etal></person-group><year>2006</year><article-title>A new mechanism of methotrexate action revealed by target screening with affinity beads</article-title><source>Mol Pharmacol</source><volume>70</volume><fpage>1832</fpage><lpage>1839</lpage><pub-id pub-id-type="pmid">16936229</pub-id></citation></ref><ref id="pmed-0050083-b013"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>McGuire</surname><given-names>JJ</given-names></name><name><surname>Hsieh</surname><given-names>P</given-names></name><name><surname>Coward</surname><given-names>JK</given-names></name><name><surname>Bertino</surname><given-names>JR</given-names></name></person-group><year>1980</year><article-title>Enzymatic synthesis of folylpolyglutamates. Characterization of the reaction and its products</article-title><source>J Biol Chem</source><volume>255</volume><fpage>5776</fpage><lpage>5788</lpage><pub-id pub-id-type="pmid">6892914</pub-id></citation></ref><ref id="pmed-0050083-b014"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Banerjee</surname><given-names>D</given-names></name><name><surname>Mayer-Kuckuk</surname><given-names>P</given-names></name><name><surname>Capiaux</surname><given-names>G</given-names></name><name><surname>Budak-Alpdogan</surname><given-names>T</given-names></name><name><surname>Gorlick</surname><given-names>R</given-names></name><etal></etal></person-group><year>2002</year><article-title>Novel aspects of resistance to drugs targeted to dihydrofolate reductase and thymidylate synthase</article-title><source>Biochim Biophys Acta</source><volume>1587</volume><fpage>164</fpage><lpage>173</lpage><pub-id pub-id-type="pmid">12084458</pub-id></citation></ref><ref id="pmed-0050083-b015"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Chabner</surname><given-names>BA</given-names></name><name><surname>Allegra</surname><given-names>CJ</given-names></name><name><surname>Curt</surname><given-names>GA</given-names></name><name><surname>Clendeninn</surname><given-names>NJ</given-names></name><name><surname>Baram</surname><given-names>J</given-names></name><etal></etal></person-group><year>1985</year><article-title>Polyglutamation of methotrexate. Is methotrexate a prodrug</article-title><source>J Clin Invest</source><volume>76</volume><fpage>907</fpage><lpage>912</lpage><pub-id pub-id-type="pmid">2413074</pub-id></citation></ref><ref id="pmed-0050083-b016"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Kager</surname><given-names>L</given-names></name><name><surname>Cheok</surname><given-names>M</given-names></name><name><surname>Yang</surname><given-names>W</given-names></name><name><surname>Zaza</surname><given-names>G</given-names></name><name><surname>Cheng</surname><given-names>Q</given-names></name><etal></etal></person-group><year>2005</year><article-title>Folate pathway gene expression differs in subtypes of acute lymphoblastic leukemia and influences methotrexate pharmacodynamics</article-title><source>J Clin Invest</source><volume>115</volume><fpage>110</fpage><lpage>117</lpage><pub-id pub-id-type="pmid">15630450</pub-id></citation></ref><ref id="pmed-0050083-b017"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Barredo</surname><given-names>JC</given-names></name><name><surname>Synold</surname><given-names>TW</given-names></name><name><surname>Laver</surname><given-names>J</given-names></name><name><surname>Relling</surname><given-names>MV</given-names></name><name><surname>Pui</surname><given-names>CH</given-names></name><etal></etal></person-group><year>1994</year><article-title>Differences in constitutive and post-methotrexate folylpolyglutamate synthetase activity in B-lineage and T-lineage leukemia</article-title><source>Blood</source><volume>84</volume><fpage>564</fpage><lpage>569</lpage><pub-id pub-id-type="pmid">7517720</pub-id></citation></ref><ref id="pmed-0050083-b018"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Masson</surname><given-names>E</given-names></name><name><surname>Relling</surname><given-names>MV</given-names></name><name><surname>Synold</surname><given-names>TW</given-names></name><name><surname>Liu</surname><given-names>Q</given-names></name><name><surname>Schuetz</surname><given-names>JD</given-names></name><etal></etal></person-group><year>1996</year><article-title>Accumulation of methotrexate polyglutamates in lymphoblasts is a determinant of antileukemic effects in vivo. A rationale for high-dose methotrexate</article-title><source>J Clin Invest</source><volume>97</volume><fpage>73</fpage><lpage>80</lpage><pub-id pub-id-type="pmid">8550853</pub-id></citation></ref><ref id="pmed-0050083-b019"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Whitehead</surname><given-names>VM</given-names></name><name><surname>Rosenblatt</surname><given-names>DS</given-names></name><name><surname>Vuchich</surname><given-names>MJ</given-names></name><name><surname>Shuster</surname><given-names>JJ</given-names></name><name><surname>Witte</surname><given-names>A</given-names></name><etal></etal></person-group><year>1990</year><article-title>Accumulation of methotrexate and methotrexate polyglutamates in lymphoblasts at diagnosis of childhood acute lymphoblastic leukemia: a pilot prognostic factor analysis</article-title><source>Blood</source><volume>76</volume><fpage>44</fpage><lpage>49</lpage><pub-id pub-id-type="pmid">1694703</pub-id></citation></ref><ref id="pmed-0050083-b020"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Cario</surname><given-names>G</given-names></name><name><surname>Stanulla</surname><given-names>M</given-names></name><name><surname>Fine</surname><given-names>BM</given-names></name><name><surname>Teuffel</surname><given-names>O</given-names></name><name><surname>Neuhoff</surname><given-names>NV</given-names></name><etal></etal></person-group><year>2005</year><article-title>Distinct gene expression profiles determine molecular treatment response in childhood acute lymphoblastic leukemia</article-title><source>Blood</source><volume>105</volume><fpage>821</fpage><lpage>826</lpage><pub-id pub-id-type="pmid">15388585</pub-id></citation></ref><ref id="pmed-0050083-b021"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lugthart</surname><given-names>S</given-names></name><name><surname>Cheok</surname><given-names>MH</given-names></name><name><surname>den Boer</surname><given-names>ML</given-names></name><name><surname>Yang</surname><given-names>W</given-names></name><name><surname>Holleman</surname><given-names>A</given-names></name><etal></etal></person-group><year>2005</year><article-title>Identification of genes associated with chemotherapy crossresistance and treatment response in childhood acute lymphoblastic leukemia</article-title><source>Cancer Cell</source><volume>7</volume><fpage>375</fpage><lpage>386</lpage><pub-id pub-id-type="pmid">15837626</pub-id></citation></ref><ref id="pmed-0050083-b022"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Rots</surname><given-names>MG</given-names></name><name><surname>Pieters</surname><given-names>R</given-names></name><name><surname>Kaspers</surname><given-names>GJ</given-names></name><name><surname>van Zantwijk</surname><given-names>CH</given-names></name><name><surname>Noordhuis</surname><given-names>P</given-names></name><etal></etal></person-group><year>1999</year><article-title>Differential methotrexate resistance in childhood T- versus common/preB-acute lymphoblastic leukemia can be measured by an in situ thymidylate synthase inhibition assay, but not by the MTT assay</article-title><source>Blood</source><volume>93</volume><fpage>1067</fpage><lpage>1074</lpage><pub-id pub-id-type="pmid">9920857</pub-id></citation></ref><ref id="pmed-0050083-b023"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Yeoh</surname><given-names>EJ</given-names></name><name><surname>Ross</surname><given-names>ME</given-names></name><name><surname>Shurtleff</surname><given-names>SA</given-names></name><name><surname>Williams</surname><given-names>WK</given-names></name><name><surname>Patel</surname><given-names>D</given-names></name><etal></etal></person-group><year>2002</year><article-title>Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling</article-title><source>Cancer Cell</source><volume>1</volume><fpage>133</fpage><lpage>143</lpage><pub-id pub-id-type="pmid">12086872</pub-id></citation></ref><ref id="pmed-0050083-b024"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Crist</surname><given-names>WM</given-names></name><name><surname>Look</surname><given-names>AT</given-names></name></person-group><year>1990</year><article-title>Biology and clinical significance of cytogenetic abnormalities in childhood acute lymphoblastic leukemia</article-title><source>Blood</source><volume>76</volume><fpage>1449</fpage><lpage>1463</lpage><pub-id pub-id-type="pmid">2207320</pub-id></citation></ref><ref id="pmed-0050083-b025"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Boyett</surname><given-names>JM</given-names></name><name><surname>Rivera</surname><given-names>GK</given-names></name><name><surname>Hancock</surname><given-names>ML</given-names></name><name><surname>Sandlund</surname><given-names>JT</given-names></name><etal></etal></person-group><year>2000</year><article-title>Long-term results of Total Therapy studies 11, 12 and 13A for childhood acute lymphoblastic leukemia at St Jude Children's Research Hospital</article-title><source>Leukemia</source><volume>14</volume><fpage>2286</fpage><lpage>2294</lpage><pub-id pub-id-type="pmid">11187920</pub-id></citation></ref><ref id="pmed-0050083-b026"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Cheng</surname><given-names>C</given-names></name><name><surname>Leung</surname><given-names>W</given-names></name><name><surname>Rai</surname><given-names>SN</given-names></name><name><surname>Rivera</surname><given-names>GK</given-names></name><etal></etal></person-group><year>2003</year><article-title>Extended follow-up of long-term survivors of childhood acute lymphoblastic leukemia</article-title><source>N Engl J Med</source><volume>349</volume><fpage>640</fpage><lpage>649</lpage><pub-id pub-id-type="pmid">12917300</pub-id></citation></ref><ref id="pmed-0050083-b027"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Sandlund</surname><given-names>JT</given-names></name><name><surname>Pei</surname><given-names>D</given-names></name><name><surname>Rivera</surname><given-names>GK</given-names></name><name><surname>Howard</surname><given-names>SC</given-names></name><etal></etal></person-group><year>2003</year><article-title>Results of therapy for acute lymphoblastic leukemia in black and white children</article-title><source>JAMA</source><volume>290</volume><fpage>2001</fpage><lpage>2007</lpage><pub-id pub-id-type="pmid">14559953</pub-id></citation></ref><ref id="pmed-0050083-b028"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Relling</surname><given-names>MV</given-names></name><name><surname>Sandlund</surname><given-names>JT</given-names></name><name><surname>Downing</surname><given-names>JR</given-names></name><name><surname>Campana</surname><given-names>D</given-names></name><etal></etal></person-group><year>2004</year><article-title>Rationale and design of Total Therapy Study XV for newly diagnosed childhood acute lymphoblastic leukemia</article-title><source>Ann Hematol</source><volume>83</volume><issue>Suppl 1</issue><fpage>S124</fpage><lpage>S126</lpage><pub-id pub-id-type="pmid">15124703</pub-id></citation></ref><ref id="pmed-0050083-b029"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lipshutz</surname><given-names>RJ</given-names></name><name><surname>Fodor</surname><given-names>SP</given-names></name><name><surname>Gingeras</surname><given-names>TR</given-names></name><name><surname>Lockhart</surname><given-names>DJ</given-names></name></person-group><year>1999</year><article-title>High density synthetic oligonucleotide arrays</article-title><source>Nat Genet</source><volume>21</volume><fpage>20</fpage><lpage>24</lpage><pub-id pub-id-type="pmid">9915496</pub-id></citation></ref><ref id="pmed-0050083-b030"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Panetta</surname><given-names>JC</given-names></name><name><surname>Wall</surname><given-names>A</given-names></name><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Relling</surname><given-names>MV</given-names></name><name><surname>Evans</surname><given-names>WE</given-names></name></person-group><year>2002</year><article-title>Methotrexate intracellular disposition in acute lymphoblastic leukemia: a mathematical model of gamma-glutamyl hydrolase activity</article-title><source>Clin Cancer Res</source><volume>8</volume><fpage>2423</fpage><lpage>2429</lpage><pub-id pub-id-type="pmid">12114448</pub-id></citation></ref><ref id="pmed-0050083-b031"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Synold</surname><given-names>TW</given-names></name><name><surname>Relling</surname><given-names>MV</given-names></name><name><surname>Boyett</surname><given-names>JM</given-names></name><name><surname>Rivera</surname><given-names>GK</given-names></name><name><surname>Sandlund</surname><given-names>JT</given-names></name><etal></etal></person-group><year>1994</year><article-title>Blast cell methotrexate-polyglutamate accumulation in vivo differs by lineage, ploidy, and methotrexate dose in acute lymphoblastic leukemia</article-title><source>J Clin Invest</source><volume>94</volume><fpage>1996</fpage><lpage>2001</lpage><pub-id pub-id-type="pmid">7525652</pub-id></citation></ref><ref id="pmed-0050083-b032"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Goeman</surname><given-names>JJ</given-names></name><name><surname>van de Geer</surname><given-names>SA</given-names></name><name><surname>de Kort</surname><given-names>F</given-names></name><name><surname>van Houwelingen</surname><given-names>HC</given-names></name></person-group><year>2004</year><article-title>A global test for groups of genes: testing association with a clinical outcome</article-title><source>Bioinformatics</source><volume>20</volume><fpage>93</fpage><lpage>99</lpage><pub-id pub-id-type="pmid">14693814</pub-id></citation></ref><ref id="pmed-0050083-b033"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Gentleman</surname><given-names>RC</given-names></name><name><surname>Carey</surname><given-names>VJ</given-names></name><name><surname>Bates</surname><given-names>DM</given-names></name><name><surname>Bolstad</surname><given-names>B</given-names></name><name><surname>Dettling</surname><given-names>M</given-names></name><etal></etal></person-group><year>2004</year><article-title>Bioconductor: open software development for computational biology and bioinformatics</article-title><source>Genome Biol</source><volume>5</volume><fpage>R80</fpage><pub-id pub-id-type="pmid">15461798</pub-id></citation></ref><ref id="pmed-0050083-b034"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Storey</surname><given-names>JD</given-names></name><name><surname>Tibshirani</surname><given-names>R</given-names></name></person-group><year>2003</year><article-title>Statistical significance for genomewide studies</article-title><source>Proc Natl Acad Sci U S A</source><volume>100</volume><fpage>9440</fpage><lpage>9445</lpage><pub-id pub-id-type="pmid">12883005</pub-id></citation></ref><ref id="pmed-0050083-b035"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Fine</surname><given-names>JP</given-names></name><name><surname>Gray</surname><given-names>RJ</given-names></name></person-group><year>1999</year><article-title>A proportional hazards model for the subdistribution of a competing risk</article-title><source>J Am Stat Assoc</source><volume>94</volume><fpage>496</fpage><lpage>509</lpage></citation></ref><ref id="pmed-0050083-b036"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Dervieux</surname><given-names>T</given-names></name><name><surname>Brenner</surname><given-names>TL</given-names></name><name><surname>Hon</surname><given-names>YY</given-names></name><name><surname>Zhou</surname><given-names>Y</given-names></name><name><surname>Hancock</surname><given-names>ML</given-names></name><etal></etal></person-group><year>2002</year><article-title>De novo purine synthesis inhibition and antileukemic effects of mercaptopurine alone or in combination with methotrexate in vivo</article-title><source>Blood</source><volume>100</volume><fpage>1240</fpage><lpage>1247</lpage><pub-id pub-id-type="pmid">12149204</pub-id></citation></ref><ref id="pmed-0050083-b037"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Golub</surname><given-names>TR</given-names></name><name><surname>Slonim</surname><given-names>DK</given-names></name><name><surname>Tamayo</surname><given-names>P</given-names></name><name><surname>Huard</surname><given-names>C</given-names></name><name><surname>Gaasenbeek</surname><given-names>M</given-names></name><etal></etal></person-group><year>1999</year><article-title>Molecular classification of cancer: class discovery and class prediction by gene expression monitoring</article-title><source>Science</source><volume>286</volume><fpage>531</fpage><lpage>537</lpage><pub-id pub-id-type="pmid">10521349</pub-id></citation></ref><ref id="pmed-0050083-b038"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Ramaswamy</surname><given-names>S</given-names></name><name><surname>Tamayo</surname><given-names>P</given-names></name><name><surname>Rifkin</surname><given-names>R</given-names></name><name><surname>Mukherjee</surname><given-names>S</given-names></name><name><surname>Yeang</surname><given-names>CH</given-names></name><etal></etal></person-group><year>2001</year><article-title>Multiclass cancer diagnosis using tumor gene expression signatures</article-title><source>Proc Natl Acad Sci U S A</source><volume>98</volume><fpage>15149</fpage><lpage>15154</lpage><pub-id pub-id-type="pmid">11742071</pub-id></citation></ref><ref id="pmed-0050083-b039"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>DeRisi</surname><given-names>J</given-names></name><name><surname>Penland</surname><given-names>L</given-names></name><name><surname>Brown</surname><given-names>PO</given-names></name><name><surname>Bittner</surname><given-names>ML</given-names></name><name><surname>Meltzer</surname><given-names>PS</given-names></name><etal></etal></person-group><year>1996</year><article-title>Use of a cDNA microarray to analyse gene expression patterns in human cancer</article-title><source>Nat Genet</source><volume>14</volume><fpage>457</fpage><lpage>460</lpage><pub-id pub-id-type="pmid">8944026</pub-id></citation></ref><ref id="pmed-0050083-b040"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wei</surname><given-names>G</given-names></name><name><surname>Twomey</surname><given-names>D</given-names></name><name><surname>Lamb</surname><given-names>J</given-names></name><name><surname>Schlis</surname><given-names>K</given-names></name><name><surname>Agarwal</surname><given-names>J</given-names></name><etal></etal></person-group><year>2006</year><article-title>Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance</article-title><source>Cancer Cell</source><volume>10</volume><fpage>331</fpage><lpage>342</lpage><pub-id pub-id-type="pmid">17010674</pub-id></citation></ref><ref id="pmed-0050083-b041"><citation citation-type="other"><person-group person-group-type="author"><name><surname>Flotho</surname><given-names>C</given-names></name><name><surname>Coustan-Smith</surname><given-names>E</given-names></name><name><surname>Pei</surname><given-names>D</given-names></name><name><surname>Cheng</surname><given-names>C</given-names></name><name><surname>Song</surname><given-names>G</given-names></name><etal></etal></person-group><year>2007</year><article-title>A set of genes that regulate cell proliferation predict treatment outcome in childhood acute lymphoblastic leukemia</article-title><source>Blood</source></citation></ref><ref id="pmed-0050083-b042"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Levy</surname><given-names>AS</given-names></name><name><surname>Sather</surname><given-names>HN</given-names></name><name><surname>Steinherz</surname><given-names>PG</given-names></name><name><surname>Sowers</surname><given-names>R</given-names></name><name><surname>La</surname><given-names>M</given-names></name><etal></etal></person-group><year>2003</year><article-title>Reduced folate carrier and dihydrofolate reductase expression in acute lymphocytic leukemia may predict outcome: a Children's Cancer Group Study</article-title><source>J Pediatr Hematol Oncol</source><volume>25</volume><fpage>688</fpage><lpage>695</lpage><pub-id pub-id-type="pmid">12972803</pub-id></citation></ref><ref id="pmed-0050083-b043"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hatse</surname><given-names>S</given-names></name><name><surname>de Clercq</surname><given-names>E</given-names></name><name><surname>Balzarini</surname><given-names>J</given-names></name></person-group><year>1999</year><article-title>Role of antimetabolites of purine and pyrimidine nucleotide metabolism in tumor cell differentiation</article-title><source>Biochem Pharmacol</source><volume>58</volume><fpage>539</fpage><lpage>555</lpage><pub-id pub-id-type="pmid">10413291</pub-id></citation></ref><ref id="pmed-0050083-b044"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Kohn</surname><given-names>KW</given-names></name></person-group><year>1996</year><article-title>Regulatory genes and drug sensitivity</article-title><source>J Natl Cancer Inst</source><volume>88</volume><fpage>1255</fpage><lpage>1256</lpage><pub-id pub-id-type="pmid">8797761</pub-id></citation></ref><ref id="pmed-0050083-b045"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Fernandes</surname><given-names>DJ</given-names></name><name><surname>Sur</surname><given-names>P</given-names></name><name><surname>Kute</surname><given-names>TE</given-names></name><name><surname>Capizzi</surname><given-names>RL</given-names></name></person-group><year>1988</year><article-title>Proliferation-dependent cytotoxicity of methotrexate in murine L5178Y leukemia</article-title><source>Cancer Res</source><volume>48</volume><fpage>5638</fpage><lpage>5644</lpage><pub-id pub-id-type="pmid">2458828</pub-id></citation></ref><ref id="pmed-0050083-b046"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Rocha</surname><given-names>JC</given-names></name><name><surname>Cheng</surname><given-names>C</given-names></name><name><surname>Liu</surname><given-names>W</given-names></name><name><surname>Kishi</surname><given-names>S</given-names></name><name><surname>Das</surname><given-names>S</given-names></name><etal></etal></person-group><year>2005</year><article-title>Pharmacogenetics of outcome in children with acute lymphoblastic leukemia</article-title><source>Blood</source><volume>105</volume><fpage>4752</fpage><lpage>4758</lpage><pub-id pub-id-type="pmid">15713801</pub-id></citation></ref><ref id="pmed-0050083-b047"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Kaaijk</surname><given-names>P</given-names></name><name><surname>Kaspers</surname><given-names>GJ</given-names></name><name><surname>Van Wering</surname><given-names>ER</given-names></name><name><surname>Broekema</surname><given-names>GJ</given-names></name><name><surname>Loonen</surname><given-names>AH</given-names></name><etal></etal></person-group><year>2003</year><article-title>Cell proliferation is related to in vitro drug resistance in childhood acute leukaemia</article-title><source>Br J Cancer</source><volume>88</volume><fpage>775</fpage><lpage>781</lpage><pub-id pub-id-type="pmid">12618889</pub-id></citation></ref><ref id="pmed-0050083-b048"><citation citation-type="journal"><person-group person-group-type="author"><name><surname>Cheok</surname><given-names>MH</given-names></name><name><surname>Yang</surname><given-names>W</given-names></name><name><surname>Pui</surname><given-names>CH</given-names></name><name><surname>Downing</surname><given-names>JR</given-names></name><name><surname>Cheng</surname><given-names>C</given-names></name><etal></etal></person-group><year>2003</year><article-title>Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells</article-title><source>Nat Genet</source><volume>34</volume><fpage>85</fpage><lpage>90</lpage><pub-id pub-id-type="pmid">12704389</pub-id></citation></ref></ref-list><fn-group><fn id="ack1" fn-type="con"><p><bold>Author contributions.</bold> MJS, C-HP, MHC, and WEE designed the experiments and the study. MJS, DP, WY, GS, CC, JCP, MVR, WEE, and MHC analyzed the data. C-HP enrolled and treated patients. NP wrote the first draft of the paper. MJS, WY, LK, GS, CC, C-HP, MVR, MHC, and WEE contributed to writing the paper. MHC collected gene expression and patient data for this study.</p></fn><fn id="n101" fn-type="financial-disclosure"><p><bold>Funding:</bold> This work was supported in part by the following NIH grants; R37 CA36401 (WEE, MVR, CHP), R01 CA78224 (WEE, MVR, CHP), RO1 CA51001 (MVR, CHP), U01 GM61393 (MVR, WEE), Cancer Center Support Grant CA21765, by an FM Kirby Clinical Research Professorship from the American Cancer Society (C-HP), and by the American Lebanese Syrian Associated Charities. MJS was supported by a CJ Martin Fellowship from the National Health and Medical Research Council of Australia. GS was supported by a fellowship from the IRCCS, “Burlo Garofolo” in Trieste, by a “Progetto Giovani Ricercatori” fellowship from the University of Trieste, and by the Italian Society of Pharmacology. Funding organizations did not have any role in study design, data collection or analysis, decision to publish, or preparation of the manuscript.</p></fn><fn id="n102" fn-type="conflict"><p><bold>Competing Interests:</bold> The authors have declared that no competing interests exist.</p></fn></fn-group></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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