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A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics.

Identifieur interne : 001086 ( Main/Exploration ); précédent : 001085; suivant : 001087

A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics.

Auteurs : Laurent Claret [France] ; Jin Y. Jin [États-Unis] ; Charles Ferté [France] ; Helen Winter [États-Unis] ; Sandhya Girish [États-Unis] ; Mark Stroh [États-Unis] ; Pei He [États-Unis] ; Marcus Ballinger [États-Unis] ; Alan Sandler [États-Unis] ; Amita Joshi [États-Unis] ; Achim Rittmeyer [Allemagne] ; David Gandara [États-Unis] ; Jean-Charles Soria [France] ; René Bruno [France]

Source :

RBID : pubmed:29685883

Descripteurs français

English descriptors

Abstract

Purpose: Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non-small cell lung cancer.Patients and Methods: OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks.Results: In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63-0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR, P < 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS.Conclusions: KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies. Clin Cancer Res; 24(14); 3292-8. ©2018 AACR.

DOI: 10.1158/1078-0432.CCR-17-3662
PubMed: 29685883


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Le document en format XML

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<name sortKey="Sandler, Alan" sort="Sandler, Alan" uniqKey="Sandler A" first="Alan" last="Sandler">Alan Sandler</name>
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<term>Algorithms (MeSH)</term>
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<term>Antibodies, Monoclonal, Humanized (adverse effects)</term>
<term>Antibodies, Monoclonal, Humanized (therapeutic use)</term>
<term>Antineoplastic Agents, Immunological (administration & dosage)</term>
<term>Antineoplastic Agents, Immunological (adverse effects)</term>
<term>Antineoplastic Agents, Immunological (therapeutic use)</term>
<term>Antineoplastic Combined Chemotherapy Protocols (adverse effects)</term>
<term>Antineoplastic Combined Chemotherapy Protocols (therapeutic use)</term>
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<term>Carcinoma, Non-Small-Cell Lung (drug therapy)</term>
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<term>Lung Neoplasms (diagnosis)</term>
<term>Lung Neoplasms (drug therapy)</term>
<term>Lung Neoplasms (mortality)</term>
<term>Models, Theoretical (MeSH)</term>
<term>Molecular Targeted Therapy (MeSH)</term>
<term>Neoplasm Staging (MeSH)</term>
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<term>Algorithmes (MeSH)</term>
<term>Anticorps monoclonaux humanisés (administration et posologie)</term>
<term>Anticorps monoclonaux humanisés (effets indésirables)</term>
<term>Anticorps monoclonaux humanisés (usage thérapeutique)</term>
<term>Antigène CD274 (antagonistes et inhibiteurs)</term>
<term>Antinéoplasiques immunologiques (administration et posologie)</term>
<term>Antinéoplasiques immunologiques (effets indésirables)</term>
<term>Antinéoplasiques immunologiques (usage thérapeutique)</term>
<term>Carcinome pulmonaire non à petites cellules (diagnostic)</term>
<term>Carcinome pulmonaire non à petites cellules (mortalité)</term>
<term>Carcinome pulmonaire non à petites cellules (traitement médicamenteux)</term>
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<term>Modèles théoriques (MeSH)</term>
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<term>Protocoles de polychimiothérapie antinéoplasique (usage thérapeutique)</term>
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<term>Tumeurs du poumon (mortalité)</term>
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<term>Antineoplastic Agents, Immunological</term>
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<term>Antibodies, Monoclonal, Humanized</term>
<term>Antineoplastic Agents, Immunological</term>
</keywords>
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<term>Antibodies, Monoclonal, Humanized</term>
<term>Antineoplastic Agents, Immunological</term>
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<term>Anticorps monoclonaux humanisés</term>
<term>Antinéoplasiques immunologiques</term>
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<term>Antineoplastic Combined Chemotherapy Protocols</term>
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<term>Antigène CD274</term>
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<term>Carcinoma, Non-Small-Cell Lung</term>
<term>Lung Neoplasms</term>
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<term>Carcinome pulmonaire non à petites cellules</term>
<term>Tumeurs du poumon</term>
</keywords>
<keywords scheme="MESH" qualifier="drug therapy" xml:lang="en">
<term>Carcinoma, Non-Small-Cell Lung</term>
<term>Lung Neoplasms</term>
</keywords>
<keywords scheme="MESH" qualifier="effets indésirables" xml:lang="fr">
<term>Anticorps monoclonaux humanisés</term>
<term>Antinéoplasiques immunologiques</term>
<term>Protocoles de polychimiothérapie antinéoplasique</term>
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<keywords scheme="MESH" qualifier="mortality" xml:lang="en">
<term>Carcinoma, Non-Small-Cell Lung</term>
<term>Lung Neoplasms</term>
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<keywords scheme="MESH" qualifier="mortalité" xml:lang="fr">
<term>Carcinome pulmonaire non à petites cellules</term>
<term>Tumeurs du poumon</term>
</keywords>
<keywords scheme="MESH" qualifier="therapeutic use" xml:lang="en">
<term>Antineoplastic Combined Chemotherapy Protocols</term>
</keywords>
<keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr">
<term>Carcinome pulmonaire non à petites cellules</term>
<term>Tumeurs du poumon</term>
</keywords>
<keywords scheme="MESH" qualifier="usage thérapeutique" xml:lang="fr">
<term>Anticorps monoclonaux humanisés</term>
<term>Antinéoplasiques immunologiques</term>
<term>Protocoles de polychimiothérapie antinéoplasique</term>
</keywords>
<keywords scheme="MESH" xml:lang="en">
<term>Algorithms</term>
<term>Humans</term>
<term>Models, Theoretical</term>
<term>Molecular Targeted Therapy</term>
<term>Neoplasm Staging</term>
<term>Prognosis</term>
<term>Proportional Hazards Models</term>
<term>Treatment Outcome</term>
<term>Tumor Burden</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr">
<term>Algorithmes</term>
<term>Charge tumorale</term>
<term>Humains</term>
<term>Modèles des risques proportionnels</term>
<term>Modèles théoriques</term>
<term>Pronostic</term>
<term>Résultat thérapeutique</term>
<term>Stadification tumorale</term>
<term>Thérapie moléculaire ciblée</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">
<b>Purpose:</b>
Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non-small cell lung cancer.
<b>Patients and Methods:</b>
OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks.
<b>Results:</b>
In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63-0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR,
<i>P</i>
< 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS.
<b>Conclusions:</b>
KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies.
<i>Clin Cancer Res; 24(14); 3292-8. ©2018 AACR</i>
.</div>
</front>
</TEI>
<pubmed>
<MedlineCitation Status="MEDLINE" Owner="NLM">
<PMID Version="1">29685883</PMID>
<DateCompleted>
<Year>2019</Year>
<Month>12</Month>
<Day>20</Day>
</DateCompleted>
<DateRevised>
<Year>2019</Year>
<Month>12</Month>
<Day>20</Day>
</DateRevised>
<Article PubModel="Print-Electronic">
<Journal>
<ISSN IssnType="Print">1078-0432</ISSN>
<JournalIssue CitedMedium="Internet">
<Volume>24</Volume>
<Issue>14</Issue>
<PubDate>
<Year>2018</Year>
<Month>07</Month>
<Day>15</Day>
</PubDate>
</JournalIssue>
<Title>Clinical cancer research : an official journal of the American Association for Cancer Research</Title>
<ISOAbbreviation>Clin Cancer Res</ISOAbbreviation>
</Journal>
<ArticleTitle>A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics.</ArticleTitle>
<Pagination>
<MedlinePgn>3292-3298</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1158/1078-0432.CCR-17-3662</ELocationID>
<Abstract>
<AbstractText>
<b>Purpose:</b>
Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non-small cell lung cancer.
<b>Patients and Methods:</b>
OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks.
<b>Results:</b>
In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63-0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR,
<i>P</i>
< 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS.
<b>Conclusions:</b>
KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies.
<i>Clin Cancer Res; 24(14); 3292-8. ©2018 AACR</i>
.</AbstractText>
<CopyrightInformation>©2018 American Association for Cancer Research.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y">
<Author ValidYN="Y">
<LastName>Claret</LastName>
<ForeName>Laurent</ForeName>
<Initials>L</Initials>
<AffiliationInfo>
<Affiliation>Clinical Pharmacology, Roche/Genentech, Marseille, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Jin</LastName>
<ForeName>Jin Y</ForeName>
<Initials>JY</Initials>
<AffiliationInfo>
<Affiliation>Clinical Pharmacology, Roche/Genentech, South San Francisco, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Ferté</LastName>
<ForeName>Charles</ForeName>
<Initials>C</Initials>
<AffiliationInfo>
<Affiliation>Gustave Roussy, Villejuif, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Winter</LastName>
<ForeName>Helen</ForeName>
<Initials>H</Initials>
<AffiliationInfo>
<Affiliation>Clinical Pharmacology, Roche/Genentech, South San Francisco, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Girish</LastName>
<ForeName>Sandhya</ForeName>
<Initials>S</Initials>
<AffiliationInfo>
<Affiliation>Clinical Pharmacology, Roche/Genentech, South San Francisco, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Stroh</LastName>
<ForeName>Mark</ForeName>
<Initials>M</Initials>
<AffiliationInfo>
<Affiliation>Clinical Pharmacology, Roche/Genentech, South San Francisco, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>He</LastName>
<ForeName>Pei</ForeName>
<Initials>P</Initials>
<AffiliationInfo>
<Affiliation>Biostatistics, Roche/Genentech, South San Francisco, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Ballinger</LastName>
<ForeName>Marcus</ForeName>
<Initials>M</Initials>
<AffiliationInfo>
<Affiliation>Clinical, Roche/Genentech, South San Francisco, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Sandler</LastName>
<ForeName>Alan</ForeName>
<Initials>A</Initials>
<AffiliationInfo>
<Affiliation>Clinical, Roche/Genentech, South San Francisco, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Joshi</LastName>
<ForeName>Amita</ForeName>
<Initials>A</Initials>
<AffiliationInfo>
<Affiliation>Clinical Pharmacology, Roche/Genentech, South San Francisco, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Rittmeyer</LastName>
<ForeName>Achim</ForeName>
<Initials>A</Initials>
<AffiliationInfo>
<Affiliation>Lungenfachklinik Immenhausen, Immenhausen, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Gandara</LastName>
<ForeName>David</ForeName>
<Initials>D</Initials>
<AffiliationInfo>
<Affiliation>University of California, Davis, Davis, California.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Soria</LastName>
<ForeName>Jean-Charles</ForeName>
<Initials>JC</Initials>
<AffiliationInfo>
<Affiliation>Gustave Roussy, Villejuif, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Bruno</LastName>
<ForeName>René</ForeName>
<Initials>R</Initials>
<AffiliationInfo>
<Affiliation>Clinical Pharmacology, Roche/Genentech, Marseille, France. rene.bruno@roche.com.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList>
<PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic">
<Year>2018</Year>
<Month>04</Month>
<Day>23</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo>
<Country>United States</Country>
<MedlineTA>Clin Cancer Res</MedlineTA>
<NlmUniqueID>9502500</NlmUniqueID>
<ISSNLinking>1078-0432</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList>
<Chemical>
<RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D061067">Antibodies, Monoclonal, Humanized</NameOfSubstance>
</Chemical>
<Chemical>
<RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D000074322">Antineoplastic Agents, Immunological</NameOfSubstance>
</Chemical>
<Chemical>
<RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D060890">B7-H1 Antigen</NameOfSubstance>
</Chemical>
<Chemical>
<RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="C423236">CD274 protein, human</NameOfSubstance>
</Chemical>
<Chemical>
<RegistryNumber>52CMI0WC3Y</RegistryNumber>
<NameOfSubstance UI="C000594389">atezolizumab</NameOfSubstance>
</Chemical>
</ChemicalList>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList>
<MeshHeading>
<DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D061067" MajorTopicYN="N">Antibodies, Monoclonal, Humanized</DescriptorName>
<QualifierName UI="Q000008" MajorTopicYN="N">administration & dosage</QualifierName>
<QualifierName UI="Q000009" MajorTopicYN="N">adverse effects</QualifierName>
<QualifierName UI="Q000627" MajorTopicYN="Y">therapeutic use</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D000074322" MajorTopicYN="N">Antineoplastic Agents, Immunological</DescriptorName>
<QualifierName UI="Q000008" MajorTopicYN="N">administration & dosage</QualifierName>
<QualifierName UI="Q000009" MajorTopicYN="N">adverse effects</QualifierName>
<QualifierName UI="Q000627" MajorTopicYN="Y">therapeutic use</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D000971" MajorTopicYN="N">Antineoplastic Combined Chemotherapy Protocols</DescriptorName>
<QualifierName UI="Q000009" MajorTopicYN="N">adverse effects</QualifierName>
<QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D060890" MajorTopicYN="N">B7-H1 Antigen</DescriptorName>
<QualifierName UI="Q000037" MajorTopicYN="N">antagonists & inhibitors</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D002289" MajorTopicYN="N">Carcinoma, Non-Small-Cell Lung</DescriptorName>
<QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName>
<QualifierName UI="Q000188" MajorTopicYN="Y">drug therapy</QualifierName>
<QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D008175" MajorTopicYN="N">Lung Neoplasms</DescriptorName>
<QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName>
<QualifierName UI="Q000188" MajorTopicYN="Y">drug therapy</QualifierName>
<QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D058990" MajorTopicYN="N">Molecular Targeted Therapy</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D009367" MajorTopicYN="N">Neoplasm Staging</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D011379" MajorTopicYN="N">Prognosis</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D016016" MajorTopicYN="N">Proportional Hazards Models</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D016896" MajorTopicYN="N">Treatment Outcome</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D047368" MajorTopicYN="N">Tumor Burden</DescriptorName>
</MeshHeading>
</MeshHeadingList>
</MedlineCitation>
<PubmedData>
<History>
<PubMedPubDate PubStatus="received">
<Year>2017</Year>
<Month>12</Month>
<Day>08</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="revised">
<Year>2018</Year>
<Month>02</Month>
<Day>22</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted">
<Year>2018</Year>
<Month>04</Month>
<Day>17</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed">
<Year>2018</Year>
<Month>4</Month>
<Day>25</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline">
<Year>2019</Year>
<Month>12</Month>
<Day>21</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez">
<Year>2018</Year>
<Month>4</Month>
<Day>25</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList>
<ArticleId IdType="pubmed">29685883</ArticleId>
<ArticleId IdType="pii">1078-0432.CCR-17-3662</ArticleId>
<ArticleId IdType="doi">10.1158/1078-0432.CCR-17-3662</ArticleId>
</ArticleIdList>
</PubmedData>
</pubmed>
<affiliations>
<list>
<country>
<li>Allemagne</li>
<li>France</li>
<li>États-Unis</li>
</country>
<region>
<li>Californie</li>
<li>Provence-Alpes-Côte d'Azur</li>
</region>
<settlement>
<li>Marseille</li>
<li>Villejuif</li>
</settlement>
</list>
<tree>
<country name="France">
<region name="Provence-Alpes-Côte d'Azur">
<name sortKey="Claret, Laurent" sort="Claret, Laurent" uniqKey="Claret L" first="Laurent" last="Claret">Laurent Claret</name>
</region>
<name sortKey="Bruno, Rene" sort="Bruno, Rene" uniqKey="Bruno R" first="René" last="Bruno">René Bruno</name>
<name sortKey="Ferte, Charles" sort="Ferte, Charles" uniqKey="Ferte C" first="Charles" last="Ferté">Charles Ferté</name>
<name sortKey="Soria, Jean Charles" sort="Soria, Jean Charles" uniqKey="Soria J" first="Jean-Charles" last="Soria">Jean-Charles Soria</name>
</country>
<country name="États-Unis">
<region name="Californie">
<name sortKey="Jin, Jin Y" sort="Jin, Jin Y" uniqKey="Jin J" first="Jin Y" last="Jin">Jin Y. Jin</name>
</region>
<name sortKey="Ballinger, Marcus" sort="Ballinger, Marcus" uniqKey="Ballinger M" first="Marcus" last="Ballinger">Marcus Ballinger</name>
<name sortKey="Gandara, David" sort="Gandara, David" uniqKey="Gandara D" first="David" last="Gandara">David Gandara</name>
<name sortKey="Girish, Sandhya" sort="Girish, Sandhya" uniqKey="Girish S" first="Sandhya" last="Girish">Sandhya Girish</name>
<name sortKey="He, Pei" sort="He, Pei" uniqKey="He P" first="Pei" last="He">Pei He</name>
<name sortKey="Joshi, Amita" sort="Joshi, Amita" uniqKey="Joshi A" first="Amita" last="Joshi">Amita Joshi</name>
<name sortKey="Sandler, Alan" sort="Sandler, Alan" uniqKey="Sandler A" first="Alan" last="Sandler">Alan Sandler</name>
<name sortKey="Stroh, Mark" sort="Stroh, Mark" uniqKey="Stroh M" first="Mark" last="Stroh">Mark Stroh</name>
<name sortKey="Winter, Helen" sort="Winter, Helen" uniqKey="Winter H" first="Helen" last="Winter">Helen Winter</name>
</country>
<country name="Allemagne">
<noRegion>
<name sortKey="Rittmeyer, Achim" sort="Rittmeyer, Achim" uniqKey="Rittmeyer A" first="Achim" last="Rittmeyer">Achim Rittmeyer</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>

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