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Efficacy of chemotherapy and atezolizumab in patients with non-small-cell lung cancer receiving antibiotics and proton pump inhibitors: pooled post hoc analyses of the OAK and POPLAR trials.

Identifieur interne : 000439 ( Main/Exploration ); précédent : 000438; suivant : 000440

Efficacy of chemotherapy and atezolizumab in patients with non-small-cell lung cancer receiving antibiotics and proton pump inhibitors: pooled post hoc analyses of the OAK and POPLAR trials.

Auteurs : M. Chalabi [Pays-Bas] ; A. Cardona [Suisse] ; D R Nagarkar [États-Unis] ; A. Dhawahir Scala [Suisse] ; D R Gandara [États-Unis] ; A. Rittmeyer [Allemagne] ; M L Albert [États-Unis] ; T. Powles [Royaume-Uni] ; M. Kok [Pays-Bas] ; F G Herrera [Suisse]

Source :

RBID : pubmed:32115349

Abstract

BACKGROUND

Preclinical data have shown that proton pump inhibitors (PPI) can modulate the microbiome, and single-arm studies suggested that antibiotics (ATB) may decrease the efficacy of immune checkpoint inhibitors (ICI), but randomized controlled trial data are lacking. This pooled analysis evaluated the effect of ATB and PPI on outcome in patients randomized between ICI and chemotherapy.

PATIENTS AND METHODS

This retrospective analysis used pooled data from the phase II POPLAR (NCT01903993) and phase III OAK (NCT02008227) trials, which included 1512 patients with previously treated non-small-cell lung cancer (NSCLC) randomly assigned to receive atezolizumab (n = 757) or docetaxel (n = 755). The main objective of this analysis was to assess the impact of ATB and PPI use on overall survival (OS) and progression-free survival (PFS).

RESULTS

A total of 169 (22.3%) patients in the atezolizumab group and 202 (26.8%) in the docetaxel group received ATB, and 234 (30.9%) and 260 (34.4%), respectively, received PPI. Multivariate analysis in all patients revealed that ATB were associated with shorter OS [hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.04-1.39], as was PPI (HR 1.26, 95% CI 1.10-1.44). Within the atezolizumab population, OS was significantly shorter in patients who received ATB (8.5 versus 14.1 months, HR 1.32, 95% CI 1.06-1.63, P = 0.01) or PPI (9.6 versus 14.5 months, HR 1.45, 95% CI 1.20-1.75, P = 0.0001). PPI use was associated with shorter PFS in the atezolizumab population (1.9 versus 2.8 months, HR 1.30, 95% CI 1.10-1.53, P = 0.001). There was no association between ATB and PPI use and PFS or OS within the docetaxel population.

CONCLUSION

In this unplanned analysis from two randomized trials, data suggest that ATB or PPI use in patients with metastatic NSCLC is associated with poor outcome and may influence the efficacy of ICI.


DOI: 10.1016/j.annonc.2020.01.006
PubMed: 32115349


Affiliations:


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<name sortKey="Powles, T" sort="Powles, T" uniqKey="Powles T" first="T" last="Powles">T. Powles</name>
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<nlm:affiliation>Centre for Experimental Cancer Medicine, Barts Cancer Institute, London, UK.</nlm:affiliation>
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<name sortKey="Herrera, F G" sort="Herrera, F G" uniqKey="Herrera F" first="F G" last="Herrera">F G Herrera</name>
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<nlm:affiliation>Immune Oncology Service, Lausanne University Hospital, Ludwig Institute for Cancer Research, Lausanne, Switzerland. Electronic address: Fernanda.Herrera@chuv.ch.</nlm:affiliation>
<country xml:lang="fr">Suisse</country>
<wicri:regionArea>Immune Oncology Service, Lausanne University Hospital, Ludwig Institute for Cancer Research, Lausanne</wicri:regionArea>
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<front>
<div type="abstract" xml:lang="en">
<p>
<b>BACKGROUND</b>
</p>
<p>Preclinical data have shown that proton pump inhibitors (PPI) can modulate the microbiome, and single-arm studies suggested that antibiotics (ATB) may decrease the efficacy of immune checkpoint inhibitors (ICI), but randomized controlled trial data are lacking. This pooled analysis evaluated the effect of ATB and PPI on outcome in patients randomized between ICI and chemotherapy.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>PATIENTS AND METHODS</b>
</p>
<p>This retrospective analysis used pooled data from the phase II POPLAR (NCT01903993) and phase III OAK (NCT02008227) trials, which included 1512 patients with previously treated non-small-cell lung cancer (NSCLC) randomly assigned to receive atezolizumab (n = 757) or docetaxel (n = 755). The main objective of this analysis was to assess the impact of ATB and PPI use on overall survival (OS) and progression-free survival (PFS).</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>RESULTS</b>
</p>
<p>A total of 169 (22.3%) patients in the atezolizumab group and 202 (26.8%) in the docetaxel group received ATB, and 234 (30.9%) and 260 (34.4%), respectively, received PPI. Multivariate analysis in all patients revealed that ATB were associated with shorter OS [hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.04-1.39], as was PPI (HR 1.26, 95% CI 1.10-1.44). Within the atezolizumab population, OS was significantly shorter in patients who received ATB (8.5 versus 14.1 months, HR 1.32, 95% CI 1.06-1.63, P = 0.01) or PPI (9.6 versus 14.5 months, HR 1.45, 95% CI 1.20-1.75, P = 0.0001). PPI use was associated with shorter PFS in the atezolizumab population (1.9 versus 2.8 months, HR 1.30, 95% CI 1.10-1.53, P = 0.001). There was no association between ATB and PPI use and PFS or OS within the docetaxel population.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>CONCLUSION</b>
</p>
<p>In this unplanned analysis from two randomized trials, data suggest that ATB or PPI use in patients with metastatic NSCLC is associated with poor outcome and may influence the efficacy of ICI.</p>
</div>
</front>
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<AbstractText Label="BACKGROUND">Preclinical data have shown that proton pump inhibitors (PPI) can modulate the microbiome, and single-arm studies suggested that antibiotics (ATB) may decrease the efficacy of immune checkpoint inhibitors (ICI), but randomized controlled trial data are lacking. This pooled analysis evaluated the effect of ATB and PPI on outcome in patients randomized between ICI and chemotherapy.</AbstractText>
<AbstractText Label="PATIENTS AND METHODS">This retrospective analysis used pooled data from the phase II POPLAR (NCT01903993) and phase III OAK (NCT02008227) trials, which included 1512 patients with previously treated non-small-cell lung cancer (NSCLC) randomly assigned to receive atezolizumab (n = 757) or docetaxel (n = 755). The main objective of this analysis was to assess the impact of ATB and PPI use on overall survival (OS) and progression-free survival (PFS).</AbstractText>
<AbstractText Label="RESULTS">A total of 169 (22.3%) patients in the atezolizumab group and 202 (26.8%) in the docetaxel group received ATB, and 234 (30.9%) and 260 (34.4%), respectively, received PPI. Multivariate analysis in all patients revealed that ATB were associated with shorter OS [hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.04-1.39], as was PPI (HR 1.26, 95% CI 1.10-1.44). Within the atezolizumab population, OS was significantly shorter in patients who received ATB (8.5 versus 14.1 months, HR 1.32, 95% CI 1.06-1.63, P = 0.01) or PPI (9.6 versus 14.5 months, HR 1.45, 95% CI 1.20-1.75, P = 0.0001). PPI use was associated with shorter PFS in the atezolizumab population (1.9 versus 2.8 months, HR 1.30, 95% CI 1.10-1.53, P = 0.001). There was no association between ATB and PPI use and PFS or OS within the docetaxel population.</AbstractText>
<AbstractText Label="CONCLUSION">In this unplanned analysis from two randomized trials, data suggest that ATB or PPI use in patients with metastatic NSCLC is associated with poor outcome and may influence the efficacy of ICI.</AbstractText>
<CopyrightInformation>Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.</CopyrightInformation>
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<Language>eng</Language>
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<DataBank>
<DataBankName>ClinicalTrials.gov</DataBankName>
<AccessionNumberList>
<AccessionNumber>NCT01903993</AccessionNumber>
<AccessionNumber>NCT02008227</AccessionNumber>
</AccessionNumberList>
</DataBank>
</DataBankList>
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<PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic">
<Year>2020</Year>
<Month>01</Month>
<Day>16</Day>
</ArticleDate>
</Article>
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<Country>England</Country>
<MedlineTA>Ann Oncol</MedlineTA>
<NlmUniqueID>9007735</NlmUniqueID>
<ISSNLinking>0923-7534</ISSNLinking>
</MedlineJournalInfo>
<CitationSubset>IM</CitationSubset>
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<Keyword MajorTopicYN="Y">antibiotics</Keyword>
<Keyword MajorTopicYN="Y">immune checkpoint inhibitors</Keyword>
<Keyword MajorTopicYN="Y">lung cancer</Keyword>
<Keyword MajorTopicYN="Y">microbiota</Keyword>
<Keyword MajorTopicYN="Y">proton pump inhibitors</Keyword>
</KeywordList>
<CoiStatement>Disclosure MC reports grants to the institute from Bristol Myers Squibb/II-ON and Roche, outside the submitted work. AC and ADS are employees of F. Hoffmann-La Roche Ltd. DRN is an employee of Genentech Inc. and reports ownership of stocks from F. Hoffmann-La Roche Ltd. DRG reports a consulting or advisory role for AstraZeneca, Celgene, CellMax Life, Genentech, Guardant Health, Lilly, Liquid Genomics, Inc., and research funding from AstraZeneca/MedImmune (Inst) and Genentech (Inst). AR reports a consultant or advisory role for AbbVie, AstraZeneca/MedImmune, Boehringer Ingelheim, Bristol-Myers Squibb, Lilly, MSD, Pfizer, and Roche/Genentech. MLA is an employee of Genentech Inc. and is now an employee of Insitro. TP reports honoraria received from Novartis, BMS, Merck, Pfizer, Roche, and AstraZeneca. MK reports funding and a speaker's fee to the institute from BMS, Roche and an unpaid advisory role for BMS, outside the submitted work. FGH reports research funding from BMS, Accuray Inc., Bioprotect, and Prostate Cancer Foundation.</CoiStatement>
</MedlineCitation>
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<PubMedPubDate PubStatus="received">
<Year>2019</Year>
<Month>07</Month>
<Day>30</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="revised">
<Year>2019</Year>
<Month>12</Month>
<Day>08</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted">
<Year>2020</Year>
<Month>01</Month>
<Day>04</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed">
<Year>2020</Year>
<Month>3</Month>
<Day>3</Day>
<Hour>6</Hour>
<Minute>0</Minute>
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<PubMedPubDate PubStatus="medline">
<Year>2020</Year>
<Month>3</Month>
<Day>3</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez">
<Year>2020</Year>
<Month>3</Month>
<Day>3</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList>
<ArticleId IdType="pubmed">32115349</ArticleId>
<ArticleId IdType="pii">S0923-7534(20)35927-5</ArticleId>
<ArticleId IdType="doi">10.1016/j.annonc.2020.01.006</ArticleId>
</ArticleIdList>
</PubmedData>
</pubmed>
<affiliations>
<list>
<country>
<li>Allemagne</li>
<li>Pays-Bas</li>
<li>Royaume-Uni</li>
<li>Suisse</li>
<li>États-Unis</li>
</country>
<region>
<li>Angleterre</li>
<li>Canton de Vaud</li>
<li>Grand Londres</li>
<li>Hollande-Septentrionale</li>
</region>
<settlement>
<li>Amsterdam</li>
<li>Lausanne</li>
<li>Londres</li>
</settlement>
</list>
<tree>
<country name="Pays-Bas">
<region name="Hollande-Septentrionale">
<name sortKey="Chalabi, M" sort="Chalabi, M" uniqKey="Chalabi M" first="M" last="Chalabi">M. Chalabi</name>
</region>
<name sortKey="Kok, M" sort="Kok, M" uniqKey="Kok M" first="M" last="Kok">M. Kok</name>
</country>
<country name="Suisse">
<noRegion>
<name sortKey="Cardona, A" sort="Cardona, A" uniqKey="Cardona A" first="A" last="Cardona">A. Cardona</name>
</noRegion>
<name sortKey="Dhawahir Scala, A" sort="Dhawahir Scala, A" uniqKey="Dhawahir Scala A" first="A" last="Dhawahir Scala">A. Dhawahir Scala</name>
<name sortKey="Herrera, F G" sort="Herrera, F G" uniqKey="Herrera F" first="F G" last="Herrera">F G Herrera</name>
</country>
<country name="États-Unis">
<noRegion>
<name sortKey="Nagarkar, D R" sort="Nagarkar, D R" uniqKey="Nagarkar D" first="D R" last="Nagarkar">D R Nagarkar</name>
</noRegion>
<name sortKey="Albert, M L" sort="Albert, M L" uniqKey="Albert M" first="M L" last="Albert">M L Albert</name>
<name sortKey="Gandara, D R" sort="Gandara, D R" uniqKey="Gandara D" first="D R" last="Gandara">D R Gandara</name>
</country>
<country name="Allemagne">
<noRegion>
<name sortKey="Rittmeyer, A" sort="Rittmeyer, A" uniqKey="Rittmeyer A" first="A" last="Rittmeyer">A. Rittmeyer</name>
</noRegion>
</country>
<country name="Royaume-Uni">
<region name="Angleterre">
<name sortKey="Powles, T" sort="Powles, T" uniqKey="Powles T" first="T" last="Powles">T. Powles</name>
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</country>
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