<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY center_name="BioProject" alias="PRJNA797954" accession="SRP404257">
    <IDENTIFIERS>
      <PRIMARY_ID>SRP404257</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject" label="primary">PRJNA797954</EXTERNAL_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>Gut microbiomes of patients under cancer immunotherapy Raw sequence reads</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Metagenomics"/>
      <STUDY_ABSTRACT>Gut microbiomes have been linked to the clinical responses of oncology patients on immunotherapies. Further investigation into potential gut microbiome biomarkers of immunotherapy response may improve patient outcomes. In this study, gut microbiome analyses of late-stage cancer patients prior to immunotherapy demonstrated specific gut bacterial taxa correlated with response status. To identify immunotherapy microbiome response biomarkers that may be generalizable across different cohorts, we conducted a combined analysis of previously published cancer immunotherapy gut microbiome datasets. Using selbal analyses, different groups of bacterial genera distinguished responders versus non-responders. The association of multiple microbiome features (bacterial taxa, Chao1 richness, other alpha diversity indexes) with immunotherapy response prompted development of a predictor of outcomes using supervised machine learning methods. Resultant models predicted response in amplicon and metagenomic sequencing validation datasets. These investigations will be important in potentially identifying response biomarkers and improving our understanding of the relationships between human microbiomes and immunotherapy responses with the goal of improving and predicting treatment outcomes.</STUDY_ABSTRACT>
      <CENTER_PROJECT_NAME>Gut microbiomes of patients under cancer immunotherapy</CENTER_PROJECT_NAME>
    </DESCRIPTOR>
    <STUDY_LINKS>
      <STUDY_LINK>
        <XREF_LINK>
          <DB>pubmed</DB>
          <ID>35875611</ID>
        </XREF_LINK>
      </STUDY_LINK>
    </STUDY_LINKS>
  </STUDY>
</STUDY_SET>
