Preventing Adverse Patient Responses to Cancer Chemotherapeutics

Abstract

The purpose of this project is to prevent adverse patient responses to the cancer drug irinotecan by analyzing the gut microbiomes of patients. The scope of this project is to study irinotecan metabolism and the microbiome over time using fecal samples from healthy individuals and metastatic colorectal cancer patients. We are happy to report a number of significant results for this year. We have sequenced the metagenomes of 39 fecal samples collected over time from six metastatic colorectal cancer patients. This unique dataset will be of substantial interest to the community because there are no available temporal metagenomic datasets for colorectal cancer patients and no datasets that include data on adverse events and treatment regimens. We note that our microbiome enzyme of interest, betaglucuronidase, which can interact with metabolites of the colorectal cancer drug irinotecan, changes its distribution in one patient during transient diarrhea. Among our publications for this year, our work describing a novel computational approach, topological data analysis, to identify metastable states and state transitions in microbiome data that are linked to patient outcomes was published (Chang, VanInsberghe, and Kelly, npj Biofilms and Microbiomes, 2020) and will be used to analyze the temporal colorectal cancer patient samples sequenced this year. We published an additional three original research papers this year which acknowledged DoD funding, bringing the total number of peer-reviewed publications this grant has supported to 11.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2021
Accession Number
AD1143199

Entities

People

  • Libusha Kelly

Organizations

  • Albert Einstein College of Medicine

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Colon Cancer
  • Computational Science
  • Covid-19
  • Data Analysis
  • Databases
  • Department Of Defense
  • Gut Microbiome
  • Information Science
  • Machine Learning
  • Mass Spectrometry
  • Microbiology
  • Microbiomes
  • Microorganisms
  • Neural Networks
  • Sars
  • Students
  • Viruses

Fields of Study

  • Biology

Readers

  • Microbial Pathology
  • Neural Network Machine Learning.
  • Personnel Management and Statistics in the Military and Department of Defense

Technology Areas

  • Biotechnology