Microbiome Profiling as a Technology to Support Personalized Medicine – PHRT


Microbiome Profiling as a Technology to Support Personalized Medicine

Short Summary

This project aims to establish the compositional analysis of microbial communities as a technology to identify links between gastrointestinal microbiota composition and the response of cancer patients to chemotherapy. Acute myeloid leukemia (AML) is an aggressive type of blood cancer and its treatment involves chemotherapy. Yet, mortality rates due to infections are high and response to therapy remains largely unpredictable. The working hypothesis of this project is that the composition of the gastrointestinal microbiome will be indicative for the large variability of observed patient responses. The long-term goal is the use of microbiome profiling to predict the risk for adverse treatment effects and to help guide more personalized treatment options than is currently possible for individual patients.


In this project, we aim to demonstrate the power and to evaluate the performance of mOTU profiling in a clinical setting focusing on AML patients undergoing chemotherapy. In close collaboration with the Divisions of Hematology and Gastroenterology at the UniversityHospital Zürich, we will collect gut microbial samples from a cohort of AML patients before, during and after induction chemotherapy. Samples subjected to mOTU profiling will be analyzed with the aim to identify potential biomarkers for patients at high risk for developing neutropenic enterocolitis, as well as overall treatment outcomes.


For AML treatment, the link between microbiota composition and the effect of different treatment options, the risk for neutropenic enterocolitis and other infections, and the overall outcome of therapy remain largely unknown. For decades, treatment options have been a matter of controversy with recent data strongly suggesting a negative impact of the use of antibiotics on the resistance against pathogens and cancer immunotherapy responses. Gut microbiome profiling as a technology will provide the molecular data required to quantitatively assess these links, and help to identify novel biomarkers for adverse treatment effects and to predict the outcomes of therapy. Upon successful implementation, clinical applicability and interoperability of this technology could be validated using samples from independent patient cohorts leading to a growing database of reference profiles that could be used to refine statistical models as the number of analyzed samples increases. Moreover, the PHRT provides an ideal framework to explore synergies in using microbiome profiles and other –omics data generated by other projects to obtain and analyze a system-wide readout from the same patients. The possibility for integrative multi–omics and/or imaging-based interrogation of clinical samples from the same patient represents an exciting outlook for the future implementation of microbiome profiling as a technology to support personalized medicine.


We have developed a methodology for microbiome profiling named “mOTU profiling” that enables the detection and accurate quantification species in complex microbial community samples by next generation sequencing. Given its unique feature of quantifying yet unknown (in addition to known) species that are missed by other metagenomic profiling methods, it shows great potential for identifying novel biomarkers, e.g. for medical treatment responses. In AML patients, in particular, the course and outcome of chemotherapy is highly heterogeneous. Neutropenic enterocolitis and infections are adverse effects that manifest in a subpopulation of patients, while risk factors remain largely unknown. Therefore, patients would greatly benefit from the identification of biomarkers that would allow for more individualized treatment strategies. Recent studies have reported gastrointestinal microbiome compositions to correlate with treatment responses in patients undergoing cancer immunotherapy. These data provide a solid basis for the goal of translating microbiome profiling as a technology to support clinical decision making for personalized medicine.

Technology Translation

Prof. Dr. Shinichi Sunagawa

ETH Zurich



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