High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients

Chi L. Nguyen; Kate A. Markey; Oriana Miltiadous; Anqi Dai; Nicholas Waters; Keimya Sadeghi; Teng Fei; Roni Shouval; Bradford P. Taylor; Chen Liao; John B. Slingerland; Ann E. Slingerland; Annelie G. Clurman; Molly A. Maloy; Lauren Bohannon; Paul A. Giardina; Daniel G. Brereton; Gabriel K. Armijo; Emily Fontana; Ana Gradissimo; Boglarka Gyurkocza; Anthony D. Sung; Nelson J. Chao; Sean M. Devlin; Ying Taur; Sergio A. Giralt; Miguel Angel Perales; Joao B. Xavier; Eric G. Pamer; Jonathan U. Peled; Antonio L.C. Gomes; Marcel R.M. van den Brink

doi:10.1016/j.cell.2023.05.007

High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients

Chi L. Nguyen, Kate A. Markey, Oriana Miltiadous, Anqi Dai, Nicholas Waters, Keimya Sadeghi, Teng Fei, Roni Shouval, Bradford P. Taylor, Chen Liao, John B. Slingerland, Ann E. Slingerland, Annelie G. Clurman, Molly A. Maloy, Lauren Bohannon, Paul A. Giardina, Daniel G. Brereton, Gabriel K. Armijo, Emily Fontana, Ana GradissimoBoglarka Gyurkocza, Anthony D. Sung, Nelson J. Chao, Sean M. Devlin, Ying Taur, Sergio A. Giralt, Miguel Angel Perales, Joao B. Xavier, Eric G. Pamer, Jonathan U. Peled, Antonio L.C. Gomes, Marcel R.M. van den Brink^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients’ longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

Original language	English
Pages (from-to)	2705-2718.e17
Journal	Cell
Volume	186
Issue number	12
DOIs	https://doi.org/10.1016/j.cell.2023.05.007
State	Published - 8 Jun 2023
Externally published	Yes

Funding

Funders	Funder number
NHIBL	R01HL151365, R01CA203950
NHLBI NIH	K08HL143189, R21AG066388, NCI P30 CA008748
National Science Foundation
National Institutes of Health	R56 AI137269-01
National Institute on Aging	P01-AG052359
National Heart, Lung, and Blood Institute	R01-HL123340, R01-HL125571
National Institute of Allergy and Infectious Diseases	2016-013, AI124275
American Society of Clinical Oncology
Parker Institute for Cancer Immunotherapy	P01-CA023766, R01- CA228308, P30 CA008748, R01-CA228358
DKMS Foundation

Keywords

16S sequencing
computational modeling
hematopoietic cell transplantation
metagenomics
microbiota
pharmacological exposures

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.cell.2023.05.007

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Nguyen, C. L., Markey, K. A., Miltiadous, O., Dai, A., Waters, N., Sadeghi, K., Fei, T., Shouval, R., Taylor, B. P., Liao, C., Slingerland, J. B., Slingerland, A. E., Clurman, A. G., Maloy, M. A., Bohannon, L., Giardina, P. A., Brereton, D. G., Armijo, G. K., Fontana, E., ... van den Brink, M. R. M. (2023). High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients. Cell, 186(12), 2705-2718.e17. https://doi.org/10.1016/j.cell.2023.05.007

@article{cf46a00badc54358a61e19afbd9edacd,

title = "High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients",

abstract = "Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients{\textquoteright} longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.",

keywords = "16S sequencing, computational modeling, hematopoietic cell transplantation, metagenomics, microbiota, pharmacological exposures",

author = "Nguyen, {Chi L.} and Markey, {Kate A.} and Oriana Miltiadous and Anqi Dai and Nicholas Waters and Keimya Sadeghi and Teng Fei and Roni Shouval and Taylor, {Bradford P.} and Chen Liao and Slingerland, {John B.} and Slingerland, {Ann E.} and Clurman, {Annelie G.} and Maloy, {Molly A.} and Lauren Bohannon and Giardina, {Paul A.} and Brereton, {Daniel G.} and Armijo, {Gabriel K.} and Emily Fontana and Ana Gradissimo and Boglarka Gyurkocza and Sung, {Anthony D.} and Chao, {Nelson J.} and Devlin, {Sean M.} and Ying Taur and Giralt, {Sergio A.} and Perales, {Miguel Angel} and Xavier, {Joao B.} and Pamer, {Eric G.} and Peled, {Jonathan U.} and Gomes, {Antonio L.C.} and {van den Brink}, {Marcel R.M.}",

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year = "2023",

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doi = "10.1016/j.cell.2023.05.007",

language = "אנגלית",

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Nguyen, CL, Markey, KA, Miltiadous, O, Dai, A, Waters, N, Sadeghi, K, Fei, T, Shouval, R, Taylor, BP, Liao, C, Slingerland, JB, Slingerland, AE, Clurman, AG, Maloy, MA, Bohannon, L, Giardina, PA, Brereton, DG, Armijo, GK, Fontana, E, Gradissimo, A, Gyurkocza, B, Sung, AD, Chao, NJ, Devlin, SM, Taur, Y, Giralt, SA, Perales, MA, Xavier, JB, Pamer, EG, Peled, JU, Gomes, ALC & van den Brink, MRM 2023, 'High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients', Cell, vol. 186, no. 12, pp. 2705-2718.e17. https://doi.org/10.1016/j.cell.2023.05.007

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T1 - High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients

AU - Nguyen, Chi L.

AU - Markey, Kate A.

AU - Miltiadous, Oriana

AU - Dai, Anqi

AU - Waters, Nicholas

AU - Sadeghi, Keimya

AU - Fei, Teng

AU - Shouval, Roni

AU - Taylor, Bradford P.

AU - Liao, Chen

AU - Slingerland, John B.

AU - Slingerland, Ann E.

AU - Clurman, Annelie G.

AU - Maloy, Molly A.

AU - Bohannon, Lauren

AU - Giardina, Paul A.

AU - Brereton, Daniel G.

AU - Armijo, Gabriel K.

AU - Fontana, Emily

AU - Gradissimo, Ana

AU - Gyurkocza, Boglarka

AU - Sung, Anthony D.

AU - Chao, Nelson J.

AU - Devlin, Sean M.

AU - Taur, Ying

AU - Giralt, Sergio A.

AU - Perales, Miguel Angel

AU - Xavier, Joao B.

AU - Pamer, Eric G.

AU - Peled, Jonathan U.

AU - Gomes, Antonio L.C.

AU - van den Brink, Marcel R.M.

PY - 2023/6/8

Y1 - 2023/6/8

N2 - Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients’ longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

AB - Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients’ longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

KW - 16S sequencing

KW - computational modeling

KW - hematopoietic cell transplantation

KW - metagenomics

KW - microbiota

KW - pharmacological exposures

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ER -

High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients

Abstract

Funding

Keywords

UN SDGs

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