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New data science approaches to visualize and understand the impact of the microbiome on risk of graft-versus-host disease

Award Number: R01HL158796
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/01/2022
PERIOD OF PERFORMANCE END DATE: 02/28/2027

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New data science approaches to visualize and understand the impact of the microbiome on risk of graft-versus-host disease - Project Summary/Abstract Allogeneic stem cell transplantation is a life-saving therapy for a variety of blood disorders, but its use is limited by a high rate of serious side eﬀects, including the development of graft-versus-host-disease (GVHD). The gut microbiome, or the composition of microorganisms populating the digestive tract, plays a key role in triggering this inﬂammatory response, and there is an urgent need to analyze patient microbiome proﬁles to both predict and mitigate risk of GVHD. However, microbiome data pose a number of statistical challenges not addressed by existing methods due to high dimensionality, heterogeneity across subjects, and complex phylogenetic relationships. In this proposal, we develop new data science approaches to make sense of microbiome data, providing insight that can guide the development of future interventions aimed at reducing GVHD incidence. We will develop accurate and eﬃcient methods for microbiome data analysis and make them available in user-friendly formats. We focus on the development of novel methods for visualization and prediction using microbiome data, as detailed in the following speciﬁc aims: Speciﬁc Aim 1: To develop and evaluate advanced tools for visualization of microbiome data. The high dimensionality and unique structure of microbiome data present challenges to eﬀective data visualization. In this aim, we will develop approaches for both unsupervised and supervised visualization of microbiome data, along with an RShiny app and QIIME2 plug-in that will make these tools accessible to both clinicians and bioinformaticians. The methods and software resulting from this aim will provide robust approaches to enable researchers to better visualize global microbiome heterogeneity across their study population, enhancing data exploration and identiﬁcation of potential confounding factors or outliers. Speciﬁc Aim 2: To develop predictive modeling approaches for binary and survival outcomes. In this aim, we will focus on selection of predictive microbiome features in the context of regression. We will carry out key advances enabling the eﬀective application of sparse modeling to predict GVHD risk: novel statistical approaches to handle binary and time-to-event outcomes, including those with competing risks, and computationally eﬃcient implementations, to be made freely available as both an R package and RShiny application. Speciﬁc Aim 3: To develop methods for understanding the impact of rare features. Current microbiome proﬁling methods allow for very ﬁne resolution of the strains present in each sample. In this aim, we propose two methods to understand the impact of rare features. We will ﬁrst develop a method to provide insight into kernel association results, by obtaining estimated eﬀect sizes for individual microbiome features. We will then develop an approach for nonparametric clustering of the regression coeﬃcients, which allows ﬂexible aggregation of the observed rare features. Successful completion of this work will result in new statistical and computational approaches to provide insights into microbiome data, generating hypotheses that can guide the development of future strategies to predict and mitigate GVHD. These methods will be disseminated through easy-to-use and eﬃcient cloud-based software implementations.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $0 )
2026	2025	THE UNIVERISTY OF TEXAS M.D. ANDERSON CANCER CENTER	1515 HOLCOMBE BLVD	HOUSTON	TX	77030	HARRIS	USA	Blood Diseases and Resources Research	000	4	1/6/2026	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $243,000 )
2025	2025	THE UNIVERISTY OF TEXAS M.D. ANDERSON CANCER CENTER	1515 HOLCOMBE BLVD	HOUSTON	TX	77030	HARRIS	USA	Blood Diseases and Resources Research	000	4	2/19/2025	NON-COMPETING CONTINUATION	$243,000
														Subtotal = $243,000

Issue Date FY: 2024 ( Subtotal = $243,000 )
2024	2024	THE UNIVERISTY OF TEXAS MD ANDERSON CANCER CENTER, A COMPONENT OF THE UNIVERISTY OF TEXAS SYSTEM	1515 HOLCOMBE BLVD	HOUSTON	TX	77030	HARRIS	USA	Blood Diseases and Resources Research	000	3	2/2/2024	NON-COMPETING CONTINUATION	$243,000
														Subtotal = $243,000

Issue Date FY: 2023 ( Subtotal = $243,000 )
2023	2023	UNIVERSITY OF TEXAS M. D. ANDERSON CANCER CENTER, THE	1515 HOLCOMBE BLVD	HOUSTON	TX	77030	HARRIS	USA	Blood Diseases and Resources Research	000	2	2/6/2023	NON-COMPETING CONTINUATION	$243,000
														Subtotal = $243,000

Issue Date FY: 2022 ( Subtotal = $283,500 )
2022	2022	UNIVERSITY OF TEXAS M. D. ANDERSON CANCER CENTER, THE	1515 HOLCOMBE BLVD	HOUSTON	TX	77030	HARRIS	USA	Blood Diseases and Resources Research	000	1	3/11/2022	NEW	$283,500
														Subtotal = $283,500

Grand Total All Awards = $1,012,500

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New data science approaches to visualize and understand the impact of the microbiome on risk of graft-versus-host disease

Award Number: R01HL158796

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 04/01/2022

PERIOD OF PERFORMANCE END DATE: 02/28/2027

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