Identifying TNFI Therapy Response in UC Patients Using Baseline Whole Blood Gene Expression Data

Shanthamallu, Uday Shankar ; Jones, Alexander ; Panés, Julián ; Corraliza Márquez, Ana Maria ; Salas, Azucena ; Akmaev, Slava ; Ghiassian, Susan Dina


Non-response to TNF inhibitor (TNFi) therapies remains a challenge in UC/IBD

management. Identification of non-responders before initiating biologic therapy can reduce costs and bolster

the principles of precision medicine. In this study, we employ a network-based approach to derive biomarkers

of response and use whole blood pre-treatment gene expression data from severe UC patients to differentiate

TNFi therapy responders and non-responders.


Clinical and molecular data for a cohort of 15 UC patients with moderate-to-severe disease activity

and active ulcers at baseline were analyzed. Prior to TNFi therapy, whole blood RNAseq data were aligned to

hg38 by STAR and transcriptome profiles were generated by RSEM. The Global Mayo UC score for each

patient was recorded before and 14 weeks after initiation of a TNFi. The pre-/post-treatment difference in

Global Mayo UC score was used to assess patient clinical response. A score reduction exceeding 3 points

after the 14-week period was interpreted as a meaningful response. To obtain the top predictive biomarkers,

PRoBeNet, a previously developed network-based algorithm, was run on the map of the Human Interactome.

This identified the most important intermediary nodes between TNF (source node), drug target, and UC

disease signature cytokines (readout nodes). We evaluated the predictive power of PRoBeNet biomarkers by

running unsupervised clustering algorithm UMAP [7] on baseline transcriptomic data of UC patients and

assessing clusters and their enrichment with response clinical outcome. For patient class assignment and

calculating significance of enrichment, we implemented Hierarchical Clustering Algorithm with L1-norm as

distance metric on top two UMAP components.


Implementing UMAP on baseline transcriptomic profile of the top 100 predicted biomarkers captured

two distinct subgroups in the UC patient cohort. UMAP clustering was performed with no prior information on

patient clinical outcome. Patient clinical outcomes on the UMAP visualization demonstrate a clear separation of

responders and non-responders (Figure 1A). Figure 1B shows patient class assignments as well as number of

responders and non-responders in each class. Cluster 1 is enriched with TNFi non-responders (p-value =

0.0089, Hypergeometric test).


Baseline whole-blood transcriptomic data contains information predictive of response to TNFi

therapy. PRoBeNet is a validated approach for identifying predictive biomarkers within transcriptomic data. The

predictive power of identified biomarkers was demonstrated in clear separation of UC responders and nonresponders.

Such predictive power is pivotal for tailoring therapeutic regimens, emphasizing the importance of

precision medicine in IBD treatment. Further studies are recommended to validate and expand upon these


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