Identification of Candidate Transcriptional Biomarkers Associated with Chronic Graft-Versus-Host Disease Following Allogeneic Hematopoietic Cell Transplantation
Authors: Kohrt Holbrook E , Tian Lu , Li Li , Alizadeh Ash A. , Hsieh Sue , Strober Samuel , Sarwal Minnie , Lowsky Robert 
Published: October 2019

Abstract
Abstract 4190

Background:
Chronic graft-versus-host disease (cGvHD) and its treatment result in significant morbidity and mortality, limiting the clinical benefit of allogeneic hematopoietic cell transplantation (HCT). The diagnosis of cGvHD can be challenging especially when diagnostic features are absent or when the clinical features are confined to internal organs (i.e., lungs). A noninvasive, diagnostic and prognostic test is needed to determine which pts have or will develop cGvHD.

Objective:
To identify candidate blood biomarkers using gene expression profiling of the circulating donor immune cell repertoire following allogeneic HCT and develop a classification rule to distinguish cGvHD from non-GvHD pts.

Methods:
In this cross sectional discovery phase study we identified 63 pts (median age 50 yrs; range 19–64) who were beyond 100 days from transplantation (median 424 days) with complete (100%) donor CD3+ T cell chimerism. Pts were divided into cGvHD and non-cGvHD groups based on conventional criteria. To adjust for the influence of immune suppression (IS) medication a prospective cohort of 7 new onset cGvHD pts who were free of IS drugs at the time of onset of cGvHD and at the time of blood sample acquisition. Peripheral blood mononuclear cells were obtained from the 70 pts on IRB approval protocols. The RNA was processed on the Agilent whole human genome 44k microarray platform. Bioinformatics programs including AILUN, GeneSpring, SAM, and PAM.

Results:
Three data sets were constructed to address the comparison of cGvHD pts on IS medication to drug free non-GvHD pts. First a ‘screening set’, compared gene expression levels in pts with cGvHD on IS (n=14) to those with cGvHD not yet on IS (n=7) using a two-sample t-test and eliminated more than 27k genes whose t-test statistics were beyond the interval of [-1,1] as differences in expression were possibly related to the presence or absence of these medication. We next applied the Prediction Analysis of Microarray (PAM) method to a ‘training set’ to discriminate 21 GvHD pts from 21 non-GvHD using the 16,103 selected probes from the screening set and found a minimum misclassification error of 6 samples was achieved with a probe set that contained 10 genes. The candidate genes represented a compensatory response to cGvHD rather than a pro-inflammatory gene signature. We next evaluated the performance of the classification rule from the ‘training set’ to differentiate cGvHD pts from non-GvHD pts in the 14 pt ‘test set’ and correctly predicted 3 new cGvHD and all 7 non-GvHD controls. We repeated PAM and adjusted for possible confounding variables, that included length in days of sample acquisition date relative to the transplant date, recipient age, donor gender, disease histology (myeloid or lymphoid malignancy), related or unrelated donor to recipient status, and type of conditioning regimen (full dose or reduced intensity conditioning) and observed a result similar to that from the unadjusted analysis. Specifically, adjusted PAM achieved a minimum cross-validated misclassification error rate of 9 among the entire cohort of 70 samples. Finally, the entire analysis was bootstrapped and cross-validated with similar results obtained; the genes with the top frequencies for discriminating cGvHD were over-expression of IL1R2, ADAMTS2 and TS3, TPST1, SESN1, AREG, IRS2, GPER, BCAT1 and CxCR7. IL1R2 is the decoy receptor for the pro-inflammatory IL1 cytokine, ADAMTS2 and TS3 are pro-collagen modifying enzymes that regulate the clipping of collagen molecules to impart structure to connective tissue, AREG is a key gene that underlies the development of oral lichen planus, and pathway analysis revealed that the combination of the other over-expressed genes converge through IL-4, Il-6 and IL-10 signaling pathways which are considered GvHD blocking signals and controllers of inflammation.

Conclusion:
In the current discovery phase study we used expression profiling of peripheral blood mononuclear cells combined with biostatical analyses and identified several candidate biomarkers that associated with chronic GvHD. The genes and signaling pathways highlighted in the current analysis suggest that compensatory responses that control inflammation or are involved with profibrotic matrix remodeling represent a dominant GvHD “gene footprint”. The identified genes have biological relevance to chronic GvHD and may further the understanding of its pathogenesis.

Disclosures:
No relevant conflicts of interest to declare.