(B-41) Biomarkers from Subcutaneous Engineered Tissues Predict Acute Rejection of Organ Allografts

Introduction:: As there is no assay to predict the risk of acute cellular allograft rejection (ACAR), clinicians rely on protocol graft biopsy and aggressive, one-size-fits-all immunosuppression¹. Immunosuppression protects grafts but increases systemic toxicities². Endomyocardial biopsy (EMB) is a flawed standard for heart transplant surveillance and diagnosing ACAR, as histological evidence of rejection inherently lags behind molecular biomarkers^1,3 and suffers from variability⁴. Noninvasive alternatives to EMB in ACAR surveillance, including gene profiling and donor-derived cell-free DNA⁵, also measure lagging indicators of ACAR. A minimally invasive surveillance method is urgently needed to quantify early risk of rejection for minimizing graft biopsy and personalizing immunosuppression (Fig 1).
We have developed porous biomaterial implants (“scaffolds”) for minimally-invasive sampling. These scaffolds amass immune cells producing biomarkers of disease as an engineered immunological niche⁶, and gene expression in biopsied scaffolds predicts disease onset in murine metastatic cancer and multiple sclerosis^7,8.



Materials and Methods:: Biomaterial scaffolds accumulate immune cells producing biomarkers of ACAR as an engineered immunological niche. We implanted subcutaneous poly-caprolactone scaffolds in murine heart or skin transplant recipients to determine if they could identify ACAR. Rag2KO mice received full MHC mismatch allografts or syngeneic controls, and ACAR was initiated as a tunable event by C57BL/6 T cell transfer (Fig 2A). Scaffolds were explanted and analyzed via histology and differential gene expression by RNA sequencing. Gene expression was analyzed by elastic net regularization for differential expression across mice, tissue, and day to generate a 10-30 gene signature of ACAR risk. We performed singular value decomposition and supervised machine learning (Random Forest^TM) to derive single-metric scores and a predictive model⁷ for the probability that a graft is rejecting.

Results, Conclusions, and Discussions:: The scaffolds provided a novel measure of graft health. We identified predictive biomarkers of ACAR conserved in both in murine heterotopic heart transplant and skin transplant using cell-capture scaffolds. We identified 43 highly differentially-expressed genes as organ-agnostic biomarkers of ACAR (FIG 2A). These 43 novel biomarkers strongly differentiate between pre-symptomatic ACAR, symptomatic ACAR, and graft recipients with healthy allografts (Fig 2B).

Gene expression in the cell-capture scaffold identified a biomarker signature of early ACAR, without invasive biopsy. Scaffold cell capture technology provides a leading indicator of ACAR by monitoring tissue-specific immune responses associated with allograft rejection. This implantable scaffold enables minimally invasive histological evaluation and molecular calculation of the early risk of rejection to reduce the frequency of routine EMB and personalize immune suppression that could prolong transplant life while minimizing risk.


Acknowledgements (Optional): :

References (Optional): : 1. Miller, CA. Heart 99, 445–453 (2013).

2. Dandel, M. Transpl. Immunol. 23, 93–103 (2010).

3. Lander, MM. Curr. Transplant. Rep. 3, 367–374 (2016).

4. Crespo-Leiro, MG. Transplantation 94, 1172–1177 (2012).

5. Verhoeven, JGHP. Ther. Drug Monit. 40, 515–525 (2018).

6. Vegas, AJ. Nat. Biotechnol. 34, 345–352 (2016).

7. Oakes, RS. Cancer Res. canres.1932.2019 (2019).

8. Morris, AH. Nat. Commun. 11, 3871 (2020).