Our Impact

Targeting the rheumatoid arthritis synovial fibroblast via cyclin dependent kinase inhibition (TRAFIC): An early phase trial 

Researchers

• Professor Andrew Filer (Department of Inflammation and Ageing, University of Birmingham)
• Professor John Isaacs (Faculty of Medical Sciences, Newcastle University)
• Dr Arthur Pratt (Faculty of Medical Sciences, Newcastle University)
• Dr David Gardner (Birmingham Tissue Analytics, University of Birmingham)
• Dr Jack McMurray (Birmingham Tissue Analytics, University of Birmingham)
• Dr Saba Nayar (Department of Inflammation and Ageing, University of Birmingham)

Technologies

• Immunohistochemistry
• Visiopharm
• Nanostring nCounter
• AKOYA PhenoImager
• Rheumatoid arthritis  

Project Description

Fibroblasts underpin synovial inflammation in rheumatoid arthritis (RA) by supporting the survival and inflammatory phenotype of neighbouring immune cell populations. Nevertheless, biological Disease-modifying anti-rheumatic drugs (DMARDs) focus on the immune component rather than directly targeting synovial fibroblasts. TRAFIC aims to determine the impact of Seliciclib, a Cyclin Dependent Kinase (CDK)- 2,7,9 inhibitor, upon fibroblast mediated inflammation among anti-TNF refractory RA patients.

Birmingham Tissue Analytics (BTA) has been involved in the delivery of Good Clinical Practice (GCP) tissue-based outcome measures for this trial, handling sample processing and storage, assay validation and sample analysis for several outcome measures:

1. Quantification of sublining CD68+ cells in response to treatment: This is known to be a sensitive biomarker of RA treatment response1. BTA developed an imaging assay in which Formalin Fixed Paraffin Embedded (FFPE) RA biopsy tissue from patients pre/post Seliciclib treatment were stained for CD68 using Leica Bond RX autostainer, imaged using a Leica Aperio AT2 slide scanner with image hosting on the Leica eslidemanager portal. Images were analysed using Visiopharm including CD68+ cell quantification.
2. Multiple IHC analysis of Seliciclib-sensitive PD biomarkers2,3: We developed multiplex IHC panels to quantify the expression of biomarkers of CDK inhibition, ki67, cleaved caspase 3, MCL-1, phospho-Retinoblastoma in either macrophage (CD68+) or fibroblast (CD90/PDPN+) populations.
3. Bulk gene expression: We analysed a targeted panel of 10 Seliciclib-sensitive PD/PK biomarkers using our Nanostring nCounter platform on RNA extracted from both synovial biopsy tissue and whole blood taken pre/post Seliciclib treatment.

_{¹ Bresnihan B, Pontifex E, Thurlings RM, Vinkenoog M, El-Gabalawy H, Fearon U, Fitzgerald O, Gerlag DM, Rooney T, van de Sande MG, Veale D, Vos K, Tak PP. Synovial tissue sublining CD68 expression is a biomarker of therapeutic response in rheumatoid arthritis clinical trials: consistency across centers. J Rheumatol. 2009 Aug;36(8):1800-2. doi: 10.3899/jrheum.090348. PMID: 19671815.}

_{² Rossi AG, Sawatzky DA, Walker A, Ward C, Sheldrake TA, Riley NA, Caldicott A, Martinez-Losa M, Walker TR, Duffin R, Gray M, Crescenzi E, Martin MC, Brady HJ, Savill JS, Dransfield I, Haslett C. Cyclin-dependent kinase inhibitors enhance the resolution of inflammation by promoting inflammatory cell apoptosis. Nat Med. 2006 Sep;12(9):1056-64. doi: 10.1038/nm1468. Epub 2006 Sep 3. Erratum in: Nat Med. 2006 Dec;12(12):1434. Dosage error in article text. PMID: 16951685.}

_{³ Hsieh WS, Soo R, Peh BK, Loh T, Dong D, Soh D, Wong LS, Green S, Chiao J, Cui CY, Lai YF, Lee SC, Mow B, Soong R, Salto-Tellez M, Goh BC. Pharmacodynamic effects of seliciclib, an orally administered cell cycle modulator, in undifferentiated nasopharyngeal cancer. Clin Cancer Res. 2009 Feb 15;15(4):1435-42. doi: 10.1158/1078-0432.CCR-08-1748. PMID: 19228744.}

Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes

Researchers

• Professor Andrew Filer (Department of Inflammation and Ageing, University of Birmingham)
• Professor Jennifer Anolik (Allergy, Immunology & Rheumatology, University of Rochester)
• Dr Saba Nayar (Department of Inflammation and Ageing, University of Birmingham)
• Dr David Gardner (Birmingham Tissue Analytics, University of Birmingham)

Technologies

• Multiplex Immunohistochemistry
• Visiopharm
• AKOYA PhenoImager
• Rheumatoid arthritis 

Project Description

This work was supported by the Accelerating Medicines Partnership® Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP® RA/SLE) Network. The study profiled synovial tissue from a cohort of 79 patients across a clinical spectrum of rheumatoid arthritis with multi-modal single-cell RNA-sequencing and surface protein data coupled with histology in order identify a cellular atlas of rheumatoid arthritis synovial tissue1.

Single-cell RNA-sequencing identified 77 distinct cell states within RA synovial biopsy tissue. Of these, 6 major cell groups; T cells, B cells, Fibroblasts, Myeloid cells, Endothelial cells, could be used to stratify patients based on relative cell type abundance (CTAPs). We performed further histological analyses to further verify these CTAPs spatially.

BTA developed a multiplex immunohistochemistry panel using the AKOYA PhenoImager platform to identify immune/stromal cell populations within these CTAPs. This panel consisted of CD3, CD34, CLIC5, CD90, HLA-DR, CD68. Image analysis including cell density quantification demonstrated that CTAPs map to cellular histology with regards to cell proportions.

References

_{¹Zhang F, Jonsson AH, Nathan A, Millard N, Curtis M, Xiao Q, Gutierrez-Arcelus M, Apruzzese W, Watts GFM, Weisenfeld D, Nayar S, Rangel-Moreno J, Meednu N, Marks KE, Mantel I, Kang JB, Rumker L, Mears J, Slowikowski K, Weinand K, Orange DE, Geraldino-Pardilla L, Deane KD, Tabechian D, Ceponis A, Firestein GS, Maybury M, Sahbudin I, Ben-Artzi A, Mandelin AM 2nd, Nerviani A, Lewis MJ, Rivellese F, Pitzalis C, Hughes LB, Horowitz D, DiCarlo E, Gravallese EM, Boyce BF; Accelerating Medicines Partnership: RA/SLE Network; Moreland LW, Goodman SM, Perlman H, Holers VM, Liao KP, Filer A, Bykerk VP, Wei K, Rao DA, Donlin LT, Anolik JH, Brenner MB, Raychaudhuri S. Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes. Nature. 2023 Nov;623(7987):616-624. doi: 10.1038/s41586-023-06708-y. Epub 2023 Nov 8. PMID: 37938773; PMCID: PMC10651487.} 

AI-enabled Analysis of H&E images from Sjögren Disease patients

Researchers

• Dr Saba Nayar (Department of Inflammation and Ageing, University of Birmingham)
• Dr Sebastian Gilbert (Birmingham Tissue Analytics, University of Birmingham)
• Dr Jeremy Pike (Advanced Research Computing, University of Birmingham)
• Professor Fabian Spill (School of Mathematics, University of Birmingham)

Technologies

• Haematoxylin and eosin
• Brightfield,
• Salivary glands
• Sjögren’s disease
• Computer vision
• Machine learning/AI
• Python
• Deep-learning 

Project Description

Analysis of microscopy images from patient biopsies, particularly Hematoxylin and Eosin (H&E) staining, is pivotal in diagnosing various diseases, including cancers, infectious diseases, and inflammatory conditions.

This Institute for Data and AI funded pump-priming project developed pilot analysis workflows to automatically analyse H&E images from Sjögren’s Disease (SjD) patients - a chronic autoimmune disease. The images are very large, so we trained a deep neural network (UNET with custom PyTorch implementation) to identify the key regions of the tissue associate with the disease —specifically, lymphomononuclear cell clusters organized as periductal infiltrates (foci), both with and without germinal centres. We then used a second pre-trained deep neural network (Hover-Net implemented in TIA-Toolbox framework) to identify the location and cell type of individual cells within these key regions.

Going forward this framework will allow the researchers to automate the analysis of H&E images and study the spatial organisation of cells within these foci and their structural variations, advancing our understanding of SjD pathology. The ability to automate H&E image analysis holds significant promise: it paves the way for optimized, tissue-based pathological scoring in clinical trials and may lead to more efficient, consistent histopathological assessments. This approach could support new diagnostic tools and deepen insights into the disease mechanisms underlying SjD.

The pipeline adhered to coding best practices to maximise reusability and was documented to a high standard making it accessible to researchers with a range of coding experience. All model training and prediction was performed on GPUs within a containerised environment run of BlueBEAR. This allowed us to efficiently train customised deep-learning models and then apply them to large scale image data.