Fadia Ashmawi

Bioinformatics Specialization · Cohort 2026

Myeloid–T-cell Communication States Predict BCG Response and Prognosis in NMIBC

This project investigates whether myeloid–T-cell communication states can distinguish BCG response in non-muscle-invasive bladder cancer (NMIBC) beyond differences in cell abundance. BCG is a standard immunotherapy for intermediate- and high-risk NMIBC, yet approximately 30–50% of high-risk patients do not achieve a durable response, and validated pre-treatment biomarkers remain limited. Because BCG efficacy depends on antigen presentation and T-cell priming, this study focuses on the signalling interface between myeloid cells and T cells rather than simply asking which cell populations are more abundant. The study reanalyses GSE322495, a CD45-enriched single-cell RNA-sequencing dataset containing 21 libraries from 19 NMIBC patients across BCG-naive, BCG-responsive, and BCG-unresponsive groups. A reproducible analysis pipeline was developed incorporating AbSeq feature separation, adaptive MAD-based quality control, SCTransform normalisation, Harmony integration, clustering and marker-based cell annotation, pseudobulk DESeq2 analysis, compositional analysis, and CellChat-based ligand–receptor communication inference restricted to bladder tissue and myeloid-to-T-cell interactions. The analysis found that cellular composition alone did not significantly distinguish the response groups, whereas predicted myeloid-to-T-cell signalling showed distinct communication states. In particular, the MHC-II:MHC-I signalling ratio was 0.33 in BCG-naive, 2.97 in BCG-responsive, and 0.68 in BCG-unresponsive tissue. This pattern remained after a sensitivity analysis removing potentially confounding CD4-receptor edges on CD8 T cells, and signalling differences were not explained simply by the number of myeloid sender cells. Overall, the findings suggest that communication state may provide complementary biological information beyond cell abundance and could represent a promising hypothesis for future BCG-response biomarker development and patient stratification. However, the results are explicitly hypothesis-generating and require validation in independent cohorts and experimental systems before clinical application.

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