PhD Scientific Days 2026

Poster Session 2.E - Pathological and Oncological Sciences

Mapping Drug Targets at Single-cell Resolution in Kidney Cancer

Name of the presenter

Dobolyi, Zsófia

Institute/workplace of the presenter

Bioinformatics Department

Authors

Zsófia Dobolyi¹, Otilia Menyhart, PhD¹, Balazs Gyorffy, Prof. Dr.¹
1: Bioinformatics Department

Text of the abstract

Introduction
Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer. Although nephrectomy can cure localized disease, 20-30% of patients present with metastatic disease, and recurrence remains a major clinical challenge.

Aims
Despite improved survival with immune checkpoint and tyrosine kinase inhibitors, resistance is common and durable responses are rare. We therefore aimed to map the cellular expression of established and novel therapeutic targets in ccRCC at single-cell resolution.

Method
We assembled an integrated single-cell RNA sequencing dataset of treatment-naive, surgically resected human ccRCC samples from the NCBI Gene Expression Omnibus and the Human Cell Atlas. After quality control, the dataset comprised ~400,000 cells across three conditions: normal kidney (68,069 cells; 33 patients), tumor-adjacent tissue (50,145 cells; 17 patients), and tumor tissue (276,564 cells; 38 patients). Data were analyzed in Python using Scanpy, with integration via scVI and cell-type annotation using a custom CellTypist-based model. Tumor cells were identified by inferred copy-number profiling. Therapeutic targets were curated from ChEMBL, and novel candidates were prioritized using AUC analysis in annotated cancer cells.

Results
We identified 43 distinct cell types across normal, adjacent, and tumour tissues. Established therapeutic targets showed limited and heterogeneous expression in malignant cells and were generally weakly expressed across the tumour microenvironment, which may contribute to the limited durability of current therapies. Cancer cells in ccRCC predominantly expressed CA9, EPAS1, and VEGFA, supporting the relevance of HIF-axis-directed therapeutic strategies. AUC analysis highlighted CD24 (AUC 0.95; 98% of cancer cells vs. 26% of non-cancer cells), SPP1 (AUC 0.89; 94% vs. 27%), and ANGPTL4 (AUC 0.91; 93% vs. 20%) as promising candidate targets.

Conclusion
We generated an integrated single-cell atlas of approximately 400,000 kidney cells spanning normal, tumor-adjacent, and tumor tissues. Current therapeutic targets showed variable and often weak expression, whereas CD24, SPP1, and ANGPTL4 emerged as promising candidates for further functional and translational validation.

Funding
Zs.D. was supported by the SE250+ fellowship.
O.M. was supported by the Janos Bolyai Scholarship and by the OTKA FK grant (FK147194).