CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data

Abstract Single-cell RNA-sequencing (scRNA-seq) facilitates the discovery of gene expression signatures that define cell states across patients, which could be used in patient stratification and precision oncology. However, the lack of standardization in computational methodologies that are used to analyze these data impedes the reproducibility of signature detection. To address this, we developed CanSig, a comprehensive benchmarking tool that evaluates methods for identifying transcriptional signatures in cancer. CanSig integrates metrics for batch correction and biological signal conservation with a transcriptional signature correlation metric to score methods according to signature rediscovery, cross-dataset reproducibility, and clinical relevance. CanSig was applied to thirteen methods on twelve scRNA-seq datasets from five human cancer types—glioblastoma, breast cancer, lung adenocarcinoma, rhabdomyosarcoma, and cutaneous squamous cell carcinoma—representing 185 patients and 174,000 malignant cells. The signatures identified with these methods correlated with clinically relevant outcomes, including patient survival and lymph node metastasis. These results identified Harmony, BBKNN, and fastMNN as the highest-scoring integration methods for discovering shared transcriptional states in cancer. Overall, CanSig provides a standardized, reproducible framework for uncovering clinically relevant cancer cell states in single-cell transcriptomics.