Leveraging single-cell sequencing to classify and characterize tumor subgroups in bulk RNA-sequencing data

Purpose: Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies. Methods: Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. To integrate interpretable features from the bulk (n = 77 samples) and single-cell profiling (∼ 10 K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models, RNA-inferred copy number variation (CNV) signals, and the initial bulk model to create a meta-model. Results: While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ∼ 8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n = 789 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (∼ 200 K cells) and bulk TCGA glioma data (n = 711 samples). Conclusion: Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.