scOPE identifies which driver-associated expression programs transfer from bulk tumors to single cells

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) offers high resolution of tumor heterogeneity but struggles to detect somatic mutations due to expression and transcription requirements.
• Bulk tumor sequencing provides matched genotype and expression data but lacks cellular resolution.
• Bridging this gap is crucial for understanding mutation-driven tumor evolution.

Purpose of the Study:

• To develop and validate a computational tool, scOPE (single-cell Oncological Prediction Explorer), for inferring cancer driver mutations from scRNA-seq data.
• To assess the transferability and accuracy of cancer-specific expression axes learned from bulk tumors onto single-cell transcriptomes.
• To establish confidence metrics for evaluating the reliability of inferred mutation-associated transcriptional programs.

Main Methods:

• scOPE learns cancer-specific, driver-associated expression axes from bulk tumor data.
• These learned axes are then projected onto single-cell transcriptomes without refitting.
• A confidence score integrating bulk transferability, spatial coherence, score concentration, and copy-number variation (CNV) agreement is used to assess program reliability.

Main Results:

• The scOPE tool demonstrated selective transferability of driver-associated expression programs across seven malignancies, with 102 out of 158 models meeting safety criteria.
• High accuracy (AUROC ≥ 0.90) was achieved for specific driver-cancer pairs, notably AML NPM1 (0.971), glioblastoma IDH1 (0.963), and pancreatic adenocarcinoma KRAS (0.944).
• A confidence score effectively prioritized validated programs in AML and revealed mutation-associated transcriptional programs enriched in mutant-labeled cells (NPM1, TP53, DNMT3A), with NPM1 separation persisting at the patient level.

Conclusions:

• scOPE enables the recovery of continuous, mutation-associated transcriptional axes from existing scRNA-seq data, providing valuable insights into tumor biology.
• The tool offers explicit diagnostics to indicate when inferred transcriptional programs should be interpreted with caution.
• Inferred scores track CNV burden and can resolve discordant malignant populations, offering a new dimension for cancer subtyping and treatment monitoring.