unCTC

unCTC provides unbiased identification and characterization of circulating tumor cells (CTCs) from single-cell RNA sequencing (scRNA-Seq) data to enable analysis of metastatic tumor heterogeneity and expression-based copy number variation.

Key Features:

• Implementation: An R package for analysis of single-cell transcriptomic data.
• Unbiased Identification: Distinguishes CTCs from white blood cells (WBCs) in scRNA-Seq data to detect atypical CTCs and reduce WBC contamination.
• DeepDictionaryLearning with K-means Clustering Cost (DDLK): A clustering method that operates in pathway space and uses a K-means clustering cost to segregate CTCs from WBCs.
• Expression-Based Copy Number Variation (CNV) Inference: Infers CNVs from gene expression profiles to reveal genomic alterations in CTCs.
• Combinatorial Marker-Based Verification: Verifies malignant phenotypes using combinations of markers rather than single-antigen methods.

Scientific Applications:

• Metastatic Cancer Research: Characterizes CTC transcriptomes and inferred CNVs to study metastasis and tumor heterogeneity.
• Disease Monitoring and Management: Provides transcriptomic and CNV information from CTCs to support tracking disease progression and evaluating treatment response.

Methodology:

Validated on scRNA-Seq data from breast cancer patients with CTCs captured and profiled using an integrated ClearCell®FX and PolarisTM workflow combining size-based separation with marker-based WBC depletion.