AITAC

AITAC infers tumor purity and absolute copy numbers from high-throughput sequencing (HTS) data to quantify tumor and normal cell contributions in mixed tumor samples.

Key Features:

• Tumor Purity Estimation: Estimates the proportion of tumor cells relative to normal cells from HTS data without requiring pre-detected mutation genotypes.
• Copy Number Inference: Infers absolute copy numbers by analyzing read depths (RDs) in genomic regions with copy number losses.
• Non-linear Modeling Approach: Employs a non-linear model correlating observed RDs with expected RDs across varying tumor purity levels.
• Exhaustive Search Strategy: Performs an exhaustive search across candidate tumor purity values and selects the value that minimizes deviations between observed and expected RDs.
• Performance Validation: Validated on simulated and real sequencing datasets, showing improved performance compared to classical methods.

Scientific Applications:

• Tumor Heterogeneity: Quantifies intra-tumor genetic diversity by combining tumor purity and copy number estimates from HTS data.
• Cancer Progression: Detects and characterizes copy number variations associated with tumor progression and prognosis in longitudinal sequencing studies.
• Personalized Medicine: Supports patient-specific genomic profiling and therapeutic decision-making through accurate purity and copy number estimation.

Methodology:

Analyzes read depths at regions with copy number losses from HTS data, applies a non-linear model relating observed and expected RDs as a function of tumor purity, and uses an exhaustive search over candidate purity values to minimize deviations; analysis does not rely on mutation genotype data.