Inferring Tumor Growth Models from Variant Allele Frequencies

Identifier:  TFM:2518

Handle: https://hdl.handle.net/20.500.11797/TFM2518

Authors:  Sanz Ruiz, Raúl

Abstract:
Understanding tumor evolution is key to improving cancer diagnosis and treatment. This thesis presents TAFI (Tumor Allele Frequency Interpreter), a statistical framework that infers evolutionary dynamics from variant allele frequency (VAF) distributions in bulk tumor sequencing data. TAFI estimates tumor purity, clonal and subclonal real mutation burdens, and classifies tumors between two growth modes: Wright-Fisher (constant) and exponential. Applied to large-scale cancer datasets, TAFI outperforms existing methods in reconstructing clonal structure and highlights associations between the classified model, gene expression, and survival. These findings support the relevance of evolutionary modeling in cancer research.