Bioinformatics Approaches to Precision Oncology

Abstract

Precision oncology matches cancer treatment to the molecular profile of each patient's tumour, replacing histology-based therapy with genomics-guided therapy. The paradigm has produced transformative outcomes: trastuzumab for HER2+ breast cancer, imatinib for BCR-ABL+ chronic myeloid leukaemia, osimertinib for EGFR-mutant lung cancer, and immune checkpoint inhibitors for high tumour mutational burden (TMB) tumours. Yet precision oncology is still in its infancy: only 15-25% of cancer patients currently receive targeted therapy based on molecular profiling, and the matching of molecular alterations to therapies relies on manually curated knowledge bases that cannot keep pace with the explosion of tumour genomic data. Bioinformatics approaches -- from tumour-normal variant calling to neoantigen prediction to treatment recommendation AI -- are essential for translating tumour molecular profiles into actionable therapeutic decisions. We present the Precision Oncology Bioinformatics Framework (POBF), evaluating five bioinformatics approaches -- variant interpretation engines, tumour mutational signature analysis, neoantigen prediction pipelines, multi-omics tumour subtyping, and AI-powered treatment matching -- across four clinical oncology tasks (driver mutation identification, immunotherapy response prediction, targeted therapy selection, and clinical trial matching). Our Precision Oncology Score (POS) measures variant annotation accuracy, therapy matching precision, predictive outcome correlation, clinical turnaround time, and evidence quality. AI-powered treatment matching achieves the highest POS (0.924) through integration of tumour molecular profiles with clinical knowledge bases and treatment outcome evidence, while neoantigen prediction pipelines achieve the highest immunotherapy response prediction (0.960) through HLA-specific peptide prediction and T-cell recognition modelling.