Panels and models for accurate prediction of tumor mutation burden in tumor samples.
Elizabeth Martínez-PérezMiguel Ángel Molina-VilaCristina Marino-BusljePublished in: NPJ precision oncology (2021)
Immune checkpoint blockade (ICB) is becoming standard-of-care in many types of human malignancies, but patient selection is still imperfect. Tumor mutation burden (TMB) is being evaluated as a biomarker for ICB in clinical trials, but most of the sequencing panels used to estimate it are inadequately designed. Here, we present a bioinformatics-based method to select panels and mathematical models for accurate TMB prediction. Our method is based on tumor-specific, forward-step selection of genes, generation of panels using a linear regression algorithm, and rigorous internal and external validation comparing predicted with experimental TMB. As a result, we propose cancer-specific panels for 14 malignancies which can offer reliable, clinically relevant estimates of TMBs. Our work facilitates a better prediction of TMB that can improve the selection of patients for ICB therapy.
Keyphrases
- clinical trial
- end stage renal disease
- healthcare
- endothelial cells
- chronic kidney disease
- high resolution
- squamous cell carcinoma
- machine learning
- genome wide
- palliative care
- newly diagnosed
- randomized controlled trial
- gene expression
- papillary thyroid
- stem cells
- single cell
- case report
- deep learning
- risk factors
- prognostic factors
- pain management
- young adults
- open label
- neural network
- patient reported
- genome wide identification
- bioinformatics analysis