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Panels and models for accurate prediction of tumor mutation burden in tumor samples.


ABSTRACT: 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.

SUBMITTER: Martinez-Perez E 

PROVIDER: S-EPMC8044185 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Panels and models for accurate prediction of tumor mutation burden in tumor samples.

Martínez-Pérez Elizabeth E   Molina-Vila Miguel Angel MA   Marino-Buslje Cristina C  

NPJ precision oncology 20210413 1


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,  ...[more]

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