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 in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-04-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-021-00169-0 |
| _version_ | 1797428641951383552 |
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| author | Elizabeth Martínez-Pérez Miguel Angel Molina-Vila Cristina Marino-Buslje |
| author_facet | Elizabeth Martínez-Pérez Miguel Angel Molina-Vila Cristina Marino-Buslje |
| author_sort | Elizabeth Martínez-Pérez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T09:01:50Z |
| format | Article |
| id | doaj.art-1b76c4e637d44f018df09d860ec13ed7 |
| institution | Directory Open Access Journal |
| issn | 2397-768X |
| language | English |
| last_indexed | 2024-03-09T09:01:50Z |
| publishDate | 2021-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj.art-1b76c4e637d44f018df09d860ec13ed72023-12-02T11:17:12ZengNature Portfolionpj Precision Oncology2397-768X2021-04-01511810.1038/s41698-021-00169-0Panels and models for accurate prediction of tumor mutation burden in tumor samplesElizabeth Martínez-Pérez0Miguel Angel Molina-Vila1Cristina Marino-Buslje2Bioinformatics Unit, Fundación Instituto Leloir, Buenos Aires, C1405BWE, Avda. Patricias Argentinas 435 C1405BWELaboratorio de Oncología/Pangaea Oncology, Hospital Universitario Quirón DexeusBioinformatics Unit, Fundación Instituto Leloir, Buenos Aires, C1405BWE, Avda. Patricias Argentinas 435 C1405BWEAbstract 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.https://doi.org/10.1038/s41698-021-00169-0 |
| spellingShingle | Elizabeth Martínez-Pérez Miguel Angel Molina-Vila Cristina Marino-Buslje Panels and models for accurate prediction of tumor mutation burden in tumor samples npj Precision Oncology |
| title | Panels and models for accurate prediction of tumor mutation burden in tumor samples |
| title_full | Panels and models for accurate prediction of tumor mutation burden in tumor samples |
| title_fullStr | Panels and models for accurate prediction of tumor mutation burden in tumor samples |
| title_full_unstemmed | Panels and models for accurate prediction of tumor mutation burden in tumor samples |
| title_short | Panels and models for accurate prediction of tumor mutation burden in tumor samples |
| title_sort | panels and models for accurate prediction of tumor mutation burden in tumor samples |
| url | https://doi.org/10.1038/s41698-021-00169-0 |
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