Strength in numbers: predicting response to checkpoint inhibitors from large clinical datasets
The advent of immune checkpoint blockers for cancer therapy has spawned great interest in identifying molecular features reflecting the complexity of tumor immunity, which can subsequently be leveraged as predictive biomarkers. In a thorough big-data approach analyzing the largest series of homogeni...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
27 January 2021
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| In: |
Cell
Year: 2021, Jahrgang: 184, Heft: 3, Pages: 571-573 |
| ISSN: | 1097-4172 |
| DOI: | 10.1016/j.cell.2021.01.008 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.cell.2021.01.008 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0092867421000088 
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| Verfasserangaben: | Albrecht Stenzinger, Daniel Kazdal, and Solange Peters |
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| 520 | |a The advent of immune checkpoint blockers for cancer therapy has spawned great interest in identifying molecular features reflecting the complexity of tumor immunity, which can subsequently be leveraged as predictive biomarkers. In a thorough big-data approach analyzing the largest series of homogenized molecular and clinical datasets, Litchfield et al. identified a set of genomic biomarkers that identifies immunotherapy responders across cancer types. | ||
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