Unknown

Dataset Information

0

Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma.


ABSTRACT: Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45-0.69 and 0.85-0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.

SUBMITTER: Du K 

PROVIDER: S-EPMC8519947 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

2021-08-15 | GSE168204 | GEO
| PRJNA706446 | ENA
| S-EPMC8069589 | biostudies-literature
| S-EPMC6094609 | biostudies-literature
| S-EPMC7831745 | biostudies-literature
| S-EPMC8220213 | biostudies-literature
| S-EPMC6932919 | biostudies-literature
| S-EPMC6898788 | biostudies-literature
| S-EPMC7409244 | biostudies-literature
| S-EPMC8458464 | biostudies-literature