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.
Highlights
Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; most of these signatures are derived from pre-treatment biopsy samples
We analyzed four published datasets and one newly generated dataset with RNA sequencing (RNAseq) data available for pre- and on-treatment tumor specimens derived from patients with metastatic melanoma who were treated with anti-PD-1/PD-L1 monotherapy, anti-PD-1/PDL1 monotherapy with prior anti-CTLA-4 monotherapy, or the combination of anti-PD-1 plus anti-CTLA-4 therapies (Fig. 1a, b)
In the Riaz et al dataset[39, 68] patients with 108 biopsies received anti-PD1 monotherapy with (59 biopsies) or without (49 biopsies) prior anti-CTLA-4 therapy (Fig. 1a and Supplementary Data 1a). After excluding those patients without response evaluation criteria in solid tumors (RECIST) (n = 4), and those without RNAseq data (n = 1), a total of 49 biopsies at the timepoint of pre-treatment and 54 biopsies at the timepoint of on-treatment were included in the analysis
Summary
Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; most of these signatures are derived from pre-treatment biopsy samples. We build pathwaybased 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. We develop pathway-based signatures to predict response of metastatic melanoma to anti-PD1-based therapies in four independent datasets with RNAseq data and clinical information available for both pre- and on-treatment metastatic melanomas. We demonstrate that pathway-based signatures derived from ontreatment tumor specimens are highly predictive of response to anti-PD1 blockade therapies in patients with metastatic melanoma
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