Dataset Information

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

ACCESS DATA

Similar Datasets

Project description:PurposeAnti-programmed cell death protein 1 (PD1)±anti-cytotoxic T-lymphocyte associated protein 4 (CTLA4) immune checkpoint inhibitors (ICIs) are standard therapeutic options for metastatic melanoma. We assessed whether biologic subtype according to primary tumor type or genomic subtype can function as predictive biomarkers for anti-PD1±anti-CTLA4 ICI in patients with advanced melanoma.MethodsWe performed a single-center retrospective cohort analysis of patients who received anti-PD1±anti-CTLA4 ICI for advanced melanoma between 2012 and 2019. Primary tumor type, BRAF and NRAS mutation status, and other covariates were abstracted from chart review. Log-rank tests and multivariable Cox regression models were used to assess differences in clinical progression-free (cPFS) and overall survival (OS).ResultsWe identified 230 patients who received 249 lines of anti-PD1±anti-CTLA4 ICI for unresectable or metastatic disease. Of these patients, 74% were cutaneous, 11% mucosal, 8% unknown primary and 7% acral. BRAF and NRAS mutations were identified in 35% and 28% of patients, respectively. In multivariable analyses of the entire cohort, acral or mucosal primary tumor type, >3 metastatic sites, elevated LDH were predictive of shorter cPFS and OS. Combination ICI therapy was associated with longer cPFS (HR 0.57, 95% CI 0.38 to 0.86, p=0.007) and OS (HR 0.42, 95% CI 0.28 to 0.65, p<0.001). Combination ICI was significantly associated with longer OS in unknown primary and mucosal melanoma. There was a non-significant trend toward longer OS with anti-PD1+anti-CTLA4 in cutaneous melanoma, but not in acral melanoma. In multivariable analyses, combination ICI was associated with longer OS in NRAS (HR 0.24, 95% CI 0.10 to 0.62, p=0.003, n=69) and BRAF V600E/K (HR 0.47, 95% CI 0.24 to 0.90, p=0.024, n=86) mutant melanoma but not BRAF/NRAS wild-type (n=94) melanoma.ConclusionsIn our cohort, primary melanoma tumor type and genomic subtype were independent predictive markers of cPFS and OS for patients with metastatic melanoma receiving anti-PD1 ICI. Primary tumor type and genomic subtype-including NRAS-should be further evaluated in prospective clinical trials to determine their value as predictive markers. Biologic subtypes may facilitate clinical decision-making when recommending combination ICI treatment (anti-PD1±anti-CTLA4) versus anti-PD1 alone for patients with metastatic melanoma.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets