Pathway Signatures Derived from On-treatment Tumor Specimens Predict Response to Anti-PD1 Blockade in Metastatic Melanoma
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ABSTRACT:
PROVIDER: PRJNA706446 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13853353_1.fastq.gz | Fastqsanger.gz | |||
| SRR13853353_2.fastq.gz | Fastqsanger.gz | |||
| SRR13853354_1.fastq.gz | Fastqsanger.gz | |||
| SRR13853354_2.fastq.gz | Fastqsanger.gz | |||
| SRR13853355_1.fastq.gz | Fastqsanger.gz |
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Project description: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 were derived from pre-treatment biopsy samples. Here, we developed pathway-based signatures that predict response of metastatic melanoma to anti-PD1-based therapies in four independent datasets with RNAseq and clinical response data available for both pre- and on-treatment metastatic melanomas. We first identified pathway signatures that were significantly enriched in tumor specimens from anti-PD1 responders (R) compared to non-responders (NR) at pre-treatment and on-treatment time points, respectively. We also identified pathway signatures that were differentially expressed in pre-treatment versus on-treatment samples derived from R. Finally, we interrogated the capacity of the two types of signatures in predicting response of metastatic melanoma to anti-PD1 therapies in comparison with existing gene expression signatures. And we also investigated the effect of biopsy sites at the same biopsy time point on predictive performance of response to anti-PD1 therapy. Overall, we demonstrate that pathway-based signatures derived from on-treatment tumor specimens are highly predictive of response to anti-PD1 blockade therapies in patients with metastatic melanoma.
2021-08-15 | GSE168204 | GEO
Project description:In this comprehensive study, the authors have developed concise models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 Immune Checkpoint Blockade (ICB) treatment in individual tumors. It's important to note that their validation was performed in smaller, independent cohorts, constrained by data availability. The authors have developed two Logistic Regression based models for Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma. The main predictive features for the Ipilimumab treated patients are MHC-II HLA, LDH at treatment initiation and the presence of lymph node metastases (LN met), chosen using forward selection methodology. The main predictive features for the Ipilimumab naive patients are tumor heterogeneity, tumor ploidy and tumor purity, chosen using forward selection methodology.
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2023-11-17 | MODEL2310150001 | BioModels
Project description:Purpose Antibody-blockade of PD-1 shows durable responses, but so far, few biomarkers accurately predict if patients with metastatic melanoma will respond. Experimental design We analyzed baseline serum samples (n=56) and tumor cell cultures (n=8), from melanoma patients treated with anti-PD1 therapy. Two approaches concentrating low abundant proteins in serum and two different mass spectrometry (MS) methods were applied: depletion of high abundant proteins with shotgun-MS, and N-glycosite enrichment combined with SWATH-MS. Tumor cell cultures were processed as whole cell lysates with subsequent shotgun-MS. Results Focusing on the non-responder proteome, we identified pathways such as inflammatory processes, cell adhesion and migration, scavenger receptor activity, RAGE receptor binding, platelet aggregation, neutrophil degranulation and integrin signaling to be upregulated. Analyzing the tumor cell proteome revealed a distinctive immunophenotype with CD antigens highly overexpressed in non-responders. Validation of a subset of these markers, performed by immunofluorescence staining of a tissue microarray, revealed a higher mean of positive cells in the tumor microenvironment of non-responders. Validation of serum proteome markers on a second baseline cohort by using a timsTOF for ion mobility MS, revealed significant differences between responder and non-responder for the proteins ORM2, SERPINA1, EFEMP1, and VASN. TCGA survival analysis demonstrated prognostic significance for multiple proteins of our signature correlating with lower overall survival in melanoma patients. Based on this study, we suggest a protein-panel for responsiveness to PD-1 blockade consisting of 14 serum and 33 tumor cell markers that will be the basis for further research.
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Project description:Recent success in cancer immunotherapy has come from the blockade of inhibitory receptors on T cells, such as programmed cell death-1, which can induce a state of T cell exhaustion upon constant antigen stimulation. Understanding miRNA regulation of PD1 can be useful to discover miRNAs for use in therapy or as prognostic markers in various diseases including cancer, autoimmunity and transplantation. We used microarrays to discover global miRNA expression changes upon PD1 upregulation and identified miRNAs that are both up- and down-regulated. B16F10 cells were injected subcutaneously into C57BL/6 mice and 16 days later CD4+PD1+ and CD4+PD1- were sorted from the lymph nodes and spleen for RNA extraction and hybridization on Affymetrix miRNA array.
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Project description: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.
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Project description:Functional gene expression signatures (FGES) from pretreatment biopsy samples have been used to predict the responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies. However, there are no predictive FGE signatures from patients receiving treatment. Here, using the Elastic Net Regression (ENLR) algorithm, we analyzed transcriptomic and matching clinical data from a dataset of patients with metastatic melanoma treated with ICB therapies and produced an FGE signature for pretreatment (FGES-PRE) and on-treatment (FGES-ON). Both the FGES-PRE and FGES-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.44-0.81 and 0.82-0.83, respectively. Then, we combined all test samples and obtained AUCs of 0.71 and 0.82 for the FGES-PRE and FGES-ON signatures, respectively. The FGES-ON signatures had a higher predictive value for prognosis than the FGES-PRE signatures. The FGES-PRE and FGES-ON signatures were divided into high- and low-risk scores using the signature score mean value. Patients with a high FGE signature score had better survival outcomes than those with low scores. Overall, we determined that the FGES-ON signature is an effective biomarker for metastatic melanoma patients receiving ICB therapy. This work would provide an important theoretical basis for applying FGE signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.
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Project description:A previously established bioassay using Jurkat cells overexpressing PD1 and Lag3 allowed for assessment of simultaneous blockade of PD1 and LAG3 pathways in an in-vitro setting and demonstrated that an antibody cocktail increased IL-2 levels 5-fold better than single agent treatment. To gain understanding of signal transduction events RNA-Seq analysis of cell pellets individually treated with LAG3 or PD1 antibodies was used to reveal modest immune activation however, 5-fold more genes were upregulated upon combination treatment. There were increases in costimulatory genes like CD28, CD5, CD6 as well as other intracellular signaling molecules like LCP2 and ITK. Given the role of ERK in immune activation of T cells, pERK levels of Jurkat cells in the assay were evaluated, indicating that ERK phosphorylation was impacted on PD1 and LAG3 engagement with their ligands and this could be reversed by antibody blockade. A small molecule phosphatase inhibitor NSC87877, when combined with the PD1 antibody, could phenocopy the effect of combining PD1 and LAG3 blocking antibodies. CD28 has a recognized role in PD1 signaling but the impact on LAG3 signaling remains unknown. CD28 knockout cells demonstrated an overall muted IL-2 response but retained combination benefit in terms of IL-2 production in the context of LAG3 and PD1 co-blockade versus individual antibody treatments. Taken together, these observations provide new insights on the impact of LAG3 and PD1 co-blockade and provides additional support for ongoing immunotherapy clinical trials that combine PD1 and LAG3 antibodies.
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