advanta immuno oncology gene expression signatures (fluidigm)
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advanta immuno oncology gene expression signatures
Advanta Immuno Oncology Gene Expression Signatures, supplied by fluidigm, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/advanta+immuno+oncology+gene+expression+signatures/Advanta+IO+Gene+Expression+Control+1/us12043870-2655-178-183
Average 93 stars, based on 1 article reviews
Advanta Immuno Oncology Gene Expression Signatures, supplied by fluidigm, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/advanta+immuno+oncology+gene+expression+signatures/Advanta+IO+Gene+Expression+Control+1/us12043870-2655-178-183
Average 93 stars, based on 1 article reviews
advanta immuno oncology gene expression signatures - by Bioz Stars,
2026-10
93/100 stars
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CRISPR:Article Title: Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer Article Snippet: .. Alternative ICI Response Predictors To compare the predictive value of the resistance signatures to that of other signatures, Applicants repeated the prediction process, as describe in Survival and ICI response predictions, for each of the following gene signatures (Table 29 and 9): (1) cell type specific signatures identified from the scRNA-seq data (as described in the Data-driven signatures of specific cell types section); (2) signatures that characterize melanoma cell states (the AXL-high, MITF-high, and cell cycle states from (Tirosh et al., 2016)); (3) six sets of genes whose CRISPR knockout in melanoma cells conferred resistance or sensitivity (FDR<0.05) to different types of immune killing according to a genome-scale in vivo screen (Manguso et al., 2017); (4) genes whose CRISPR knockout in melanoma cells conferred resistance to T cell killing (top 10 and top 50) in a genome-scale co-culture screen (Patel et al., 2017); (5) immune-related signatures that were identified based on the analysis of multiple pembrolizumab clinical datasets, and were shown to predict the response to pembrolizumab in an independent cohort (Ayers et al., 2017); (6) the Fluidigm Article Title: Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer Article Snippet: The data was obtained from Table S1 of (22); (4) The genes whose guides were most preferentially and significantly enriched (top 10 and top 50) in the co-culture conditions in the genome-scale CRISPR screen of (19), also designed to identify key regulators of immune evasion and ICR in melanoma; (5) Immune-related signatures that were identified based on the analysis of multiple Pembrolizumab clinical datasets, and were shown to predict the response to Pembrolizumab in an independent cohort (23); (6) The Fluidigm AdvantaTM Immuno-Oncology Gene Expression signatures (www.fluidigm.com/applications/advanta-immuno-oncology-gene-expression-assay); and (7) PDL1 expression. .. The data was obtained from Table S1 of (22); (4) The genes whose guides were most preferentially and significantly enriched (top 10 and top 50) in the co-culture conditions in the genome-scale CRISPR screen of (19), also designed to identify key regulators of immune evasion and ICR in melanoma; (5) Immune-related signatures that were identified based on the analysis of multiple Pembrolizumab clinical datasets, and were shown to predict the response to Pembrolizumab in an independent cohort (23); (6) The Fluidigm Knock-Out:Article Title: Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer Article Snippet: .. Alternative ICI Response Predictors To compare the predictive value of the resistance signatures to that of other signatures, Applicants repeated the prediction process, as describe in Survival and ICI response predictions, for each of the following gene signatures (Table 29 and 9): (1) cell type specific signatures identified from the scRNA-seq data (as described in the Data-driven signatures of specific cell types section); (2) signatures that characterize melanoma cell states (the AXL-high, MITF-high, and cell cycle states from (Tirosh et al., 2016)); (3) six sets of genes whose CRISPR knockout in melanoma cells conferred resistance or sensitivity (FDR<0.05) to different types of immune killing according to a genome-scale in vivo screen (Manguso et al., 2017); (4) genes whose CRISPR knockout in melanoma cells conferred resistance to T cell killing (top 10 and top 50) in a genome-scale co-culture screen (Patel et al., 2017); (5) immune-related signatures that were identified based on the analysis of multiple pembrolizumab clinical datasets, and were shown to predict the response to pembrolizumab in an independent cohort (Ayers et al., 2017); (6) the Fluidigm In Vivo:Article Title: Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer Article Snippet: .. Alternative ICI Response Predictors To compare the predictive value of the resistance signatures to that of other signatures, Applicants repeated the prediction process, as describe in Survival and ICI response predictions, for each of the following gene signatures (Table 29 and 9): (1) cell type specific signatures identified from the scRNA-seq data (as described in the Data-driven signatures of specific cell types section); (2) signatures that characterize melanoma cell states (the AXL-high, MITF-high, and cell cycle states from (Tirosh et al., 2016)); (3) six sets of genes whose CRISPR knockout in melanoma cells conferred resistance or sensitivity (FDR<0.05) to different types of immune killing according to a genome-scale in vivo screen (Manguso et al., 2017); (4) genes whose CRISPR knockout in melanoma cells conferred resistance to T cell killing (top 10 and top 50) in a genome-scale co-culture screen (Patel et al., 2017); (5) immune-related signatures that were identified based on the analysis of multiple pembrolizumab clinical datasets, and were shown to predict the response to pembrolizumab in an independent cohort (Ayers et al., 2017); (6) the Fluidigm Expressing:Article Title: Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer Article Snippet: .. Alternative ICI Response Predictors To compare the predictive value of the resistance signatures to that of other signatures, Applicants repeated the prediction process, as describe in Survival and ICI response predictions, for each of the following gene signatures (Table 29 and 9): (1) cell type specific signatures identified from the scRNA-seq data (as described in the Data-driven signatures of specific cell types section); (2) signatures that characterize melanoma cell states (the AXL-high, MITF-high, and cell cycle states from (Tirosh et al., 2016)); (3) six sets of genes whose CRISPR knockout in melanoma cells conferred resistance or sensitivity (FDR<0.05) to different types of immune killing according to a genome-scale in vivo screen (Manguso et al., 2017); (4) genes whose CRISPR knockout in melanoma cells conferred resistance to T cell killing (top 10 and top 50) in a genome-scale co-culture screen (Patel et al., 2017); (5) immune-related signatures that were identified based on the analysis of multiple pembrolizumab clinical datasets, and were shown to predict the response to pembrolizumab in an independent cohort (Ayers et al., 2017); (6) the Fluidigm Article Title: Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer Article Snippet: The data was obtained from Table S1 of (22); (4) The genes whose guides were most preferentially and significantly enriched (top 10 and top 50) in the co-culture conditions in the genome-scale CRISPR screen of (19), also designed to identify key regulators of immune evasion and ICR in melanoma; (5) Immune-related signatures that were identified based on the analysis of multiple Pembrolizumab clinical datasets, and were shown to predict the response to Pembrolizumab in an independent cohort (23); (6) The Fluidigm AdvantaTM Immuno-Oncology Gene Expression signatures (www.fluidigm.com/applications/advanta-immuno-oncology-gene-expression-assay); and (7) PDL1 expression. .. The data was obtained from Table S1 of (22); (4) The genes whose guides were most preferentially and significantly enriched (top 10 and top 50) in the co-culture conditions in the genome-scale CRISPR screen of (19), also designed to identify key regulators of immune evasion and ICR in melanoma; (5) Immune-related signatures that were identified based on the analysis of multiple Pembrolizumab clinical datasets, and were shown to predict the response to Pembrolizumab in an independent cohort (23); (6) The Fluidigm |