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dc.contributor.author | Conroy, Jeffrey M. | - |
dc.contributor.author | Pabla, Sarabjot | - |
dc.contributor.author | Nesline, Mary K. | - |
dc.contributor.author | Glenn, Sean T. | - |
dc.contributor.author | Papanicolau-Sengos, Antonios | - |
dc.contributor.author | Burgher, Blake | - |
dc.contributor.author | Andreas, Jonathan | - |
dc.contributor.author | Giamo, Vincent | - |
dc.contributor.author | Wang, Yirong | - |
dc.contributor.author | Lenzo, Felicia L. | - |
dc.contributor.author | Bshara, Wiam | - |
dc.contributor.author | Khalil, Maya | - |
dc.contributor.author | Dy, Grace K. | - |
dc.contributor.author | Madden, Katherine G. | - |
dc.contributor.author | Shirai, Keisuke | - |
dc.contributor.author | Dragnev, Konstantin | - |
dc.contributor.author | Tafe, Laura J. | - |
dc.contributor.author | Zhu, Jason | - |
dc.contributor.author | Labriola, Matthew | - |
dc.contributor.author | Marin, Daniele | - |
dc.contributor.author | McCall, Shannon J. | - |
dc.contributor.author | Clarke, Jeffrey | - |
dc.contributor.author | George, Daniel J. | - |
dc.contributor.author | Zhang, Tian | - |
dc.contributor.author | Zibelman, Matthew | - |
dc.contributor.author | Ghatalia, Pooja | - |
dc.contributor.author | Aráujo-Fernández, Isabel | - |
dc.contributor.author | Cruz Merino, L. de la | - |
dc.contributor.author | Singavi, Arun | - |
dc.contributor.author | George, Ben | - |
dc.contributor.author | MacKinnon, Alexander C. | - |
dc.contributor.author | Thompson, Jonathan | - |
dc.contributor.author | Singh, Rajbir | - |
dc.contributor.author | Jacob, Robin | - |
dc.contributor.author | Kasuganti, Deepa | - |
dc.contributor.author | Shah, Neel | - |
dc.contributor.author | Day, Roger | - |
dc.contributor.author | Galluzzi, Lorenzo | - |
dc.contributor.author | Gardner, Mark | - |
dc.contributor.author | Morrison, Carl | - |
dc.date.accessioned | 2020-06-09T10:26:33Z | - |
dc.date.available | 2020-06-09T10:26:33Z | - |
dc.date.issued | 2019 | - |
dc.identifier | doi: 10.1186/s40425-018-0489-5 | - |
dc.identifier | e-issn: 2051-1426 | - |
dc.identifier.citation | Journal for ImmunoTherapy of Cancer 7: 18 (2019) | - |
dc.identifier.uri | http://hdl.handle.net/10261/213880 | - |
dc.description.abstract | [Background] PD-L1 immunohistochemistry (IHC) has been traditionally used for predicting clinical responses to immune checkpoint inhibitors (ICIs). However, there are at least 4 different assays and antibodies used for PD-L1 IHC, each developed with a different ICI. We set to test if next generation RNA sequencing (RNA-seq) is a robust method to determine PD-L1 mRNA expression levels and furthermore, efficacy of predicting response to ICIs as compared to routinely used, standardized IHC procedures. | - |
dc.description.abstract | [Methods] A total of 209 cancer patients treated on-label by FDA-approved ICIs, with evaluable responses were assessed for PD-L1 expression by RNA-seq and IHC, based on tumor proportion score (TPS) and immune cell staining (ICS). A subset of serially diluted cases was evaluated for RNA-seq assay performance across a broad range of PD-L1 expression levels. | - |
dc.description.abstract | [Results] Assessment of PD-L1 mRNA levels by RNA-seq demonstrated robust linearity across high and low expression ranges. PD-L1 mRNA levels assessed by RNA-seq and IHC (TPS and ICS) were highly correlated (p < 2e-16). Sub-analyses showed sustained correlation when IHC results were classified as high or low by clinically accepted cut-offs (p < 0.01), and results did not differ by tumor type or anti-PD-L1 antibody used. Overall, a combined positive PD-L1 result (≥1% IHC TPS and high PD-L1 expression by RNA-Seq) was associated with a 2-to-5-fold higher overall response rate (ORR) compared to a double negative result. Standard assessments of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) showed that a PD-L1 positive assessment for melanoma samples by RNA-seq had the lowest sensitivity (25%) but the highest PPV (72.7%). Among the three tumor types analyzed in this study, the only non-overlapping confidence interval for predicting response was for “RNA-seq low vs high” in melanoma. | - |
dc.description.abstract | [Conclusions] Measurement of PD-L1 mRNA expression by RNA-seq is comparable to PD-L1 expression by IHC both analytically and clinically in predicting ICI response. RNA-seq has the added advantages of being amenable to standardization and avoidance of interpretation bias. PD-L1 by RNA-seq needs to be validated in future prospective ICI clinical studies across multiple histologies. | - |
dc.description.sponsorship | This research was funded by OmniSeq, Inc. (Buffalo, NY). | - |
dc.language | eng | - |
dc.publisher | BMJ Publishing Group | - |
dc.relation.isversionof | Publisher's version | - |
dc.rights | openAccess | - |
dc.subject | Atezolizumab | - |
dc.subject | Avelumab | - |
dc.subject | Cancer immunotherapy | - |
dc.subject | Durvalumab | - |
dc.subject | Nivolumab | - |
dc.subject | Pembrolizumab | - |
dc.subject | PD-L1 | - |
dc.subject | Biomarkers | - |
dc.title | Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors | - |
dc.type | artículo | - |
dc.identifier.doi | 10.1186/s40425-018-0489-5 | - |
dc.relation.publisherversion | http://dx.doi.org/10.1186/s40425-018-0489-5 | - |
dc.date.updated | 2020-06-09T10:26:33Z | - |
dc.rights.license | http://creativecommons.org/licenses/by/4.0/ | - |
dc.contributor.funder | OmniSeq | - |
dc.relation.csic | Sí | - |
dc.identifier.pmid | 30678715 | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.openairetype | artículo | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
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