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Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors
by
Zibelman, Matthew
, Galluzzi, Lorenzo
, Wang, Yirong
, George, Ben
, Dragnev, Konstantin
, Clarke, Jeffrey
, Zhang, Tian
, Thompson, Jonathan
, Nesline, Mary K.
, Andreas, Jonathan
, Zhu, Jason
, McCall, Shannon J.
, Madden, Katherine G.
, Araujo-Fernandez, Isabel
, Singh, Rajbir
, Jacob, Robin
, Kasuganti, Deepa
, Burgher, Blake
, Shirai, Keisuke
, Papanicolau-Sengos, Antonios
, Labriola, Matthew
, Glenn, Sean T.
, Dy, Grace K.
, Khalil, Maya
, Singavi, Arun
, Day, Roger
, Marin, Daniele
, George, Daniel J.
, Conroy, Jeffrey M.
, Lenzo, Felicia L.
, Morrison, Carl
, MacKinnon, Alexander C.
, Pabla, Sarabjot
, Ghatalia, Pooja
, Shah, Neel
, Bshara, Wiam
, de la Cruz-Merino, Luis
, Giamo, Vincent
, Tafe, Laura J.
, Gardner, Mark
in
Antibodies
/ Antineoplastic Agents, Immunological - therapeutic use
/ Atezolizumab
/ Avelumab
/ B7-H1 Antigen - antagonists & inhibitors
/ B7-H1 Antigen - genetics
/ B7-H1 Antigen - metabolism
/ Cancer
/ cancer immunotherapy
/ Cancer patients
/ Clinical trials
/ Cloning
/ Comparative analysis
/ Drug approval
/ Durvalumab
/ FDA approval
/ Gene expression
/ Humans
/ Immune checkpoint inhibitors
/ Immune response
/ Immunohistochemistry
/ Immunology
/ Immunotherapy
/ Immunotherapy Biomarkers
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Melanoma
/ Messenger RNA
/ Metastasis
/ Monoclonal antibodies
/ Neoplasms - drug therapy
/ Neoplasms - genetics
/ Neoplasms - metabolism
/ Nivolumab
/ Oncology
/ Patients
/ Pembrolizumab
/ Research Article
/ RNA
/ RNA sequencing
/ RNA, Messenger - genetics
/ RNA-Seq
/ Targeted cancer therapy
/ Tumors
2019
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Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors
by
Zibelman, Matthew
, Galluzzi, Lorenzo
, Wang, Yirong
, George, Ben
, Dragnev, Konstantin
, Clarke, Jeffrey
, Zhang, Tian
, Thompson, Jonathan
, Nesline, Mary K.
, Andreas, Jonathan
, Zhu, Jason
, McCall, Shannon J.
, Madden, Katherine G.
, Araujo-Fernandez, Isabel
, Singh, Rajbir
, Jacob, Robin
, Kasuganti, Deepa
, Burgher, Blake
, Shirai, Keisuke
, Papanicolau-Sengos, Antonios
, Labriola, Matthew
, Glenn, Sean T.
, Dy, Grace K.
, Khalil, Maya
, Singavi, Arun
, Day, Roger
, Marin, Daniele
, George, Daniel J.
, Conroy, Jeffrey M.
, Lenzo, Felicia L.
, Morrison, Carl
, MacKinnon, Alexander C.
, Pabla, Sarabjot
, Ghatalia, Pooja
, Shah, Neel
, Bshara, Wiam
, de la Cruz-Merino, Luis
, Giamo, Vincent
, Tafe, Laura J.
, Gardner, Mark
in
Antibodies
/ Antineoplastic Agents, Immunological - therapeutic use
/ Atezolizumab
/ Avelumab
/ B7-H1 Antigen - antagonists & inhibitors
/ B7-H1 Antigen - genetics
/ B7-H1 Antigen - metabolism
/ Cancer
/ cancer immunotherapy
/ Cancer patients
/ Clinical trials
/ Cloning
/ Comparative analysis
/ Drug approval
/ Durvalumab
/ FDA approval
/ Gene expression
/ Humans
/ Immune checkpoint inhibitors
/ Immune response
/ Immunohistochemistry
/ Immunology
/ Immunotherapy
/ Immunotherapy Biomarkers
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Melanoma
/ Messenger RNA
/ Metastasis
/ Monoclonal antibodies
/ Neoplasms - drug therapy
/ Neoplasms - genetics
/ Neoplasms - metabolism
/ Nivolumab
/ Oncology
/ Patients
/ Pembrolizumab
/ Research Article
/ RNA
/ RNA sequencing
/ RNA, Messenger - genetics
/ RNA-Seq
/ Targeted cancer therapy
/ Tumors
2019
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Do you wish to request the book?
Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors
by
Zibelman, Matthew
, Galluzzi, Lorenzo
, Wang, Yirong
, George, Ben
, Dragnev, Konstantin
, Clarke, Jeffrey
, Zhang, Tian
, Thompson, Jonathan
, Nesline, Mary K.
, Andreas, Jonathan
, Zhu, Jason
, McCall, Shannon J.
, Madden, Katherine G.
, Araujo-Fernandez, Isabel
, Singh, Rajbir
, Jacob, Robin
, Kasuganti, Deepa
, Burgher, Blake
, Shirai, Keisuke
, Papanicolau-Sengos, Antonios
, Labriola, Matthew
, Glenn, Sean T.
, Dy, Grace K.
, Khalil, Maya
, Singavi, Arun
, Day, Roger
, Marin, Daniele
, George, Daniel J.
, Conroy, Jeffrey M.
, Lenzo, Felicia L.
, Morrison, Carl
, MacKinnon, Alexander C.
, Pabla, Sarabjot
, Ghatalia, Pooja
, Shah, Neel
, Bshara, Wiam
, de la Cruz-Merino, Luis
, Giamo, Vincent
, Tafe, Laura J.
, Gardner, Mark
in
Antibodies
/ Antineoplastic Agents, Immunological - therapeutic use
/ Atezolizumab
/ Avelumab
/ B7-H1 Antigen - antagonists & inhibitors
/ B7-H1 Antigen - genetics
/ B7-H1 Antigen - metabolism
/ Cancer
/ cancer immunotherapy
/ Cancer patients
/ Clinical trials
/ Cloning
/ Comparative analysis
/ Drug approval
/ Durvalumab
/ FDA approval
/ Gene expression
/ Humans
/ Immune checkpoint inhibitors
/ Immune response
/ Immunohistochemistry
/ Immunology
/ Immunotherapy
/ Immunotherapy Biomarkers
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Melanoma
/ Messenger RNA
/ Metastasis
/ Monoclonal antibodies
/ Neoplasms - drug therapy
/ Neoplasms - genetics
/ Neoplasms - metabolism
/ Nivolumab
/ Oncology
/ Patients
/ Pembrolizumab
/ Research Article
/ RNA
/ RNA sequencing
/ RNA, Messenger - genetics
/ RNA-Seq
/ Targeted cancer therapy
/ Tumors
2019
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Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors
Journal Article
Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors
2019
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Overview
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.
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.
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.
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.
Publisher
BioMed Central,BioMed Central Ltd,BMJ Publishing Group LTD,BMJ Publishing Group
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