Journal for ImmunoTherapy of Cancer (Jan 2019)

Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors

  • Jeffrey M. Conroy,
  • Sarabjot Pabla,
  • Mary K. Nesline,
  • Sean T. Glenn,
  • Antonios Papanicolau-Sengos,
  • Blake Burgher,
  • Jonathan Andreas,
  • Vincent Giamo,
  • Yirong Wang,
  • Felicia L. Lenzo,
  • Wiam Bshara,
  • Maya Khalil,
  • Grace K. Dy,
  • Katherine G. Madden,
  • Keisuke Shirai,
  • Konstantin Dragnev,
  • Laura J. Tafe,
  • Jason Zhu,
  • Matthew Labriola,
  • Daniele Marin,
  • Shannon J. McCall,
  • Jeffrey Clarke,
  • Daniel J. George,
  • Tian Zhang,
  • Matthew Zibelman,
  • Pooja Ghatalia,
  • Isabel Araujo-Fernandez,
  • Luis de la Cruz-Merino,
  • Arun Singavi,
  • Ben George,
  • Alexander C. MacKinnon,
  • Jonathan Thompson,
  • Rajbir Singh,
  • Robin Jacob,
  • Deepa Kasuganti,
  • Neel Shah,
  • Roger Day,
  • Lorenzo Galluzzi,
  • Mark Gardner,
  • Carl Morrison

DOI
https://doi.org/10.1186/s40425-018-0489-5
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 11

Abstract

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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. 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.

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