Steve Lu1, Julie E Stein1, David L Rimm2, Daphne W Wang1, J Michael Bell1, Douglas B Johnson3, Jeffrey A Sosman4, Kurt A Schalper2, Robert A Anders5, Hao Wang6, Clifford Hoyt7, Drew M Pardoll8,9, Ludmila Danilova6,9, Janis M Taube1,5,8,9. 1. Department of Dermatology, Johns Hopkins Medical Institutions, Baltimore, Maryland. 2. Department of Pathology, Yale University School of Medicine, New Haven, Connecticut. 3. Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. 4. Department of Medicine, Division of Hematology-Oncology, Northwestern University Medical Center, and Robert H. Lurie Cancer Center, Chicago, Illinois. 5. Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, Maryland. 6. Division of Biostatistics & Bioinformatics at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Medical Institutions, Baltimore, Maryland. 7. Akoya Biosciences, Hopkinton, Massachusetts. 8. Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, Maryland. 9. Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins Medical Institutions, Baltimore, Maryland.
Abstract
IMPORTANCE: PD-L1 (programmed cell death ligand 1) immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) assays have been used to assess pretreatment tumor tissue to predict response to anti-PD-1/PD-L1 therapies. However, the relative diagnostic performance of these modalities has yet to be established. OBJECTIVE: To compare studies that assessed the diagnostic accuracy of PD-L1 IHC, TMB, GEP, and mIHC/IF in predicting response to anti-PD-1/PD-L1 therapy. EVIDENCE REVIEW: A search of PubMed (from inception to June 2018) and 2013 to 2018 annual meeting abstracts from the American Association for Cancer Research, American Society of Clinical Oncology, European Society for Medical Oncology, and Society for Immunotherapy of Cancer was conducted to identify studies that examined the use of PD-L1 IHC, TMB, GEP, and mIHC/IF assays to determine objective response to anti-PD-1/PD-L1 therapy. For PD-L1 IHC, only clinical trials that resulted in US Food and Drug Administration approval of indications for anti-PD-1/PD-L1 were included. Studies combining more than 1 modality were also included. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Two reviewers independently extracted the clinical outcomes and test results for each individual study. MAIN OUTCOMES AND MEASURES: Summary receiver operating characteristic (sROC) curves; their associated area under the curve (AUC); and pooled sensitivity, specificity, positive and negative predictive values (PPV, NPV), and positive and negative likelihood ratios (LR+ and LR-) for each assay modality. RESULTS: Tumor specimens representing over 10 different solid tumor types in 8135 patients were assayed, and the results were correlated with anti-PD-1/PD-L1 response. When each modality was evaluated with sROC curves, mIHC/IF had a significantly higher AUC (0.79) compared with PD-L1 IHC (AUC, 0.65, P < .001), GEP (AUC, 0.65, P = .003), and TMB (AUC, 0.69, P = .049). When multiple different modalities were combined such as PD-L1 IHC and/or GEP + TMB, the AUC drew nearer to that of mIHC/IF (0.74). All modalities demonstrated comparable NPV and LR-, whereas mIHC/IF demonstrated higher PPV (0.63) and LR+ (2.86) than the other approaches. CONCLUSIONS AND RELEVANCE: In this meta-analysis, tumor mutational burden, PD-L1 IHC, and GEP demonstrated comparable AUCs in predicting response to anti-PD-1/PD-L1 treatment. Multiplex immunohistochemistry/IF and multimodality biomarker strategies appear to be associated with improved performance over PD-L1 IHC, TMB, or GEP alone. Further studies with mIHC/IF and composite approaches with a larger number of patients will be required to confirm these findings. Additional study is also required to determine the most predictive analyte combinations and to determine whether biomarker modality performance varies by tumor type.
IMPORTANCE: PD-L1 (programmed cell death ligand 1) immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) assays have been used to assess pretreatment tumor tissue to predict response to anti-PD-1/PD-L1 therapies. However, the relative diagnostic performance of these modalities has yet to be established. OBJECTIVE: To compare studies that assessed the diagnostic accuracy of PD-L1 IHC, TMB, GEP, and mIHC/IF in predicting response to anti-PD-1/PD-L1 therapy. EVIDENCE REVIEW: A search of PubMed (from inception to June 2018) and 2013 to 2018 annual meeting abstracts from the American Association for Cancer Research, American Society of Clinical Oncology, European Society for Medical Oncology, and Society for Immunotherapy of Cancer was conducted to identify studies that examined the use of PD-L1 IHC, TMB, GEP, and mIHC/IF assays to determine objective response to anti-PD-1/PD-L1 therapy. For PD-L1 IHC, only clinical trials that resulted in US Food and Drug Administration approval of indications for anti-PD-1/PD-L1 were included. Studies combining more than 1 modality were also included. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Two reviewers independently extracted the clinical outcomes and test results for each individual study. MAIN OUTCOMES AND MEASURES: Summary receiver operating characteristic (sROC) curves; their associated area under the curve (AUC); and pooled sensitivity, specificity, positive and negative predictive values (PPV, NPV), and positive and negative likelihood ratios (LR+ and LR-) for each assay modality. RESULTS: Tumor specimens representing over 10 different solid tumor types in 8135 patients were assayed, and the results were correlated with anti-PD-1/PD-L1 response. When each modality was evaluated with sROC curves, mIHC/IF had a significantly higher AUC (0.79) compared with PD-L1 IHC (AUC, 0.65, P < .001), GEP (AUC, 0.65, P = .003), and TMB (AUC, 0.69, P = .049). When multiple different modalities were combined such as PD-L1 IHC and/or GEP + TMB, the AUC drew nearer to that of mIHC/IF (0.74). All modalities demonstrated comparable NPV and LR-, whereas mIHC/IF demonstrated higher PPV (0.63) and LR+ (2.86) than the other approaches. CONCLUSIONS AND RELEVANCE: In this meta-analysis, tumor mutational burden, PD-L1 IHC, and GEP demonstrated comparable AUCs in predicting response to anti-PD-1/PD-L1 treatment. Multiplex immunohistochemistry/IF and multimodality biomarker strategies appear to be associated with improved performance over PD-L1 IHC, TMB, or GEP alone. Further studies with mIHC/IF and composite approaches with a larger number of patients will be required to confirm these findings. Additional study is also required to determine the most predictive analyte combinations and to determine whether biomarker modality performance varies by tumor type.
Authors: Siwen Hu-Lieskovan; Srabani Bhaumik; Kavita Dhodapkar; Jean-Charles J B Grivel; Sumati Gupta; Brent A Hanks; Sylvia Janetzki; Thomas O Kleen; Yoshinobu Koguchi; Amanda W Lund; Cristina Maccalli; Yolanda D Mahnke; Ruslan D Novosiadly; Senthamil R Selvan; Tasha Sims; Yingdong Zhao; Holden T Maecker Journal: J Immunother Cancer Date: 2020-12 Impact factor: 13.751
Authors: Jon Zugazagoitia; Swati Gupta; Yuting Liu; Kit Fuhrman; Scott Gettinger; Roy S Herbst; Kurt A Schalper; David L Rimm Journal: Clin Cancer Res Date: 2020-04-06 Impact factor: 12.531
Authors: Franck Housseau; Robert A Anders; Nicolas J Llosa; Brandon Luber; Nicholas Siegel; Anas H Awan; Teniola Oke; Qingfeng Zhu; Bjarne R Bartlett; Laveet K Aulakh; Elizabeth D Thompson; Elizabeth M Jaffee; Jennifer N Durham; Cynthia L Sears; Dung T Le; Luis A Diaz; Drew M Pardoll; Hao Wang Journal: Cancer Immunol Res Date: 2019-08-22 Impact factor: 11.151
Authors: Emily Z Keung; Melissa Burgess; Ruth Salazar; Edwin R Parra; Jaime Rodrigues-Canales; Vanessa Bolejack; Brian A Van Tine; Scott M Schuetze; Steven Attia; Richard F Riedel; James Hu; Scott H Okuno; Dennis A Priebat; Sujana Movva; Lara E Davis; Damon R Reed; Alexandre Reuben; Christina L Roland; Denise Reinke; Alexander J Lazar; Wei-Lien Wang; Jennifer A Wargo; Hussein A Tawbi Journal: Clin Cancer Res Date: 2020-01-03 Impact factor: 12.531
Authors: Takehito Shukuya; Vikas Ghai; Joseph M Amann; Tamio Okimoto; Konstantin Shilo; Taek-Kyun Kim; Kai Wang; David P Carbone Journal: J Thorac Oncol Date: 2020-06-19 Impact factor: 15.609
Authors: Leisha A Emens; Sylvia Adams; Ashley Cimino-Mathews; Mary L Disis; Margaret E Gatti-Mays; Alice Y Ho; Kevin Kalinsky; Heather L McArthur; Elizabeth A Mittendorf; Rita Nanda; David B Page; Hope S Rugo; Krista M Rubin; Hatem Soliman; Patricia A Spears; Sara M Tolaney; Jennifer K Litton Journal: J Immunother Cancer Date: 2021-08 Impact factor: 13.751
Authors: Sneha Berry; Nicolas A Giraldo; Benjamin F Green; Alexander S Szalay; Janis M Taube; Tricia R Cottrell; Julie E Stein; Elizabeth L Engle; Haiying Xu; Aleksandra Ogurtsova; Charles Roberts; Daphne Wang; Peter Nguyen; Qingfeng Zhu; Sigfredo Soto-Diaz; Jose Loyola; Inbal B Sander; Pok Fai Wong; Shlomit Jessel; Joshua Doyle; Danielle Signer; Richard Wilton; Jeffrey S Roskes; Margaret Eminizer; Seyoun Park; Joel C Sunshine; Elizabeth M Jaffee; Alexander Baras; Angelo M De Marzo; Suzanne L Topalian; Harriet Kluger; Leslie Cope; Evan J Lipson; Ludmila Danilova; Robert A Anders; David L Rimm; Drew M Pardoll Journal: Science Date: 2021-06-11 Impact factor: 47.728
Authors: Lakshmi Nayak; Annette M Molinaro; Katherine Peters; Jennifer L Clarke; Justin T Jordan; John de Groot; Leia Nghiemphu; Thomas Kaley; Howard Colman; Christine McCluskey; Sarah Gaffey; Timothy R Smith; David J Cote; Mariano Severgnini; Jennifer H Yearley; Qing Zhao; Wendy M Blumenschein; Dan G Duda; Alona Muzikansky; Rakesh K Jain; Patrick Y Wen; David A Reardon Journal: Clin Cancer Res Date: 2020-11-16 Impact factor: 12.531
Authors: Marco Bandini; Jeffrey S Ross; Daniele Raggi; Andrea Gallina; Maurizio Colecchia; Roberta Lucianò; Patrizia Giannatempo; Elena Farè; Filippo Pederzoli; Marco Bianchi; Renzo Colombo; Giorgio Gandaglia; Nicola Fossati; Laura Marandino; Umberto Capitanio; Federico Deho'; Siraj M Ali; Russell Madison; Jon H Chung; Andrea Salonia; Alberto Briganti; Francesco Montorsi; Andrea Necchi Journal: J Natl Cancer Inst Date: 2021-01-04 Impact factor: 13.506