Tina Krieger1, Isobel Pearson2, Judith Bell2, Jim Doherty3, Paul Robbins3. 1. RTI Health Solutions, The Pavilion, Towers Business Park, Wilmslow Road, Didsbury, Manchester, M20 2LS, UK. tkrieger@rti.org. 2. RTI Health Solutions, The Pavilion, Towers Business Park, Wilmslow Road, Didsbury, Manchester, M20 2LS, UK. 3. Pfizer, Inc., La Jolla, USA.
Abstract
BACKGROUND: To achieve optimal outcomes, an individual approach is needed in the treatment and care of patients. The potential value of tumor mutational burden (TMB) status and/or programmed cell death ligand 1 (PD-L1) expression as biomarkers to predict which patients are most likely to respond to checkpoint inhibitors has been explored in many studies. The goal of this targeted literature review is to identify data available for TMB status and/or PD-L1 expression that predict response to checkpoint inhibitors and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies. METHODS: Targeted literature searches were performed using electronic medical databases (MEDLINE, Embase, and BIOSIS) and internet searches of specified sites. Bibliographies of key systematic literature reviews and meta-analyses also were reviewed for studies of interest. RESULTS: The review identified 27 studies of non-small cell lung cancer (NSCLC), 40 studies of melanoma, 10 studies of urothelial cancer, and 5 studies of renal cell cancer indications. Studies also were identified in other cancer types, e.g., colorectal, breast, gastric, and Merkel cell cancer and squamous-cell carcinoma of the head and neck. Twelve trials, including six in NSCLC and four in melanoma, evaluated TMB as a predictor of outcomes. A TMB of ≥10 mutations per megabase was shown to be an effective biomarker in the CheckMate 227 study. PD-L1 expression was included in the majority of identified studies and was found to predict response in in melanoma and in all types of NSCLC. Prediction of response was not a prespecified analysis in some studies; others had small sample sizes and wide confidence intervals. A clear predictive trend for PD-L1 expression was not identified in renal, breast, gastric, or Merkel cell cancer. CONCLUSION: Based on data contained in this review, assessment of TMB status and PD-L1 expression may help enhance the prediction of response to checkpoint inhibition in some tumors, such as NSCLC and melanoma. In this rapidly growing area of research, further exploratory biomarkers are being investigated including tumor-infiltrating lymphocytes, immune profiling (e.g., effector T cells or regulatory T cells), epigenetic signatures, T-cell receptor repertoire, proteomics, microbiome, and metabolomics.
BACKGROUND: To achieve optimal outcomes, an individual approach is needed in the treatment and care of patients. The potential value of tumor mutational burden (TMB) status and/or programmed cell death ligand 1 (PD-L1) expression as biomarkers to predict which patients are most likely to respond to checkpoint inhibitors has been explored in many studies. The goal of this targeted literature review is to identify data available for TMB status and/or PD-L1 expression that predict response to checkpoint inhibitors and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies. METHODS: Targeted literature searches were performed using electronic medical databases (MEDLINE, Embase, and BIOSIS) and internet searches of specified sites. Bibliographies of key systematic literature reviews and meta-analyses also were reviewed for studies of interest. RESULTS: The review identified 27 studies of non-small cell lung cancer (NSCLC), 40 studies of melanoma, 10 studies of urothelial cancer, and 5 studies of renal cell cancer indications. Studies also were identified in other cancer types, e.g., colorectal, breast, gastric, and Merkel cell cancer and squamous-cell carcinoma of the head and neck. Twelve trials, including six in NSCLC and four in melanoma, evaluated TMB as a predictor of outcomes. A TMB of ≥10 mutations per megabase was shown to be an effective biomarker in the CheckMate 227 study. PD-L1 expression was included in the majority of identified studies and was found to predict response in in melanoma and in all types of NSCLC. Prediction of response was not a prespecified analysis in some studies; others had small sample sizes and wide confidence intervals. A clear predictive trend for PD-L1 expression was not identified in renal, breast, gastric, or Merkel cell cancer. CONCLUSION: Based on data contained in this review, assessment of TMB status and PD-L1 expression may help enhance the prediction of response to checkpoint inhibition in some tumors, such as NSCLC and melanoma. In this rapidly growing area of research, further exploratory biomarkers are being investigated including tumor-infiltrating lymphocytes, immune profiling (e.g., effector T cells or regulatory T cells), epigenetic signatures, T-cell receptor repertoire, proteomics, microbiome, and metabolomics.
Authors: Alessandra Raimondi; Pierangela Sepe; Emma Zattarin; Alessia Mennitto; Marco Stellato; Melanie Claps; Valentina Guadalupi; Elena Verzoni; Filippo de Braud; Giuseppe Procopio Journal: Front Oncol Date: 2020-08-12 Impact factor: 6.244
Authors: Sofia Sagredou; Panagiotis Dalezis; Eirini Papadopoulou; Maria Voura; Maria V Deligiorgi; Michail Nikolaou; Mihalis I Panayiotidis; George Nasioulas; Vasiliki Sarli; Dimitrios T Trafalis Journal: Pharmaceutics Date: 2021-06-15 Impact factor: 6.321
Authors: Eda G Ramirez-Valles; Alicia Rodríguez-Pulido; Marcelo Barraza-Salas; Isaac Martínez-Velis; Iván Meneses-Morales; Víctor M Ayala-García; Carlos A Alba-Fierro Journal: Technol Cancer Res Treat Date: 2020 Jan-Dec