You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Pembrolizumab for the treatment of non-small cell lung cancer In the last years, a spectacular development of immunotherapeutic agents aimed at the PD-1 / PD-L1 axis has taken place. This development of these checkpoint inhibitors has greatly influenced our approach to the treatment of lung cancer in first and second line. The limited toxicity profile and the ability to treat for prolonged periods, even in smokers, is a welcome expansion of the therapeutic arsenal of the oncologist. Areas covered: This review highlights the results of recent clinical trials on pembrolizumab for the treatment of non-small cell lung cancer. The authors discuss both first and second line treatment with pembrolizumab as monotherapy and in combination therapies. Additionally, implications of the PD-L1 immunohistochemistry assay with the 22C3 antibody and its use in clinical practice and trials is discussed. Expert commentary: A higher overall response, overall survival and a moderate toxicity profile is observed with the use of pembrolizumab, compared to chemotherapy, in both first and second line. These promising results have already translated into the registration of pembrolizumab in first and second line in patients with a high expression of PD-L1. However, as PD-L1 staining does not sufficiently discriminate responders from non-responders for all checkpoint inhibitors, there still is a need for a better predictive biomarker.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["expression", "lung cancer", "non-small cell lung cancer"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": ["immunotherapeutic agents", "pembrolizumab", "checkpoint inhibitors", "22C3 antibody", "PD-1", "Pembrolizumab"], "ClinicalAttribute": ["biomarker"], "Eukaryote": [], "Finding": ["non-responders", "responders", "smokers", "overall response"], "Food": [], "HealthCareActivity": ["monotherapy", "first", "combination therapies", "second line treatment", "registration", "immunohistochemistry assay", "chemotherapy", "second line", "treat", "staining", "treatment"], "InjuryOrPoisoning": ["toxicity"], "IntellectualProduct": ["review"], "MedicalDevice": [], "Organization": [], "PopulationGroup": [], "ProfessionalOrOccupationalGroup": ["authors", "oncologist"], "ResearchActivity": ["clinical trials", "trials", "clinical practice"], "SpatialConcept": [], "Virus": []}