{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11513122","patent":{"patent_number":"US-11513122","title":"Predicting response to PD-1 axis inhibitors","assignee":null,"inventors":[],"filing_date":"2019-03-25T00:00:00.000Z","publication_date":"2022-11-29T00:00:00.000Z","cpc_codes":["G01N","G01N","G01N","G01N"],"num_claims":3,"abstract":"The invention is concerned with a method of predicting response to a PD-1 axis inhibitor such as anti-PD-L1 antibody by determining the abundance of dendritic cells (DCs) in a tumor tissue sample. The abundance of DCs characterized by enhanced expressions of XCR1, IRF8, BATF3 and FLT3 predicts clinical response to the PD-L1 blockade 5 treatment."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Predicting response to PD-1 axis inhibitors","description":"The invention is concerned with a method of predicting response to a PD-1 axis inhibitor such as anti-PD-L1 antibody by determining the abundance of dendritic cells (DCs) in a tumor tissue sample. The","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11513122","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11513122","citation_suggestion":"Patentable. \"Predicting response to PD-1 axis inhibitors\" (US-11513122). https://patentable.app/patents/US-11513122","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11513122","json":"https://patentable.app/api/llm-context/US-11513122","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T13:22:55.340Z"}