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4. LCOR mediates interferon-independent tumor immunogenicity and responsiveness to immune-checkpoint blockade in triple-negative breast cancer. Pérez-Núñez I; Rozalén C; Palomeque JÁ; Sangrador I; Dalmau M; Comerma L; Hernández-Prat A; Casadevall D; Menendez S; Liu DD; Shen M; Berenguer J; Ruiz IR; Peña R; Montañés JC; Albà MM; Bonnin S; Ponomarenko J; Gomis RR; Cejalvo JM; Servitja S; Marzese DM; Morey L; Voorwerk L; Arribas J; Bermejo B; Kok M; Pusztai L; Kang Y; Albanell J; Celià-Terrassa T Nat Cancer; 2022 Mar; 3(3):355-370. PubMed ID: 35301507 [TBL] [Abstract][Full Text] [Related]
5. CD52 mRNA expression predicts prognosis and response to immune checkpoint blockade in melanoma. de Vos-Hillebrand L; Fietz S; Hillebrand P; Kulcsár Z; Diop MY; Hollick S; Maas AP; Strieth S; Landsberg J; Dietrich D Pigment Cell Melanoma Res; 2024 Mar; 37(2):309-315. PubMed ID: 37975535 [TBL] [Abstract][Full Text] [Related]
6. HLA class II immunogenic mutation burden predicts response to immune checkpoint blockade. Shao XM; Huang J; Niknafs N; Balan A; Cherry C; White J; Velculescu VE; Anagnostou V; Karchin R Ann Oncol; 2022 Jul; 33(7):728-738. PubMed ID: 35339648 [TBL] [Abstract][Full Text] [Related]
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8. Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients. Shen HY; Xu JL; Zhu Z; Xu HP; Liang MX; Xu D; Chen WQ; Tang JH; Fang Z; Zhang J Neoplasia; 2023 Nov; 45():100942. PubMed ID: 37839160 [TBL] [Abstract][Full Text] [Related]
9. A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. Sahni S; Wang B; Wu D; Dhruba SR; Nagy M; Patkar S; Ferreira I; Day CP; Wang K; Ruppin E Nat Commun; 2024 Oct; 15(1):8867. PubMed ID: 39402030 [TBL] [Abstract][Full Text] [Related]
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