000096119 001__ 96119
000096119 005__ 20210902121854.0
000096119 0247_ $$2doi$$a10.3390/cancers12102974
000096119 0248_ $$2sideral$$a120699
000096119 037__ $$aART-2020-120699
000096119 041__ $$aeng
000096119 100__ $$aSesma, A.
000096119 245__ $$aFrom tumor mutational burden to blood T cell receptor: Looking for the best predictive biomarker in lung cancer treated with immunotherapy
000096119 260__ $$c2020
000096119 5060_ $$aAccess copy available to the general public$$fUnrestricted
000096119 5203_ $$aDespite therapeutic advances, lung cancer (LC) is one of the leading causes of cancer morbidity and mortality worldwide. Recently, the treatment of advanced LC has experienced important changes in survival benefit due to immune checkpoint inhibitors (ICIs). However, overall response rates (ORR) remain low in unselected patients and a large proportion of patients undergo disease progression in the first weeks of treatment. Therefore, there is a need of biomarkers to identify patients who will benefit from ICIs. The programmed cell death ligand 1 (PD-L1) expression has been the first biomarker developed. However, its use as a robust predictive biomarker has been limited due to the variability of techniques used, with different antibodies and thresholds. In this context, tumor mutational burden (TMB) has emerged as an additional powerful biomarker based on the observation of successful response to ICIs in solid tumors with high TMB. TMB can be defined as the total number of nonsynonymous mutations per DNA megabases being a mechanism generating neoantigens conditioning the tumor immunogenicity and response to ICIs. However, the latest data provide conflicting results regarding its role as a biomarker. Moreover, considering the results of the recent data, the use of peripheral blood T cell receptor (TCR) repertoire could be a new predictive biomarker. This review summarises recent findings describing the clinical utility of TMB and TCR (TCRB) and concludes that immune, neontigen, and checkpoint targeted variables are required in combination for accurately identifying patients who most likely will benefit of ICIs.
000096119 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000096119 590__ $$a6.639$$b2020
000096119 591__ $$aONCOLOGY$$b51 / 242 = 0.211$$c2020$$dQ1$$eT1
000096119 592__ $$a1.818$$b2020
000096119 593__ $$aOncology$$c2020$$dQ1
000096119 593__ $$aCancer Research$$c2020$$dQ1
000096119 655_4 $$ainfo:eu-repo/semantics/review$$vinfo:eu-repo/semantics/publishedVersion
000096119 700__ $$0(orcid)0000-0003-0154-0730$$aPardo, J.$$uUniversidad de Zaragoza
000096119 700__ $$aCruellas, M.
000096119 700__ $$aGálvez, E.M.
000096119 700__ $$aGascón, M.
000096119 700__ $$aIsla, D.
000096119 700__ $$0(orcid)0000-0003-3043-147X$$aMartínez-Lostao, L.$$uUniversidad de Zaragoza
000096119 700__ $$aOcáriz, M.
000096119 700__ $$0(orcid)0000-0002-9600-8116$$aPaño, J.R.$$uUniversidad de Zaragoza
000096119 700__ $$aQuílez, E.
000096119 700__ $$0(orcid)0000-0002-3888-7036$$aRamírez, A.
000096119 700__ $$0(orcid)0000-0003-3387-0558$$aTorres-Ramón, I.$$uUniversidad de Zaragoza
000096119 700__ $$aYubero, A.
000096119 700__ $$aZapata, M.
000096119 700__ $$aLastra, R.
000096119 7102_ $$11011$$2566$$aUniversidad de Zaragoza$$bDpto. Microb.Ped.Radio.Sal.Pú.$$cÁrea Inmunología
000096119 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000096119 773__ $$g12, 10 (2020), 2974 [19 pp]$$pCancers$$tCancers$$x2072-6694
000096119 8564_ $$s259711$$uhttps://zaguan.unizar.es/record/96119/files/texto_completo.pdf$$yVersión publicada
000096119 8564_ $$s501648$$uhttps://zaguan.unizar.es/record/96119/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000096119 909CO $$ooai:zaguan.unizar.es:96119$$particulos$$pdriver
000096119 951__ $$a2021-09-02-10:31:29
000096119 980__ $$aARTICLE