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Predictive Value of Combining Biomarkers for Clinical Outcomes in Advanced Non-Small Cell Lung Cancer Patients Receiving Immune Checkpoint Inhibitors.
Kao, Chester; Powers, Eric; Wu, Yuan; Datto, Michael B; Green, Michelle F; Strickler, John H; Ready, Neal E; Zhang, Tian; Clarke, Jeffrey M.
Afiliação
  • Kao C; Department of Medicine, Duke University School of Medicine, Durham, NC.
  • Powers E; Department of Medicine, Duke University School of Medicine, Durham, NC.
  • Wu Y; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.
  • Datto MB; Department of Pathology, Duke University School of Medicine, Durham, NC.
  • Green MF; Department of Pathology, Duke University School of Medicine, Durham, NC.
  • Strickler JH; Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Cancer Institute, Duke University, Durham, NC.
  • Ready NE; Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Cancer Institute, Duke University, Durham, NC.
  • Zhang T; Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Cancer Institute, Duke University, Durham, NC; Duke Cancer Institute Center for Prostate and Urologic Cancers, Duke University, Durham, NC.
  • Clarke JM; Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Cancer Institute, Duke University, Durham, NC. Electronic address: jeffrey.clarke@duke.edu.
Clin Lung Cancer ; 22(6): 500-509, 2021 11.
Article em En | MEDLINE | ID: mdl-33972172
INTRODUCTION: A high tumor mutational burden (TMB) (≥10 mut/Mb) has been associated with improved clinical benefit in non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICI) and is a tumor agnostic indication for pembrolizumab across tumor types. We explored whether combining TMB with programmed cell death ligand 1 (PD-L1) and pretreatment neutrophil-lymphocyte ratio (NLR) was associated with improved outcomes in ICI-treated NSCLC. METHODS: We retrospectively analyzed patients treated with ICI with Foundation One genomic testing, including TMB. Optimal cutoff for prediction of response by TMB was determined by receiver operating characteristic analysis, and area under the curve (AUC) was calculated for all 3 biomarkers and combinations. Cox model was used to assess prognostic factors of overall survival (OS) and time to progression (TTP). Survival cutoffs calculated with Kaplan-Meier survival curves were TMB ≥10 mut/Mb, PD-L1 ≥50%, NLR <5, and combined biomarkers. RESULTS: Data from 88 patients treated were analyzed. The optimal TMB cutoff was 9.24 mut/Mb (AUC, 0.62), improving to 0.74 combining all 3 biomarkers. Adjusted Cox model showed that TMB ≥10 mut/Mb was an independent factor of OS (hazard ratio [HR], 0.31; 95% confidence interval; 0.14-0.69; P = .004) and TTP (HR, 0.46; 95% CI, 0.27-0.77; P = .003). The combination of high TMB with positive PD-L1 and low NLR was significantly associated with OS (P = .038) but not TTP. CONCLUSIONS: TMB has modest predictive and prognostic power for clinical outcomes after ICI treatment. The combination of TMB, PD-L1, and NLR status improves this power.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Avaliação de Resultados em Cuidados de Saúde / Carcinoma Pulmonar de Células não Pequenas / Inibidores de Checkpoint Imunológico / Neoplasias Pulmonares Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Avaliação de Resultados em Cuidados de Saúde / Carcinoma Pulmonar de Células não Pequenas / Inibidores de Checkpoint Imunológico / Neoplasias Pulmonares Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article