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• W3025865536 abstract "Abstract Background It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC). Materials and methods We assessed TMB in surgical tumor specimens by performing whole exome sequencing. IME profiles, including PD‐L1 tumor proportion score (TPS), stromal CD8 tumor‐infiltrating lymphocyte (TIL) density, and stromal Foxp3 TIL density, were quantified by digital pathology using a machine learning algorithm. To detect factors associated with TMB, clinical data, and IME factors were assessed by means of a multiple regression model. Results We analyzed tumors from 200 of the 246 surgically resected NSCLC patients between September 2014 and September 2015. Patient background: median age (range) 70 years (39‐87); male 37.5%; smoker 27.5%; pathological stage (p‐stage) I/II/III, 63.5/22.5/14.0%; histological type Ad/Sq, 77.0/23.0%; primary tumor location upper/lower, 58.5/41.5%; median PET SUV 7.5 (0.86‐29.8); median serum CEA (sCEA) level 3.4 ng/mL (0.5‐144.3); median serum CYFRA 21‐1 (sCYFRA) level 1.2 ng/mL (1.0‐38.0); median TMB 2.19/ Mb (0.12‐64.38); median PD‐L1 TPS 15.1% (0.09‐77.4); median stromal CD8 TIL density 582.1/mm 2 (120.0‐4967.6);, and median stromal Foxp3 TIL density 183.7/mm 2 (6.3‐544.0). The multiple regression analysis identified three factors associated with higher TMB: smoking status: smoker, increase PET SUV, and sCEA level: >5 ng/mL ( P < .001, P < .001, and P = .006, respectively). Conclusions The IME factors assessed were not associated with TMB, but our findings showed that, in addition to smoking, PET SUV and sCEA levels may be independent predictors of TMB. TMB and IME factors are independent factors in resected NSCLC." @default.
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• W3025865536 date "2020-05-12" @default.
• W3025865536 modified "2023-10-16" @default.
• W3025865536 title "Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole‐slide images" @default.
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• W3025865536 doi "https://doi.org/10.1002/cam4.3107" @default.
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