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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

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. IM...

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Autores principales: Ono, Akira, Terada, Yukihiro, Kawata, Takuya, Serizawa, Masakuni, Isaka, Mitsuhiro, Kawabata, Takanori, Imai, Toru, Mori, Keita, Muramatsu, Koji, Hayashi, Isamu, Kenmotsu, Hirotsugu, Ohshima, Keiichi, Urakami, Kenichi, Nagashima, Takeshi, Kusuhara, Masatoshi, Akiyama, Yasuto, Sugino, Takashi, Ohde, Yasuhisa, Yamaguchi, Ken, Takahashi, Toshiaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333844/
https://www.ncbi.nlm.nih.gov/pubmed/32400056
http://dx.doi.org/10.1002/cam4.3107
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author Ono, Akira
Terada, Yukihiro
Kawata, Takuya
Serizawa, Masakuni
Isaka, Mitsuhiro
Kawabata, Takanori
Imai, Toru
Mori, Keita
Muramatsu, Koji
Hayashi, Isamu
Kenmotsu, Hirotsugu
Ohshima, Keiichi
Urakami, Kenichi
Nagashima, Takeshi
Kusuhara, Masatoshi
Akiyama, Yasuto
Sugino, Takashi
Ohde, Yasuhisa
Yamaguchi, Ken
Takahashi, Toshiaki
author_facet Ono, Akira
Terada, Yukihiro
Kawata, Takuya
Serizawa, Masakuni
Isaka, Mitsuhiro
Kawabata, Takanori
Imai, Toru
Mori, Keita
Muramatsu, Koji
Hayashi, Isamu
Kenmotsu, Hirotsugu
Ohshima, Keiichi
Urakami, Kenichi
Nagashima, Takeshi
Kusuhara, Masatoshi
Akiyama, Yasuto
Sugino, Takashi
Ohde, Yasuhisa
Yamaguchi, Ken
Takahashi, Toshiaki
author_sort Ono, Akira
collection PubMed
description 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.
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spelling pubmed-73338442020-07-07 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 Ono, Akira Terada, Yukihiro Kawata, Takuya Serizawa, Masakuni Isaka, Mitsuhiro Kawabata, Takanori Imai, Toru Mori, Keita Muramatsu, Koji Hayashi, Isamu Kenmotsu, Hirotsugu Ohshima, Keiichi Urakami, Kenichi Nagashima, Takeshi Kusuhara, Masatoshi Akiyama, Yasuto Sugino, Takashi Ohde, Yasuhisa Yamaguchi, Ken Takahashi, Toshiaki Cancer Med Cancer Prevention 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. John Wiley and Sons Inc. 2020-05-12 /pmc/articles/PMC7333844/ /pubmed/32400056 http://dx.doi.org/10.1002/cam4.3107 Text en © 2020 Shizuoka Cancer Center. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Prevention
Ono, Akira
Terada, Yukihiro
Kawata, Takuya
Serizawa, Masakuni
Isaka, Mitsuhiro
Kawabata, Takanori
Imai, Toru
Mori, Keita
Muramatsu, Koji
Hayashi, Isamu
Kenmotsu, Hirotsugu
Ohshima, Keiichi
Urakami, Kenichi
Nagashima, Takeshi
Kusuhara, Masatoshi
Akiyama, Yasuto
Sugino, Takashi
Ohde, Yasuhisa
Yamaguchi, Ken
Takahashi, Toshiaki
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
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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Cancer Prevention
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333844/
https://www.ncbi.nlm.nih.gov/pubmed/32400056
http://dx.doi.org/10.1002/cam4.3107
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