Cargando…

Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging

Objectives: Tumor pathology examination especially epidermal growth factor receptor (EGFR) mutations molecular testing has been integral part of lung cancer clinical practices. However, the EGFR mutations spatial distribution characteristics remains poorly investigated, which is critical to tumor he...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Min, He, Jiuming, Li, Tiegang, Hu, Haixu, Li, Xiaofei, Xing, Hao, Wang, Jun, Yang, Fan, Ma, Qunfeng, Liu, Bing, Tang, Chuanhao, Abliz, Zeper, Liu, Xiaoqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722907/
https://www.ncbi.nlm.nih.gov/pubmed/31555581
http://dx.doi.org/10.3389/fonc.2019.00804
_version_ 1783448649292840960
author Zhang, Min
He, Jiuming
Li, Tiegang
Hu, Haixu
Li, Xiaofei
Xing, Hao
Wang, Jun
Yang, Fan
Ma, Qunfeng
Liu, Bing
Tang, Chuanhao
Abliz, Zeper
Liu, Xiaoqing
author_facet Zhang, Min
He, Jiuming
Li, Tiegang
Hu, Haixu
Li, Xiaofei
Xing, Hao
Wang, Jun
Yang, Fan
Ma, Qunfeng
Liu, Bing
Tang, Chuanhao
Abliz, Zeper
Liu, Xiaoqing
author_sort Zhang, Min
collection PubMed
description Objectives: Tumor pathology examination especially epidermal growth factor receptor (EGFR) mutations molecular testing has been integral part of lung cancer clinical practices. However, the EGFR mutations spatial distribution characteristics remains poorly investigated, which is critical to tumor heterogeneity analysis and precision diagnosis. Here, we conducted an exploratory study for label-free lung cancer pathology diagnosis and mapping of EGFR mutation spatial distribution using ambient mass spectrometry imaging (MSI). Materials and Methods: MSI analysis were performed in 55 post-operative non-small cell lung cancer (NSCLC) tumor and paired normal tissues to distinguish tumor from normal and classify pathology. We then compared diagnostic sensitivity of MSI and ADx-amplification refractory mutation system (ARMS) for the detection of EGFR mutation in pathological confirmed lung adenocarcinoma (AC) and explored EGFR mutations associated biomarkers to depict EGFR spatial distribution base on ambient MSI. Results: Of 55 pathological confirmed NSCLC, MSI achieved a diagnostic sensitivity of 85.2% (23/27) and 82.1% (23/28) for AC and squamous cell carcinoma (SCC), respectively. Among 27 AC, there were 17 EGFR-wild-type and 10 EGFR-mutated-positive samples detected by ARMS, and MSI achieved a diagnostic sensitivity of 82.3% (14/17) and 80% (8/10) for these two groups. Several phospholipids were specially enriched in AC compared with SCC tissues, with the higher ions intensity of phospholipids in EGFR-mutated-positive compared with EGFR-wild-type AC tissues. We also found EGFR mutations distribution was heterogeneous in different regions of same tumor by multi-regions ARMS detection, and only the regions with higher ions intensity of phospholipids were EGFR-mutated-positive. Conclusion: MSI method could accurately distinguish tumor pathology and subtypes, and phospholipids were reliable EGFR mutations associated biomarkers, phospholipids imaging could intuitively visualize EGFR mutations spatial distribution, may facilitate our understanding of tumor heterogeneity.
format Online
Article
Text
id pubmed-6722907
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-67229072019-09-25 Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging Zhang, Min He, Jiuming Li, Tiegang Hu, Haixu Li, Xiaofei Xing, Hao Wang, Jun Yang, Fan Ma, Qunfeng Liu, Bing Tang, Chuanhao Abliz, Zeper Liu, Xiaoqing Front Oncol Oncology Objectives: Tumor pathology examination especially epidermal growth factor receptor (EGFR) mutations molecular testing has been integral part of lung cancer clinical practices. However, the EGFR mutations spatial distribution characteristics remains poorly investigated, which is critical to tumor heterogeneity analysis and precision diagnosis. Here, we conducted an exploratory study for label-free lung cancer pathology diagnosis and mapping of EGFR mutation spatial distribution using ambient mass spectrometry imaging (MSI). Materials and Methods: MSI analysis were performed in 55 post-operative non-small cell lung cancer (NSCLC) tumor and paired normal tissues to distinguish tumor from normal and classify pathology. We then compared diagnostic sensitivity of MSI and ADx-amplification refractory mutation system (ARMS) for the detection of EGFR mutation in pathological confirmed lung adenocarcinoma (AC) and explored EGFR mutations associated biomarkers to depict EGFR spatial distribution base on ambient MSI. Results: Of 55 pathological confirmed NSCLC, MSI achieved a diagnostic sensitivity of 85.2% (23/27) and 82.1% (23/28) for AC and squamous cell carcinoma (SCC), respectively. Among 27 AC, there were 17 EGFR-wild-type and 10 EGFR-mutated-positive samples detected by ARMS, and MSI achieved a diagnostic sensitivity of 82.3% (14/17) and 80% (8/10) for these two groups. Several phospholipids were specially enriched in AC compared with SCC tissues, with the higher ions intensity of phospholipids in EGFR-mutated-positive compared with EGFR-wild-type AC tissues. We also found EGFR mutations distribution was heterogeneous in different regions of same tumor by multi-regions ARMS detection, and only the regions with higher ions intensity of phospholipids were EGFR-mutated-positive. Conclusion: MSI method could accurately distinguish tumor pathology and subtypes, and phospholipids were reliable EGFR mutations associated biomarkers, phospholipids imaging could intuitively visualize EGFR mutations spatial distribution, may facilitate our understanding of tumor heterogeneity. Frontiers Media S.A. 2019-08-28 /pmc/articles/PMC6722907/ /pubmed/31555581 http://dx.doi.org/10.3389/fonc.2019.00804 Text en Copyright © 2019 Zhang, He, Li, Hu, Li, Xing, Wang, Yang, Ma, Liu, Tang, Abliz and Liu. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Zhang, Min
He, Jiuming
Li, Tiegang
Hu, Haixu
Li, Xiaofei
Xing, Hao
Wang, Jun
Yang, Fan
Ma, Qunfeng
Liu, Bing
Tang, Chuanhao
Abliz, Zeper
Liu, Xiaoqing
Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging
title Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging
title_full Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging
title_fullStr Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging
title_full_unstemmed Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging
title_short Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging
title_sort accurate classification of non-small cell lung cancer (nsclc) pathology and mapping of egfr mutation spatial distribution by ambient mass spectrometry imaging
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722907/
https://www.ncbi.nlm.nih.gov/pubmed/31555581
http://dx.doi.org/10.3389/fonc.2019.00804
work_keys_str_mv AT zhangmin accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT hejiuming accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT litiegang accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT huhaixu accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT lixiaofei accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT xinghao accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT wangjun accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT yangfan accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT maqunfeng accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT liubing accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT tangchuanhao accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT ablizzeper accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging
AT liuxiaoqing accurateclassificationofnonsmallcelllungcancernsclcpathologyandmappingofegfrmutationspatialdistributionbyambientmassspectrometryimaging