Molecular profiling of non-small cell lung cancer
Lung cancer is generally treated with conventional therapies, including chemotherapy and radiation. These methods, however, are not specific to cancer cells and instead attack every cell present, including normal cells. Personalized therapies provide more efficient treatment options as they target t...
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creator | Forsythe, Marika L Alwithenani, Akram Bethune, Drew Castonguay, Mathieu Drucker, Arik Flowerdew, Gordon French, Daniel Fris, John Greer, Wenda Henteleff, Harry MacNeil, Mary Marignani, Paola Morzycki, Wojciech Plourde, Madelaine Snow, Stephanie Xu, Zhaolin |
description | Lung cancer is generally treated with conventional therapies, including chemotherapy and radiation. These methods, however, are not specific to cancer cells and instead attack every cell present, including normal cells. Personalized therapies provide more efficient treatment options as they target the individual's genetic makeup. The goal of this study was to identify the frequency of causal genetic mutations across a variety of lung cancer subtypes in the earlier stages. 833 samples of non-small cell lung cancer from 799 patients who received resection of their lung cancer, were selected for molecular analysis of six known mutations, including EGFR, KRAS, BRAF, PIK3CA, HER2 and ALK. A SNaPshot assay was used for point mutations and fragment analysis searched for insertions and deletions. ALK was evaluated by IHC +/- FISH. Statistical analysis was performed to determine correlations between molecular and clinical/pathological patient data. None of the tested variants were identified in most (66.15%) of cases. The observed frequencies among the total samples vs. only the adenocarcinoma cases were notable different, with the highest frequency being the KRAS mutation (24.49% vs. 35.55%), followed by EGFR (6.96% vs. 10.23%), PIK3CA (1.20% vs. 0.9%), BRAF (1.08% vs. 1.62%), ALK (0.12% vs. 0.18%), while the lowest was the HER2 mutation (0% for both). The statistical analysis yielded correlations between presence of a mutation with gender, cancer type, vascular invasion and smoking history. The outcome of this study will provide data that helps stratify patient prognosis and supports development of more precise treatments, resulting in improved outcomes for future lung cancer patients. |
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These methods, however, are not specific to cancer cells and instead attack every cell present, including normal cells. Personalized therapies provide more efficient treatment options as they target the individual's genetic makeup. The goal of this study was to identify the frequency of causal genetic mutations across a variety of lung cancer subtypes in the earlier stages. 833 samples of non-small cell lung cancer from 799 patients who received resection of their lung cancer, were selected for molecular analysis of six known mutations, including EGFR, KRAS, BRAF, PIK3CA, HER2 and ALK. A SNaPshot assay was used for point mutations and fragment analysis searched for insertions and deletions. ALK was evaluated by IHC +/- FISH. Statistical analysis was performed to determine correlations between molecular and clinical/pathological patient data. None of the tested variants were identified in most (66.15%) of cases. The observed frequencies among the total samples vs. only the adenocarcinoma cases were notable different, with the highest frequency being the KRAS mutation (24.49% vs. 35.55%), followed by EGFR (6.96% vs. 10.23%), PIK3CA (1.20% vs. 0.9%), BRAF (1.08% vs. 1.62%), ALK (0.12% vs. 0.18%), while the lowest was the HER2 mutation (0% for both). The statistical analysis yielded correlations between presence of a mutation with gender, cancer type, vascular invasion and smoking history. The outcome of this study will provide data that helps stratify patient prognosis and supports development of more precise treatments, resulting in improved outcomes for future lung cancer patients.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0236580</identifier><identifier>PMID: 32756609</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Adenocarcinoma ; Biology and Life Sciences ; Brain research ; Cancer research ; Cancer therapies ; Care and treatment ; Chemotherapy ; Consent ; Correlation ; Correlation analysis ; Development and progression ; Epidermal growth factor ; Epidermal growth factor receptors ; ErbB-2 protein ; Gene mutation ; Genetic aspects ; Genetic research ; Health aspects ; Kinases ; Lung cancer ; Lung diseases ; Medical research ; Medicine ; Medicine and Health Sciences ; Metastasis ; Mutation ; Non-small cell lung cancer ; Non-small cell lung carcinoma ; Oncology ; Pathology ; Patients ; Precision medicine ; Radiation ; Samples ; Sarcoma ; Small cell lung carcinoma ; Squamous cell carcinoma ; Statistical analysis ; Statistical methods ; Statistics ; Surgery ; Tumors</subject><ispartof>PloS one, 2020-08, Vol.15 (8), p.e0236580-e0236580</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Forsythe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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The outcome of this study will provide data that helps stratify patient prognosis and supports development of more precise treatments, resulting in improved outcomes for future lung cancer patients.</description><subject>Adenocarcinoma</subject><subject>Biology and Life Sciences</subject><subject>Brain research</subject><subject>Cancer research</subject><subject>Cancer therapies</subject><subject>Care and treatment</subject><subject>Chemotherapy</subject><subject>Consent</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Development and progression</subject><subject>Epidermal growth factor</subject><subject>Epidermal growth factor receptors</subject><subject>ErbB-2 protein</subject><subject>Gene mutation</subject><subject>Genetic aspects</subject><subject>Genetic research</subject><subject>Health aspects</subject><subject>Kinases</subject><subject>Lung cancer</subject><subject>Lung diseases</subject><subject>Medical 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one</jtitle><date>2020-08-05</date><risdate>2020</risdate><volume>15</volume><issue>8</issue><spage>e0236580</spage><epage>e0236580</epage><pages>e0236580-e0236580</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Lung cancer is generally treated with conventional therapies, including chemotherapy and radiation. These methods, however, are not specific to cancer cells and instead attack every cell present, including normal cells. Personalized therapies provide more efficient treatment options as they target the individual's genetic makeup. The goal of this study was to identify the frequency of causal genetic mutations across a variety of lung cancer subtypes in the earlier stages. 833 samples of non-small cell lung cancer from 799 patients who received resection of their lung cancer, were selected for molecular analysis of six known mutations, including EGFR, KRAS, BRAF, PIK3CA, HER2 and ALK. A SNaPshot assay was used for point mutations and fragment analysis searched for insertions and deletions. ALK was evaluated by IHC +/- FISH. Statistical analysis was performed to determine correlations between molecular and clinical/pathological patient data. None of the tested variants were identified in most (66.15%) of cases. The observed frequencies among the total samples vs. only the adenocarcinoma cases were notable different, with the highest frequency being the KRAS mutation (24.49% vs. 35.55%), followed by EGFR (6.96% vs. 10.23%), PIK3CA (1.20% vs. 0.9%), BRAF (1.08% vs. 1.62%), ALK (0.12% vs. 0.18%), while the lowest was the HER2 mutation (0% for both). The statistical analysis yielded correlations between presence of a mutation with gender, cancer type, vascular invasion and smoking history. The outcome of this study will provide data that helps stratify patient prognosis and supports development of more precise treatments, resulting in improved outcomes for future lung cancer patients.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>32756609</pmid><doi>10.1371/journal.pone.0236580</doi><tpages>e0236580</tpages><orcidid>https://orcid.org/0000-0002-8553-4081</orcidid><orcidid>https://orcid.org/0000-0002-3551-8673</orcidid><oa>free_for_read</oa></addata></record> |
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source | Public Library of Science (PLoS) Journals Open Access; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Adenocarcinoma Biology and Life Sciences Brain research Cancer research Cancer therapies Care and treatment Chemotherapy Consent Correlation Correlation analysis Development and progression Epidermal growth factor Epidermal growth factor receptors ErbB-2 protein Gene mutation Genetic aspects Genetic research Health aspects Kinases Lung cancer Lung diseases Medical research Medicine Medicine and Health Sciences Metastasis Mutation Non-small cell lung cancer Non-small cell lung carcinoma Oncology Pathology Patients Precision medicine Radiation Samples Sarcoma Small cell lung carcinoma Squamous cell carcinoma Statistical analysis Statistical methods Statistics Surgery Tumors |
title | Molecular profiling of non-small cell lung cancer |
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