Dataset Information

Genome analysis of peeling archival cytology samples detects driver mutations in lung cancer.

ABSTRACT: INTRODUCTIONS:When tumor tissue samples are unavailable to search for actionable driver mutations, archival cytology samples can be useful. We investigate whether archival cytology samples can yield reliable genomic information compared to corresponding formalin-fixed paraffin-embedded (FFPE) tumor samples. PATIENTS AND METHODS:Pretreatment class V archival cytology samples with adequate tumor cells were selected from 172 lung cancer patients. The genomic profiles of the primary lung tumors have been analyzed through whole-exome regions of 53 genes. We compared the genomic profiles based on the oncogenicity and variant allele frequency (VAF) between the archival cytology and the corresponding primary tumors. We also analyzed the genomic profiles of serial cytological samples during the treatment of EGFR-TKI. RESULTS:A total of 43 patients were analyzed with the paired samples for DNA mutations and other three patients were analyzed for their fusion genes. A total of 672 mutations were detected. Of those, 106 mutations (15.8%) were shared with both samples. Sixty of seventy-seven (77.9%) shared mutations were oncogenic or likely oncogenic mutations with VAF ?10%. As high as 90% (9/10) actionable driver mutations and ALK and ROS1 fusion genes were successfully detected from archival cytology samples. Sequential analysis revealed the dynamic changes in EGFR-TKI-resistant mutation (EGFR p.T790M) during the course of treatment. CONCLUSION:Archival cytology sample with adequate tumor cells can yield genetic information compared to the primary tumors. If tumor tissue samples are unavailable, we can use archival cytology samples to search for actionable driver mutations.

SUBMITTER: Kunimasa K

PROVIDER: S-EPMC7333826 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genome analysis of peeling archival cytology samples detects driver mutations in lung cancer.

Kunimasa Kei K Hirotsu Yosuke Y Amemiya Kenji K Nagakubo Yuki Y Goto Taichiro T Miyashita Yoshihiro Y Kakizaki Yumiko Y Tsutsui Toshiharu T Otake Sotaro S Kobayashi Hiroaki H Higuchi Rumi R Inomata Kie K Kumagai Takashi T Mochizuki Hitoshi H Nakamura Harumi H Nakatsuka Shin-Ichi SI Nishino Kazumi K Imamura Fumio F Kumagai Toru T Oyama Toshio T Omata Masao M

Cancer medicine 20200429 13

<h4>Introductions</h4>When tumor tissue samples are unavailable to search for actionable driver mutations, archival cytology samples can be useful. We investigate whether archival cytology samples can yield reliable genomic information compared to corresponding formalin-fixed paraffin-embedded (FFPE) tumor samples.<h4>Patients and methods</h4>Pretreatment class V archival cytology samples with adequate tumor cells were selected from 172 lung cancer patients. The genomic profiles of the primary l ...[more]

PMID: 32351019

Similar Datasets

Project description:MotivationNext-generation sequencing technologies have accelerated the discovery of single nucleotide variants in the human genome, stimulating the development of predictors for classifying which of these variants are likely functional in disease, and which neutral. Recently, we proposed CScape, a method for discriminating between cancer driver mutations and presumed benign variants. For the neutral class, this method relied on benign germline variants found in the 1000 Genomes Project database. Discrimination could, therefore, be influenced by the distinction of germline versus somatic, rather than neutral versus disease driver. This motivates this article in which we consider predictive discrimination between recurrent and rare somatic single point mutations based solely on using cancer data, and the distinction between these two somatic classes and germline single point mutations.ResultsFor somatic point mutations in coding and non-coding regions of the genome, we propose CScape-somatic, an integrative classifier for predictively discriminating between recurrent and rare variants in the human cancer genome. In this study, we use purely cancer genome data and investigate the distinction between minimal occurrence and significantly recurrent somatic single point mutations in the human cancer genome. We show that this type of predictive distinction can give novel insight, and may deliver more meaningful prediction in both coding and non-coding regions of the cancer genome. Tested on somatic mutations, CScape-somatic outperforms alternative methods, reaching 74% balanced accuracy in coding regions and 69% in non-coding regions, whereas even higher accuracy may be achieved using thresholds to isolate high-confidence predictions.Availability and implementationPredictions and software are available at http://CScape-somatic.biocompute.org.uk/.Contactmark.f.rogers.phd@gmail.com or C.Campbell@bristol.ac.uk.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Genome analysis of peeling archival cytology samples detects driver mutations in lung cancer.

Publications

Genome analysis of peeling archival cytology samples detects driver mutations in lung cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets