Dataset Information

Next-generation sequencing for molecular diagnosis of lung adenocarcinoma specimens obtained by fine needle aspiration cytology.

ABSTRACT: Identification of multi-gene variations has led to the development of new targeted therapies in lung adenocarcinoma patients, and identification of an appropriate patient population with a reliable screening method is the key to the overall success of tumor targeted therapies. In this study, we used the Ion Torrent next-generation sequencing (NGS) technique to screen for mutations in 89 cases of lung adenocarcinoma metastatic lymph node specimens obtained by fine-needle aspiration cytology (FNAC). Of the 89 specimens, 30 (34%) were found to harbor epidermal growth factor receptor (EGFR) kinase domain mutations. Seven (8%) samples harbored KRAS mutations, and three (3%) samples had BRAF mutations involving exon 11 (G469A) and exon 15 (V600E). Eight (9%) samples harbored PIK3CA mutations. One (1%) sample had a HRAS G12C mutation. Thirty-two (36%) samples (36%) harbored TP53 mutations. Other genes including APC, ATM, MET, PTPN11, GNAS, HRAS, RB1, SMAD4 and STK11 were found each in one case. Our study has demonstrated that NGS using the Ion Torrent technology is a useful tool for gene mutation screening in lung adenocarcinoma metastatic lymph node specimens obtained by FNAC, and may promote the development of new targeted therapies in lung adenocarcinoma patients.

SUBMITTER: Qiu T

PROVIDER: S-EPMC4464150 | biostudies-other | 2015

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:INTRODUCTION. Gene expression profiling has emerged as a potentially useful tool for personalized management of breast cancer. Adequate RNA can be obtained for this scope from dedicated fine-needle aspiration cytology (FNAC), with the limitation that separate sampling prohibits direct comparison with the material used for cytology. The aim of this study was to investigate whether syringe leftovers of cytological smears obtained in diagnostic FNACs can be successfully used to extract RNA for reliable PCR- and microarray-based gene expression analysis. METHODS. Twenty-eight FNACs were performed under ultrasound guide. After preparing two smears for cytological diagnosis, the syringes were immediately rinsed with Trizol® to extract RNA from residual material. The abundance and type of cells obtained from the smears was recorded and compared to RNA amounts and quality as determined by spectrophotometry and RT-PCR. FNAC leftovers of eleven breast cancers of different size, histological type, grade and immunoprofile were then selected for gene expression profiling using oligonucleotide microarrays. RESULTS. FNAC leftovers yielded an average of over 10 micrograms of RNA, more proportional to the amount of cells on the smear than to the lesion size, and adequate for expression analysis in 26/28 cases. The samples chosen for microarray analysis were clustered according to four criteria: (i) gene expression profiles of all detected genes; (ii) gene expression profiles of a previously defined stromal gene signature; (iii) “biological scores”, related to diagnostic cytological variables; (iv) “technical scores”, related to RNA/cRNA/microarray quality controls. Clusters generated by both expression profiles partitioned the samples in well-distinguished subgroups that were highly overlapping with clusters obtained using biological scores. Conversely, poor overlap was observed with clusters based on technical scores, demonstrating that sample divergences in RNA quality and amounts due to acquisition, handling and processing did not severely influence gene expression profiling. CONCLUSIONS. The syringe leftover after the smearing procedures of FNAC samples is suitable for RNA banking even when taken from small, non-palpable breast lesions, which guarantees congruity between morphological and molecular analysis. RNA yield mostly correlates with cellularity of the smears, and gene expression profiles are consistent with the cytologic features, and therefore with tumour biology.

Dataset Information

Next-generation sequencing for molecular diagnosis of lung adenocarcinoma specimens obtained by fine needle aspiration cytology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets