Dataset Information

Comparative transcriptomic characterisation of canine prostate fine-needle-aspiration and tissue samples - from sampling to sequencing

ABSTRACT: Ultrasound-guided fine-needle aspiration (US-FNA) biopsy is a widely used minimally invasive sampling procedure for cytological diagnosis. This study investigates the feasibility of using US-FNA samples for both cytological diagnosis and whole transcriptome RNA-sequencing analysis (RNA-Seq), with the ultimate aim of improving canine prostate cancer management. The feasibility of the US-FNA procedure was evaluated intra vitam on 43 dogs. Additionally, aspirates from 31 euthanised dogs were collected for standardising the procedure. Each aspirate was separated into two subsamples: for cytology and RNA extraction. Additional prostate tissue samples served as control for RNA quantity and quality evaluation, and differential expression analysis. The US-FNA sampling procedure was feasible in 95% of dogs. RNA isolation of US-FNA samples was successfully performed using phenol-chloroform extraction. The extracted RNA of 56% of a subset of US-FNA samples met the quality requirements for RNA-seq. Expression analysis revealed that only 153 genes were exclusively differentially expressed between non-malignant US-FNAs and tissues. Moreover, only 36 differentially expressed genes were associated with the US-FNA sampling technique and unrelated to the diagnosis. Furthermore, the gene expression profiles clearly distinguished between non-malignant and malignant samples. This proves US-FNA to be useful for molecular profiling.

ORGANISM(S): Canis lupus familiaris

PROVIDER: GSE122916 | GEO | 2019/10/08

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background. Although gene expression arrays have been used to generate molecular predictors of relapse and drug sensitivity in breast cancer, no large study describes genes and exons differentially expressed between breast cancer and benign lesions. Methods. One hundred and sixty-five tumour samples were obtained by fine needle aspiration (FNA). cDNA were hybridized on Splice ArrayTM. A nearest centroid prediction rule was developed to classify lesions as malignant or benign on a training set, and its performance evaluated on an independent validation set. A two-way ANOVA model was used to identify probesets that present a differential expression between cancer and benign lesions while adjusting for scan dates. P-values were adjusted for False Discovery Rate. Findings. Overall 120 breast cancers and 45 benign lesions were included in the study. A 1228-probeset molecular classifier for breast cancer diagnosis was generated from the training set (n=94). This signature accurately classified all samples (100% accuracy, 95% exact CI: 96-100%). In the validation set (n=71), the molecular predictor accurately classified 68 out of 71 tumours (96%, 95% CI: 88-99%). When the 165 samples were taken into account, 37 858 exon-probesets (5.4%) and 3733 genes (20%) were found to be differentially expressed between malignant and benign conditions (adjusted p-value<0.05). Pathway analyses showed that genes involved in spliceosome assembly were significantly enriched in malignant condition (permutation p=0.002). In the same population of 165 samples, 956 exon-probesets presented both a higher intensity and higher splice index in breast cancer, although located on unchanged genes. Interpretation. The present study provides a thorough description of differentially expressed exons between breast cancer and benign lesions, and emphasizes the contribution of spliceosome and alternative transcripts to the molecular portrait of breast malignancy. This allowed the development of a molecular classifier for breast cancer diagnosis using FNA.

Project description:Although fine needle aspiration (FNA) cytology is helpful in determining whether thyroid nodules are benign or malignant, this distinction remains challenging in follicular neoplasms. Identification of genomic alterations in cytological specimens that distinguish between benign and malignant follicular tumors would have great clinical value. This study identified chromosomal imbalances in a set of 126 thyroid frozen follicular lesions characterized by histopathology, using aCGH, FISH, and DNA ploidy determination. An evaluation algorithm was developed based on a panel of three FISH experiments to better distinguish between follicular adenomas and carcinomas of the thyroid. This algorithm detects structural abnormalities that were observed in about half of the follicular thyroid carcinomas. These molecular markers may be therefore useful as an adjunct for making a differential diagnosis of follicular adenoma versus follicular carcinoma using cytological material obtained by FNA. Further information on the values in the comment columns: [FISH_1p_Loss : Status for abnormality found in terms of copy number variation for chr1p by FISH], [FISH_22_Loss : Status for abnormality found in terms of copy number variation for chr22 by FISH], [FISH_X_Gain : Status for abnormality found in terms of copy number variation for chrX by FISH], [FISH_Global : Status for abnormality found in terms of copy number variation for at least one of these (chr1p, chr22, chrX) by FISH], [DNA_Index : Measured by flow cytometry], [KRAS_Mutation : Mutation status for KRAS, obtained from Sanger sequencing. wt for wild-type, mut for mutant], [HRAS_Mutation : Mutation status for HRAS, obtained from Sanger sequencing. wt for wild-type, mut for mutant], [NRAS_Mutation : Mutation status for NRAS, obtained from Sanger sequencing. wt for wild-type, mut for mutant], [BRAF_Mutation : Mutation status for BRAF, obtained from Sanger sequencing. wt for wild-type, mut for mutant], [CTNNB1_Mutation : Mutation status for CTNNB1, obtained from Sanger sequencing. wt for wild-type, mut for mutant], [PIK3CA_Mutation : Mutation status for PIK3CA, obtained from Sanger sequencing. wt for wild-type, mut for mutant], [PAX8-PPARg_Fusion : Status for the PAX8-PPARG gene fusion, obtained from qRT-PCR. pos for presence of fusion, neg for absence], [RET-PTC_Fusion : Status for the RET-PTC gene fusion, obtained from qRT-PCR. pos for presence of fusion, neg for absence], [Mutation_Status : Global mutation status, combining results from HRAS, NRAS mutation status, and PAX8-PPARG fusion status], [Tumor_Size_cm : Tumor size in centimeters], [pT : Classes defined according to the size of the tumors (for more information, check AJCC classification)], [pN : Classes defined according to the number of nodes involved or not (for more information, check AJCC classification)], [pM : Classes defined according to the presence or absence of distant metastases (for more information, check AJCC classification)], [Carcinoma_Histo_Type : Histological type for carcinoma. MIF for minimally invasive, WIF for widely invasive], [Adenoma_Histo_Type : Histological type for adenoma. MIC for microvesicular, MAC for macrovesicular, BOTH for both], [Follow-up_month : Patient follow-up in months], [Alive_status : Patient alive status. yes for alive patient, no for deceased patient], [Metastasis_status : Presence of metastasis during the follow-up. yes for positive, no for negative, possible when uncertainty remains].

Dataset Information

Comparative transcriptomic characterisation of canine prostate fine-needle-aspiration and tissue samples - from sampling to sequencing

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets