Biology subjects

Nadine Botherel

Publications and source records attributed to Nadine Botherel.

2 recordsLinked to original sources

bioRxiv2020

Interest of circulating tumor DNA as a biomarker for canine cancers: illustration in histiocytic sarcoma, oral malignant melanoma and multicentric lymphoma

Circulating tumor DNA (ctDNA) has become an attractive biomarker in human oncology and may be informative in cancer-affected dogs. By performing ddPCR or PARR methods, we detected tumor-specific point mutations, copy number alterations and chromosomal rearrangements in the plasma of cancer-affected dogs. It allowed the detection of ctDNA in 2/8 (25%) oral malignant melanoma cases, 12/13 (92.3%) lymphoma cases and 21/23 (91.3%) histiocytic sarcoma (HS) cases. The value of ctDNA to diagnose HS was explored in 133 dogs including 49 with HS. In this cohort, screening recurrent PTPN11 mutations in plasma had a specificity of 98.8%, and a sensitivity between 42.8-77% according to HS clinical presentation, being higher in internal forms, especially with pulmonary location. Regarding lymphoma, the follow-up of four dogs showed that the minimal residual disease detection by targeting lymphoma-specific antigen receptor rearrangement in the plasma was concordant with the clinical evaluation. Moreover, ctDNA analysis appeared interesting to assess treatment response and to predict relapse. This study shows that ctDNA is detectable in the plasma of cancer-affected dogs and is a relevant biomarker for diagnosis and clinical follow-up. With a growing interest in integrating natural canine tumors to explore new therapies, this biomarker appears promising in comparative oncology research.

cancer biology↗

bioRxiv2016

FEELnc: A tool for Long non-coding RNAs annotation and its application to the dog transcriptome

Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. Among the plethora of reconstructed transcripts, one of the main bottlenecks consists in correctly identifying the different classes of RNAs, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program which accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE datasets. The program also provides several specific modules that enable to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to annotate lncRNAs even in the absence of training set of noncoding RNAs. We used FEELnc on a real dataset comprising 20 new canine RNA-seq samples produced in the frame of the European LUPA consortium to expand the canine genome annotation and classified 10,374 novel lncRNAs and 58,640 new mRNA transcripts. FEELnc represents a standardized protocol for identifying and annotating lncRNAs and is freely accessible at https://github.com/tderrien/FEELnc.

Bioinformatics↗