HEMASEQ

The project aims extending our understanding of genetic alterations in two haematological malignancies, multiple myeloma (MM) and chronic myelomonocytic leukemia (CMML) using high-throughput sequencing data (HS). This project will feature HS data for patient’s diagnosis and prognosis improvement in CMML and resistance to treatment response in MM. We had a large number of sequence data from tumor RNA (RNA-seq) and DNA samples (Whole DNA-seq from tumor and non tumor DNA) to carry out our main objectives: i/the data mining of RNA-seq to explore alteration of spliced event and chimeric RNAs (cRNA) including fusions genes, and ii/the use of whole DNA-seq to identify novel DNA alterations with a specific investigation of mobile elements. In CMML, these data have contributed to the first analysis of genetic alterations at the whole genome level (Merlevede et al, Nat Com, in press). A full molecular portrait of drug resistant cell lines and link with primary MMCs of patients is in progress with whole DNA-seq analysis. The study included eight human myeloma cell lines, representative of the molecular heterogeneity of the disease, characterized in term of short tandem repeat molecular markers and the corresponding resistant cell lines to the four major drugs used in myeloma (Bortezomib, Lenalidomide, dexamethasone and melphalan). The analysis required to use and adapt complex and efficient methods for whole genome analysis (among them, BWA, GATK, SamTools, SomaticSniper, VarScan2, Annovar…) with the help of several bioinformatics teams (CNG and Institut Gustave Roussy). The project resides in the collaboration of experts in cancer and computational biology and the data will be used to improve the developments of new tools and pipelines specifically to explore the biological potential of new spliced events and cRNA extracted from RNA-seq and compared to DNA-seq.

Publications :

• Merlevede J. et al. Mutation allele burden remains unchanged in chronic myelomonocytic leukaemia responding to hypomethylating agents. Nat Commun. 2016, 7: 10767
• Audoux J. et al. DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition. Genome Biology 2017, vol. 18, issue 1