Project description:Whole exome sequencing (the SureSelectXT Mouse All Exon Kit) was done on leukemias from NP23-NHD13 double transgenic mice with strain background designated “C57Bl6-NIH”.
Project description:Purpose: To analyze the expression of two different CD19 isoforms during BiTE treatment, and to further determine cell lineage specific molecules before and after BiTE treatment Methods: For transcriptome sequencing, we isolated patient's bone marrow samples, quality of total RNA was evaluated by Agilent 2100. RNA libraries were prepared for sequencing using NEBNext® UltraTM RNA Library Prep Kit for Illumina (NEB,USA). RNA sequencing process and the data acquisition were finished on Novogene Experimental Department according to the manufacturer’s protocol. Reads were aligned to hg38 reference genome. Relative abundance of CD19 and other gene transcripts were mapped and estimated at both gene and transcript level (FPKM) by feature Counts. Results: The expression of CD19 isoform with exon 2 deletion was found at diagnosis before BiTE therapy. The patient did not achieve remission after BiTE treatment, and the expression of CD19 antigen turned negative by flow cytometry detection. But the expression ratio of exon 2 deleted CD19 was not increased, and the flow cytometry phenotype and transcriptome sequencing confirmed that no linage switching occurred, which suggested the expression of CD19 isoform caused by exon alternative splicing and lineage switching was not the driving mechanism of CD19 epitope loss in this patient. Immune escape could not be prevented by targeting alternative exons. This patient achieved complete remission by sequential infusions of our own developed CD22 CAR-T and CD19 CAR-T after disease progression Conclusions: transcriptome sequencing confirmed that no linage switching occurred in this patient, which suggested the expression of CD19 isoform caused by exon alternative splicing and lineage switching was not the driving mechanism of CD19 epitope loss in this patient.
Project description:Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. DNA copy number profiles generated with a new tool, ENCODER, were compared to DNA copy number profiles from SNP6, NimbleGen and low-coverage Whole Genome Sequencing.