Transcriptome analysis of breast cancer clinical specimens with long-read sequencer

C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Senior Staff Scientist, Division of Cellular Signaling, Research Institute, National Cancer Center
    Biography

      Masahito Kawazu began his medical research after studying medicine at the University of Tokyo and being trained as a hematologist. After earning a degree in research on transcription factors and conducting research on transcriptional regulators as a postdoctoral fellow, he joined  Department of Cellular Signaling, The University of Tokyo, in 2010 and started his research on genomic medicine. His research focuses on genomic abnormalities of cancer cells. He aims to find genomic abnormalities that drive the growth of cancer or that affect the immune environment of cancer. He performed a genome analysis of breast cancer and microsatellite instability-high colorectal cancer, and clarified the characteristics of their genomic abnormality. Recently, his team is trying to analyze the abnormal transcripts resulting from the structural variations (chromosomal rearrangement) of cancer genome using a long-read sequencer.


    Abstract

    Changes in transcriptional regulation are thought to be one of the key drivers of carcinogenesis. Although next-generation sequencer revolutionized transcriptome analysis, there are limitations in the analysis of full-length transcripts with short-read sequencing data. We developed a multi-sample long-read transcriptome assembly pipline, MuSTA, and showed through simulation that a transcriptome can be constructed from the transcripts represented by the target samples, enabling accurate evaluation of transcriptional regulation. RNA extracted from 22 breast cancer clinical specimens were subjected to Iso-seq full-length transcriptome sequencing using Sequel (PacBio). The MuSTA pipeline was applied to the long-read sequencing data to successfully obtain a full-length transcriptome for the entire cohort. By comparing isoform existence and expression between estrogen receptor positive and triple-negative subtypes, we obtained a comprehensive set of subtype-specific isoforms and differentially used isoforms which consisted of both known and unannotated isoforms. We also found that the exon-intron structure of fusion transcripts depends on the features of the involved genomic regions, and that three-piece fusion transcripts were transcribed from complex structural variations.

    Learning Objectives:

    1. To understand the somatic structural variations of cancer genomes

    2. To understand the technical difficulties in transcript analysis with long-read sequencers

    3. To find out how transcriptome analysis using a long-read sequencer is useful for understanding cancer biology


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