Long-Read Sequencing of Human Transcription Provides Insights into Metastatic Cancer
Dr. Jianhua Luo presenting Loop Genomics webinar on Feb. 25
February 25, 2020 | 1 – 2 pm EST
To register, please follow this link: https://www.labpulse.com/index.aspx?sec=eba&sub=eml&pag=dis&itemId=800736
This webinar will discuss a study that used long-read transcriptome sequencing to explore the distribution of isoforms in colon cancer samples and their metastasis counterparts.
The complexity of mammalian gene expression involves the combinatorial use of exons during RNA splicing. The selective splicing process generates a plethora of isoforms per gene and accounts for what is arguably the largest source of variation in transcriptome diversity and adaptability. However, the quantification of the diversity of mammalian transcriptome is impeded by the lack of accurate, quantitative, and affordable long-read isoform sequencing.
Accurate analyses of the distribution of isoforms, fusion gene isoforms, and point mutation isoforms remains a huge challenge for human malignancies. In this webinar, Jianhua Luo of the University of Pittsburgh will discuss a study that used the ability to capture transcripts from user-defined sets of genes together with synthetic long-read sequencing of full-length mRNA to characterize the long-read transcriptomes from three pairs of colon cancers and their metastasis counterparts.
Dr. Luo will share how the study demonstrated a unique pattern of RNA isoform redistribution and enrichment for specific mutated isoforms and fusions in metastatic cancer cells in comparison with their primary cancer counterparts. The isoform switching and mutation-enriched isoforms are predicted to have subtle effects on protein structure, which may differentially impact protein signal transduction and response to drug treatment.
The results demonstrate that the use of probe capture and long-read sequencing provides focus and granularity that was previously inaccessible in transcriptome analysis. Full-length transcriptome analysis may be essential for our understanding of gene expression regulation in human cancers.