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SpliceHetero: An information theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq


Autoři: Minsu Kim aff002;  Sangseon Lee aff002;  Sangsoo Lim aff001;  Sun Kim aff001
Působiště autorů: Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea aff001;  Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea aff002;  Bioinformatics Institute, Seoul National University, Seoul, 08826, Korea aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223520

Souhrn

Motivation

Intratumor heterogeneity (ITH) represents the diversity of cell populations that make up cancer tissue. The level of ITH in a tumor is usually measured by a genomic variation profile, such as copy number variation and somatic mutation. However, a recent study has identified ITH at the transcriptome level and suggested that ITH at gene expression levels is useful for predicting prognosis. Measuring ITH levels at the spliceome level is a natural extension. There are serious technical challenges in measuring spliceomic ITH (sITH) from bulk tumor RNA sequencing (RNA-seq) due to the complex splicing patterns.

Results

We propose an information-theoretic method to measure the sITH of bulk tumors to overcome the above challenges. This method has been extensively tested in experiments using synthetic data, xenograft tumor data, and TCGA pan-cancer data. As a result, we showed that sITH is closely related to cancer progression and clonal heterogeneity, along with clinically significant features such as cancer stage, survival outcome and PAM50 subtype. As far as we know, it is the first study to define ITH at the spliceome level. This method can greatly improve the understanding of cancer spliceome and has great potential as a diagnostic and prognostic tool.

Klíčová slova:

Breast cancer – Cancer genomics – Evolutionary genetics – Introns – RNA sequencing – Somatic mutation – Transcriptome analysis – RNA alignment


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