Quantification of mutant-allele expression at isoform level in cancer from RNA-seq data

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• DNA mutations are key drivers of cancer, but RNA expression analysis often overlooks mutation status.
• Current allele-specific expression (ASE) methods focus on gene-level, missing isoform-specific mutation impacts.
• Assessing functional consequences of mutations requires isoform-level analysis.

Purpose of the Study:

• To develop and implement a method for quantifying mutant-allele expression at the isoform level.
• To evaluate the accuracy and performance of this new method compared to existing approaches.
• To explore the clinical utility of isoform-level mutant expression analysis in cancer patient datasets.

Main Methods:

• Developed the MAX (Mutant-Allele expression quantification) method.
• Constructed mutant references by incorporating all possible mutant wild-type isoforms.
• Applied an alternating expectation-maximization (EM) algorithm to RNA-seq read-count data.
• Validated MAX using simulation studies and a real acute myeloid leukemia dataset.

Main Results:

• MAX demonstrates strong performance in simulations, outperforming standard isoform quantification methods.
• MAX achieves higher accuracy than conventional gene-based ASE methods like ASEP.
• Analysis of acute myeloid leukemia data identified a patient subgroup with NPM1 mutations responding to kinase inhibitors.

Conclusions:

• Quantifying mutant-allele expression at the isoform level is feasible and provides valuable insights.
• This approach enhances the assessment of DNA mutation functional impact in cancer.
• Isoform-level analysis holds potential for identifying novel therapeutic strategies and patient subgroups.