Systematic transcriptome analysis reveals tumor-specific isoforms for ovarian cancer diagnosis and therapy

Area of Science:

• Oncology
• Bioinformatics
• Molecular Biology

Background:

• Tumor-specific molecules are crucial for advancing cancer detection, diagnosis, prognosis, and therapy.
• Publicly available transcriptome sequencing data (RNA-seq) offers a rich resource for discovering these molecules, but remains underexplored.
• High-grade serous ovarian cancer (HGS-OvCa) presents a significant clinical challenge, necessitating novel biomarkers.

Purpose of the Study:

• To identify and validate novel mRNA isoforms with tumor-specific expression in high-grade serous ovarian cancer (HGS-OvCa).
• To develop and apply custom bioinformatic algorithms for analyzing large-scale RNA-seq datasets.
• To assess the potential of identified isoforms for targeted cancer therapies.

Main Methods:

• Utilized custom bioinformatic algorithms to analyze 296 HGS-OvCa tumor and 1,839 normal RNA-seq datasets.
• Prioritized mRNA isoforms based on their likelihood of tumor-specific expression.
• Validated top-ranked isoforms using high-throughput RT-qPCR on tumor and normal tissue samples.

Main Results:

• Identified six mRNA isoforms expressed in the majority of examined HGS-OvCa tumors but not in normal tissues.
• Discovered an additional 11 isoforms expressed in most tumors and only one normal tissue (fallopian or colon).
• The top 5% of ranked isoforms (n=33) are enriched for oncogenic and developmental loci, including ETV4, FOXM1, and FGFRL1.

Conclusions:

• The identified tumor-specific mRNA isoforms represent promising candidates for early detection, diagnosis, and targeted therapeutic strategies in HGS-OvCa.
• Many isoforms encode proteins with unique sequences, enabling specific targeting via monoclonal antibodies or T-cell-based vaccines.
• The developed systematic approach is adaptable for discovering tumor-specific molecules across numerous other cancer types.