# Integrated RNA-seq and RT-qPCR Workflow Identifies Non-IGH Fusion Transcripts as Individualized Molecular Markers for Monitoring Multiple Myeloma

## TL;DR

A new RNA-seq and RT-qPCR method identifies fusion transcripts in multiple myeloma to track disease progression, especially in non-secretory cases.

## Contribution

An integrated RNA-seq and RT-qPCR workflow is introduced for identifying non-IGH fusion transcripts as personalized molecular markers in multiple myeloma.

## Key findings

- 362 fusion events were identified in MM patients, with 190 non-immunoglobulin fusions characterized.
- Five recurrent fusions were detected across nine patients, linked to RNA splicing and cancer pathways.
- Quantitative fusion transcript monitoring detected relapse earlier than flow cytometry, including in non-secretory MM.

## Abstract

Background: Multiple myeloma (MM) is a hematologic malignancy characterized by clonal plasma cell expansion and diverse genomic rearrangements, including immunoglobulin heavy chain (IGH) translocations. Although RNA sequencing enables the comprehensive detection of IGH-associated fusions, routine molecular monitoring remains limited, particularly in non-secretory MM (NSMM), which lacks measurable serologic markers. Methods: Here, we contracted an integrated system combining RNA sequencing (RNA-seq) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) to identify and validate fusion gene-based molecular markers for minimal residual disease (MRD) monitoring. Results: The global fusion landscape was delineated by the sequencing analysis of bone marrow samples from 22 newly diagnosed patients with MM. A total of 362 fusion events were identified, of which 190 non-immunoglobulin fusions were selected for detailed characterization. Recurrent breakpoints were concentrated on chromosomes 1 and 19, and five recurrent fusions, DDX5::EEF1A1, OAZ1::KLF2, OAZ1::KLF16, PFKFB3::LINC02649, and PLXNB2::SCO2, were detected across nine patients. Functional enrichment analyses indicated the significant involvement of these genes in RNA splicing regulation, transcriptional misregulation in cancer-related pathways, and focal adhesion processes. Twenty-three fusion transcripts were validated using RT-PCR and Sanger sequencing, demonstrating high specificity for MM. Longitudinal monitoring revealed that the quantitative assessment of fusion transcript levels enabled earlier relapse detection than flow cytometry, including in NSMM, where conventional MRD tools are ineffective. Conclusions: These findings suggest that individualized fusion transcripts serve as robust molecular markers for MRD surveillance. The proposed RNA-seq–RT-qPCR pipeline offers a clinically practical strategy to enhance precision diagnosis and personalized treatment in MM.