The Actionable Transcriptome: A Guide to RNA Sequencing in Precision Oncology
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Moving beyond DNA mutations in precision oncology: a new framework for integrating RNA-seq data into clinical decision support.
Over the past decade, precision oncology has evolved from treatments for specific cancer types toward molecular biomarker-driven treatments that are independent of tumor histology (histology-agnostic). Approvals such as larotrectinib for NTRK fusions, dabrafenib for the BRAF V600E mutation or pembrolizumab for high tumor mutational burden (TMB-H) are the clearest examples of this transformation. However, using DNA sequencing (DNA-seq) alone means missing the submerged part of the iceberg.
Why RNA sequencing? Where DNA-seq and IHC fall short
Proteins are the main executors of cellular activities during oncogenesis. Although protein presence is traditionally measured by immunohistochemistry (IHC), this method generally targets a single biomarker. RNA-seq, on the other hand, can assess hundreds of genes simultaneously and quantitatively.
Many gene fusions occur in intronic regions of DNA and may be missed by standard DNA-seq panels. Because RNA-seq focuses directly on exon junctions, it detects actionable fusions, including NTRK, RET and FGFR2, with much greater sensitivity.
The presence of targets such as TROP2, HER3 and folate receptor-α is critical for antibody–drug conjugates (ADCs) and CAR-T cell therapies. By quantifying the abundance of these targets in the tumor, RNA-seq makes treatment selection more precise.
Mutations such as MET exon 14 skipping may be complex at the DNA level but are clearly visible at the RNA level. RNA-seq is statistically superior to DNA-based tests for detecting these critical variants.
Beyond tests such as MammaPrint or Oncotype DX, complex RNA signatures indicating the immune state of the tumor microenvironment or homologous recombination deficiency (HRD) serve as prognostic and predictive compasses.
The actionable transcriptome: clinical applications
The researchers have compiled an «actionable transcriptome» list to enable RNA data to inform treatment decisions. This list targets epigenetic changes and deviations in protein production that are not seen in DNA but are detected at the RNA level.
| RNA-Based Biomarker | Clinical Application and Example | Targeted Treatment |
|---|---|---|
| Gene Fusions | FGFR2 Fusions (Cholangiocarcinoma) | Pemigatinib, Futibatinib |
| Oncogene Overexpression | MET RNA Overexpression (NSCLC) | Capmatinib, Tepotinib |
| Surface Antigens | TACSTD2 (TROP2) mRNA Expression | Sacituzumab Govitecan (ADC) |
| Tumor Suppressor Loss | Low MTAP or BRCA1 RNA | PRMT5 or PARP Inhibitors |
| Immune Signatures | Interferon-γ Signatures | Immune Checkpoint Inhibitors |
Pan-cancer approach and benchmark analysis
One of the biggest challenges with RNA-seq is defining what constitutes «high» gene expression. The article proposes comparing a tumor's RNA expression not only with its own tumor type but also with a broad dataset covering all cancer types, such as TCGA Pan-Cancer. For example, using average TROP2 expression in breast cancer as a reference, finding this level of expression in a rare cancer may be a «precise» indication that the patient could benefit from an ADC treatment.
Clinical Applications of RNA-Derived Biomarkers in Precision Oncology
The figure below summarizes how RNA sequencing (RNA-seq) is used to guide diagnostic, prognostic and predictive decisions for patients with cancer. The diagram details eight distinct clinical application panels (a-h), ranging from identifying antibody targets (a-c) to analyzing oncogenic pathways (d), identifying neoantigens (f) and detecting gene fusions (g), each critical to precision medicine.
The Role of the Actionable Transcriptome in Precision Oncology
The figure below shows how DNA-seq, RNA-seq and IHC are combined and complement one another to identify actionable biomarkers, including genomic alterations, transcript signatures and protein expression, that can guide treatment selection in patients with cancer. The detected biomarkers determine the most appropriate therapeutic actions, such as targeted therapies, immunotherapies and neoantigen targets.
Integration into clinical workflows and technical challenges
RNA's greater instability compared with DNA and its rapid degradation are the biggest obstacles for clinical laboratories. However, current technologies, including anchored multiplex PCR and exome-capture mRNA-seq, have made it possible to obtain high-quality RNA data even from formalin-fixed, paraffin-embedded tissue (FFPE) samples.
DROZDOGAN Academy Commentary
For years on our healing journey with cancer, we have read the cell's «book», its DNA. This article reminds us that how loudly the cell reads those pages (RNA) is just as important as what is written in the book. The actionable transcriptome is the next giant step in our vision of precise guidance in oncology.
This remarkable intersection of bioinformatics and clinical practice opens new windows for patients we previously called «untreatable» or «without a target». Biological restoration means more than eliminating a mutation: it means realigning the cell's disrupted orchestra at the RNA level. The oncology of the future will be built under the compassionate guidance of these advanced technologies, capable of hearing even the cell's silent cries.
Scientific Sources and References
- 1. Johnson A, Meric-Bernstam F, et al. The actionable transcriptome: a framework for incorporating RNA sequencing into precision oncology. Nat Rev Clin Oncol. 2026;23:213-229. doi:10.1038/s41571-025-01110-1
- 2. Meric-Bernstam F, et al. ADC targets across various tumour types: TCGA Pan-Cancer analysis. Nature Medicine. 2024.
- 3. NCCN Guidelines v1.2026. Non-Small Cell Lung Cancer - RNA-based NGS fusion detection.
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