Evidence map›Paper›PMID 41492079›Full record

ReviewNature reviews. Clinical oncology2026

The actionable transcriptome: a framework for incorporating RNA sequencing into precision oncology.

Amber Johnson, Yifei Shen, Xiaofeng Zheng, Fei Su, Jia Zeng, Burak Uzunparmak, Eun-Kyoung Breuer, Vijaykumar Holla, Michael Kahle, Sun-Hee Kim and 13 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Clinical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Amber JohnsonMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Yifei ShenDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Xiaofeng ZhengDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Fei SuMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0001-5834-4074
Jia ZengMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Burak UzunparmakDepartment of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Eun-Kyoung BreuerMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Vijaykumar HollaMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Michael KahleMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Sun-Hee KimMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Thuy VuMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-8163-6144
Kenna R Mills ShawMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ecaterina Ileana DumbravaDepartment of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-1959-0536
Timothy A YapDepartment of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-2154-3309
Jordi RodonDepartment of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0001-6467-3632
Mark RoutbortMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Keyur PatelMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Shashikant KulkarniMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Donna HanselMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Julian BryanMolecular Diagnostics Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Anil KorkutDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ken ChenDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0003-4013-5279
Funda Meric-BernstamDepartment of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. fmeric@mdanderson.org.ORCID http://orcid.org/0000-0001-6816-6072

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Comprehensive, multiplexed RNA sequencing (RNA-seq) is increasingly being incorporated into molecular tumour-profiling assays owing to overall cost-effectiveness related to enhanced detection of clinically actionable biomarkers. RNA-seq assays are now quite robust, with turnaround times of a few weeks and reasonable costs that support integration into routine clinical workflows. In this Perspective, we propose a framework for incorporating RNA levels and other RNA-seq data into precision oncology that considers RNA levels of oncogenes, tumour suppressors and diverse therapeutic targets, as well as multigene diagnostic, prognostic and predictive signatures; chimeric gene fusion transcripts; splice variants; RNA-based variant and/or mutation calling; and oncoviral gene expression; among other factors. Additionally, we provide a list of genes representing the current actionable transcriptome focused on the predictive utility of RNA-level overexpression of oncogenes and underexpression of tumour-suppressor genes. This list also includes cell-surface targets, given that RNA profiling has demonstrated that many tumour types have clinically relevant expression levels of such targets. The RNA expression levels of certain genes might also be associated with biological features such as homologous recombination deficiency and DNA mismatch repair defects. Taken together, comprehensive RNA profiling can help to identify additional actionable alterations beyond those detected via DNA sequencing and overcome the challenges faced by conventional low-plex assays (such as immunohistochemistry and in situ hybridization), thereby enhancing therapeutic opportunities for personalized cancer care.

Indexed as

Gene Expression ProfilingMedical OncologyNeoplasmsPrecision MedicineSequence Analysis, RNATranscriptomeBiomarkers, TumorHumansBiomarkers, Tumor

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.