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.
CHAT GPT Discusses
1. What kind of paper this is (important framing)
This is not a discovery paper and not a technology benchmarking study. It is a Perspective that does three deliberate things:
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Defines a new clinical construct: the actionable transcriptome
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Proposes an operational framework for how RNA-seq should sit alongside DNA-seq and IHC in routine precision oncology
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Normalizes RNA expression as a therapeutic biomarker, not merely a confirmatory or exploratory signal
That framing is strategic: the authors are arguing for a practice change, not a technical upgrade.
2. The core idea: “The actionable transcriptome”
The conceptual move
Historically, RNA-seq in oncology has been treated as:
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a fusion finder, or
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a signature generator, or
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a confirmatory layer for DNA findings
This paper reframes RNA-seq as a systematic source of actionability in its own right, across multiple orthogonal dimensions.
They define the actionable transcriptome as RNA-derived features that can directly inform:
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therapy selection
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trial eligibility
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diagnosis, prognosis, or prediction
Importantly, actionability is defined at the RNA level, not merely inferred from DNA.
3. The framework: how RNA-seq adds actionability beyond DNA
The authors lay out seven major categories of RNA-derived actionability. Individually familiar, but novel in aggregate and in clinical positioning.
1. Actionable gene fusions (the familiar entry point)
Yes, RNA-seq excels at fusion detection—but they emphasize why this matters clinically:
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Intronic breakpoints evade DNA panels
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RNA enriches expressed, oncogenic fusions
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RNA detects fusions with low DNA VAF
They cite real-world data showing ~30% of actionable fusions detected only by RNA-seq.
Tactical novelty: RNA-seq is positioned as preferable, not complementary, for fusion detection in diseases like NSCLC (aligned with NCCN).
2. RNA overexpression of oncogenes (this is a pivot)
This is one of the paper’s quietly radical moves.
They argue that:
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RNA overexpression can be actionable even without amplification
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Epigenetic or regulatory mechanisms matter clinically
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RNA expression is closer to the drug target than DNA copy number
Example logic:
If a pathway is druggable because amplification predicts response, then RNA overexpression should be interrogated directly, even when amplification is absent.
They are explicit that clinical evidence is still thin, but they deliberately build a forward-looking framework to operationalize this anyway.
3. RNA underexpression of tumor suppressors
This mirrors the oncogene argument, but for synthetic lethality and pathway vulnerability:
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PTEN RNA loss → PI3K/AKT/mTOR sensitivity
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BRCA1 RNA loss → PARP inhibitor relevance
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MTAP RNA loss → PRMT5/MAT2A strategies
Key point:
RNA loss can reflect epigenetic silencing invisible to DNA sequencing.
4. Cell-surface target expression (a major strategic expansion)
This is arguably the most strategically important section of the paper.
RNA-seq is framed as a multiplex surfaceome scanner:
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ADC targets (TROP2, HER2, Nectin-4, FRα, etc.)
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Bispecific antibodies
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CAR-T / CAR-NK targets
Why this matters:
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IHC is low-plex and serial
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RNA-seq can prioritize targets first, then confirm by IHC
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Enables histology-agnostic target discovery
The TCGA pan-cancer analyses are used not as discovery, but as proof of feasibility.
Strategic shift: RNA-seq becomes a target-discovery triage layer for biologics, not just a molecular pathology readout.
5. Multigene expression signatures
This is familiar territory (Oncotype, MammaPrint, PAM50), but the novelty is integration, not invention.
They emphasize:
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Prognostic vs predictive distinction
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Pan-cancer immune signatures
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Microenvironment subtypes
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Synthetic lethality–based transcriptomic predictors
RNA-seq allows these signatures to be embedded into comprehensive profiling, rather than ordered separately.
6. Variant calling and confirmation via RNA
This is presented cautiously but provocatively:
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RNA confirms expression of DNA variants
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RNA can detect variants missed by DNA due to depth
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RNA has amplification advantages but higher error rates
They stop short of endorsing RNA-only variant calling—but clearly suggest future convergence.
7. Neoantigen expression and splicing
RNA-seq uniquely enables:
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Confirmation that neoantigens are actually expressed
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Detection of exon skipping (e.g., MET exon 14)
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Alternative transcripts with therapeutic relevance
This links RNA-seq directly to:
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personalized vaccines
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TCR-based therapies
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resistance mechanisms
4. The actionable transcriptome list (why this matters)
The authors go further than most perspectives by actually building a gene list, grounded in:
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MD Anderson’s PODS knowledgebase
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CNV-actionable genes reinterpreted at RNA level
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Surface antigen drug pipelines (FDA + trials)
Crucially:
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They acknowledge evidence gaps
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They label genes as “literature-based” vs “potentially actionable”
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They treat the list as dynamic, not authoritative
This is a governance move, not just a data move.
5. The hardest problem they confront head-on: expression thresholds
This is where the paper does real intellectual work.
They systematically analyze four ways to define “high” or “low” RNA expression:
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Within-cohort (same tumor type)
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Tumor vs matched normal
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Pan-cancer percentile benchmarking
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Benchmark-tumor anchoring (e.g., compare to TNBC for TROP2)
The benchmark-tumor approach is particularly clever:
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If a drug works in Tumor A at expression level X
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Then Tumor B with similar RNA levels should be considered plausible
This is not FDA labeling logic—it’s drug-development logic imported into diagnostics.
6. Tactical vs strategic novelty (your key journal club slide)
What is tactically new
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RNA-seq as preferred fusion detection in some cancers
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Anchored multiplex PCR and capture strategies explained clinically
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Systematic pan-cancer expression benchmarking
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RNA-first triage for ADC and CAR targets
What is strategically new
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RNA-seq repositioned as a co-equal decision layer with DNA and IHC
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Expression itself framed as actionable biology, not just a correlate
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Diagnostics aligned with modern drug pipelines (ADCs, bispecifics, cell therapy)
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A practical answer to: “What do we do when DNA is quiet?”
In short:
This paper is about expanding the actionable search space, not refining variant detection.
7. How to summarize the paper in one sentence (journal-club closer)
Johnson et al. argue that RNA-seq should no longer be viewed as a fusion add-on or research tool, but as a systematic, multiplex, clinically actionable layer that reveals therapeutic opportunities invisible to DNA-centric precision oncology.
If you want, next I can:
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help you build journal club slides (one slide per RNA modality),
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draft anticipated skeptic questions + responses, or
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map this framework directly onto current payer / FDA / MolDx realities, which is where this gets politically interesting.
Sure — here is the same content recast into coherent narrative paragraphs, with bullets used only where they genuinely clarify structure, not as a crutch. I’ve written this in a way that should read smoothly as speaker notes or a handout for a journal club, rather than as a slide outline.
This paper is not a discovery study or a technical comparison of RNA-seq platforms. It is a Perspective that argues for a shift in how RNA sequencing is conceptually and operationally positioned in precision oncology. The authors are not claiming that RNA-seq is new or underused; rather, they argue that it has been under-theorized as a source of clinical actionability. Their central contribution is to define and formalize what they call the “actionable transcriptome”—that is, RNA-derived features that can directly inform therapy selection, trial eligibility, diagnosis, prognosis, or prediction, rather than merely supporting DNA findings.
Historically, RNA-seq has entered clinical oncology through narrow use cases: most prominently fusion detection, occasionally gene-expression signatures, and sometimes confirmation that a DNA alteration is actually expressed. The authors argue that this framing is too limited and increasingly misaligned with modern oncology, where therapeutic targets extend well beyond DNA sequence alterations. Their strategic move is to treat RNA-seq as a co-equal decision layer alongside DNA sequencing and immunohistochemistry, capable of independently revealing actionable biology.
The paper proposes a structured framework in which RNA-seq contributes actionability across multiple dimensions. Some of these are familiar, but the novelty lies in their integration and clinical framing. RNA-seq remains essential for identifying gene fusions, particularly those with intronic breakpoints or low DNA variant allele frequency, and the authors reinforce current guideline trends (for example in NSCLC) that increasingly favor RNA-based fusion detection. However, fusion detection is presented not as the endpoint of RNA-seq utility, but as merely the most mature example.
A more consequential shift comes in how the authors treat RNA expression itself as actionable biology. They argue that overexpression of oncogenes at the RNA level may have therapeutic relevance even in the absence of gene amplification, particularly when pathway activity rather than genomic structure is what ultimately determines drug sensitivity. Conversely, underexpression of tumor-suppressor genes at the RNA level may signal epigenetic silencing or regulatory suppression that creates vulnerabilities exploitable through synthetic lethality or pathway inhibition. This reframes RNA expression from a descriptive readout into a potential surrogate for functional pathway state.
One of the most strategically important sections of the paper concerns cell-surface target expression. The authors emphasize that many modern therapies—antibody–drug conjugates, bispecific antibodies, CAR-T and CAR-NK therapies—are fundamentally expression-dependent rather than mutation-dependent. Immunohistochemistry remains the clinical standard for assessing such targets, but it is inherently low-plex and serial. RNA-seq, by contrast, enables simultaneous, quantitative assessment of dozens of surface targets, allowing it to function as a triage or prioritization layer. In this framework, RNA-seq identifies candidate targets broadly, after which IHC can be applied selectively for confirmation. This is a subtle but important inversion of current workflows.
The authors also integrate multigene expression signatures—diagnostic, prognostic, and predictive—into the actionable transcriptome concept. While assays such as Oncotype DX, MammaPrint, PAM50, and Decipher are already clinically established, they are typically ordered as standalone tests. RNA-seq allows these signatures to be embedded within comprehensive molecular profiling, reducing fragmentation and enabling joint interpretation with genomic alterations, immune signatures, and microenvironmental features. The paper emphasizes the importance of distinguishing prognostic from predictive signatures and highlights emerging pan-cancer immune and microenvironmental transcriptomic classifiers.
Another area the authors explore, cautiously but deliberately, is the role of RNA-seq in variant detection and confirmation. RNA-seq can confirm that DNA variants are expressed, detect variants missed by DNA sequencing due to depth limitations, and quantify allele-specific expression. At the same time, they acknowledge technical challenges, including higher transcriptional error rates and RNA editing. The paper stops short of endorsing RNA-only variant calling, but it clearly gestures toward future convergence rather than permanent separation between DNA- and RNA-based mutation detection.
The authors go beyond conceptual framing by constructing an explicit actionable transcriptome gene list, grounded in the MD Anderson Precision Oncology Decision Support (PODS) knowledgebase. Because direct clinical evidence linking RNA expression levels to therapeutic response is still limited, they adopt a pragmatic strategy: genes that are actionable at the copy-number level are provisionally treated as potentially actionable at the RNA level, while genes with direct expression-based evidence are labeled accordingly. Cell-surface targets are deliberately emphasized, reflecting the current drug-development landscape. Importantly, the authors treat this list as dynamic and explicitly acknowledge evidence gaps, framing it as a living framework rather than a fixed authority.
One of the most valuable—and intellectually honest—parts of the paper is its treatment of expression thresholds, which remains one of the biggest obstacles to clinical RNA-seq interpretation. The authors systematically examine different strategies for defining high or low expression, including comparison within tumor type, comparison to matched normal tissue, pan-cancer percentile benchmarking, and benchmarking against tumor types in which a given therapy is already known to work. The last approach is particularly novel: if a drug is effective in a “benchmark” cancer at a certain expression range, then tumors of other histologies with comparable RNA levels should at least be considered biologically plausible targets. This imports drug-development reasoning into diagnostic interpretation, rather than relying solely on disease-specific norms.
Taken together, the tactical novelty of the paper lies in its detailed operationalization of RNA-seq across fusion detection, expression-based targeting, surface antigen prioritization, and signature integration. The strategic novelty lies in its broader repositioning of RNA-seq as a systematic engine for expanding the actionable search space, particularly for patients whose tumors are genomically quiet by DNA sequencing alone. Rather than treating RNA as confirmatory or exploratory, the authors argue that it should be understood as a primary lens on tumor biology, aligned with the realities of modern therapeutic development.
If you want, I can next condense this further into a spoken journal-club summary, help you anticipate skeptical questions (for example from DNA-centric molecular pathologists), or translate this framework into current regulatory and payer realities, where the conceptual ambition of the paper will meet its biggest constraints.
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You are a PhD student in history of medicine, in the year 2050, focused on the initial boom in genomics in medicine that occurred circa 2000-2030. Johnson et al is now recognized as a pivotal and very important paper. Present the thesis of your PhD thesis. This may be similar to an executive summary or perhaps more ike a condensed version for a book chapter in a conference proceedings.
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Thesis Summary (2050)
My dissertation argues that Johnson et al., “The Actionable Transcriptome” marked the conceptual turning point at which genomics in medicine finally escaped what I call DNA determinism—the late-20th- and early-21st-century assumption that clinically meaningful cancer biology must be legible primarily at the level of genomic sequence. While the period from roughly 2000 to 2030 is often described as the “genomic revolution,” I contend that much of this era was in fact a prolonged genotype-first interregnum, during which clinical innovation lagged behind molecular capability. Johnson et al. crystallized a different epistemology: that expression, not alteration alone, is the clinically actionable unit of cancer biology.
The paper’s importance does not lie in any single technical advance—RNA sequencing, gene fusions, expression signatures, and even RNA-based diagnostics all predated it. Rather, its significance lies in the way it reassembled familiar tools into a new clinical logic. Johnson et al. did something unusual for their time: they treated RNA not as an auxiliary confirmation of DNA findings, but as an independent source of therapeutic knowledge, capable of generating actionability on its own terms. In doing so, they helped resolve a central contradiction of early precision oncology—namely, that most patients underwent increasingly comprehensive DNA sequencing, yet the majority remained “non-actionable” under prevailing interpretive frameworks.
My thesis situates The Actionable Transcriptome as the moment when oncology formally began to pivot from a mutation-centric model of disease to a systems-oriented model of therapeutic vulnerability. Johnson et al. reframed RNA expression as a proxy for pathway state, drug target availability, immune contexture, and cellular identity. This reframing aligned diagnostics with the therapeutic realities of the 2010s and 2020s, when the fastest-growing classes of cancer therapies—antibody–drug conjugates, bispecific antibodies, cell therapies, and immune modulators—were increasingly indifferent to DNA sequence but exquisitely sensitive to protein presence, abundance, and cellular context.
A central argument of my dissertation is that Johnson et al. succeeded because they did not merely advocate for RNA sequencing; they solved the interpretive problem that had long constrained its clinical adoption. By introducing the concept of an “actionable transcriptome,” they provided a shared vocabulary that allowed clinicians, laboratorians, and drug developers to speak coherently about RNA-derived findings. Crucially, they acknowledged evidentiary gaps rather than obscuring them, proposing pragmatic heuristics—such as pan-cancer benchmarking and benchmark-tumor anchoring—that allowed RNA expression to be used responsibly even before randomized trial validation caught up.
From a historical perspective, the paper also represents a subtle but consequential shift in medical authority. Earlier genomic medicine privileged regulatory validation (FDA-approved companion diagnostics) and technological certainty (high-fidelity DNA sequencing). Johnson et al., by contrast, legitimized biological plausibility and therapeutic analogy as clinical reasoning tools. Their willingness to treat expression similarity across histologies as meaningful anticipated the histology-agnostic regulatory and reimbursement frameworks that would fully mature in the 2030s.
In the broader arc of my dissertation, I argue that The Actionable Transcriptome should be read not simply as an oncology paper, but as a foundational document of post-genomic medicine. It marked the moment when molecular diagnostics stopped asking only “What is mutated?” and began asking “What is functionally available to intervene upon?” In retrospect, this shift explains why subsequent decades saw greater clinical yield from fewer sequencing modalities: medicine had learned how to interpret molecular data in ways aligned with biological action, not just molecular novelty.
In sum, Johnson et al. did not close the genomic era; they completed it. By re-centering expression, context, and actionability, they transformed genomics from a catalog of alterations into a practical grammar of intervention. My thesis argues that this conceptual transition—not the initial sequencing boom itself—was the true inflection point that allowed precision oncology to mature into routine, outcome-altering medical practice.
