From genome to therapy: cancer as a genomic disease, CRISPR editing and mRNA vaccines
An anchor article on precision oncology and biotechnology — separating, with rigor and governance, what is already approved clinical reality from what remains investigational promise, at the crossroads of genomics and artificial intelligence.
Cancer as a disease of the genome: drivers, passengers and instability
Few mutations command; most are noise. Telling one from the other is what separates a therapeutic target from biological chance.
From a molecular standpoint, cancer is best understood as a disease of the genome that accumulates over the course of a lifetime. Tumor cells carry acquired genetic alterations, and one conceptual distinction organizes the entire field: driver mutations — few and selectively advantageous, which effectively propel growth — versus passenger mutations, the vast majority, biologically inert from the standpoint of selection. A classic synthesis estimated that typical tumors harbor 2 to 8 driver mutations surrounded by many passengers, providing the basis for separating what is a target from what is genomic noise (Vogelstein, 2013).
Against this backdrop acts genomic instability. In the framework of the hallmarks of cancer, genomic instability and mutation are described as enabling characteristics — they are not the engine of the tumor itself, but the mechanism that generates the genetic diversity upon which selection operates. The 2022 update to that framework added dimensions such as phenotypic plasticity, non-mutational epigenetic reprogramming and the role of polymorphic microbiomes, broadening the reading beyond strict genetics (Hanahan, 2022).
It is from this biology that the logic of precision medicine is born. If it is a specific molecular alteration — and not merely the organ of origin — that underlies the tumor's behavior, then characterizing that alteration can guide treatment. The promise is appealing, but it demands interpretive discipline: identifying a driver mutation does not guarantee that a targeted therapy exists against it, nor that the response will be durable. The rest of this article traces where this logic has already translated into approved practice and where it remains a hypothesis under test.
Targeted and tissue-agnostic therapy: when the biomarker defines the treatment
A single molecular marker can guide management across dozens of distinct tumors — biology takes precedence over topography.
The most consolidated expression of precision oncology is tissue-agnostic therapy: the kind in which a molecular biomarker defines management regardless of the affected organ. The first mature example is mismatch repair deficiency (dMMR) and high microsatellite instability (MSI-high). An early study showed that this signature — and not the tumor type — predicted response to PD-1 blockade with pembrolizumab, even if in a small cohort, serving as proof of concept for the notion of an agnostic predictive biomarker (Le, 2015).
This hypothesis was later consolidated on a larger scale. In the KEYNOTE-158 study, pembrolizumab produced an objective response of approximately 34.3% across 27 non-colorectal MSI-H/dMMR tumor types — a body of evidence that underpinned the drug's tissue-agnostic regulatory approval for this population (Marabelle, 2020). A calibration is warranted: a response rate of about one third is clinically relevant, but it is far from meaning universal benefit or cure.
The second example is NTRK gene fusions. Larotrectinib achieved an objective response of about 75% across 17 distinct tumor types carrying these fusions, demonstrating that a single molecular alteration can define treatment regardless of the organ (Drilon, 2018). Entrectinib, with central nervous system penetration, validated the same target, including responses in metastatic CNS disease (Doebele, 2020). These are, note, rare alterations — the value of the approach depends on adequate molecular testing to identify them.
The table below summarizes the examples that have already migrated from hypothesis to regulatory practice.
| Biomarker | Targeted therapy | Key evidence | Regulatory status |
|---|---|---|---|
| MSI-high / dMMR | Pembrolizumab | ~34.3% objective response across 27 non-colorectal types (KEYNOTE-158) | Approved, tissue-agnostic |
| NTRK gene fusion | Larotrectinib | ~75% objective response across 17 tumor types | Approved, tissue-agnostic |
| NTRK gene fusion | Entrectinib | Durable responses, with CNS activity | Approved, tissue-agnostic |
CRISPR in the clinic: what has already been approved and what is still investigational
The first approved gene-editing therapy is not against cancer — it is for blood diseases. Precision of language here is factual precision.
CRISPR-Cas9 gene editing is no longer exclusively laboratory-based, but it is essential to map precisely what has reached the clinic. The approved milestone is ex vivo editing: the patient's own hematopoietic stem cells are removed, edited outside the body — in this case, targeting the BCL11A gene to reactivate fetal hemoglobin — and reinfused. The trial that grounded this strategy eliminated vaso-occlusive crises and transfusion dependence in the first patients (Frangoul, 2021). This foundation supported exagamglogene autotemcel (Casgevy), approved by the FDA on December 8, 2023 for sickle cell anemia and on January 16, 2024 for transfusion-dependent beta-thalassemia; in 2026, the indication was expanded to children from 2 years of age. Let the essential be recorded: these are hereditary hematologic diseases, not cancer.
In vivo editing — in which the editor is administered directly into the bloodstream to act inside the body — had its first human proof of concept with NTLA-2001 in transthyretin amyloidosis, reducing circulating TTR protein after a single infusion (Gillmore, 2021). This program (nexiguran ziclumeran) advanced to phase 3 studies by 2026, but remains investigational, without approval. Its trajectory also illustrates the real limits of the technology: the phase 3 trials even underwent a temporary regulatory pause after a serious hepatic adverse event, a reminder that long-term safety is a hypothesis to be verified, not a premise.
In cancer specifically, CRISPR enters via the route of cell therapy. The first human trial of CRISPR-edited T cells — with deletion of TRAC, TRBC and PDCD1 and transduction of an anti-NY-ESO-1 receptor — proved feasible and safe in refractory cancer, with cell persistence for up to 9 months (Stadtmauer, 2020). It is crucial to read this result for what it is: a phase 1 study designed to assess feasibility and safety, not to prove efficacy. The distinction between 'approved' and 'investigational' organizes the following table.
| Approach | Example | Phase / status (2026) | Measured outcome |
|---|---|---|---|
| Ex vivo editing (stem cells) | Exa-cel (Casgevy) | Approved (FDA 2023–2024; expanded to ≥2 years in 2026) | End of vaso-occlusive crises / transfusion independence |
| In vivo editing | NTLA-2001 (nexiguran ziclumeran) | Investigational (phase 3 ongoing) | Reduction of serum TTR after single infusion |
| CRISPR in T cells (cancer) | Edited anti-NY-ESO-1 T cells | Investigational (phase 1) | Safety and feasibility; persistence ~9 months |
mRNA vaccines beyond COVID: personalized neoantigens and the role of AI
A positive phase 2b signal is not an approval. Enthusiasm and evidence are not the same currency.
The mRNA platform that scaled up during the pandemic opened a second front in oncology: personalized neoantigen vaccines. The idea is to sequence each patient's tumor, identify the mutations that generate abnormal proteins (neoantigens) and produce a bespoke immunizing agent that teaches the immune system to recognize them. In resected high-risk melanoma, the mRNA-4157/V940 vaccine (intismeran autogene) added to pembrolizumab prolonged recurrence-free survival compared with pembrolizumab alone, in a randomized phase 2b study (Weber, 2024). The result is promising and longer follow-up data have sustained the signal, but confirmation depends on ongoing phase 3 studies — and, as of 2026, there is no approval.
In pancreatic cancer, a historically bleak prognostic scenario, the personalized vaccine autogene cevumeran induced neoantigen-specific T cells that correlated with delayed recurrence after surgery (Rojas, 2023). Here caution must be explicit: this is a phase 1 study, with a small cohort, measuring immune response and association with outcome — not survival benefit proven at large scale. Association is not causation.
Artificial intelligence participates in this genome-to-therapy axis above all in predicting which neoantigens have a chance of being recognized by the immune system, a combinatorial step in which machine learning accelerates prioritization. On the structural plane, AlphaFold achieved near-experimental accuracy in predicting the three-dimensional shape of proteins through deep learning, illustrating how AI shortens the path from hypothesis to drug discovery (Jumper, 2021). The correct framing is that of a tool: AI generates and ranks hypotheses; it does not replace experimental validation nor clinical trials. Finally, a caveat of responsibility: the data on personalized cancer vaccines pertain to a specific therapeutic context and should not be extrapolated to general debates for or against vaccines — what counts is peer-reviewed evidence and real approvals.
Governance and limits: the somatic-germline line, off-target, cost and access
Editing a patient's cells and editing what they pass on to their children are ethical acts of radically distinct natures.
No honest discussion of gene editing can dispense with governance. The central distinction is between somatic and germline editing. Somatic editing alters cells of the patient's own body, without transmission to offspring — it is the one in approved clinical use (such as exa-cel) and in trials. Germline editing, heritable by subsequent generations, remains widely proscribed by international ethical-scientific consensus, for reasons that combine safety, irreversibility and social implications. Under no circumstances should it be suggested that germline editing is an accepted practice: it is not.
On the technical plane, three uncertainties weigh. First, off-target effects: the editor may, in principle, cut unintended sites of the genome, which demands vigilance. Second, long-term durability and safety are unknown — the approved therapies are recent, and the follow-up horizon is still short; the regulatory pause experienced by an in vivo editing program after a serious hepatic adverse event is a concrete reminder of this. Third, even efficacy demonstrated in a trial does not eliminate individual variability of response.
There is also the dimension of cost, access and equity, frequently omitted amid technological enthusiasm. Gene-editing therapies and personalized vaccines involve high cost and complex logistics — individual sequencing, bespoke manufacturing, specialized centers. Without confronting this point, a distorted expectation is created: a technology that exists but is not accessible is not, yet, a public health solution. Separating evidence from expectation, here, is also a matter of informational justice.
Synthesis: what is already reality and what is still promise
Calibrated language is not timidity — it is the way to respect both the science and the patient who reads it.
The genome-to-therapy axis has progressed in a real, but uneven, way. Already approved clinical reality: the tissue-agnostic biomarkers (MSI-high/dMMR guiding pembrolizumab; NTRK fusions guiding larotrectinib and entrectinib), which define treatment by molecular biology and not by organ; and ex vivo CRISPR editing for sickle cell anemia and beta-thalassemia — which, it bears repeating, are blood diseases, not cancer.
Remaining promising, yet investigational in 2026: in vivo CRISPR editing (human proof of concept, without approval), CRISPR applied to T cells against cancer (phase 1, focus on safety), and personalized mRNA neoantigen vaccines (positive signal in phase 2b in melanoma, phase 1 in pancreas, phase 3 ongoing). AI is an already-consolidated tool for accelerating hypotheses, not a source of autonomous clinical decision-making.
The final reading must be calibrated and without a promise of cure. The measured outcomes are objective response, recurrence-free survival, biomarker reduction and safety — not guaranteed eradication of the disease. Distinguishing association from causation, phase 1 from regulatory approval, and evidence from expectation is not excess caution: it is the very substance of the scientific rigor that this field, moving fast, most demands.
| Item | Situation in 2026 | Calibrated reading |
|---|---|---|
| Agnostic biomarkers (MSI-H, NTRK) | Approved clinical reality | Define treatment by molecular biology, not by organ |
| Ex vivo CRISPR (sickle cell, beta-thalassemia) | Approved (FDA) | Hematologic diseases, not cancer; foundation of gene therapy |
| In vivo CRISPR / edited T cells | Investigational | Proof of concept; without efficacy proven at large scale |
| mRNA neoantigen vaccines | Investigational | Positive signal in phase 2b; phase 3 ongoing |
| AI in discovery (e.g., AlphaFold) | Consolidated tool | Accelerates hypotheses; does not replace clinical validation |
Why this matters for your care
This article is strictly educational and informational in purpose and does not constitute medical advice, diagnosis or treatment recommendation, in accordance with CFM guidelines. The therapies discussed here include approved interventions and, for the most part, still-investigational approaches, whose use depends on individual evaluation by a specialist physician in the context of care or of a regulated clinical trial. No content promises a cure, and associations observed in studies do not, by themselves, establish a causal relationship. To organize your history and your health priorities before a clinical conversation, start with the Functional Self-Assessment. To go deeper with other reviewed and referenced texts, visit the Library. The statements about regulatory status and trial phases reflect verification of the indexed literature and of official communications through July 2026 and may change as new data emerge.
References
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Educational and scientific content. It does not constitute diagnosis, prescription or individual clinical guidance, and does not replace a medical consultation. Management decisions must be individualized by a physician.