Ready for your all-in-one single cell sequencing solution?
Contact us
Single Cell Sequencing in Cancer mRNA Vaccine Development
Single Cell Sequencing in Cancer mRNA Vaccine Development

Cancer mRNA Vaccines: What Single Cell Sequencing tells us

9’

Three years after Katalin Karikó and Drew Weissman won the 2023 Nobel Prize for the mRNA base modifications that made COVID-19 vaccines possible, that same technology made the news again, this time for personalized cancer treatment.

In August 2026, Merck and Moderna reported that intismeran autogene (mRNA-4157) plus Keytruda met both its primary endpoint (recurrence-free survival) and a key secondary endpoint (distant metastasis-free survival) in a Phase 3 trial of 1,137 patients with resected melanoma. This marked the first personalized cancer vaccine to do so in a phase 3 trial.

A few days later, with far less media coverage, BioNTech announced the termination of its Phase 2 trial (BNT122-01) of autogene cevumeran (BNT122/RO7198457) as monotherapy in resected colorectal cancer. The independent data safety monitoring board judged that further follow-up was unlikely to change the efficacy outcome. Autogene cevumeran continues in other settings, including a pancreatic cancer trial with checkpoint inhibition and chemotherapy.

Two personalized mRNA neoantigen vaccine programs, two different readouts. Meanwhile, more than 100 mRNA cancer vaccine trials are still underway in this heated race. The contrast raises the question: what contributes to a trial’s success, and how do we optimize new vaccines to improve their efficacy?

Single cell sequencing data may hold many of the answers.

How mRNA cancer vaccines work

Vaccines have historically relied on peptide epitopes. However, this approach has struggled in oncology: peptide-based cancer vaccines have shown low immunogenicity and irregular clinical responses, and producing each candidate peptide requires its own chemical synthesis and validation, which limits how well personalization scales (Garg et al., 2026).

mRNA takes a different route. Rather than manufacturing a protein synthetically, an mRNA cancer vaccine delivers mRNA encoding tumor neoantigens to antigen-presenting cells. Once inside the cell, the mRNA is translated into antigen protein, which is then degraded by the proteasome and loaded onto MHC class I molecules for recognition by cytotoxic CD8+ T cells. At the same time, antigen fragments taken up by neighboring antigen-presenting cells are cross-presented on MHC class II to activate CD4+ helper T cells. The mRNA itself is also detected by the cell’s own pattern-recognition receptors (TLR3, TLR7, TLR8, RIG-I, MDA5), triggering type-I interferon and pro-inflammatory signaling that matures dendritic cells and amplifies the response (Garg et al., 2026).

Two approaches to mRNA cancer vaccines

mRNA cancer vaccines split into two design philosophies. Personalized neoantigen vaccines sequence each patient’s tumor, predict which mutations produce immunogenic, HLA-binding peptides, and encode a patient-specific set of neoantigens (Katsikis et al., 2023).

“Off-the-shelf” antigen vaccines, on the other hand, encode shared tumor-associated antigens for a given cancer type. These can be pre-manufactured and given immediately. However, because the antigens are normal self-proteins that are simply over-expressed in tumors, the T cell responses they raise are held back by the same immune tolerance mechanisms that protect healthy tissue (Cudog et al., 2026).

Challenges faced by mRNA cancer vaccines

Regardless of the approach, mRNA cancer vaccines face three intrinsic challenges: tumor heterogeneity and antigen escape, delivery efficiency, and the immunosuppressive tumor microenvironment (Garg et al., 2026).

First, tumor cells are not a uniform population: a vaccine that primes T cells against neoantigens expressed by specific subclones may allow subclones with downregulated expression of those neoantigens to survive. Targeted neoantigens can also become silenced as an evasion mechanism, resulting in immune escape.

Second, mRNA vaccines may have varying efficiency in reaching different antigen-presenting cell subpopulations, resulting in cell-specific and patient-specific differences in efficacy.

Lastly, the hardest version of this problem is the cold tumor, where even a well-designed vaccine has few T cells to prime and little inflammation to work with.

Single cell sequencing is well positioned to measure each of these challenges and potentially identify mechanisms for overcoming them.

Measuring heterogeneity in mRNA vaccine response

Conventional readouts like flow cytometry and ELISA can tell us that, on a population level, the frequency of activated T cells increased. That’s useful for quality control, but it doesn’t tell us what pathway changed, what can be fine-tuned, or why.

Single cell sequencing identifies which specific subpopulations responded and what those responses were, turning a “did it work” readout into a “here’s what to do about it” readout.

Lin et al. (2026) described a neoantigen mRNA vaccine for hepatocellular carcinoma. Running single cell RNA sequencing on the tumor-infiltrating immune cells, they identified an ISG15+ CD8+ T cell population that appeared after vaccination, expressing GZMB, IFN-γ, CD69, and Ki67 — consistent with an activated, proliferating, cytotoxic phenotype.

Figure 1. STNvac vaccination reshapes the tumor immune landscape and induces a cytotoxic ISG15+ CD8+ T cell population. (A) UMAP visualization of single cell transcriptomes from immune cell populations in STNvac- and PBS-treated tumors, identifying six main populations. (B) The relative proportions of the cell populations in each treatment group. (C) Quantification of ISG15+ CD8+ T cell density co-expressing GZMB and IFN-γ, from multicolor immunofluorescence staining of tumor sections. Image adapted from Lin et al. (2026). Licensed under CC.BY.4.0.

In order to identify mutation-specific tumor killing activities of the mRNA vaccine, they paired single cell transcriptomes with targeted mutation detection. This allowed them to track which individual tumor cells still carried specific neoantigen-associated mutations after treatment. Subclones expressing the most immunogenic neoantigens (Ptpn2_I383T, Traf7_C403W) were preferentially cleared, while subclones carrying weakly immunogenic neoantigens survived.

Figure 2. Targeted mutation detection maps neoantigen mutation burden across individual tumor cells. Heatmap shows the number of neoantigen-associated mutations detected in each tumor cell from STNvac- and PBS-treated tumors. Image adapted from Lin et al. (2026). Licensed under CC.BY.4.0.

The cell-specific readout can help scientists fine-tune immunotherapy strategy. It identifies the immune sub populations responsible for the tumor killing activities, and identifies tumor cells at risk of immune escape.

Single cell data can identify mRNA delivery efficiency in individual cells

How efficiently mRNA reaches its target cell types is directly tied to treatment efficacy.

Dendritic cells are an important target for mRNA delivery, but their uptake of mRNA varies. To track biodistribution of mRNA, luciferase mRNA is commonly used in in vivo models. However, this is impractical in clinical settings.

Single cell sequencing, combined with targeted panels, can be used to track mRNA uptake as well as persistence post-treatment, providing cell-by-cell measurement of the efficiency of mRNA delivery.

Single cell sequencing can identify mechanisms to overcome the immunosuppressive microenvironment

Even in the most challenging cases of “cold” tumors, single cell sequencing can provide valuable guidance on how to overcome immunosuppressive environments.

Cai et al. (2025) combined a personalized neoantigen vaccine with an anti-mesothelin antibody in pancreatic cancer, a tumor type known for its immunosuppressive microenvironment. Single cell sequencing revealed a fibroblast subpopulation — antigen-presenting cancer-associated fibroblasts (apCAFs) — a small, MHC-II-high subset expressing high levels of mesothelin. Cell–cell communication analysis showed apCAFs interacting directly with naive CD4+ T cells through antigen-presentation ligand–receptor pairs, converting them into immunosuppressive Tregs. That finding prompted the authors to pair the vaccine with an anti-mesothelin antibody to deplete the apCAFs, which in turn increased neoantigen-specific CD8+ T cell infiltration.

Figure 3. Single cell sequencing identified fibroblast subtypes. A) UMAP plot based on single cell transcriptomics of fibroblasts, with seven subpopulations identified. B) UMAP plots of the same data separated by treatment groups. C) Pseudotime analysis of fibroblast populations, colored by subtypes (left) and by pseudotime (right). Image adapted from Cai et al. (2025). Licensed under CC.BY.4.0.

Zhang et al. (2025), on the other hand, used single cell sequencing to understand why an mRNA vaccine isn’t enough by itself. The authors compared an mRNA vaccine against an oncolytic virus (OV) in an HPV-related tumor model. Single cell RNA sequencing and flow cytometry showed the mRNA vaccine alone effectively primed antigen-specific T cells, but on its own did little to change the immunosuppressive composition of the tumor itself. The oncolytic virus, on the other hand, enhanced cytotoxic T cell infiltration and repolarized neutrophils and macrophages toward anti-tumor phenotypes, but without the same strength of systemic priming. A combination of these two approaches produced significant tumor regression that neither approach achieved alone.

In both cases, single cell sequencing helped identify the specific cell population responsible for immunosuppression, and suggested a combination therapy to overcome it.

Conclusion

mRNA cancer vaccines open new avenues for cancer immunotherapy, but barriers to effective treatment remain. Single cell sequencing helps identify differential responses across tumor subtypes, pinpoint mechanisms of immune escape and suppression, and suggest ways to overcome these challenges.

Precision therapy needs precision measurement. It is time to rethink clinical care, moving from the resolution of individual patients to individual cells.

References

1. Merck & Moderna. Merck and Moderna Announce Phase 3 INTerpath-001 Trial of Intismeran Autogene Plus KEYTRUDA® Met Endpoints of Recurrence-Free Survival (RFS) and Distant Metastasis-Free Survival (DMFS) in Patients with Resected Melanoma. Press release, August 2026. merck.com

2. BioNTech SE. BioNTech Provides Update on Phase 2 Clinical Trial of Autogene Cevumeran in Resected Colorectal Cancer. Press release, August 2026. biontech.com

3. Garg P, Salgia R, Singhal SS. mRNA-based cancer vaccines: A new frontier in personalized immunotherapy. BBA – Reviews on Cancer. 2026;1881:189577. doi.org/10.1016/j.bbcan.2026.189577

4. Katsikis PD, Ishii KJ, Schliehe C. Challenges in developing personalized neoantigen cancer vaccines. Nature Reviews Immunology. 2023;24(3):213–227. doi.org/10.1038/s41577-023-00937-y

5. Cudog CJA, Arcilla TAA, Gregorio AMD, et al. Mechanistic Insights into Off-the-Shelf vs. Personalized mRNA Cancer Vaccines: A Comparative Review of BNT111 and BNT122. J. 2026;9(2):15. doi.org/10.3390/j9020015

6. Lin X, Chen G, Tang R, et al. Spleen-targeted neoantigen mRNA vaccine induces ISG15+ CD8+ T cell-mediated tertiary lymphoid structure formation in hepatocellular carcinoma. Cell Reports Medicine. 2026;7(5):102754. doi.org/10.1016/j.xcrm.2026.102754

7. Cai Z, Li Z, Zhong W, et al. Targeting Mesothelin Enhances Personalized Neoantigen Vaccine Induced Antitumor Immune Response in Orthotopic Pancreatic Cancer Mouse Models. Advanced Science. 2025. doi.org/10.1002/advs.202407976

8. Zhang K, Zuo D, Wang Z, et al. Heterologous prime-boost with an mRNA vaccine and an oncolytic virus enhances tumor regression through overcoming intratumoral immune suppression. Cell Reports. 2025;44(6):115745. doi.org/10.1016/j.celrep.2025.115745

A post by Yingting Wang

Yingting earned her PhD from the National University of Singapore, specializing in cell biology and tissue engineering. She has eight years of laboratory and commercial experience in single cell multi-omics, including roles in R&D, sales, technical support, and scientific communication.

Check out our latest blog posts

Learn more