HMN 2026: How Five mutational ‘fingerprints’ could help predict how visible tumors are to the immune system

Researchers from the HUN-REN Szeged Biological Research Centre and HCEMM have just published a new study suggesting that it’s not simply the number of tumor mutations that matters for immunotherapy, but the kind of mutation patterns they create. The team found a distinct “fingerprint” linked to DNA repair problems and chemical exposures that can leave tumors surprisingly hard for the immune system to spot, even when mutation counts are high. The study also points out that a patient’s genetics, including common HLA types in Europeans, can shift how visible the same tumor looks to T cells. The paper was published recently in Molecular Systems Biology.

Five recurring protein mutation patterns

Cancer cells carry thousands of mutations, but not all mutations are created equal. Some make tumors highly visible to the immune system, while others help cancers hide. In this study, researchers have discovered that across thousands of human cancers, there are five dominant patterns of protein-altering mutations—called amino acid substitution signatures—and these patterns help determine how tumors interact with the immune system.

When DNA in a cell is damaged by environmental exposures (like tobacco smoke or UV light) or internal errors during replication and repair, the resulting mutations change the building blocks of proteins—amino acids. By analyzing nearly 9,300 cancer genomes from diverse cancer types, the team found that instead of a random jumble of changes, nearly all tumors are dominated by one of five characteristic substitution signatures.

How mutation patterns shape immunity

Crucially, these five signatures are not only molecular fingerprints of how the mutations arose—they also influence how well the immune system “sees” the tumor. Some mutation patterns tend to create highly immunogenic protein fragments (neoantigens) that alert immune cells, while others produce less recognizable neoantigens, leading to “cold” tumors that evade immune attack.

“Despite the diversity of mutational processes, their protein-level consequences converge into just five recurring fingerprints, which can strongly influence immune recognition,” says Dr. Szilvia Juhász, Head of the Cancer Microbiome Research Group at HCEMM and one of the study’s lead authors.

A mutation signature tied to treatment failure

One of the most striking findings involves a mutational pattern linked to DNA repair defects and chemical exposures. Tumors dominated by this pattern often respond poorly to immune checkpoint inhibitor therapies, even when their overall mutational burden is high. In other words, a tumor can harbor many mutations and still generate too few effective immune targets.

“Mutational burden alone is insufficient. Qualitative, protein-level consequences of mutations are critical for understanding why immunotherapy fails in many patients,” emphasizes Dr. Benjamin Papp, researcher at the HUN-REN Szeged Biological Research Centre and co–first author of the study.

However, the study also shows that certain genetic variants in the human immune system—such as specific HLA class I types common in Europeans—can partially counteract this effect by better presenting some of these mutated peptides to T cells. This suggests that the same tumor may be more immunologically visible in one patient than in another.

Toward more precise, personalized immunotherapy

Taken together, the findings point toward a more refined framework for predicting immunotherapy response. “Tumor visibility to the immune system is not determined by mutation numbers alone, but also by the protein-level patterns those mutations create,” says Dr. Máté Manczinger, Head of the Systems Immunology Research Group at the HUN-REN Szeged Biological Research Centre and senior author of the study. “These findings support a new framework for truly personalized immunotherapy, integrating tumor genomics with the patient’s immunogenetic background.”

Beyond scientific insight, the work also carries broader societal relevance. More accurate prediction of therapy response could help reduce unnecessary treatments, limit avoidable side effects, and shorten the time needed to identify effective therapies for individual patients.

The study was carried out through a close collaboration between the Systems Immunology Research Group at the HUN-REN Szeged Biological Research Centre, the HCEMM Cancer Microbiome Research Group, and contributions from the Evolutionary Systems Biology Research Group led by Csaba Pál.

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HUN-REN Szegedi Biológiai Kutatóközpont

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