The research team, led by Chao Yu and Siyu Liu with corresponding authors Yakun Wang and Xiaotian Zhang, assembled a cohort of 563 patients with stage III or IV gastric cancer or gastroesophageal junction adenocarcinoma, all of whom had paired tumor and normal tissue analyzed by targeted sequencing. The investigators call this the QZ563 cohort. Within it, a subset of 391 patients carried detailed clinicopathologic, biomarker, treatment, and survival information, forming the Clinical391 cohort used for deeper outcome analyses. By layering somatic mutations, tumor mutational burden, microsatellite instability status, metastatic patterns, and treatment-related biomarkers onto this clinical foundation, the team built a multidimensional map of advanced gastric cancer as it actually presents in Chinese hospitals.

The mutational landscape that emerged was dominated by familiar names. TP53 was altered in 70.8 percent of tumors, cementing its role as the single most frequently disrupted gene in advanced gastric cancer. Alongside it, ARID1A, CDH1, and PIK3CA ranked among the most commonly mutated genes, a pattern consistent with prior genomic surveys of the disease. But the study’s real contribution lies not in cataloging the usual suspects. Instead, the researchers asked how clinically actionable biomarkers carve up the patient population, and whether the resulting categories carry meaningful biological and prognostic differences that standard staging alone cannot capture.

One of the most clinically consequential findings concerns a small but important subgroup: patients whose tumors lack any of the established treatment-related biomarkers. The team labeled this group treatment-biomarker-negative gastric cancer, and their analysis revealed that these tumors are molecularly heterogeneous rather than uniformly indolent or uniformly aggressive. In other words, patients who test negative for actionable markers are not a single biological entity; they represent a mixed collection of genomic states that current biomarker panels do not resolve. The authors are careful to note that this subgroup warrants further validation, but the implication is provocative. A negative biomarker result may conceal as much biological diversity as a positive one, and refining how these patients are characterized could eventually open new therapeutic doors.

Metastatic phenotype added another layer of clinically relevant heterogeneity. By comparing tumors from patients with peritoneal involvement against those with liver metastases, the researchers identified distinct patterns of pathway-level mutation preference, suggesting that the route a gastric cancer takes when it spreads is written, at least in part, into its genome. This distinction matters because peritoneal and liver metastases carry different prognoses and respond differently to systemic therapy. A genomic framework that recognizes metastatic phenotype as a biologically meaningful axis, rather than a mere anatomical detail, could help clinicians anticipate disease trajectories and select treatments more rationally for stage IV patients.

The study’s most striking molecular discovery, however, centers on two chromatin-regulating genes: KMT2C and KMT2D. Loss-of-function alterations in these genes, which disrupt histone methyltransferase complexes that help orchestrate gene expression, were present in 6.4 percent of the QZ563 cohort. That modest frequency conceals a dramatic association. Among tumors carrying KMT2C or KMT2D loss-of-function changes, 48.3 percent were microsatellite instability-high, compared with just 2.7 percent of tumors lacking these alterations, a difference the authors report as highly statistically significant. In plain terms, when the epigenetic machinery governed by KMT2C and KMT2D breaks down, the genome’s mismatch repair system appears far more likely to falter as well, unleashing the mutational storm that defines MSI-high disease.

Crucially, the association did not stop at microsatellite status. Within both microsatellite-stable and MSI-high strata, KMT2C/D loss-of-function tumors showed higher tumor mutational burden than their wild-type counterparts, indicating that these chromatin alterations push genomic instability upward even within established MSI categories. The researchers then cross-checked this pattern against The Cancer Genome Atlas stomach adenocarcinoma dataset, known as TCGA-STAD, and found the same molecular signature, lending independent support to the finding. Yet a cautionary note accompanies the excitement: KMT2C/D loss-of-function status was not associated with a survival benefit in this cohort. High mutational burden is often assumed to predict better responses to immunotherapy, but the data here suggest that KMT2C/D-linked hypermutation alone does not translate into improved outcomes, at least within this retrospective framework.

To move from association toward therapeutic hypothesis, the team turned to DepMap, a large-scale cancer dependency resource that catalogs which genes cancer cell lines cannot survive without. Their analyses nominated WRN, a DNA helicase gene, as a candidate dependency in KMT2C/D loss-of-function MSI models. WRN dependency in MSI-high cancers has emerged in recent years as one of the most tantalizing synthetic lethal opportunities in oncology, and this study extends that concept into the KMT2C/D-altered subset. The authors are explicit that this remains hypothesis-generating and requires functional validation before any clinical translation, but the nomination of a druggable vulnerability within a molecularly defined subgroup exemplifies how large clinical-genomic datasets can seed the next generation of precision trials.

The study also integrated transcriptomic data from TCGA-STAD to probe what KMT2C/D loss-of-function means biologically within the MSI background, examining immune-related and non-immune signature scores between altered and wild-type tumors. Sensitivity analyses confirmed the directional concordance of co-mutation patterns between the Chinese cohort and TCGA, and the researchers stratified their TMB comparisons by MSI status to ensure the association was not simply an artifact of hypermutation. Methodologically, the work illustrates the value of pairing a well-annotated single-institution cohort with public genomic resources: the clinical depth of the Chinese data anchors the findings in real-world patient care, while the external datasets guard against cohort-specific artifacts.

For patients and clinicians, the takeaway is twofold. First, biomarker testing in advanced gastric cancer is not merely a pass-fail gate; the clinical and genomic context surrounding a biomarker result, including metastatic pattern and chromatin gene status, carries information that current decision trees largely ignore. Second, the KMT2C/D-MSI axis offers a potential new lens for subdividing the immunotherapy-sensitive population, one that could eventually explain why some hypermutated tumors respond spectacularly to checkpoint inhibitors while others do not. The study, approved by the Ethics Committee of Peking University Cancer Hospital and funded by Chinese national and municipal science programs, is retrospective by design, and the authors themselves frame the WRN finding as requiring experimental confirmation. Even so, in a disease where one in five patients may harbor molecular features invisible to standard panels, this 563-patient genomic atlas marks a meaningful step toward treatment maps that reflect the full genetic complexity of gastric cancer.

Subject of Research: Clinical-genomic stratification of treatment biomarkers and KMT2C/D-associated microsatellite instability heterogeneity in advanced Chinese gastric cancer

Article Title: Clinical-genomic stratification of treatment biomarkers and KMT2C/D-linked MSI heterogeneity in Chinese gastric cancer: a retrospective cohort study

Article References: Yu, C., Liu, S., Zhou, Z., Qin, N., Yang, L., Wang, J., Ding, M., Chong, X., Wang, Y., & Zhang, X. (2026). Clinical-genomic stratification of treatment biomarkers and KMT2C/D-linked MSI heterogeneity in Chinese gastric cancer: a retrospective cohort study. BMC Cancer. https://doi.org/10.1186/s12885-026-17051-6

Image Credits: AI Generated

DOI: 10.1186/s12885-026-17051-6

Keywords: gastric cancer, KMT2C, KMT2D, microsatellite instability, tumor mutational burden, WRN dependency, treatment biomarkers, tumor heterogeneity, precision oncology, targeted therapy, immunotherapy biomarkers, retrospective cohort study