Recent genomic, single-cell sequencing, and spatial transcriptomics studies converge

The gap

Evidence profile

Sourced from the future work of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Hippo-YAP/TAZ signaling in gastric cancer: orchestrating epithelial-stromal heterogeneity, plasticity, and therapy resistance (2026) · Cancer Heterogeneity and Plasticity · doi

  Recent genomic, single-cell sequencing, and spatial transcriptomics studies converge on a central conclusion: gastric cancer cannot be fully described by a set of static subtypes [12]. Instead, tumors move through a landscape of epithelial lineages and stromal reprogramming shaped by inflammation, mechanics, and therapy [13]. Framing YAP/TAZ as coordinators of epithelial-stromal plasticity connects genotype, histology, and microenvironmental architecture, motivating new classification and treatment paradigms [33]. Priorities include mapping YAP/TAZ activity and ecotypes in large, clinically annotated cohorts; defining how events such as CDH1 loss and RHOA pathway alterations rewire Hippo dependence; and developing preclinical models that preserve tumor-stroma coupling to evaluate how Hippo-targeted interventions reshape heterogeneity and plasticity [43,49,58]. Ultimately, clinically useful frameworks will need to combine stable molecular strata with dynamic ecotype readouts, enabling therapies that not only match baseline subtype but also anticipate and prevent transitions into drug-tolerant, metastatic states [34,46]. Such approaches could illuminate new means to manage the devastating disease of advanced gastric cancer [35]. Cancer Heterogeneity and Plasticity 2026;3(3):0008 Page 12 of 17

  generalfuture work
  Keywords: cancer plasticity gastric epithelial stromal clinically hippo heterogeneity recent genomic single cell sequencing spatial transcriptomics
• Candidate Spatial Niches Associated with Gastric Cancer Metastasis and Treatment Resistance: Functional Classification, Evidence Boundaries, and a Translational Framework (2026) · Cells · doi

  The value of a spatial-niche framework is not in renaming existing spatial phenom- ena, but rather in establishing an evidence framework linking tissue context, neighbor- hoods characterized using prespecified and transparently reported spatial rules, and local functional programs with malignant phenotypes or clinical outcomes. At present, gastric cancer lacks spatial-niche metrics that have undergone sufficient clinical validation for routine application. The next stage of research should prioritize three questions: whether candidate-niche assignments and spatial metrics can be reproducibly identified across patients, subtypes, sampling methods, and platforms; whether key neighborhoods have functional roles in metastasis or specific treatment-failure phenotypes; and whether pre-existing, selectively retained, and treatment-induced or treatment-remodeled states can be distinguished us- ing longitudinal samples. Only after these questions are addressed can spatial niches https://doi.org/10.3390/cells15181676 Cells 2026, 15, 1676 22 of 30 move beyond descriptive tissue maps toward operational variables for gastric cancer me- tastasis research, therapeutic-response prediction, and combination-intervention design. Supplementary Materials: The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cells15181676/s1, Table S1, a 66-entry study-level evidence map with framework assignment, retrieval provenance, evidence-stage grading, and evidence boundaries; Search Log, formal PubMed/Web of Science rerun strings and counts, eligibility bound- aries, deduplication audit, screening transparency, and included-study provenance; Table S2, rec- ommended reporting checklist for the operational identification and quantification of candidate spatial niches; Table S3, illustrative application of the proposed framework to representative pub- lished gastric cancer studies; and Table S4, two-rater illustrative framework application and agree- ment assessment. Author Contributions: Y.H. and J.Z. contributed equally to this work. Conceptualization, Y.H., J.Z., T.J. and G.Z.; methodology, Y.H., J.Z., T.J. and G.Z.; investigation and literature search, Y.H., J.Z., J.L., B.B., M.Z. and T.G.; writing—original draft preparation, Y.H., J.Z., J.L., B.B., M.Z. and T.G.; writing—review and editing, Y.H., J.Z., J.L., B.B., M.Z., T.G., T.J. and G.Z.; visualization, Y.H., J.Z. and M.Z.; supervision, T.J. and G.Z.; project administration, T.J. and G.Z.; funding acquisition, T.J. and G.Z. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Natural Science Foundation of China (U23A20499), the Zhejiang Provincial Leading Project for Leading Geese Plan (2026C02A1089), and the Transformation Project of the Chunyan Project (CY202302). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All study-level evidence extraction and framework classifications generated for this review are provided in Supplementary Table S1 and the associated Search Log. No new primary patient-level or experimental data were generated. Acknowledgments: We appreciate the valuable technical and experimental support from Zhejiang Chinese Medical University, especially the Medical Research Center, Academy of Chinese Medical Sciences, and from Zhejiang Provincial Hospital of Traditional Chinese Medicine. Conflicts of Interest: The authors declare no conflicts of interest.

  generalfuture work
  Keywords: spatial framework evidence project niche gastric cancer application whether treatment cells level search review zhejiang
• Tumor‑immune spatiotemporal co‑evolution: A new paradigm for understanding and overcoming therapy resistance in metastatic castration‑resistant prostate cancer (Review) (2026) · International Journal of Molecular Medicine · doi

  The tumor‑immune spatiotemporal co‑evolution paradigm reframes mcRPc resistance as an ecosystem‑level adapta‑ tion unfolding across both temporal and spatial dimensions. This conceptual shift carries profound implications for future research and clinical practice. The spatial architec‑ ture of the TME, defined by the cellular networks of Tregs, MdScs, TAMs and cAFs that actively silence antitumor immunity, must be integrated into routine clinical trial design. Andersen et al (124) revealed strong associations between SFRP4 and extracellular matrix remodeling in Pca, suggesting that spatial biomarkers may guide patient stratification while Blanke et al (125) defined CAFs subtypes that independently predict patient outcomes. These spatially resolved signatures, when combined with the temporal dynamics of clonal evolution documented by Zivanovic et al (126), offer a comprehensive view of the ecological drivers of resistance that can inform biopsy strategies and treatment selection. However, several technical limitations currently constrain the clinical translation of spatial transcriptomics in mcRPc. First, most studies are limited by small sample sizes, which restricts statistical power for identifying robust spatial signa‑ tures associated with therapy response (35,56,65). Second, the lack of longitudinally matched specimens, that is, tumor samples collected before, during and after therapy from the same patient, prevents direct tracking of spatiotemporal evolution under therapeutic selection pressure, limiting current understanding to cross‑sectional snapshots rather than dynamic trajectories (27,59). Third, current spatial resolution, while improving, remains insufficient to resolve single‑cell interactions within complex niches; most platforms capture spots containing multiple cells, obscuring the precise spatial relationships between individual immune, stromal and tumor cells (56,65). Fourth, integrating spatial transcriptomics with other omics layers (metabolomics, proteomics, and longi‑ tudinal liquid biopsy data) remains technically challenging and computationally intensive (27,127). Addressing these limitations will require multi‑center consortia to collect larger, longitudinally sampled cohorts; development of higher‑resolution spatial technologies capable of single‑cell or near‑single‑cell resolution; and standardized analytical pipelines for multi‑omics integration. The recognition that pre‑existing castration‑tolerant progenitors and adaptive resistance mechanisms operate from the earliest stages of therapy underscores the urgent need for dynamic monitoring and evolutionary‑informed treat‑ ment sequencing. Mathematical modelling has emerged as a powerful tool to address this challenge. Gallaher et al (122) developed models of intermetastatic and intrametastatic hetero‑ geneity that can simulate adaptive therapy cycling dy

  generalfuture work
  Keywords: spatial therapy tumor evolution resistance clinical patient resolution single cell immune spatiotemporal mcrpc temporal defined

Questions about this gap