Genki Okumura

Resistance to immune checkpoint inhibitors remains a major challenge in oncology, yet the mechanisms that selectively disable activating pathways are poorly defined. Here, we identify tumor-derived soluble CD155 (sCD155) as a systemic checkpoint that rewires DNAM-1 signaling to drive immunotherapy resistance. High plasma sCD155 levels correlate with impaired anti-PD-1 responses in patients with non-small cell lung cancer. Mechanistically, sCD155 selectively suppresses DNAM-1-dependent activation of CD8+ T and NK cells, uncoupling ICB from cytotoxic function. Intriguingly, a selective anti-sCD155 monoclonal antibody does not neutralize the ligand, but rather converts it into an activating scaffold. This complex induces Fc(gamma)R-anchored DNAM-1 microcluster formation and robust downstream signaling, effectively switching the checkpoint into a co-stimulatory signal. This reprogramming restores CTL function, suppresses metastasis, and augments PD-1/TIGIT blockade to achieve durable immunity. Our findings establish antibody-mediated receptor architecture rewiring as a therapeutic principle to overcome cancer immune resistance.

Introduction: Glycolysis-driven hepatocellular carcinoma (HCC) has been associated with resistance to immune checkpoint inhibitors; however, clinical implementation of glycolysis-targeted therapies remains limited by the reliance on transcriptomic subclassification. Methods: We developed a logistic regression model using clinical and pathological features to predict transcriptome-defined glycolysis subclass (training cohort, n = 113). An integer-based scoring system was derived for bedside application. Model performance was assessed using classification metrics, with external validation in TCGA-LIHC cohort. Imaging correlations were analyzed with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) in a clinical cohort (n = 133) lacking transcriptomic data. In addition, pathology-free LASSO model was also developed using 18F-FDG PET/CT, contrast-enhanced CT/Gd-MRI, and serum biomarkers. Results: The integer logistic model (cutoff ≥3), incorporating serum α-fetoprotein, serum albumin, arterial enhancement pattern, and histopathological phenotypes (macrotrabecular-massive, compact, and pseudoglandular), achieved an area under the receiver operating characteristic curve (AUROC) of 0.905, accuracy of 0.850, and F1 score of 0.80 in the training cohort, with consistent performance across subgroups. External validation using the fully annotated subset of the TCGA-LIHC cohort (n = 70) demonstrated preserved model performance (AUROC = 0.825). When applied to the clinical cohort, predicted glycolysis-enhanced HCCs demonstrated significantly higher 18F-FDG uptake and distinct hepatobiliary phase defect on Gd-EOB-DTPA-enhanced MRI. Patients classified as the glycolysis subclass showed a trend toward shorter overall survival (median 95 months, p = 0.079) and significantly shorter recurrence-free survival (median 14.4 vs. 37.7 months, p = 0.017). Visual 18F-FDG PET positivity was strongly associated with the glycolysis subclass (p < 0.001) and corresponded to a tumor-to-liver uptake ratio ≥1.51. A pathology-free LASSO model using four non-invasive variables demonstrated robust predictive performance (AUROC = 0.893). Elevated glycolytic activity was exploratively correlated with lower CD8+ T-cell infiltration and increased PD-1+CTLA-4+CD8+ T cells. Conclusion: We provide a transcriptomics-independent logistic regression model for identifying glycolysis-enhanced HCC. This approach enables practical metabolic subclassification of HCC using routinely available clinical data. Macrotrabecular-massive and compact histopathological patterns, inhomogeneous arterial hyperenhancement, elevated serum α-fetoprotein levels, and 18F-FDG PET positivity are characteristic features of this subclass and may serve as potential indicators to inform the applicability of glycolysis-targeting therapies.

Supplementary Table 1. Data summary of patients on dialysis. Supplementary Table 2. Significantly mutated genes in tumor in patients on dialysis. Supplementary Table 3. Data summary of patients not on HD. Supplementary Table 4. Genes sepcific to ACD. Supplementary Table 5. Gene list of pure PT cells. Supplementary Table 6. Genes sepcific to the cyst cluster. Supplementary Table 7. Recurrently mutated genes detected in LMD.

Supplementary Figure 1: a, Frequencies of mutations identified in ACD-RCC (left), ESRD-ccRCC (middle), and sporadicccRCC (right). Supplementary Figure 2: a, UMAP plot of the transcriptomes of tumours from HD patients. Supplementary Figure 3: a, eGFR in the expression-defined cluster groups. The green and pink groups correspond to the groups shown in Figure 3a. Supplementary Figure 4: a, Representative images of each marker via mIHC. Supplementary Figure 5: a, Spatial expression patterns of PT genes and DT genes in the renal cortex of patients not on HD. Scale bar, 2 mm.

Patients with end-stage renal disease (ESRD) are at increased risk of acquired cystic kidney disease (ACKD) and renal tumors, but the molecular basis of such disorders remains elusive. In this study, we performed spatial immunogenomic analyses on 268 specimens from 43 patients with ACKD and associated tumors. The molecular landscape of ESRD-associated tumors is different from that of sporadic renal cell carcinoma. Single-cell and spatial analyses have shown that ACKD is under an inflammatory environment and that the renal cysts in ACKD are derived from proximal tubular cells that have survived the destructive effects of inflammation. Interestingly, we revealed that cysts are formed by clonal expansion and that paracrine activation of the MET tyrosine kinase pathway by the surrounding environment nurtures cysts. Collectively, our results identify the oncogenic mechanisms of ESRD-associated renal tumors and will pave the way for improving the prognosis of patients with ESRD.Significance:We elucidated the molecular basis of renal cancer arising in patients with ESRD and show that the inflammatory environment of ESRD gives rise to field cancerization.See related commentary by Sager et al., p. 426

Supplementary Figure 6: a, Expression patterns of PT-related gene sets in UMAP plots of the integrated scRNA-seq data of the renal cortex of 5 HD patients with ACD-RCC. Supplementary Figure 7: a, Inferred CNV profiles of cyst cells with macrophages used as references. The yellow and green clusters in each figure represent heterogeneous expression clusters of cells within the same cyst. Supplementary Figure 8: a, Expression levels of HGF (macrophage) and MET (cyst) genes in UMAP plots of the integrated scRNA-seq data.

Patients with end-stage renal disease (ESRD) are at increased risk of acquired cystic kidney disease (ACKD) and renal tumors, but the molecular basis of such disorders remains elusive. In this study, we performed spatial immunogenomic analyses on 268 specimens from 43 patients with ACKD and associated tumors. The molecular landscape of ESRD-associated tumors is different from that of sporadic renal cell carcinoma. Single-cell and spatial analyses have shown that ACKD is under an inflammatory environment and that the renal cysts in ACKD are derived from proximal tubular cells that have survived the destructive effects of inflammation. Interestingly, we revealed that cysts are formed by clonal expansion and that paracrine activation of the MET tyrosine kinase pathway by the surrounding environment nurtures cysts. Collectively, our results identify the oncogenic mechanisms of ESRD-associated renal tumors and will pave the way for improving the prognosis of patients with ESRD. Significance: We elucidated the molecular basis of renal cancer arising in patients with ESRD and show that the inflammatory environment of ESRD gives rise to field cancerization. See related commentary by Sager et al., p. 426