NPJ Precision Oncology最新文献

While cancer immunotherapy has transformed clinical management for cancer patients, its low response rates remain a critical challenge to be addressed. Tumor immune evasion now extends beyond the tumor microenvironment (TME), as advanced tumors induce extramedullary hematopoiesis (EMH) in the spleen, leading to a substantial expansion of erythroid progenitor cells (EPCs) with potent immunosuppressive capacity. EPCs are typically transient populations in erythroid maturation and differentiation; however, under tumor burden, they undergo profound metabolic reprogramming that exacerbates their immunosuppressive effects. This review examines the role and mechanisms of tumor-hijacked metabolic reprogramming in EPCs and provides strategies for targeting this reprogramming to potentiate cancer immunotherapy. In particular, we synthesize the metabolic interplay between EPCs, tumor cells, and immune cells, integrating EPC metabolic reprogramming with established concepts of tumor cell metabolism and immunometabolism. Furthermore, this review outlines future directions for the field, including multi-modal approaches to decipher the mechanisms of EPC metabolic reprogramming, biomarker development, and metabolism-based targeted therapies, all aimed at improving survival and prognosis for cancer patients.

Resistance to poly (ADP-ribose) polymerase inhibitors (PARPis) like niraparib represents a major therapeutic challenge in ovarian cancer (OC). This study elucidates a novel resistance mechanism driven by the minichromosome maintenance proteins 2 and 5 (MCM2/5). In niraparib-resistant (NirR) OC cells, RNA-seq revealed upregulation of MCM2 and MCM5, which was functionally linked to enhanced proliferation and homologous recombination repair. Co-immunoprecipitation confirmed strengthened MCM2/5 interaction in NirR cells. Genetic knockdown of MCM2/5 resensitized NirR cells to niraparib, while their overexpression conferred resistance in parental cells. Mechanistically, the upregulation of MCM2/5 was transcriptionally regulated by the E2F1 transcription factor, activated via the CDK4/6-RB pathway. Consequently, pharmacological inhibition of CDK4/6 downregulated MCM2/5 expression and, when combined with niraparib, synergistically suppressed NirR tumor growth both in vitro and in vivo. Our findings identify the MCM2/5 complex as a critical mediator of PARPi resistance and establish the therapeutic potential of combining PARPis with CDK4/6 inhibitors to overcome this resistance in ovarian cancer.

The spatial organization of immune and tumor cells within the tumor microenvironment (TME) has a critical influence on antitumor immunity and patient survival. However, hand-engineered metrics such as cell densities or pairwise proximity scores fail to capture the complexity of local cell-cell interactions. Understanding these higher-order spatial patterns and their relation to patient outcomes is especially important for non-small cell lung cancer (NSCLC), the deadliest cancer worldwide. Here, we elucidate the NSCLC TME using a graph neural network (GNN)-based framework to model spatially localized cellular neighborhoods in multiplex immunofluorescence data from a clinical cohort of 506 patients. The GNN predicted patient survival with high accuracy (concordance index: 0.82) and remained a significant prognostic factor when adjusted for clinical covariates. Interpretability analyses revealed that specific combinations of cell types, particularly involving CD8+ T cells, PD-L1+ immune cells, and FOXP3+ regulatory T cells, modulated predictions depending on their spatial context. In-silico manipulation experiments applied to the trained GNN, used here as an interpretable surrogate model, suggested that the impact of CD8+ cells on survival were estimated as favorable when in direct tumor contact and less favorable when adjacent to immunosuppressive cells. Latent-space clustering identified distinct TME states predictive of outcome, reflecting varying balances of immune activation and evasion. Our approach underscores the prognostic significance of spatially resolved immune-tumor interactions, providing a blueprint for developing next-generation spatial biomarkers to guide precision treatment strategies in NSCLC.

Gastric cancer remains highly lethal, yet how protein S-palmitoylation shapes tumour ecosystems and clinical outcome is unclear. We integrated single-cell RNA sequencing (119,931 cells from 25 gastric tumours) with spatial transcriptomics and bulk cohorts to delineate palmitoylation-linked states across malignant, immune, and stromal compartments. A palmitoylation-high malignant programme partitioned into three metastasis-enriched subclusters with increased fatty-acid metabolism and Ras-MAPK signalling and predicted worse survival. Spatial mapping and ligand-receptor inference revealed co-localised niches where palmitoylation-high tumour cells interacted with immunosuppressive myeloid cells and distinct CAF subsets, with strengthened pro-angiogenic and pro-fibrotic cues. We derived and validated an 87-gene multicellular palmitoylation signature for risk stratification, and higher scores were consistently associated with adverse outcomes in external cohorts. Drug-response modelling highlighted vulnerabilities involving the HSP90-PI3K/MAPK axis. Functional assays and xenografts confirmed SH3BGRL as a key driver within this poor-prognosis programme.

Although neoadjuvant chemotherapy (NACT) is commonly used for advanced ovarian cancer, patient outcomes vary substantially. We developed a graph convolutional network (GCN) that integrates patient-specific baseline clinical variables and computed tomography-derived radiomic features while modeling inter-patient relationships to improve outcome prediction beyond standard models. The GCN operates without reliance on high-performance computing resources and predicts long-term overall survival (OS) while stratifying short-term surgical outcomes (R0 resection). The GCN was compared with the CA-125 ELIMination rate constant K (KELIM) score and three Cox-based comparator models. Model performance was evaluated using the concordance index (C-index) for OS, area under the receiver operating characteristic curve for 3-year OS, Kaplan-Meier survival analysis, and R0 resection stratification. The GCN demonstrated strong OS prognosis performance (C-index = 0.73, 0.72, and 0.70 across the training and two external test datasets), stratified surgical outcomes, and identified 16.30% of patients with low KELIM scores but favorable survival.

Antibody-drug conjugates (ADCs) demonstrate therapeutic potential, but aggressive triple-negative breast cancers (TNBCs) require precise target selection and antibody optimisation. We identified chondroitin sulfate proteoglycan 4 (CSPG4) expression in neoadjuvant treatment-resistant TNBC to guide ADC development. Three anti-CSPG4 IgG1 antibodies with distinct variable regions (225.28S, 763.74, and 9.2.27) were engineered and compared. 225.28S IgG1 demonstrated the most efficient internalisation and potent cancer cell cytotoxicity when conjugated to the tubulin inhibitor MMAE. To determine the optimal isotype, we generated 225.28S IgG4 and directly compared it with 225.28S IgG1. The IgG1 isotype showed superior internalisation and killing activity as an MMAE-conjugated ADC. Conjugation of 225.28S IgG1 to the topoisomerase inhibitor DXd produced an ADC with a drug-to-antibody ratio (DAR) of 8. This ADC was capable of robust internalisation into cancer cells and tumour cell cytotoxicity in vitro, and significant growth restriction of two CSPG4-expressing TNBC patient-derived xenografts (PDX) implanted orthotopically in mouse mammary fat pads. Unconjugated 225.28S IgG1 also limited TNBC xenograft growth in immunodeficient mice engrafted with human immune cells, confirming Fc-mediated functional activity. These studies identify 225.28S IgG1 as the optimal clone and isotype, supporting a next-generation DXd-conjugated ADC as a promising therapeutic strategy for hard-to-treat CSPG4-expressing TNBC.

Sarcomas are aggressive, immunologically cold tumors with limited benefit from immune-checkpoint blockade (ICB). Through integrated multi-omics, functional, and clinical analyses, we identify pyruvate dehydrogenase alpha 1 (PDHA1)-a cuproptosis-linked metabolic gene-as a driver of sarcoma progression and immune evasion. PDHA1 is consistently overexpressed across TCGA/GEO/ICGC cohorts and associates with poor prognosis, stromal activation, and reduced immune scores; single-cell RNA-seq of the immune compartment shows PDHA1 expression across multiple immune populations, with higher levels in T cells and monocytes/dendritic cells. PDHA1 knockdown diminishes proliferation, invasion, clonogenicity, and PD-L1 levels while increasing apoptosis. Mechanistically, PDHA1 elevates E2F1, which binds and transactivates the PD-L1 promoter; rescue assays confirm E2F1-dependent PD-L1 induction. Copper chelation with tetrathiomolybdate lowers lipoylated DLAT and suppresses the PDHA1-E2F1-PD-L1 axis. In 3D spheroids, xenografts, and multiplex immunofluorescence, high PDHA1 aligns with larger tumors, higher Ki-67/BCL-2, lower cleaved caspase-3, increased PD-L1, and reduced CD8⁺ T-cell infiltration. PDHA1 hypomethylation correlates with worse survival. PDHA1 status also modulates sensitivity to phenformin and the E2F1 pathway inhibitor NSC-207895. Collectively, PDHA1 orchestrates a cuproptosis-associated E2F1-PD-L1 program that promotes immune exclusion yet predicts ICB responsiveness, supporting PDHA1 as a clinically actionable biomarker and metabolic-immunologic target in sarcoma.

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide, yet current prognostic stratification is hindered by tumor heterogeneity. Here, we developed a deep learning radiomics model (DLRM), optimized through systematic evaluation of ten machine learning algorithms across 117 combinations, using venous-phase computed tomography (CT) images of 1183 patients from four centers. The resulting risk stratification stratified patients into high- and low-risk groups with distinct survival outcomes, and integration with clinical factors further improved prediction. Integrative transcriptomic and metabolomic analyses revealed that high-risk tumors were enriched for extracellular matrix (ECM)-related pathways associated with tumor progression, whereas low-risk tumors exhibited immune-related signatures, including higher CD8⁺ T-cell infiltration. Both omics consistently identified butanoate metabolism and nitrogen metabolism as protective pathways, validated in an independent public cohort (n = 417). This integrative analytic framework provides robust risk stratification and uncovers biological processes with potential therapeutic relevance.

Head and neck squamous cell carcinoma (HNSCC) represents a leading global malignancy among head and neck cancers. While chemotherapy serves as a standard adjuvant treatment, cisplatin resistance frequently compromises therapeutic outcomes. PANoptosis is an integrated inflammatory cell death pathway governed by PANoptosome complexes. It critically influences chemotherapy response, though its regulatory mechanisms remain incompletely characterized. NADH dehydrogenase (ubiquinone) 1 alpha subcomplex subunit 4-like 2 (NDUFA4L2), a subunit of respiratory chain complex I, has been identified as a critical regulator of cell survival. Our multi-platform investigation employed HNSCC cell lines, patient-derived organoids, tongue orthotopic xenograft models in C57BL/6 mice and Tgfbr1/Pten 2cKO mice to elucidate the role of NDUFA4L2 in cisplatin resistance. Bioinformatic analysis and clinical samples indicate that elevated NDUFA4L2 is associated with poor survival rates and low sensitivity to chemotherapy in HNSCC patients. Through in vitro and in vivo studies, we found that NDUFA4L2-KO in combination with cisplatin suppresses glycolysis levels, thereby inhibiting AIM2 inflammasome activation. Consequently, it triggers tumor cell PANoptosis, remodels the immunosuppressive tumor microenvironment, and enhances antitumor efficacy. These findings establish NDUFA4L2 as both a prognostic biomarker and therapeutic target for overcoming cisplatin resistance in HNSCC through PANoptosis modulation.

Circular RNAs (circRNAs) have emerged as important regulators in cancer biology, but their roles in acute myeloid leukemia (AML) remain poorly characterized due to limited sample sizes and technical challenges in RNA sequencing. Here, we analyze RNA-sequencing data from 315 Swedish AML patients to create the most comprehensive circRNA profile in AML to date. We identify 5,711 high-confidence circRNAs across 315 AML samples, including 402 differentially expressed between AML and healthy controls, with host genes enriched in hematopoietic pathways. We further discover two circRNAs including hsa_circ_0024048 (p = 2.16×10⁻⁶, FDR = 0.012) and hsa_circ_0084678 (p = 1.33×10⁻⁵, FDR = 0.075) whose high expression is associated with significantly improved overall survival, a relationship not observed in their respective host genes. Furthermore, these circRNAs are associated with sensitivities of several drugs, as validated in external datasets (p < 0.05). We identify 451 circRNAs with ELN2022 risk group-specific expression patterns, highlighting circRNA heterogeneity. Subtype analysis further reveals that hsa_circ_0080850 is specifically associated with worse survival (p = 2.13×10⁻⁵ and lower remission rates (38.9% vs 74.7%) within the ELN2022 Favorable subgroup. To conclude, this study establishes the most comprehensive circRNA landscape in AML to date and demonstrates their potential as biomarkers and therapeutic targets, suggesting further investigation into circRNA-driven precision medicine in AML.