NPJ Precision Oncology最新文献

The significant heterogeneity and complex morphology of invasive pulmonary adenocarcinoma (IPA) make grading challenging for pathologists. However, thorough investigations into radiopathomics features extracted from computed tomography (CT) and whole slide images (WSIs) for IPA grading and their biological significance remain limited. We aim to integrate multi-omics analysis to establish a robust grading model for IPA and reveal its biological significance. This multicenter study encompassed 988 patients who underwent radical surgical resection and received a pathological confirmation of IPA. Through integrated analysis of radiomics and pathomics, we constructed and validated an optimal ensemble learning grading model, which integrates multi-scale and multi-modal characteristics, achieved AUCs of 0.885, 0.920, 0.833, and 0.905 in the internal and external validation sets. Further systematic analysis of paired CT, WSIs, and RNA sequencing, two co-expression modules, 23 hub genes, and 680 significant pathways associated with grading were identified. Moreover, the reproducibility of the radiopathomics phenotypes, linked to multiple biological pathways-including signal transduction, cell differentiation, DNA damage and repair, cell proliferation and growth, metabolism, and metastasis and invasion-has been validated. In conclusion, the integration of radiological and pathological characteristics enhances the accuracy in differentiating high-grade IPA, offering a robust approach for grading. Multi-scale imaging biomarkers may promote personalized treatment.

Pancreatic ductal adenocarcinoma (PDAC) features a dense desmoplastic stroma, limiting drug delivery and promoting chemoresistance. While anti-angiogenic strategies have shown limited success, underexplored pro-angiogenic approaches may improve perfusion and treatment efficacy. Cold atmospheric plasma (CAP) generates reactive oxygen and nitrogen species and demonstrates anti-cancer effects at longer treatments and pro-healing, angiogenic effects at shorter treatments. We investigated the impact of short CAP treatment on angiogenesis in PDAC using a triple co-culture spheroid model and a turkey in ovo model, mimicking the tumor microenvironment. CAP was applied via the kINPen MED for 15-60 s in vitro and 10-30 s in ovo. In vitro, CAP inhibited endothelial tube formation treatment time-dependently and reduced VEGF-A secretion, while other angiogenic factors remained unchanged. In ovo, vascularization around the tumor was slightly increased in the BH tumors, while not significantly altered within the tumors, except for a significant reduction after 30 s of CAP treatment. Additionally, a significant increase in tumor weight was observed following short CAP treatment. These findings suggest that under standard conditions, short CAP treatment preserves vascular quiescence in PDAC and may exert subtle tumor-modulating effects, highlighting the importance of treatment parameters and model complexity in CAP-based therapeutic strategies.

Triple-negative breast cancer (TNBC) is resistant to radiotherapy due to tumor hypoxia and abnormal angiogenesis, necessitating strategies to enhance therapeutic outcomes. This study evaluates the use of minibeam radiation therapy (MBRT), delivered through a novel 3D-printed collimator made of polylactic acid (PLA) and tungsten, to modulate the TNBC microenvironment and potentially overcome radioresistance. Three collimator configurations (400, 600, 800 μm beam widths) were tested. Mice received MBRT (150 Gy) or conventional radiotherapy (CRT, 7 or 15 Gy), with tumor responses assessed using histology, RNA sequencing, and immunohistochemistry. The measured beam FWHM values for the MBRT 0.4, 0.6, and 0.8 groups were 419 ± 23 μm, 575 ± 31 μm, and 798 ± 50 μm, respectively, while the CTC distances were 832 ± 25 μm, 1296 ± 21 μm, and 1651 ± 49 μm. MBRT generated stable, spatially fractionated dose distributions with high peak-to-valley ratios. Compared to CRT at equivalent valley doses, MBRT significantly reduced tumor growth, proliferation, and hypoxia while increasing necrosis. Mechanistically, MBRT downregulated HIF-1α/VEGFR signaling, alleviating hypoxia and angiogenesis, and enhanced vascular normalization via increased pericyte coverage. These findings suggest MBRT reprograms the TNBC microenvironment, supporting its potential as a radiosensitizing strategy for clinical translation.

The classification of breast cancer (BC) based on HER2 expression is undergoing significant changes. While traditional approaches have focused on HER2-positive and HER2-negative categories, emerging evidence highlights varied therapeutic responses depending on the level of HER2 protein expression. Breast cancers are now immunohistochemically (IHC) scored into five subgroups, which define two primary therapeutic groups: HER2-positive (IHC 2+ amplified and 3 + ) and HER2-negative (IHC 0, 1 + , and 2+ non-amplified). Recent advances, particularly in antibody-drug conjugates (ADCs), have led to further subclassification of HER2-negative BC into HER2-Low and HER2-null (IHC 0). Also, for HER-positive subgroups, a differential response to HER2-targeted therapies is seen. This evolving landscape challenges the traditional use of HER2 as a diagnostic marker and underscores the need for a deeper understanding of HER2 biology. This review addresses these complexities, focusing on the emerging HER2-Low and Ultralow subtypes, and evaluates the distinct therapeutic responses across the spectrum of HER2 expression in different BC subtypes.

While advances in the understanding of tumor biology through multi-omics profiling hold the promise of substantially improving patient outcomes, the cost implications of such strategies remain unclear. We therefore performed a comparative cost analysis of patients treated either within the Tumor Profiler (TuPro) melanoma project or from a control cohort who received treatment after standard next-generation sequencing testing. After adjustment of cohorts through inverse probability of treatment weighting, we found no evidence of statistically significant differences in total costs between the two cohorts (95% confidence interval -10% to +67%). Importantly, treatment costs (95% confidence interval -28% to +41%) were similar between the two cohorts. In conclusion, we found no evidence that treatment recommendations guided by advanced multi-omics profiling led to significantly higher treatment costs in a Swiss context.

Hepatocellular carcinoma (HCC) exhibits profound cellular heterogeneity, the understanding of which is critical for improving prognosis and therapy. Using single-cell RNA sequencing of 32,247 cells from human HCC samples, we characterized the tumor ecosystem and identified five malignant hepatocyte subpopulations with distinct molecular profiles and stage-specific enrichment. Among these, the S100A6⁺ C1 and S100A9⁺ C4 subpopulations were predominantly associated with advanced tumors and actively remodeled the tumor microenvironment through enhanced signaling pathways such as MDK and MIF. We further identified PGAM2 as a key transcriptional regulator in early-stage tumors, whose activity correlated with sialylation-a process linked to immune evasion. Based on these findings, we developed a prognostic model integrating PGAM2 and sialylation-related genes, which robustly stratified patients into high- and low-risk groups with significantly different survival outcomes, immune contextures, and predicted therapeutic responses. Functional experiments validated AGRN, a component of the signature, as a functional driver of HCC proliferation and invasion. Collectively, our results decode the cellular and molecular heterogeneity of HCC, provide a clinically relevant prognostic tool, and highlight potential targets for further investigation.

Colorectal cancer (CRC) is a globally prevalent malignancy with high mortality rates. Cancer-associated fibroblasts (CAFs) are crucial in CRC progression and therapeutic response. This study systematically screened 22 CAF-related prognostic genes using single-cell and spatial transcriptomics analysis. By integrating 101 combinations of 10 machine learning algorithms, we developed and validated a comprehensive predictive model (CRPS) based on large-scale public and in-house datasets (1,541 patients in total), which exhibited superior prognostic predictability compared to 58 existing CRC prognostic models. CRPS score not only effectively evaluates biological functions, immune infiltration, and gene mutation levels, but also serves as a valuable tool for predicting immunotherapy efficacy in various cohorts (478 patients in total). In-house single-cell and spatial transcriptomics data, microarray cohort analysis, and experimental validation revealed that model key gene HSPB1 is closely associated with malignant transformation and subtype conversion of CAFs. In vitro and in vivo experiments further demonstrated that HSPB1-overexpressing CAFs enhance tumor cell malignancy, underscoring the therapeutic promise of targeting the HSPB1-CAF axis in CRC.

Gastric cancer (GC) remains a global clinical challenge due to late diagnosis, high heterogeneity, and poor prognosis. Tumor stemness has emerged as a key factor driving tumor aggressiveness and therapeutic resistance. However, the systematic characterization of high-stemness GC cells and their molecular features remains limited. We integrated single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq data to identify and characterize high-stemness GC cells. Stemness scores were calculated using CytoTRACE, and malignant cells were classified into high stemness (top 25% CytoTRACE-scored cells, HighStem), dynamic transition stemness (DTStem), and low stemness (LowStem) subpopulations based on the quartile method cutoff. ScPagwas and cell-cell communication profiling were used to explore genomic instability, genetic susceptibility, and microenvironmental interactions. HighStem-specific co-expression modules were identified via high-dimensional WGCNA (hdWGCNA), and features were screened using six machine learning algorithms. A benchmark model was constructed for HighStem prediction and interpreted using SHAP analysis. HighStem GC cells exhibited enhanced intercellular signaling, metabolic reprogramming, and stemness-related pathway activity. Five genes-APMAP, MAPRE1, GLB1, TSPAN6, and CDKN2A-were identified as robust HighStem features. Spatial and bulk transcriptomic validation confirmed their tumor-specific expression and prognostic relevance. The Support Vector Machine (SVM) model incorporating these genes achieved high accuracy (AUC = 0.973) in distinguishing HighStem cells, demonstrating strong clinical utility at the scRNA-seq level. In addition, experimental validation through knockdown of core genes (APMAP, CDKN2A, TSPAN6, MAPRE1, and GLB1) in SGC7901 and HGC-27 gastric cancer cell lines revealed a significant reduction in JAK1-STAT3 pathway activity, supporting their functional involvement in tumor stemness regulation. Furthermore, knockdown of these genes increased the sensitivity of GC cells to chemotherapeutic agents like 5-FU and cisplatin, indicating their potential role in chemoresistance. This study provides a comprehensive molecular and functional characterization of high-stemness GC cells. The identified signature genes and predictive models offer novel insights into GC stemness biology and could guide personalized therapeutic strategies. Furthermore, our findings suggest that the core genes identified in this study may serve as potential biomarkers for predicting treatment outcomes and monitoring therapeutic resistance in GC.

While some urological cancer survivors may develop a second primary malignancy, the mechanism is unclear. We assess the causal associations and genetic comorbidity among urological cancers, including prostate (PCa), testicular (TC), bladder (BCa), and kidney cancer (KC). We revealed extensive causal associations among 6 of the 12 trait pairs, with a bidirectional interaction between PCa and TC (OR = 1.91, 95% confidence interval, 1.81-2.00, P = 5.41 × 10^-142). We confirmed strong genome-wide and localized genetic correlations between PCa and TC, alongside tissue-specific heritability enrichment in prostate tissue for both of them. A total of 16 potential functional genes were identified, among which CHMP4C emerged as a shared risk factor for both PCa and TC, with links to poor prognosis. This study clarifies the genetic causality and comorbidity of urological cancers, showing PCa and TC share a similar genetic background. CHMP4C is a risk factor linked to poor prognosis in both, offering novel insights for clinical management.

Immunological intratumor heterogeneity (ImTH) describes the variability in the types, spatial distribution, and functional states of immune cells within tumors. While evidence suggests that ImTH influences tumor progression and therapeutic response, few studies have provided a quantitative characterization of ImTH. Here, we present Scoring Immunological Intratumor Heterogeneity (ScImTH), a novel algorithm that quantifies ImTH by calculating the Shannon entropy of immune cell type proportions within the tumor microenvironment. Using bulk, single-cell, and spatial transcriptomic datasets, we show that reduced ScImTH scores are associated with unfavorable survival outcomes, tumor progression-related molecular and phenotypic features, immunosuppressive states, and resistance to immunotherapy across multiple cancer types. Compared with existing measures of tumor immunity, such as immune score and B-cell receptor diversity, the ScImTH score demonstrated stronger and more consistent associations with clinicopathological features. Notably, the ScImTH score outperformed established biomarkers, including tumor mutational burden and PD-L1 expression, in predicting immunotherapy response. These findings highlight the clinical potential of the ScImTH score as a biomarker for cancer prognosis and immunotherapy stratification. More broadly, our results support the hypothesis that loss of immune diversity is a hallmark of tumor progression.