Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Lavinia Ghiani, Simona Citro, Alessandro Medda et al.

Abstract Background Head and Neck Squamous Cell Carcinoma (HNSCC) are classified in two main subtypes: HPV-positive (HPV+), driven by human papillomavirus (HPV) infections, and HPV-negative (HPV-), associated with environmental risk factors. Despite molecular and clinicopathological differences, neither subtype has effective tailored therapies. Since high-risk HPV oncoproteins E6/E7 affect several epigenetic regulators, characterizing the epigenetic landscape of HPV+ and HPV- HNSCC may uncover novel subtype-specific biomarkers and therapeutic targets. Methods Histone post-translational modifications were profiled in HPV+ and HPV- HNSCC tissues and cell lines using super-SILAC mass spectrometry. The same analysis was performed and combined with RNA-sequencing on E6/E7-transduced human primary keratinocytes (HKs) to identify relevant histone modifiers affected by HPV oncoproteins. Candidate gene was validated via E6/E7-mediated-siRNA knockdown in HPV + cell lines. Western Blot, RT-qPCR and Immunohistochemistry assessed gene expression. NSD2 expression was examined in patients’ tissue samples, TCGA data and 14 HNSCC cell lines. shRNA-mediated NSD2 knockdown followed by RNA-seq, cell proliferation and migration assays evaluated its oncogenic role in HNSCC. CaCl2 treatments were used to investigate NSD2’s role in epithelial differentiation, while ALDH-positive cells were quantified by flow-cytometry. NSD2 overexpression was used to confirm results. Results HPV+ HNSCC exhibited elevated H3K36me2 levels, compared to HPV-. This alteration is driven by E6/E7-induced NSD2 upregulation. NSD2, a histone methyltransferase specific for H3K36 di-methylation is overexpressed in HPV+ relative to HPV- HNSCC and in both subtypes compared to normal tissue, suggesting crucial implications in HNSCC. Functional assays revealed that NSD2 promotes cell proliferation and migration in both the subtypes. Notably, we identified a novel role for NSD2 in inhibiting epithelial cell differentiation, particularly in HPV+ HNSCC, where its upregulation mediates the E6/E7-induced differentiation blockade. Conclusions We identified a novel HPV-driven epigenetic signature in HNSCC marked by increased H3K36me2 and its writer, NSD2. Our study highlights H3K36me2 as a potential biomarker for patient stratification and positions NSD2 as a promising therapeutic target across HNSCC subtypes, modulating both common and subtype-specific oncogenic pathways. Specifically, NSD2 inhibition in HPV+ tumors restores epithelial differentiation, offering a potential strategy to arrest tumor progression.

Yinghua Zhu, Hongde Li, Hongbo Hu et al.

Guangyuan Chen, Zhonghua Fu, Xulin He et al.

Abstract Glioblastoma (GBM), a highly aggressive malignant brain tumor, leads to poor clinical outcomes and low long-term survival rates. Using bioinformatics approaches, we analyzed single-cell RNA sequencing (scRNA-seq) data (GSE273274) from glioma patient samples in this study. It was found that mesenchymal-like (MES-like) cells in the GBM center (GC) significantly facilitated GBM’s aggressive spread and microenvironmental reprogramming. Further analysis on MES-like cells suggested that these cells may participate in the progression from glial differentiation to invasive metastasis. Differential expression analysis, univariate Cox regression, and LASSO regression identified COL22A1 as a key gene. COL22A1 was highly expressed in GC compared to GBM surrounding tissue (GS) and exhibited potential connection to aberrant phosphatidylinositol 3-kinase (PI3K) signaling activation, indicating that COL22A1 may act as a potential regulator of GBM, promoting malignant processes such as proliferation, apoptosis resistance, migration, and invasion. In vitro experiments showed that COL22A1 overexpression (COL22A1-OE) U-87 cells exhibited enhanced proliferation, viability, migration, and invasion. Moreover, immunoblotting of BCL-2 and BAX revealed that COL22A1-OE increased resistance to apoptosis in U-87 cells. However, all these effects were reversed upon silencing of COL22A1, which suggests COL22A1 could be a promising new target for GBM therapy.

Marta Contreiras Silva, Daniel Faria, Laura Balbi et al.

Personalized oncology aims to tailor treatment strategies to the unique molecular and clinical profiles of individual patients, moving beyond the traditional paradigm of treating the disease not the patient. Achieving this vision requires the integration and interpretation of vast, heterogeneous biomedical data within a meaningful scientific framework. Knowledge graphs, structured according to biomedical ontologies, offer a powerful approach to contextualize and interconnect diverse datasets, enabling more precise and informed clinical decision-making. We present ECKO (Explainable Clinical Knowledge for Oncology), a comprehensive knowledge graph that integrates 33 biomedical ontologies and aggregates data from multiple studies to create a unified resource optimized for data-driven clinical applications in oncology. Designed to support personalized drug recommendations, ECKO facilitates the identification of optimal therapeutic options by linking patient-specific molecular data to relevant pharmacological knowledge. It provides transparent, interpretable explanations for drug recommendations, fostering greater trust and understanding among clinicians and researchers. This resource represents a significant advancement toward explainable, scalable, and clinically actionable personalized medicine in oncology, with potential applications in biomarker discovery, treatment optimization, and translational research.

Matthew W. Kenaston, Umair Ayub, Mihir Parmar et al.

Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology decision support that is not captured by accuracy-based evaluation. In this two-cohort retrospective study, we developed a hierarchical taxonomy of reasoning errors from GPT-4 chain-of-thought responses to real oncology notes and tested its clinical relevance. Using breast and pancreatic cancer notes from the CORAL dataset, we annotated 600 reasoning traces to define a three-tier taxonomy mapping computational failures to cognitive bias frameworks. We validated the taxonomy on 822 responses from prostate cancer consult notes spanning localized through metastatic disease, simulating extraction, analysis, and clinical recommendation tasks. Reasoning errors occurred in 23 percent of interpretations and dominated overall errors, with confirmation bias and anchoring bias most common. Reasoning failures were associated with guideline-discordant and potentially harmful recommendations, particularly in advanced disease management. Automated evaluators using state-of-the-art language models detected error presence but could not reliably classify subtypes. These findings show that large language models may provide fluent but clinically unsafe recommendations when reasoning is flawed. The taxonomy provides a generalizable framework for evaluating and improving reasoning fidelity before clinical deployment.

Uma Pisarović, Taichi Ochi, Iryna Samarska et al.

In recent years, light-sheet fluorescence microscopy (LSFM) has emerged as a powerful tool for visualizing and analyzing cancer tissue samples, including patient-derived specimens, organoids, biopsies, and murine models. In this work, we highlight the current applications of deep tissue LSFM in oncology and illustrate its use across a variety of human cancer tissues, including - but not limited to - prostate and breast. Here, we discuss that the potential integration of advanced LSFM technologies into clinical workflows to enable high-throughput, three-dimensional imaging of intact cancer specimens. This approach holds significant potential to enhance diagnostic precision and provide novel insights into tumour architecture and morphology.

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Justus Kaufmann, Maximilian Haist, Ivan-Maximiliano Kur et al.

The growth pattern of oropharyngeal squamous cell carcinomas (OPSCC) varies from compact tumor cell aggregates to diffusely infiltrating tumor cell-clusters. The influence of the growth pattern on local tumor control and survival has been studied mainly for surgically treated oral cavity carcinomas on a visual basis. In this study, we used multiplex immunofluorescence staining (mIF) to examine the antigens pan-cytokeratin, p16INK4a, Ki67, CD271, PD-L1, and CD8 in pretherapeutic biopsies from 86 OPSCC. We introduce Tumor-stroma contact ratio (TSC), a novel parameter, to quantify the relationship between tumor cells in contact with the stromal surface and the total number of epithelial tumor cells. mIF tumor cores were analyzed at the single-cell level, and tumor-stromal contact area was quantified using the R package ''Spatstat''. TSC was correlated with the visually assessed invasion pattern by two independent investigators. Furthermore, TSC was analyzed in relation to clinical parameters and patient survival data to evaluate its potential prognostic significance.Higher TSC correlated with poor response to (chemo-)radiotherapy (r = 0.3, p < 0.01), and shorter overall (OS) and progression-free (PFS) survival (median OS: 13 vs 136 months, p < 0.0001; median PFS: 5 vs 85 months, p < 0.0001). Visual categorization of growth pattern according to established criteria of tumor aggressiveness showed interobserver variability increasing with more nuanced categories (2 categories: k = 0.7, 95 %-CI: 0.55 - 0.85; 4 categories k = 0.48, 95 %-CI: 0.35 - 0.61).In conclusion, TSC is an objective and reproducible computer-based parameter to quantify tumor-stroma contact area. We demonstrate its relevance for the response of oropharyngeal carcinomas to primary (chemo-)radiotherapy.

Xue Yang, Zexuan Liu, Weiwei Tang et al.

Endometrial carcinoma (ECa) is the fourth most common cancer among women. The oncogene PELP1 is frequently overexpressed in a variety of cancers, including ECa. We recently generated SMIP34, a small‐molecule inhibitor of PELP1 that suppresses PELP1 oncogenic signaling. In this study, we assessed the effectiveness of SMIP34 in treating ECa. Treatment of established and primary patient‐derived ECa cells with SMIP34 resulted in a significant reduction of cell viability, colony formation ability, and induction of apoptosis. RNA‐seq analyses showed that SMIP34‐regulated genes were negatively correlated with ribosome biogenesis and eukaryotic translation pathways. Mechanistic studies showed that the Rix complex, which is essential for ribosomal biogenesis, is disrupted upon SMIP34 binding to PELP1. Biochemical assays confirmed that SMIP34 reduced ribosomal biogenesis and new protein synthesis. Further, SMIP34 enhanced the efficacy of mTOR inhibitors in reducing viability of ECa cells. SMIP34 is also effective in reducing cell viability in ECa organoids in vitro and explants ex vivo. Importantly, SMIP34 treatment resulted in a significant reduction of the growth of ECa xenografts. Collectively, these findings underscore the potential of SMIP34 in treating ECa.

Xiaohong Yi, Xianwen Zhang, Claire H. Zhao et al.

SOWAHA is a protein-coding gene, also known as ANKRD43. Studies have indicated that SOWAHA can serve as a prognostic biomarker in colorectal cancer and pancreatic cancer. However, there are few reports about SOWAHA in other types of cancer and the specific mechanism of action of SOWAHA in cancer is also not clear. Based on National Center for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), cBioPortal, Human Protein Atlas (HPA), etc., we adopted bioinformatics methods to uncover the potential tumor genomic features of SOWAHA, including the correlation with prognosis, gene mutation, immune cell infiltration, and DNA methylation in different tumors and evaluated the association with tumor heterogeneity, stemness, chemokines chemokine receptors, and immunomodulators in pan-cancer. Besides, we knocked down SOWAHA in SW620 cells and performed RNA-seq analysis, then we conducted functional enrichment to uncover the biological significance of the gene set. SOWAHA has early diagnostic potential, and low expression of SOWAHA was associated with poor prognosis in was associated with poor prognosis in GBMLGG, PAAD, READ, etc. SOWAHA is associated with most tumor immune-infiltrating cells in pan-cancer. SOWAHA correlates with DNA methylation, tumor heterogeneity, and stemness in many epithelial carcinomas. Furthermore, SOWAHA is involved in many enzyme activity and metabolic pathways, mainly metabolic programming pathways in cancer. Additionally, we identified two potential transcription factors of SOWAHA, TBX4, and FOXP2, which are dysregulated in SW620 cells. Besides, the cell proliferation and viability in siSOWAHA groups are better than in siNC groups.SOWAHA, identified as a suppressor gene, and its role in the progression of colorectal cancer is primarily mediated through metabolic reprogramming mechanisms.

Linkai Peng, Zheyuan Zhang, Gorkem Durak et al.

Pancreatic cancer remains one of the leading causes of cancer-related mortality worldwide. Precise segmentation of pancreatic tumors from medical images is a bottleneck for effective clinical decision-making. However, achieving a high accuracy is often limited by the small size and availability of real patient data for training deep learning models. Recent approaches have employed synthetic data generation to augment training datasets. While promising, these methods may not yet meet the performance benchmarks required for real-world clinical use. This study critically evaluates the limitations of existing generative-AI based frameworks for pancreatic tumor segmentation. We conduct a series of experiments to investigate the impact of synthetic \textit{tumor size} and \textit{boundary definition} precision on model performance. Our findings demonstrate that: (1) strategically selecting a combination of synthetic tumor sizes is crucial for optimal segmentation outcomes, and (2) generating synthetic tumors with precise boundaries significantly improves model accuracy. These insights highlight the importance of utilizing refined synthetic data augmentation for enhancing the clinical utility of segmentation models in pancreatic cancer decision making including diagnosis, prognosis, and treatment plans. Our code will be available at https://github.com/lkpengcs/SynTumorAnalyzer.

Kevin Leder, Ruping Sun, Zicheng Wang et al.

In this study, we develop consistent estimators for key parameters that govern the dynamics of tumor cell populations when subjected to pharmacological treatments. While these treatments often lead to an initial reduction in the abundance of drug-sensitive cells, a population of drug-resistant cells frequently emerges over time, resulting in cancer recurrence. Samples from recurrent tumors present as an invaluable data source that can offer crucial insights into the ability of cancer cells to adapt and withstand treatment interventions. To effectively utilize the data obtained from recurrent tumors, we derive several large number limit theorems, specifically focusing on the metrics that quantify the clonal diversity of cancer cell populations at the time of cancer recurrence. These theorems then serve as the foundation for constructing our estimators. A distinguishing feature of our approach is that our estimators only require a single time-point sequencing data from a single tumor, thereby enhancing the practicality of our approach and enabling the understanding of cancer recurrence at the individual level.

Shuning Ding, Niu Qiao, Qingchen Zhu et al.

Abstract Background Characterizing the unique immune microenvironment of each tumor is of great importance for better predicting prognosis and guiding cancer immunotherapy. However, the unique features of the immune microenvironment of triple negative breast cancer (TNBC) compared with other subtypes of breast cancer remain elusive. Therefore, we aimed to depict and compare the immune landscape among TNBC, human epidermal growth factor receptor 2‐positive (HER2+) breast cancer, and luminal‐like breast cancer. Methods Single‐cell RNA sequencing (scRNA‐seq) was performed on CD45+ immune cells isolated from human normal breast tissues and primary breast tumors of various subtypes. By analyzing the scRNA‐seq data, immune cell clusters were identified and their proportions as well as transcriptome features were compared among TNBC, human HER2+ breast cancer, and luminal‐like breast cancer. Pseudotime and cell‐cell communication analyses were also conducted to characterize the immune microenvironment. Results ScRNA‐seq data of 117,958 immune cells were obtained and 31 immune clusters were identified. A unique immunosuppressive microenvironment in TNBC was decoded as compared to that in HER2+ or luminal‐like breast cancer, which was characterized by higher proportions of regulatory T cells (Tregs) and exhausted CD8+ T cells and accompanied by more abundant plasma cells. Tregs and exhausted CD8+T cells in TNBC exhibited increased immunosuppression signature and dysfunctional scores. Pseudotime analyses showed that B cells tended to differentiate to plasma cells in TNBC. Cell‐cell communication analyses indicated that these unique features are fostered by the diversified T cell‐B cell crosstalk in TNBC. Based on the T cell‐B cell crosstalk, a prognostic signature was established that could effectively predict the prognosis status for patients with TNBC. Additionally, it was found that TNBC had a higher proportion of cytotoxic natural killer (NK) cells, whereas HER2+ or luminal‐like breast cancer lost this feature, suggesting that HER2+ or luminal‐like breast cancer, but not TNBC, may benefit from NK‐based immunotherapy. Conclusions This study identified a distinct immune feature fostered by T cell‐B cell crosstalk in TNBC, which provides better prognostic information and effective therapeutic targets for breast cancer.

Atte K Lahtinen, Jessica Koski, Jarmo Ritari et al.

DENG Yuxiao, ZHAO Xianyuan, GAO Yuan

With the improvements in surgical technique, the indications for liver transplantation(LT) are being expanded to more complex and severe patients, who not only have liver function in the terminal stage, but also extrahepatic organs involve of varying degrees dysfunction. It was demonstrated that the strategy of extrahepatic organs management in early period after LT was associated with improving recovery. This paper summarized the strategy of extrahepatic organs management in early period after adult LT. Continuing to monitor system hemodynamics is helpful for stable graft perfusion. It is necessary to manage derangements of preload, myocardial contractility, and afterload. Early postoperative extubation is recommended by current guidelines. Routine screening pulmonary complications may help weaning from the ventilator. The diagnosis of acute kidney injury(AKI) after LT has been reported to using Kidney Disease: Improving Global Outcomes(KDIGO) criteria, a delayed calcineurin inhibitor initiation strategy may be beneficial for renal protection. Currently, there is no consensus on timing of renal replacement therapy initiation in patients with AKI. Patients with cirrhosis and a history of hepatic encephalopathy(HE) displays impaired neurological recovery, the diagnosis of HE is crucial and challenging.

Amin Zia

Numerous epidemiological studies suggest febrile infections could confer long-term immunity to certain types of cancers, though the precise mechanisms for this phenomenon remain unclear. Systemic heat-shock responses to fever may be key to understanding the overlapping outcomes of immune responses to infection and cancer. To investigate this hypothesis, we performed epitope discovery between heat-shock proteins (HSP) and cancer-associated antigens (CAA) and annotated the results with experimentally validated epitopes in the Immune Epitope Database (IEDB) (Vita et al., 2019). Further, epitopes were matched with their homologs in human pathogens. Results identified 94 epitopes shared between HSPs and CAAs, with experimental evidence of presentation at MHC molecules and with high homology to several epitopes of human pathogens. The identified epitopes can be used as candidates for designing cancer vaccines. They may also be used to identify autoreactive antibodies or TCR specificities that, as antibody drugs and cell therapies, would reproduce the effect of febrile infection in conferring cancer immunity. Our results support the hypothesis that the loss of self-tolerance to HSPs during febrile infection confers tumor immunity through molecular mimicry.

Yu Shi, Hannah Tang, Michael Baine et al.

Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to provide a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where contrast is especially poor owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the interslice connection data that the existing 2D CT image synthesis models lack. Our innovation is to develop a 3D U-Net architecture for the generator to improve shape and texture learning for PDAC tumors and pancreatic tissue. Our approach offers a promising path to tackle the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, this model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.

Wenyi Luo, Todd M. Stevens, Phillip Stafford et al.

Nuclear protein of testis (NUT), a protein product of the NUTM1 gene (located on the long arm of chromosome 15) with highly restricted physiologic expression in post-meiotic spermatids, is the oncogenic driver of a group of emerging neoplasms when fused with genes involved in transcription regulation. Although initially identified in a group of lethal midline carcinomas in which NUT forms fusion proteins with bromodomain proteins, NUTM1-rearrangement has since been identified in tumors at non-midline locations, with non-bromodomain partners and with varied morphology. The histologic features of these tumors have also expanded to include sarcoma, skin adnexal tumors, and hematologic malignancies that harbor various fusion partners and are associated with markedly different clinical courses varying from benign to malignant. Most of these tumors have nondescript primitive morphology and therefore should be routinely considered in any undifferentiated neoplasm. The diagnosis is facilitated by the immunohistochemical use of the monoclonal C52 antibody, fluorescence in situ hybridization (FISH), and, recently, RNA-sequencing. The pathogenesis is believed to be altered expression of oncogenes or tumor suppressor genes by NUT-mediated genome-wide histone modification. NUTM1-rearranged neoplasms respond poorly to classical chemotherapy and radiation therapy. Targeted therapies such as bromodomain and extraterminal domain inhibitor (BETi) therapy are being developed. This current review provides an update on NUTM1-rearranged neoplasms, focusing on the correlation between basic sciences and clinical aspects.

Kai Zhou, Anqiang Wang, Jingtao Wei et al.

BackgroundHepatoid adenocarcinoma of the stomach (HAS) is a rare type of gastric cancer, but the role of perioperative chemotherapy is still poorly understood. The aim of this retrospective study was to investigate the associations between perioperative chemotherapy and prognosis of HAS.MethodWe retrospectively analyzed patients with locally advanced HAS who received radical surgery in Peking University Cancer Hospital between November 2009 and October 2020. Patients were divided into neoadjuvant chemotherapy-first (NAC-first) group and surgery-first group. The relationships between perioperative chemotherapy and prognosis of HAS were analyzed using univariate, multivariate survival analyses and propensity score matching analysis (PSM).ResultsA total of 100 patients were included for analysis, including 29 in the NAC-first group and 71 in the surgery-first group. The Her-2 amplification in HAS patients was 22.89% (19/83). For NAC-first group, 4 patients were diagnosed as tumor recession grade 1 (TRG1), 4 patients as TRG 2, and 19 patients as TRG 3. No significant difference in prognosis between the surgery-first group and the NAC-first group (P=0.108) was found using PSM analysis. In the surgery-first group, we found that the survival rate was better in group of ≥6 cycles of adjuvant chemotherapy than that of &lt;6 cycles (P=0.013).ConclusionNAC based on platinum and fluorouracil may not improve the Overall survival (OS) and Disease-free survival time (DFS) of patients with locally advanced HAS. Patients who received ≥6 cycles of adjuvant chemotherapy had better survival. Therefore, the combination treatment of radical gastrectomy and sufficient adjuvant chemotherapy is recommended for patients with locally advanced HAS.

Qixin Hu, Junfei Xiao, Yixiong Chen et al.

We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and texture, which even medical professionals can confuse with real tumors; (2) effective for AI model training, which can perform liver tumor segmentation similarly to a model trained on real tumors - this result is unprecedented because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to the model trained on real tumors. This result also implies that manual efforts for developing per-voxel annotation of tumors (which took years to create) can be considerably reduced for training AI models in the future. Moreover, our synthetic tumors have the potential to improve the success rate of small tumor detection by automatically generating enormous examples of small (or tiny) synthetic tumors.