Mitosis is a critical phase of the cell cycle and a vulnerable point where cancer cells can be disrupted, causing cell death and inhibiting tumor growth. Challenges such as drug resistance persist in clinical applications. During mitosis, mRNA translation is generally downregulated, while non-canonical translation of specific transcripts continues. Here, we show that mitotic cancer cells redistribute ribosomes toward the 5' untranslated region (5' UTR) and beginning of the coding sequence (CDS), enhancing translation of thousands of upstream open reading frames (uORFs) and upstream overlapping open reading frames (uoORFs). This mitotic induction of uORF/uoORF enriches human leukocyte antigen (HLA) presentation of non-canonical peptides on the surface of cancer cells after mitotic inhibitor treatment. Functional assays indicate these epitopes provoke cancer-cell killing by T cells. Our findings highlight the therapeutic potential of targeting uORF/uoORF-derived epitopes with mitotic inhibitors to enhance immune recognition and tumor cell elimination.

The Greatwall kinase inhibits PP2A-B55 phosphatase activity during mitosis to stabilise critical Cdk1-driven mitotic phosphorylation. Although Greatwall represents a potential oncogene and prospective therapeutic target, our understanding of the cellular and molecular consequences of chemical Greatwall inactivation remains limited. To address this, we introduce C-604, a highly selective Greatwall inhibitor, and characterise both immediate and long-term cellular responses to the chemical attenuation of Greatwall activity. We demonstrate that Greatwall inhibition causes systemic destabilisation of the mitotic phosphoproteome, premature mitotic exit and pleiotropic cellular pathologies. Importantly, we show that the cellular and molecular abnormalities associated with reduced Greatwall activity are specifically dependent on the B55α isoform, rather than other B55 variants, underscoring PP2A-B55α phosphatases as key mediators of the cytotoxic effects of Greatwall-targeting agents in human cells. Additionally, we establish that sensitivity to Greatwall inhibition varies in different cell line models and that dependency on Greatwall activity reflects the balance between Greatwall and B55α expression levels. Our findings highlight Greatwall dependency as a cell-specific vulnerability and propose the B55α-to-Greatwall expression ratio as a predictive biomarker of cellular responses to Greatwall-targeted therapeutics.

Repeated oncogenic mutations and polyclonal proliferation are evident in cancers. However, little is known about the polyclonal principles governing the systemic cancerous lineage during immunotherapy. Here, we examine a unique autopsy case of metastatic urothelial carcinoma that exhibits different treatment responses to anti-PD-1 therapy at each tumor site. By performing in-depth analyses of different multiregional bulk tumor masses, we reveal that subsets of subclones acquire potential driver mutations under treatment selection pressure. Spatial transcriptomics analysis reveals that subclones resistant to immunotherapy form distinct immunosuppressive environments consistent with their habitats. Furthermore, different cancer hallmarks are identified in each of the subclones that expand under immunotherapy at single-cell level; for example, one subclone is more proliferative, and another is more stem-cell-like. In summary, this study provides an overall picture of the polyclonal competition and changes in the immune microenvironment that are related to resistance to immunotherapy in patients with malignancies.

The efficacy, safety and ideal treatment duration of an adjuvant epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) for patients with resected EGFR-mutated non-small-cell lung cancer (NSCLC) were not known until 2014, when this study was initiated. In this phase 3 ICTAN trial (GASTO1002, NCT01996098), patients with completely resected, EGFR-mutated, stage II-IIIA NSCLC after adjuvant chemotherapy were assigned in a 1:1:1 ratio to receive icotinib (125 mg, three times daily) for 12 months, to receive icotinib for 6 months, or to undergo observation. The primary endpoint was disease-free survival (DFS). This trial was terminated early. A total of 251 patients were randomized. Adjuvant icotinib for 12 months significantly improved DFS (hazard ratio [HR]: 0.40, 95% confidence interval [CI], 0.27-0.61; P < 0.001) and overall survival (OS; HR: 0.55, 95% CI, 0.32-0.96; P = 0.032) compared with observation. Adjuvant icotinib of 6 months also significantly improved DFS (HR: 0.41, 95% CI, 0.27-0.62; P < 0.001) and OS (HR: 0.56, 95% CI, 0.32-0.98; P = 0.038) compared with observation. Adjuvant icotinib for 12 months did not improve DFS (HR: 0.97; P = 0.89) or OS (HR: 1.00; P = 0.99) compared with 6 months of this drug. Rates of adverse events of grade 3 or higher were 8.3%, 6.0% and 2.4% for the 12-month icotinib, 6-month icotinib, and observation groups, respectively. Adjuvant icotinib for 12 months or 6 months following adjuvant chemotherapy improved DFS and OS compared with observation in patients with resected EGFR-mutated stage II-IIIA NSCLC with a manageable safety profile, supporting it as a potential treatment option.

Epithelial ovarian cancer (EOC) remains one of the most lethal gynecological malignancies. Although treatment options for newly diagnosed advanced EOC include primary debulking surgery (PDS) or neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS), the comparative effectiveness of these strategies remains uncertain across different disease stages.

RPS20 is a proposed colorectal cancer (CRC) predisposition gene, with only four families with putative loss-of-function (pLoF) variants published. The prevalence, phenotypic spectrum, and clinical management recommendations for RPS20 heterozygotes remain unknown.

This Special Article provides a comprehensive review and expert commentary on the prospective clinical implementation of artificial intelligence (AI) in the detection of prostate cancer from digital prostate biopsies, as presented in the original research by Flach et al. It contextualizes the study within broader developments in digital pathology and AI, addressing barriers to adoption and the implications for diagnostic workflows and pathology practice.

In single-cell datasets, patient labels indicating disease status (e.g., "sick" or "not sick") are typically available, but individual cell labels indicating which of a patient's cells are associated with their disease state are generally unknown. To address this, we introduce mixture modeling for multiple-instance learning (MMIL), an expectation-maximization approach that trains cell-level binary classifiers using only patient-level labels. Applied to primary samples from patients with acute leukemia, MMIL accurately separates leukemia from nonleukemia baseline cells, including rare minimal residual disease (MRD) cells; generalizes across tissues and treatment time points; and identifies biologically relevant features with accuracy approaching that of a hematopathologist. MMIL can also incorporate cell labels when they are available, creating a robust framework for leveraging both labeled and unlabeled cells. MMIL provides a flexible modeling framework for cell classification, especially in scenarios with unknown gold-standard cell labels.

Thyroid nodules are primarily diagnosed using ultrasound imaging (USI), but its low specificity leads to unnecessary fine-needle aspiration biopsies (FNABs). In particular, USI's limited ability to differentiate follicular neoplasms from benign nodules contributes to suboptimal biopsy decision-making. We propose a dual-modal imaging approach that combines multiparametric photoacoustic imaging (PAI) and USI to support smarter biopsy decisions. In 106 patients with 29 benign nodules, 45 papillary thyroid carcinomas, and 32 follicular neoplasms, three PAI-derived parameters-the photoacoustic spectral gradient, oxygen saturation, and skewness of the oxygen saturation distribution-were combined using a support vector machine. Following USI-based American Thyroid Association (ATA) guidelines, they were used to develop the ATA-Photoacoustic (ATAP) scoring system. The ATAP score achieved 97% sensitivity and 38% specificity in distinguishing nodules requiring FNAB. Our approach enabled better identification of benign nodules, reducing unnecessary FNAB in 11 of the 29 benign cases. This dual-modal strategy can assess thyroid nodules, effectively reducing unnecessary biopsies while maintaining high diagnostic accuracy.

Neurofibromin/NF1 is a RAS (rat sarcoma virus) GTPase-activating protein and estrogen receptor (ER) transcriptional corepressor. NF1low status, identified by copy number loss or low mRNA/protein expression, is associated with endocrine therapy resistance in ~20% of ER+/HER2- (human epidermal growth factor receptor 2) early-stage breast cancers. The identification of targeted treatments for NF1low ER+/HER2- breast cancer is therefore a priority. In this study, proteogenomic analysis of ER+/HER2- breast cancer demonstrated that NF1low tumors exhibited elevated cyclin-dependent kinase 4/6 (CDK4/6) activity. In cell lines, NF1 deletion had a dual effect on CDK4 activity: first, by promoting ER recruitment to CCND1 (cyclin D1), thereby increasing CDK4-cyclin D1 complex formation, and second, by activating C-RAF (rapidly accelerated fibrosarcoma), which drove phosphorylation of the CDK4 activation loop. Preclinical modeling demonstrated that NF1low ER+ cancer cells were more sensitive to fulvestrant combined with a CDK4/6 inhibitor versus fulvestrant alone, with the induction of cell death in vitro and durable tumor regressions in ER+ NF1low patient-derived xenograft models in vivo. Furthermore, NF1low ER+/HER2- tumors were more sensitive to neoadjuvant aromatase inhibitor (AI) plus palbociclib than to neoadjuvant AI alone, as indicated by suppression of mRNA-based proliferation scores. These data are consistent with a model whereby ER and RAS coactivation upon NF1 loss can drive CDK4/6 activity and endocrine therapy resistance but renders NF1low ER+ tumors susceptible to CDK4/6 inhibition. Development of clinical-grade NF1 diagnostics should be prioritized to determine whether NF1low ER+ breast cancers should receive adjusted adjuvant treatment recommendations that reflect increased responsiveness to CDK4/6 inhibition.

A 17-year-old male presented with a painful subungual mass, which was clinically diagnosed as a subungual exostosis prior to surgical referral. Few reported cases of subungual osteochondroma exist in the literature, and those published describe skin or nail deformities resulting from the lesion. These deformities can easily be misdiagnosed as subungual exostosis by clinical examination alone. The characteristic findings in this case resulted in a diagnosis of subungual osteochondroma, which was successfully resolved following surgical excision. This report highlights the clinical, radiographic, and histopathologic characteristics of subungual osteochondroma, and differentiates it from subungual exostosis. The results report on the success of a 2-year post-surgical audit of patient-related outcomes.

Current prognostic models for colorectal liver metastases (CRLM) primarily incorporate clinicopathologic features assessed at a single time point, resulting in a static risk assessment for individuals. Given that tumor progression is a dynamic process, especially for patients with CRLM, and patients' data are continuously collected during the follow-up visits, dynamic prediction is a natural model for risk assessments via reflecting the latest prognosis, whenever new marker measurements are available.

Low-grade gliomas (LGGs) represent a complex and aggressive category of brain tumors. Despite recent advancements in molecular subtyping and characterization, the necessity to identify additional molecular subtypes and biomarkers remains. To delineate survival subtypes in LGG, we propose a deep learning (DL)-based multi-omics SurvivalNet (MOST) model. By integrating histological RNA-seq, miRNA-seq, and DNA methylation data obtained from The Cancer Genome Atlas (TCGA), we applied the MOST model to analyze data from 497 LGG patients. We employed consensus clustering to reveal heterogeneous subtypes, validated our findings using an internal validation set through a supervised classification algorithm, and further evaluated the robustness of our model in an independent external cohort. The DL-based MOST model identified two optimal patient subtypes with significant differences in survival (P = 3.07E - 16) and demonstrated a robust model fit (C = 0.92 ± 0.02). This multi-omics model was validated using external Chinese Glioma Genome Atlas (CCGA) datasets, including RNA-Seq (N = 497, C = 0.85), miRNA array (N = 89, C = 0.80), and DNA methylation (N = 89, C = 0.61). High-risk subcategories exhibited increased expression of the homeobox (HOX) family genes, regulation of cholesterol homeostasis, glycolysis, epithelial-mesenchymal transition pathway enrichment, and a high density of M2 macrophages. Our study utilized deep learning to identify multi-omics features associated with differential survival outcomes in patients with LGG. This work is anticipated to significantly enhance prognosis prediction for LGG due to its robustness within the cohorts.

Recent studies have revealed that the tumor microenvironment (TME) mediates neutrophil activation through multicomponent collaborative regulatory mechanisms, serving as a key driver of NETs formation. Cytokines, platelets, and complements activate neutrophil signaling pathways, ultimately forming NETs with protumorigenic properties in the TME. Notably, exogenous stimuli such as surgery, chronic stress, and pathogens can regulate NETs formation by remodeling the TME. However, the regulatory factors that induce NETs formation have not yet been systematically elucidated. Despite the identification of several factors that initiate the formation of NETs, the potential involvement of additional mechanisms remains inadequately understood. There is an urgent need for thorough mechanistic investigations utilizing cell lines and animal models in cancer. These studies will yield essential insights necessary for the formulation of targeted therapeutic approaches. This review delves into the mechanisms behind neutrophil extracellular traps (NETs) formation, tumor microenvironmental regulation of NETs formation, and exogenous stimuli that mediate tumor biological behaviors by inducing NETs. Gaining insight into these mechanisms will enhance our understanding of NETs and aid in crafting more precise strategies to inhibit tumor progression.

Phosphatidylcholine (PC), a core component of eukaryotic cell membranes essential for maintaining membrane integrity, has emerged as a critical regulator in oncogenic metabolic reprogramming. Accumulating evidence reveals that dysregulated PC metabolism constitutes a central mechanism driving malignant tumor progression. This review systematically delineates the biosynthetic pathways (Kennedy pathway, PEMT pathway, Lands cycle) and catabolic processes (phospholipase-mediated hydrolysis via PLA2, PC-PLC, and PLD) governing PC homeostasis. We highlight how PC metabolic networks orchestrate pro-tumorigenic effects via multifaceted mechanisms, such as enhancing membrane biosynthesis to support rapid tumor proliferation, activating some proliferative signaling cascades coupled with apoptosis suppression, remodeling the immunosuppressive microenvironment, et al. Notably, small-molecule inhibitors targeting key PC metabolic enzymes (e.g., RSM-932A, FIPI) demonstrate promising anti-tumor efficacy in preclinical models, though therapeutic outcomes are constrained by metabolic plasticity and tumor heterogeneity. By integrating recent advances in lipidomics and spatial metabolomics, this synthesis not only deciphers the evolutionary logic underlying PC-driven oncogenesis but also proposes innovative therapeutic strategies combining metabolic inhibitors with immune checkpoint modulators. Our analysis provides a conceptual framework for targeting phospholipid vulnerabilities in cancer, paving the way for precision oncology applications.

Accurate preoperative tumor sizing is critical for optimal surgical planning in breast cancer. Contrast-enhanced mammography (CEM) has emerged as a promising modality, yet its accuracy relative to conventional imaging and pathology requires further validation.

This study aimed to investigate the consistency of lesion identification by Prostate Imaging Reporting and Data System (PI-RADS) and the related clinical and histological characteristics in a high-volume tertiary care center.

Accurate subtype identification of lymphoma cancer is crucial for effective diagnosis and treatment planning. Although standard deep learning algorithms have demonstrated robustness, they are still prone to overfitting and limited generalization, necessitating more reliable and robust methods.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, with limited effective therapeutic options due to late diagnosis, aggressive progression, and rapid development of drug resistance. In pursuit of novel treatments, this study reports the design, synthesis, and biological evaluation of a new series of topsentin derivatives, featuring a 1,2,4-oxadiazole core. The newly synthesized derivatives were screened for antiproliferative activity against multiple PDAC cell lines (SUIT-2, Patu-T, and PANC-1), identifying several compounds with potent growth-inhibitory effects, particularly on SUIT-2 and Patu-T cells. Further studies demonstrated that these compounds also significantly inhibited cell migration and reduced clonogenic potential, with IC50 values in the micromolar range. The results suggest that these marine-inspired 1,2,4-oxadiazole derivatives effectively target key hallmarks of PDAC, including proliferation, migration, and colony formation, supporting their further development as promising candidates for overcoming drug resistance and metastatic progression in pancreatic cancer.

Colorectal cancer (CRC) remains a predominant cause of global cancer-related mortality, highlighting the pressing demand for innovative therapeutic strategies. Natural polysaccharides have emerged as promising candidates in cancer research due to their multifaceted anticancer mechanisms and tumor-suppressive potential across diverse malignancies. In this study, we enzymatically extracted a polysaccharide, named ERPP, from Ruditapes philippinarum and comprehensively evaluated its anti-colorectal cancer activity. We conducted in vitro assays, including CCK-8 proliferation, clonogenic survival, scratch wound healing, and Annexin V-FITC/PI apoptosis staining, and the results demonstrated that ERPP significantly inhibited HT-29 cell proliferation, suppressed colony formation, impaired migratory capacity, and induced apoptosis. JC-1 fluorescence assays provided further evidence of mitochondrial membrane potential (MMP) depolarization, as manifested by a substantial reduction in the red/green fluorescence ratio (from 10.87 to 0.35). These antitumor effects were further validated in vivo using a zebrafish HT-29 xenograft model. Furthermore, ERPP treatment significantly attenuated tumor angiogenesis and downregulated the expression of the vascular endothelial growth factor A (Vegfaa) gene in the zebrafish xenograft model. Mechanistic investigations revealed that ERPP primarily activated the mitochondrial apoptosis pathway. RT-qPCR analysis showed an upregulation of the pro-apoptotic gene Bax and a downregulation of the anti-apoptotic gene Bcl-2, leading to cytochrome c (CYCS) release and caspase-3 (CASP-3) activation. Additionally, ERPP exhibited potent antioxidant capacity, achieving an 80.2% hydroxyl radical scavenging rate at 4 mg/mL. ERPP also decreased reactive oxygen species (ROS) levels within the tumor cells, thereby augmenting anticancer efficacy through its antioxidant activity. Collectively, these findings provide mechanistic insights into the properties of ERPP, underscoring its potential as a functional food component or adjuvant therapy for colorectal cancer management.