Sacituzumab govitecan (SG) is a key treatment for metastatic triple-negative breast cancer (mTNBC). Neutropenia is a common toxicity, and the optimal use of G-CSF (primary prophylaxis versus reactive administration) remains uncertain. We evaluated the association of primary G-CSF prophylaxis with clinical outcomes and treatment-related toxicities.

We conducted a multinational retrospective real-world cohort study of 303 patients with mTNBC treated with SG in the second or a later line. Efficacy endpoints included progression-free survival (PFS) and overall survival (OS), analyzed using the Kaplan-Meier method. Adverse events were assessed according to CTCAE criteria.

Of 303 patients, 100 were included in the primary prophylaxis group and 203 in the no primary prophylaxis group. Median PFS was 4.2 months in the primary prophylaxis group versus 5.1 months in the no primary prophylaxis group (p = 0.66), with 6-month PFS rates of 38.5% vs. 42.1%, respectively. Median OS was 10.9 vs. 11.6 months (p = 0.95), with 12-month OS rates of 44.9% vs. 47.1%. The frequencies of the recorded individual AEs were similar between groups, apart from grade 3-4 neutropenia, which was less frequent in the primary prophylaxis group (33.0% vs. 50.7%, p = 0.005). Febrile neutropenia occurred in 4.0% vs. 4.4% of patients (p = 1.00). Other recorded AEs (anemia, diarrhea, nausea, vomiting, alopecia) were comparable. Dose reductions due to toxicity were similar (36.0% vs. 38.4%, p = 0.71). In the no primary prophylaxis group, 74.4% subsequently received secondary G-CSF.

In this retrospective real-world cohort, primary G-CSF prophylaxis was associated with a lower frequency of grade 3-4 neutropenia. No statistically significant differences in PFS, OS, or febrile neutropenia were observed between groups. These findings support a risk-adapted rather than universal approach to primary G-CSF prophylaxis.

The utility of whole-genome sequencing (WGS) for detecting childhood leukemia predisposition remains unclear. We perform a nationwide, prospective, population-based study of 181 children with acute leukemia to assess diagnostic yield and clinical utility of a uniformly applied three-pronged strategy, including systematic phenotyping, germline WGS-based 189-gene panel and tumor sequencing of positive cases. Trio-WGS is performed in 11 high-suspicion families. Nine patients have pathogenic germline alterations: six (3.3%) with leukemia predisposition syndromes (TP53, CEBPA, DNMT3A, trisomy 21) and three (1.6%) with solid tumor predisposition syndromes (MSH6, PALB2, SDHA). Trio-WGS identifies one likely-pathogenic de novo DNMT3A variant. Six of nine diagnoses are previously unrecognized. Findings lead to tailored surveillance in 8/9 patients and treatment modifications in 4/9. Here, we show that this strategy gives a modest diagnostic yield. Nevertheless, the actionability of the findings supports its feasibility and validity in practice. Whether to restrict analysis to leukemia-relevant genes or broaden the panel should reflect local resources and counseling capacity.

Current artificial intelligence systems for leukemia detection typically rely on costly whole-slide scanners, limiting accessibility in low-resource settings. We present ALLocate, a low-cost, artificial intelligence-powered microscope plugin that enables self-driving microscopy for leukemia detection. ALLocate attaches directly to conventional microscopes and provides automated analysis at a fraction of the cost of a whole-slide scanner. We evaluate its robustness at three levels: region-of-interest identification, cell detection, and end-to-end slide-level diagnosis. The system is trained and evaluated using more than 11,000 annotated regions and 130,000 annotated cells and is further validated using independent multi-institutional cohorts, including 165 physical bone marrow smear slides. ALLocate achieves an area under the receiver operating characteristic curve greater than 0.99 for region-of-interest identification, a mean average precision at 50% intersection over union of 0.90 for cell detection, and 88% accuracy for slide-level diagnosis on glass slides without requiring a whole-slide scanner. These results suggest that ALLocate provides an accurate, generalizable, and cost-effective approach for automated bone marrow smear screening, helping bridge the gap between AI innovation and practical deployment in resource-limited settings where access to specialist expertise may be limited.

Distinguishing early melanoma from borderline, atypical, or biologically intermediate melanocytic lesions remains one of the most consequential and urgent diagnostic challenges in dermato-oncology, since melanoma causes nearly 95% of skin cancer deaths. This narrative review establishes the biological relevance and evaluates the clinical maturity of mass spectrometry-based proteomics, with particular emphasis on spatially resolved approaches, as a powerful strategy to address this critical diagnostic gap. We position spatial proteomics within the broader landscape of DNA-, RNA-, and protein-based molecular diagnostics for functional manifestation, highlighting that genomic and transcriptomic methods offer standardized, higher-throughput workflows but limited insight into the tumor microenvironment and functional protein-level biology. Laser capture microdissection-mass spectrometry, imaging mass spectrometry, and Deep Visual Proteomics each preserve spatial and single-cell resolution. However, protein expression is inherently plastic, shaped by microenvironment, tissue handling, and technical artifact, and no existing study has been powered specifically for the diagnostically indeterminate categories where clinical need is greatest. We discuss how compartment-resolved "mitochondrial-high" profiles and "immune-low" microenvironment states, evidence largely extrapolated from progression and metastatic biology, might inform future risk stratification. Spatial proteomics represents a biologically promising, but not yet clinically validated, extension of morphology-led melanoma diagnosis, with the potential to resolve functional states within annotated tumor compartments. Integrated with AI-guided tissue annotation, compartment-resolved mass spectrometry and spatial proteomic profiling could, following prospective validation, help improve the distinction between borderline melanocytic lesions and melanoma while refining patient risk stratification.

Glioblastoma is one of the most malignant primary tumors in adults. The shortages of the traditional tumor models obstruct the development of basic research and therapeutic modes. Application of organoid is critical to keep tumoral heterogeneity and microenvironment. The current method for generating glioblastoma organoid is sophisticated, laborious, and inconvenient.

We collected glioblastoma tissue samples from patients and used an improved methodology to generate organoids. Subsequently, the collected suspension during the process was used to isolate primary tumor cells. Organoid were used to assess its drug susceptibility. Finally, we generated organoids of other CNS tumors with the same method.

The improved method took less time and lowered technical requirements. Glioblastoma organoids generated with our improved method can be cultured for further research. Primary glioblastoma cells isolated from collected medium were proved with immunofluorescence. Patient-derived glioblastoma organoids showed consistent drug susceptibility with the patients. Tumor organoids were also generated successfully from patient's lymphoma and low-grade glioma samples.

We improved an easier and rapider method to generate glioma organoids from tissue samples and avoided wasting primary tumor cells in used medium. This method established a technical foundation for the widely application of organoids in neuro-oncology tumor research and clinical decision-making.

Thyroid carcinoma (THCA) represents the most prevalent malignancy of the endocrine system. Although the majority of patients exhibit favorable clinical outcomes, a subset still faces significant clinical challenges, including distant metastasis, recurrence, and poor response to therapeutic interventions, all of which substantially compromise quality of life and overall survival (OS).

Neutrophils, as pivotal components of the innate immune system, exhibit dichotomous roles in cancer biology, exerting both tumor-promoting and tumor-suppressive functions. However, the prognostic value of neutrophil-related genes (NRGs) and their contribution to the molecular stratification of THCA remain poorly defined. Our aim was to identify and evaluate NRG biomarkers with the potential to inform personalized therapeutic strategies for patients with THCA.

We first identified differentially expressed genes (DEGs) between THCA tumor and normal samples. Prognosis-associated DEGs were filtered using univariate Cox regression analysis. Subsequently, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were employed to identify a core set of prognostic NRGs. A risk model was constructed based on these core genes and validated in independent testing cohorts. Furthermore, consensus clustering was applied to delineate THCA molecular subtypes based on NRG expression profiles, followed by comprehensive analyses of functional enrichment and immune microenvironment characteristics.

The prognostic signature derived from NRGs demonstrated robust and consistent predictive performance across the training and validation datasets. Patients with high NRG-based risk scores exhibited significantly poorer OS and a more immunologically complex tumor microenvironment (TME) than their low-risk counterparts. Consensus clustering further revealed 2 distinct molecular subtypes of THCA, each characterized by divergent immune infiltration patterns and functional pathway enrichment, suggesting biological heterogeneity with potential therapeutic implications.

Our study highlights NRGs as promising prognostic biomarkers in THCA, uncovering distinct molecular subtypes with potential relevance for personalized therapeutic stratification. These findings underscore the clinical value of NRGs and emphasize the need for further mechanistic investigations to advance our understanding of THCA biology and refine precision treatment strategies.

Sporadic pancreatic neuroendocrine tumors (pNETs) with wild-type MEN1 represent a major yet largely ignored subset whose biology and metastatic potential remain poorly understood. Because metastasis can occur despite low histologic grade and modest mutational burden, we hypothesized that metastatic competence in MEN1-wild-type pNETs reflects quantitative reinforcement of shared oncogenic pathways rather than distinct mutational processes. We profiled 75 primary WHO G1/G2 pNETs by whole-exome and RNA sequencing, including 25% with lymph node and/or liver metastasis, and integrated genomic and transcriptomic data to connect pathway lesions with expression state. Metastatic tumors showed a slight increase in mutation frequency but conserved base-substitution spectra relative to non-metastatic cases, and adverse clinicopathologic features were enriched in Grade 2 disease. Aggregating alterations to pathways revealed broad convergence on canonical networks, with transcriptomic analyses demonstrating cohort-wide enrichment of Calcium, WNT, and KRAS/PI3K-AKT programs in metastasis. Intersection of significantly mutated genes with differentially expressed genes identified a focused 29-gene overlap, including RYR1 and ZNF273, that marks these convergent axes and distinguishes metastatic from non-metastatic tumors. Gene set enrichment confirmed preferential activation of Calcium, WNT, and PI3K-AKT signaling in metastatic tumors, consistent with a network-intensity model of progression. Finally, upstream-regulator analysis (iPathwayGuide) and gene-centric perturbation mapping (Gene2Drug) nominated candidate targeted and repurposable agents predicted to reverse the metastatic expression phenotype and flagged drugs unlikely to provide benefit, yielding a prioritized, testable therapeutic shortlist which includes fasudil and spaglumic acid. Convergent, domain-specific mutational patterns in highly mutated genes such as ZNF273 and CLCA1 define a molecular signature that could stratify metastatic risk in low-grade pNETs. Functional validation identified ZNF273 and RYR1 as candidate effectors of metastatic fitness in MEN1-wild-type pNETs, as siRNA-mediated silencing reduced cell viability, clonogenicity, spheroid growth, and migration in BON1 and QGP1 models. Predicted compounds showed in vitro activity, with doxorubicin exhibiting the strongest cytotoxic effect, supporting a therapeutically tractable KRAB-ZNF/calcium signaling axis in metastatic pNETs. Collectively, our data reframe metastasis in MEN1-wild-type low-grade pNETs as a property of pathway state rather than mutation quantity and provide a translational blueprint for biomarker-guided therapy development focused on Calcium, WNT, and KRAS/PI3K hubs.

Previous research has predominantly focused on the regulatory role of lncRNA HOTAIR in gene silencing by interacting with the Polycomb repressive complex 2 (PRC2), but its direct role as a transcriptional activator remains less well characterized. Here, our study reveals a novel regulatory mechanism of HOTAIR, which facilitates prostate cancer (PCa) progression by enhancing transcription elongation. Integrating ChIRP-seq, RNA-seq, and ChIP-seq, we found that HOTAIR co-localizes with AR and facilitates the interaction between CDK9 and Pol II. This function requires the 1-442 nt region and intact RNA, as deletion or RNase treatment disrupts the interaction. Re-expression of full-length HOTAIR, but not the deletion mutant, restores nascent transcription of target genes; conversely, CDK9 inhibition suppresses this elongation effect. We identified MMP14 and TNFAIP2 as functional downstream targets; ectopic expression of either rescues colony formation and invasive migration upon HOTAIR depletion. HOTAIR knockdown reduced Ser2P and Ser5P signals at target loci, yet RNA pull-down showed no direct binding to Pol II Ser5P, implying that any effect on initiation is indirect. This elongation activity operates in both AR-positive and AR-negative cells, with AR acting as a context-specific recruiter. Collectively, these findings support a model in which HOTAIR functions as an elongation activator and suggest potential therapeutic targets in prostate cancer.

Ferroptosis is an emerging form of programmed cell death driven by the excessive accumulation of iron-dependent lipid peroxides, which plays a pivotal regulatory role in suppressing tumor initiation and progression. In recent years, protein post-translational modification (PTM), as a core molecular mechanism regulating protein function, stability, and subcellular localization, occupies a central position in the precise targeted regulation of ferroptosis. This review systematically summarizes and elucidates the core molecular regulatory mechanisms of ferroptosis, focusing on the dynamic regulatory pathways and effects of common protein PTM (including ubiquitination, phosphorylation, acetylation and lactylation) on key ferroptosis regulators. Meanwhile, it deeply analyzes the complex interactive regulatory network between ferroptosis and tumor immunity, and elaborates on the specific pathways by which ferroptosis remodels the tumor immune microenvironment through inducing immunogenic cell death (ICD) and releasing damage-associated molecular patterns (DAMPs). More importantly, this paper systematically discusses the potential application value of ferroptosis-related protein PTM in overcoming tumor immune escape, enhancing the antitumor activity of T cells and natural killer cells, and improving the clinical efficacy of immune checkpoint inhibitors (ICIs). Finally, it summarizes the core challenges existing in this research field and prospects the future development of novel PTM-based combination antitumor strategies targeting ferroptosis and personalized antitumor therapy. This review aims to provide new theoretical references and experimental evidence for the clinical prevention, treatment and intervention of malignant tumors.

Discoidin domain receptor 1 (DDR1), a collagen-activated receptor tyrosine kinase, is essential for tumor cell proliferation, invasion and drug resistance. Our study demonstrated that the expression and phosphorylation of DDR1 were associated not only with the poor outcome of non-small cell lung cancer (NSCLC), but also with low sensitivity to docetaxel. Moreover, our kinase profiling identified that anlotinib, an oral small-molecule tyrosine kinase inhibitor, could significantly inhibit DDR1 phosphorylation. Further data revealed that anlotinib could increase the sensitivity to docetaxel in cell lines, cell-derived and patient-derived tumor xenografts through down-regulating DDR1 phosphorylation, which no longer existed in DDR1-KO A549 cells. Mechanistically, anlotinib could enhance the sensitivity to docetaxel through inhibiting DDR1-mediated glycolysis. Additionally, the clinical trial demonstrated that anlotinib could increase the efficacy of docetaxel, with an improved overall response rate of 35.6%, a prolonged median progression-free survival by 5.3 months and a median overall survival by 14.0 months compared with historical data. In summary, our study not only highlights the new function of DDR1 in energy metabolism but also provides further evidence for the combination of anlotinib and docetaxel as a preferable strategy in clinical practice.