bims-necame Biomed News
on Metabolism in small cell neuroendocrine cancers
Issue of 2025–12–28
three papers selected by
Grigor Varuzhanyan, UCLA



  1. Curr Oncol. 2025 Nov 21. pii: 654. [Epub ahead of print]32(12):
       BACKGROUND: Small cell neuroendocrine carcinoma of the cervix (SCNEC) is a rare and highly aggressive malignancy with limited prognostic biomarkers available for clinical use. Inflammatory markers derived from complete blood count (CBC) have been shown to reflect the systemic immune response and tumor progression in various cancers, but their prognostic value in SCNEC remains unclear.
    METHODS: We retrospectively analyzed clinical data from patients diagnosed with SCNEC between 2004 and 2024 across two centers. Internal validation was performed by dividing patients into training and test cohorts. Cox regression analyses and Kaplan-Meier survival analyses were used to evaluate prognostic factors and treatment outcomes. Inverse probability of treatment weighting (IPTW) was applied to reduce baseline imbalances. Patients were randomly divided into training and test cohorts. A nomogram was constructed to predict 3-year and 5-year progression-free survival (PFS) with performance evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
    RESULTS: 196 participants were included in the study. Age, FIGO 2018 stage, surgery, neutrophil-to-lymphocyte ratio (NLR), and monocyte-to-lymphocyte ratio (MLR) were independently associated with PFS. High MLR (>0.19) was significantly linked to shorter PFS. After IPTW adjustment, the protective effect of low MLR on PFS remained significant (p = 0.029). The constructed nomogram demonstrated excellent predictive performance, with area under the curve (AUC) values of 0.799 and 0.787 for 3-year and 5-year PFS in the training cohort, and 0.802 for endpoints in the test cohort.
    CONCLUSIONS: MLR was identified as an independent prognostic biomarker for PFS in SCNEC, with potential value in risk stratification and personalized treatment strategies. Additionally, we developed a reliable nomogram that accurately predicts 3-year and 5-year PFS, serving as a practical tool for individualized prognosis and clinical decision-making.
    Keywords:  inflammatory biomarkers; monocyte-to-lymphocyte ratio; nomogram; prognostic model; small cell neuroendocrine carcinoma of the cervix
    DOI:  https://doi.org/10.3390/curroncol32120654
  2. bioRxiv. 2025 Dec 19. pii: 2025.12.16.694669. [Epub ahead of print]
      Small-cell lung cancer (SCLC) is an aggressive neuroendocrine carcinoma characterized by high numbers of circulating tumor cells (CTCs). We applied CyTOF and a 20-marker antibody panel to detect and phenotype CTCs directly in liquid biopsies of 51 SCLC patients (treatment-naïve, chemotherapy and immunotherapy-treated, and tarlatamab-treated), of which a subset were longitudinally tracked. Unsupervised clustering revealed distinct cell populations enriched in patient liquid biopsies compared to those from healthy donors. Further analysis identified CTC populations of the three established SCLC subtypes driven by the high expression of ASCL1, NeuroD1, and POU2F3 transcription factors respectively. Significant differences in CTC EMT markers, established therapeutic targets (e.g. DLL3), and subtype heterogeneity were observed between naïve versus treated samples. Changes in subtype proportions were observed in longitudinally tracked samples in both treatment modalities. Our study demonstrates the utility of CyTOF for high-resolution CTC profiling, offering dynamic insights into CTC heterogeneity, treatment response, and resistance mechanisms.
    Highlights: CTCs can be detected, subtyped and phenotyped in SCLC liquid biopsies using CyTOFCTC subtypes and EMT states are differentially associated with treatment modalityCTC DLL3 levels and epithelial features increase following anti-DLL3 BiTE therapyCyTOF CTC subtyping can predict disease aggressivenessLongitudinal tracking reveals CTC plasticity and therapy response correlations.
    DOI:  https://doi.org/10.64898/2025.12.16.694669
  3. BMC Med Imaging. 2025 Dec 23.
      
    Keywords:  Clinical decision support; Large language models; Neuroendocrine tumors; PET/CT; TNM staging
    DOI:  https://doi.org/10.1186/s12880-025-02092-3