bims-necame Biomed News
on Metabolism in small cell neuroendocrine cancers
Issue of 2026–01–18
eight papers selected by
Grigor Varuzhanyan, UCLA
  1. SEZ6 expression and lineage plasticity in small cell lung cancer and transformed non-small cell lung cancer.
  2. Spatial transcriptomics reveals molecular heterogeneity and subtype-specific therapeutic targets in small cell lung cancer.
  3. Personalized drug screening and risk assessment in patient-derived gastroenteropancreatic neuroendocrine neoplasms.
  4. Baseline ¹8F-FDG PET/CT radiomics predict early progression and survival in small cell lung cancer.
  5. Hypoxic-immune axis orchestrates metastatic dissemination via HIF isoform imbalance in pancreatic neuroendocrine tumors.
  6. Molecular decoupling of lineage identity and morphology in aggressive variant prostate cancer.
  7. Quantitative analysis for identifying molecular subtypes of small cell lung cancer via two-dimensional and three-dimensional contrast-enhanced computed tomography images: a preliminary study.
  8. Circulating Tumor Cells Predict Response to the DLL3-targeting Bispecific Antibody Tarlatamab.
  1. Lung Cancer. 2026 Jan 10. pii: S0169-5002(26)00008-5. [Epub ahead of print]213 108913
    SEZ6 expression and lineage plasticity in small cell lung cancer and transformed non-small cell lung cancer.
    Jennifer A Marks, Kieran Sweeney, Andrew Elliott, Brinda Gupta, Ari VanderWalde, Sonam Puri, Misty Dawn Shields, Jorge J Nieva, Heloisa P Soares, Patrick C Ma, Balazs Halmos, Stephen V Liu.
       INTRODUCTION: Small cell lung cancer (SCLC) and extrapulmonary neuroendocrine (NE) tumors are aggressive malignancies with limited treatment options. Seizure-related homolog 6 (SEZ6) is a potential therapeutic target, but its expression in these tumors remains poorly understood. Lineage plasticity contributes to resistance in non-small cell lung cancer (NSCLC), where some cases can undergo SCLC-transformation after targeted therapy. We aimed to characterize SEZ6 expression across NE tumors and presumed NSCLC-to-SCLC transformations.
    METHODS: DNA and RNA sequencing were performed for SCLC, NSCLC, and NE samples. Samples were stratified by SEZ6 RNA expression quartiles and classified into subtypes based on ASCL1, NEUROD1, and POU2F3 expression. Significance was tested using the Mann-Whitney U test. Real-world overall survival was obtained from insurance claims data, with p-values calculated using the log-rank test. Paired samples for NSCLC-to-SCLC transformation were identified by sequential biopsies classified as NSCLC followed by SCLC.
    RESULTS: RNA sequencing was performed on 1318 SCLC and 2218 NE samples. Median SEZ6expression was higher in SCLC (39.7 transcripts per million (TPM)) than in NE tumors (20.8 TPM, p<0.0001) and NSCLC (1.34 TPM, p<0.001). Among NE tumors, median SEZ6 expression was highest in prostate (52.0 TPM, p=0.0016 vs SCLC) and lowest in adrenal gland tumors (1.2 TPM, p<0.0001 vs SCLC). In SCLC,SEZ6expression was positively correlated with ASCL1(p=0.44, p<0.0001) andNEUROD1(p=0.16, p<0.0001) expression but notPOU2F3(p=-0.04, p=0.1253). Median survival was longest in SEZ6-Q2 for both SCLC and NE (13.0 mos. and 33.7 mos., respectively). NSCLC-to-SCLC transformation samples showed numerically higher SEZ6 expression post-transformation (median: 86.2 vs 2.4 TPM).
    CONCLUSIONS: SEZ6expression is higher in SCLC than in NE tumors, with notable heterogeneity by subtype, warranting consideration of expanded use of SEZ6-directed therapy. Translational Relevance Statement: This study establishes SEZ6 as a promising therapeutic target in small cell lung cancer (SCLC) and transformed non-small cell lung cancer (NSCLC), demonstrating its significantly elevated expression compared to neuroendocrine (NE) tumors and NSCLC. The positive correlation of SEZ6 expression with NE lineage markers, particularly in ASCL1 and NEUROD1 subtypes, highlights its role as a lineage-specific marker, guiding the development of SEZ6-targeted antibody-drug conjugates (ADCs). Additionally, the increased SEZ6 expression following NSCLC-to-SCLC transformation suggests that SEZ6-targeted therapies could address resistance mechanisms in transformed tumors. Importantly, the association between high SEZ6 expression and shorter survival indicates that integrating SEZ6 status into diagnostic workflows could help stratify patients by risk and guide therapeutic decision-making. The findings from this study will inform future clinical trials, aiming to implement SEZ6-targeted treatments as part of precision oncology strategies for aggressive NE malignancies.
    Keywords:  Lineage plasticity; NSCLC; Neuroendocrine tumors; SCLC; SEZ6
    DOI:  https://doi.org/10.1016/j.lungcan.2026.108913
  2. NPJ Precis Oncol. 2026 Jan 16.
    Spatial transcriptomics reveals molecular heterogeneity and subtype-specific therapeutic targets in small cell lung cancer.
    Tongji Xie, Le Tang, Guangyu Fan, Xiaohong Han, Yuankai Shi.
      Small cell lung cancer (SCLC) is a highly aggressive malignancy with strong associations to smoking, characterized by initial platinum sensitivity followed by rapid recurrence and poor long-term survival. The evolutionary processes driving this high plasticity and intratumoral heterogeneity remain inadequately understood, hampering the development of effective therapies. In this study, we established a comprehensive spatial transcriptomic (ST) landscape of SCLC. Our approach integrated two key methodological innovations: the Edgeindex metric for the quantitative assessment of tumor spatial architecture, and a specialized artificial neural network (ANN) model for precise tumor annotation. Utilizing this analytical framework, we systematically resolved SCLC heterogeneity across clinical, spatial, functional, and temporal dimensions. Furthermore, pathway enrichment analysis was performed to explore the underlying molecular mechanisms. This work provides a multi-dimensional resource for deciphering the complexity of SCLC.
    DOI:  https://doi.org/10.1038/s41698-025-01243-7
  3. J Clin Endocrinol Metab. 2026 Jan 10. pii: dgaf705. [Epub ahead of print]
    Personalized drug screening and risk assessment in patient-derived gastroenteropancreatic neuroendocrine neoplasms.
    Christoph J Auernhammer, Katharina Wang, Umberto Maccio, Thomas Knösel, Maximilian P Hungbauer, Katharina Schilbach, Julian Maurer, Lea Peischer, Astrid Reul, Elena Kuzmenko, Edlira Luca, Julia Hamati, Diana Vetter, Jose Oberholzer, Ralph Fritsch, Karel Pacak, Ashley B Grossman, Felix Beuschlein, Martin Reincke, Constanze Hantel, Kathrin Zitzmann, Svenja Nölting.
       BACKGROUND: Precision medicine has transformed many areas in oncology. However, it remains largely unexplored in metastatic gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs), where there is a need for further innovative therapies.
    METHODS: To evaluate individual tumor responses to different agents, we have established a standardized personalized drug screening and risk assessment platform utilizing patient-derived GEP-NEN primary cultures (n=23, 16/23 from metastatic tumors, n=12 small intestinal neuroendocrine tumors [siNETs], n=10 pancreatic NETs [pNETs], n=1 neuroendocrine carcinoma [NEC]). We assessed primary culture cell viability, performed signaling pathway analysis by Automated Western blotting and immunohistochemically evaluated tumor composition.
    RESULTS: Systematic drug testing of 27 agents including signaling inhibitors (i) (mTORi everolimus, tyrosine kinase inhibitors cabozantinib/sunitinib, AKTi capivasertib, PI3Ki alpelisib, CDK4/6i ribociclib), DNA damage response inhibitors (PARPi niraparib, WEE1i adavosertib, ATRi berzosertib), chemotherapeutics (temozolomide, 5-fluorouracil, lurbinectedin), drug repurposed agents (zoledronic acid) and a personalized risk assessment (GLP-2 analog teduglutide, GLP-1 analog semaglutide, sex hormones) was performed. We demonstrated significant group effects and individualized responsiveness/resistance data. We identified differences in drug response between pNETs/siNETs and between GEP-NETs/GEP-NEC, respectively.
    CONCLUSIONS: We provide novel data on the efficacy of putative and established therapies in patient-derived GEP-NEN primary cultures. Our standardized platform for personalized drug screening and risk assessment in GEP-NEN primary cultures enables prediction of individual tumor treatment response in this orphan disease.
    Keywords:  neuroendocrine tumor; personalized drug testing; precision medicine; primary cultures
    DOI:  https://doi.org/10.1210/clinem/dgaf705
  4. BMC Cancer. 2026 Jan 13.
    Baseline ¹8F-FDG PET/CT radiomics predict early progression and survival in small cell lung cancer.
    Cheng-Yin Liu, Li-Fan Lin, Chen-Liang Tsai, Tsai-Wang Huang, Yu-Cheng Wu, Chia-Hsin Liu.
       BACKGROUND: To determine whether baseline ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomic markers of thoracic tumor burden and metabolic heterogeneity predict early progression and overall survival (OS) in patients with small cell lung cancer (SCLC) receiving first-line platinum-etoposide chemotherapy.
    METHODS: We retrospectively analyzed 45 patients with SCLC who underwent baseline ¹⁸F-FDG PET/CT before chemotherapy. Radiomic features were extracted from thoracic tumor volumes, including metabolic tumor volume (MTV), total lesion glycolysis (TLG), standardized uptake value (SUV) histogram parameters, and gray-level co-occurrence matrix (GLCM) metrics. Univariable and multivariable Cox regression assessed associations with progression-free survival (PFS) and OS. Receiver operating characteristic (ROC) analysis, with area under the ROC curve (AUC), assessed discriminatory ability for early progression (PFS < 6 months) and short-term mortality (OS < 12 months).
    RESULTS: Higher log-transformed TLG (logTLG) was correlated with shorter PFS and OS in univariable analysis. In multivariable models, logTLG independently predicted PFS (hazard ratio [HR] 2.20, 95% confidence interval [CI] 1.12-4.30; p = 0.021) and OS (HR 2.54, 95% CI 1.24-5.20; p = 0.011). Age (HR 1.07 per year; 95% CI 1.02-1.12, p = 0.007) and stage IV vs. III (HR 2.46, 95% CI 1.06-5.71; p = 0.037) were additional OS predictors. GLCM texture features were not significant. ROC analysis showed MTV (AUC 0.88) and serum lactate dehydrogenase (LDH) (AUC 0.74) best predicted early progression, while age (AUC 0.79), stage (AUC 0.73), and SUV entropy (AUC 0.75) predicted short-term mortality. Kaplan-Meier curves confirmed poorer survival with high logTLG, advanced stage, and older age.
    CONCLUSIONS: Baseline thoracic PET/CT radiomics, particularly logTLG, independently predict survival in SCLC. MTV and LDH identify early progression, while age, stage, and SUV entropy predict short-term mortality, supporting integrated imaging-clinical risk stratification.
    Keywords:  Metabolic tumor volume; Prognosis; Radiomics; Small cell lung cancer; Total lesion glycolysis; ¹⁸F-FDG PET/CT
    DOI:  https://doi.org/10.1186/s12885-025-15523-9
  5. iScience. 2026 Jan 16. 29(1): 114340
    Hypoxic-immune axis orchestrates metastatic dissemination via HIF isoform imbalance in pancreatic neuroendocrine tumors.
    Jianli Lin, Yi Lin, Min Cai, Yaoqi Chen, Xiafang Lin, Lizhi Li, Jianlin Lai, Huping Huang, Jinxin Li, Qinwen Liu, Qinyu Liu, Yinghua Luo, Xin Chen, Jinsheng Liu.
      Metastatic dissemination underpins mortality in pancreatic neuroendocrine tumors (PNETs), where the hypoxic, immunosuppressive microenvironment facilitates progression. Non-genetic determinants, including hypoxia-inducible factor (HIF) isoforms, preceding metastatic traits can disrupt immune homeostasis and promote aggression. However, the dynamics of HIF-immune crosstalk in PNET metastasis remain elusive. Using multi-omics and organoid models of KRAS-mutated PNETs, we uncovered rapid HIF isoform shifts, with HIF-1α/β overexpression and HIF-2α suppression emerging as pivotal. This imbalance is pronounced in advanced and metastatic PNETs. The hypoxic-immune axis is swiftly activated under pseudohypoxia and sustains in disseminated cells. It fuels immune evasion and invasion by enriching immunosuppressive cells and altering checkpoint signaling, interacting with KRAS-driven succinate accumulation. We propose that HIF isoform imbalance arises early in PNET evolution and orchestrates metastatic dissemination.
    Keywords:  Cancer; Cell biology; Molecular biology
    DOI:  https://doi.org/10.1016/j.isci.2025.114340
  6. medRxiv. 2026 Jan 09. pii: 2026.01.07.26343520. [Epub ahead of print]
    Molecular decoupling of lineage identity and morphology in aggressive variant prostate cancer.
    Chennan Li, JuanJuan Yin, Melissa L Abel, Dana S Vargas Solivan, Kinjal Bhadresha, Sumeyra Kartal, Samantha Nichols, Kanak Parmar, Joseph Twohig, Tri M Truong, Cindy H Chau, Kathleen Kelly, William D Figg, Anish Thomas, Adam G Sowalsky.
      Aggressive variant prostate cancer (AVPC) is a lethal subtype of prostate cancer characterized by its androgen independence, resistance to chemotherapy, and display of neuroendocrine features which can emerge either de novo or via transformation after a prior diagnosis of adenocarcinoma. The poor clinical outcomes in patients with AVPC are associated with its profound molecular heterogeneity. In this study, we analyzed 23 consecutive AVPC cases treated at a dedicated small-cell clinic (2017-2025) using clinicogenomic and transcriptomic profiling. Transformed AVPC exhibited significantly shorter overall survival times than de novo AVPC (11.8 vs 26.0 months, P < 0.001). Integrative genomic analyses identified residual androgen signaling in subsets of cases harboring neuroendocrine lineage programs, highlighting a decoupling of lineage identity and morphology. To facilitate mechanistic and pharmacologic studies, we established NCI-LYM-1, a patient-derived organoid/PDX from an AR-negative, ASCL1+/SYP+ lymph node metastasis, which faithfully recapitulates the donor tumor's molecular and phenotypic features. Short- and long-read whole-genome sequencing combined with optical genome mapping identified biallelic inactivation of PTEN , TP53 , RB1 and BRCA2 as potential drivers, demonstrating clonal concordance with circulating tumor DNA from the original patient donor. Pathway and perturbation analyses suggested that NCI-LYM-1 harbored a strong dependency on apoptotic pathways, which was confirmed by in vitro organoid testing with the BCL-2/BCL-xL inhibitor navitoclax (IC 50 : 0.27 µM) and the MCL-1 inhibitor AZD-5991 (IC 50 : 0.060 µM). Overall, NCI-LYM-1 recapitulates the clinical aggressiveness and heterogeneity of AVPC, providing a tractable platform to identify novel precision therapies.
    DOI:  https://doi.org/10.64898/2026.01.07.26343520
  7. J Thorac Dis. 2025 Dec 31. 17(12): 11172-11185
    Quantitative analysis for identifying molecular subtypes of small cell lung cancer via two-dimensional and three-dimensional contrast-enhanced computed tomography images: a preliminary study.
    Xu Jiang, Li Liu, Meng-Wen Liu, Jiu-Ming Jiang, Si-Jie Hu, Jia-Liang Ren, Li Zhang, Jian-Xin Zhang, Lin Yang, Meng Li.
       Background: Small cell lung cancer (SCLC) comprises distinct molecular subtypes [neuroendocrine (NE) vs. non-NE] that have different prognoses, with NE tumors generally exhibiting a more aggressive clinical course. However, identifying these subtypes usually requires invasive tissue sampling. Radiomics-the extraction of quantitative features from medical images-offers a potential noninvasive alternative. This study aimed to predict the NE subtype of SCLC using radiomics analysis of contrast-enhanced computed tomography (CECT) images, and to compare a two-dimensional (2D) radiomics approach with a three-dimensional (3D) approach.
    Methods: In this single-center retrospective study, we included 51 patients with resected SCLC (NE subtype n=39, non-NE n=12) between 2005 and 2016, all with preoperative CECT scans and known molecular subtype confirmed by immunohistochemistry. Radiomics features were extracted from arterial-phase CECT images using both a 2D (single largest cross-sectional slice) and 3D (whole tumor volume) segmentation of the primary tumor. Radiomics-based logistic regression models were trained to classify NE vs. non-NE subtypes. Model performance was evaluated using receiver operating characteristic analysis [area under the curve (AUC)] with bootstrap 95% confidence intervals (CIs). A combined model incorporating radiomics and clinical factors was also tested. Additionally, we explored the association of the radiomics signature with recurrence-free survival (RFS) via Kaplan-Meier curves and Cox proportional-hazards analysis.
    Results: The 2D radiomics model achieved an AUC of 0.806 (95% CI: 0.666-0.945) for distinguishing NE vs. non-NE subtypes, comparable to the 3D model (AUC 0.784, 95% CI: 0.634-0.934; P=0.75 or 2D vs. 3D). At the optimal cutoff, the 2D model yielded 64.1% sensitivity and 83.3% specificity. The radiomics signature remained an independent predictor of NE subtype in a combined model [adjusted odds ratio (OR) 6.22, P=0.005], and the addition of radiomics improved the combined model's AUC to 0.861 (vs. 0.673 for clinical factors alone). No conventional clinical or CT features alone were significant predictors. Notably, the 2D radiomics score also stratified patients' outcomes: those predicted as NE subtype had a 5-year RFS of 48.1%, compared to 62.5% for non-NE (log-rank P=0.03). In multivariable Cox analysis, a higher radiomics score showed a trend toward shorter RFS [hazard ratios (HRs) 1.46 per SD increase, P=0.08].
    Conclusions: Quantitative analysis of CECT images via radiomics can noninvasively distinguish NE and non-NE molecular subtypes of SCLC. A simplified 2D radiomics approach performed comparably to 3D volumetric analysis for subtype classification and also demonstrated prognostic relevance. Radiomics could serve as a valuable adjunct for SCLC subtype identification and risk stratification, potentially guiding more personalized treatment decisions.
    Keywords:  Small cell lung cancer (SCLC); contrast-enhanced computed tomography (CECT); molecular subtypes; quantitative analysis; radiomics
    DOI:  https://doi.org/10.21037/jtd-2025-1041
  8. Cancer Discov. 2026 Jan 14.
    Circulating Tumor Cells Predict Response to the DLL3-targeting Bispecific Antibody Tarlatamab.
    Avanish Mishra, Catherine B Meador, Kruthika Kikkeri, Quinn Cunneely, Maoxuan Lin, Thomas J Carmona-LaSalle, Shih-Bo Huang, Remy Bell, Victor Putaturo, Weikun Xia, Joyce H Liang, Jacy Fang, Sarah San Vicente, Caroline E Zielinski, Subba R Digumarthy, Yin P Hung, Beow Y Yeap, Jon F Edd, Michael S Lawrence, Moshe Sade-Feldman, Debattama R Sen, Mehmet Toner, Shyamala Maheswaran, Justin F Gainor, Daniel A Haber.
      The bispecific antibody tarlatamab recruits T cells to cancers expressing the neuroendocrine epitope DLL3. Tarlatamab is effective in small cell lung cancer (SCLC), but clinical outcomes vary, and no biomarkers enable patient selection. Single-cell RNA sequencing of SCLC biopsies identifies heterogeneity in DLL3 expression, and analysis of circulating tumor cells (CTCs) distinguishes individual patients as predominantly DLL3Pos or DLL3Low. In a prospective cohort of 20 patients, pretreatment DLL3 expression on CTCs predicts tarlatamab clinical benefit (85% sensitivity, 100% specificity). Necrotic CTC clusters in blood accompany treatment-induced tumor lysis. Acquired resistance to tarlatamab is associated in some cases with loss of DLL3 expression, but persistence of other targetable neuro-endocrine epitopes; in other patients, DLL3 is retained on CTCs, but accompanied by systemic markers of T cell dysfunction. Quantitation of DLL3-positive CTCs identifies patients likely to benefit from tarlatamab, and longitudinal monitoring may guide therapeutic decision-making at the time of acquired resistance.
    DOI:  https://doi.org/10.1158/2159-8290.CD-25-1483
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