bims-necame Biomed News
on Metabolism in small cell neuroendocrine cancers
Issue of 2026–03–01
seven papers selected by
Grigor Varuzhanyan, UCLA
  1. Immunohistochemical profiling of small cell lung cancer reveals subtype heterogeneity and a tuft cell-like subtype characterized by LRMP and TRPM5.
  2. Genetic and epigenetic mechanisms underlying treatment-induced neuroendocrine prostate cancer.
  3. Novel Blood-Based Extracellular Vesicle-Derived Biomarkers in Small Cell Lung Cancer Identified via Proximity Extension Assay.
  4. PRC2 loss impairs small cell lung cancer tumorigenesis and enhances sensitivity to G9a/GLP inhibition.
  5. Subclassification of Small Cell Lung Cancer Based on Gene Expression Signatures and Machine Learning.
  6. Eukaryote initiation factor 6 modulates small-cell lung carcinoma plasticity via the integrin-FAK signaling axis.
  7. A hierarchical Bayesian framework for inferring mitochondrial clonal selection from single-cell data.
  1. Virchows Arch. 2026 Feb 25.
    Immunohistochemical profiling of small cell lung cancer reveals subtype heterogeneity and a tuft cell-like subtype characterized by LRMP and TRPM5.
    Wafaa A Mahmoud, Mohammed S Alorjani, Samir M Al Bashir, Qosay M Sharqiah, Sura B Alomari, Noor N Al Bzour, Saad A Mahmoud.
      Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumor with marked molecular heterogeneity. Recent transcriptomic studies have identified a tuft cell-like SCLC subtype defined by POU2F3 expression, but its immunohistochemical features remain incompletely characterized. The aim of this study is to describe the immunohistochemical expression of the subtype-defining transcription factors ASCL1, NEUROD1, POU2F3, and YAP1 in SCLC and to assess protein expression of tuft cell markers in the POU2F3-positive subset. We conducted an integrated analysis combining in silico evaluation of a published SCLC RNA-sequencing dataset with immunohistochemical profiling of 80 tumors. Protein expression of ASCL1, NEUROD1, POU2F3, YAP1, LRMP, and TRPM5 was quantified using H-scores, and associations were analyzed with chi-square or Fisher's exact tests. In silico analysis confirmed that SCLC-POU2F3 tumors exhibit a distinct tuft cell-like transcriptomic signature, with upregulation of tuft cell marker genes LRMP, TRPM5, AVIL, ASCL2, and COLCA2. IHC showed ASCL1, NEUROD1, POU2F3, and YAP1 expressions in 60%, 38%, 16%, and 6% of cases, respectively. Based on ASCL1/NEUROD1 expression patterns, cases were classified as ASCL1+/NEUROD1- (39%), ASCL1-/NEUROD1+ (16%), ASCL1+/NEUROD1+ (21%), and ASCL1-/NEUROD1- (24%). Among double-negative tumors, 9% were POU2F3-positive and 15% POU2F3-negative. High POU2F3 expression occurred exclusively in double-negative tumors (p < 0.001). LRMP and TRPM5 selectively marked POU2F3-high, double-negative tumors. SCLC exhibits molecular heterogeneity, with ASCL1- and NEUROD1-driven tumors representing the most frequent subtypes. A distinct subset of ASCL1/NEUROD1 double-negative tumors showed high POU2F3 expression together with LRMP and TRPM5, supporting a tuft cell-like subtype and refining the immunohistochemical characterization of this rare SCLC subset.
    Keywords:  Immunohistochemistry; LRMP; POU2F3; Small cell lung cancer; TRPM5; Tuft cell
    DOI:  https://doi.org/10.1007/s00428-026-04467-5
  2. J Natl Cancer Cent. 2026 Feb;6(1): 88-97
    Genetic and epigenetic mechanisms underlying treatment-induced neuroendocrine prostate cancer.
    Viriya Keo, Xiaodong Lu, Jonathan C Zhao, Jindan Yu.
      Treatment-induced neuroendocrine prostate cancer (t-NEPC) is a lethal subtype of castration-resistant prostate cancer (CRPC) characterized by unique pathological features and molecular changes, including the loss of androgen receptor (AR) activities and the gain of neuroendocrine gene expression. The incidence of t-NEPC has increased substantially in the last decade, in up to 20 % of CRPC cases, largely due to intensive treatment of advanced prostate cancer (PCa) with AR pathway inhibitors (ARPi). While genomic alterations between CRPC and t-NEPC are largely conserved, their epigenetic programs are markedly distinct. The molecular mechanisms underlying the neuroendocrine transformation (NET) of PCa are rapidly emerging. Here, we first briefly summarize the genetic drivers of t-NEPC and then comprehensively review 2D and 3D chromatin alterations, including changes in DNA methylation, histone modifications, chromatin accessibility, and 3D chromatin organization, during NET of PCa. We then review key molecular regulators, including lineage-specific transcription factors and chromatin modifiers, of such epigenetic programs. Lastly, we discuss evidence that suggests a mixed model of clonal selection and transformation that underlies NEPC progression.
    Keywords:  Chromatin; Clonal Selection; Epigenetics; Lineage Plasticity; Methylation; Neuroendocrine Prostate Cancer; Transcription Factors
    DOI:  https://doi.org/10.1016/j.jncc.2025.11.001
  3. Cancers (Basel). 2026 Feb 10. pii: 580. [Epub ahead of print]18(4):
    Novel Blood-Based Extracellular Vesicle-Derived Biomarkers in Small Cell Lung Cancer Identified via Proximity Extension Assay.
    Hubert Krzyslak, Weronika Maria Szejniuk, Marwan Malluhi, Henrik Steglich-Arnholm, Ursula Falkmer, Jonas Ellegaard Mortensen, Søren Risom Kristensen, Shona Pedersen.
      Background: Small cell lung cancer (SCLC) is an aggressive malignancy with rapid progression and early metastasis, yet it lacks validated biomarkers for early diagnosis and treatment monitoring. Extracellular vesicles (EVs) are nano-sized particles released by cells that carry cargo reflective of their origin, making them promising candidates for liquid biopsy-based biomarker discovery. Methods: Plasma-derived EVs were isolated from 29 SCLC patients and 28 healthy controls (HCs) using ultracentrifugation and characterized via nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM). Proteomic profiling was performed using the Olink® Immuno-Oncology panel. Group differences were analyzed using multivariate models (Boruta, Elastic Net, and partial least squares discriminant analysis). Diagnostic potential was assessed using ROC analysis, and predictive response associations were evaluated by correlating EV protein changes with tumor size reduction (FDR-adjusted Spearman). Results: Of the 58 detected proteins, 36 were significantly upregulated in SCLC. PDGF-B, CXCL5, CCL17, EGF, and LAP-TGF-β1 showed good discriminatory performance (AUC 0.95-0.98). Additionally, a two-protein panel (LAP-TGF-β1 and PDGF-B) achieved an out-of-fold AUC of 0.96 (CI 0.89-1.00). NOS3, VEGFR-2, and ANGPT2 levels correlated inversely with tumor reduction after chemotherapy. Conclusions: These exploratory findings highlight EV proteomics as a minimally invasive platform for potential SCLC diagnosis and monitoring.
    Keywords:  Olink; PDGF-B; TGF-β1; biomarker; extracellular vesicles; liquid biopsy; proximity extension assay; small cell lung cancer
    DOI:  https://doi.org/10.3390/cancers18040580
  4. Commun Biol. 2026 Feb 21.
    PRC2 loss impairs small cell lung cancer tumorigenesis and enhances sensitivity to G9a/GLP inhibition.
    Jawahar Kopparam, Gayathri Chandrasekaran, Danielle Hulsman, Liesa-Marie Pilger, Karen Stevers, Ji-Ying Song, Miguel Hernandez-Quiles, Jelmer Dijkstra, Jitendra Badhai, Nick Landman, Michiel Vermeulen, Maarten van Lohuizen.
      Effective targeted therapies for small cell lung cancer (SCLC) remain a significant challenge. Targeting the epigenome to overcome immune evasion and chemoresistance represents a promising strategy to improve treatment outcomes. In this study, we explored the role of Polycomb repressive complex 2 (PRC2), a key transcriptional repressor, by dissecting its structural and enzymatic functions. Conditional deletion of Eed, a core structural component of PRC2, prevented tumor formation in an autochthonous SCLC model. In contrast, enzymatic inhibition of EZH2 had no impact on tumor growth but significantly altered the PRC2 interactome, unveiling novel targets for drug development. Since EZH2 inhibitors are already clinically approved for other cancers, we conducted a focused combination drug screen to enhance their therapeutic potential in SCLC. Our findings revealed that prolonged EZH2 inhibition sensitized neuroendocrine cancer cells to G9a/GLP inhibition. Transcriptomic analysis revealed that the drug combination triggered an oxidative stress response by modulating the expression of cellular oxidases, an effect that could be reversed by antioxidant treatment. These results underscore the critical role of PRC2's structural functions in SCLC and identify promising drug combinations to enhance the efficacy of EZH2 inhibitors.
    DOI:  https://doi.org/10.1038/s42003-026-09677-w
  5. Cancer Res Commun. 2026 Feb 23.
    Subclassification of Small Cell Lung Cancer Based on Gene Expression Signatures and Machine Learning.
    Nicole Kiedanski, Julian Kreis, Lucia Spangenberg, Eike Staub.
      Small-cell lung cancer (SCLC) is frequently subdivided into four molecular subtypes according to the activity of key transcription factors (TFs) NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY). There is no consensus on the diagnostic procedures to determine these subtypes. Downstream transcriptional programs of the four TFs could play an important role in the development of an advanced SCLC subtyping approach. We analyzed transcriptomic and genomic sequencing data from a novel cohort of 460 real-world patients with SCLC. We extracted gene expression signatures specific for the four TFs and used them as features to train machine-learning models to predict SCLC-NAPY subtypes in a nested cross-validation approach. Our ML model for transcriptional programs downstream of the four NAPY TFs predicted the NAPY subtypes at an average accuracy of ~90% in clinical SCLC samples and cell lines. We assessed genomic alterations and our compendium of cancer pathway signatures RosettaSX for subtype-specific signals in our SCLC cohort. Survival analyses of an extensive-stage SCLC subset revealed significant prognostic differences and predictive capacities for several molecular phenotypes. We propose a diagnostic algorithm for NAPY classification which demands that high expression of a NAPY TFs is matched by a high signal of its downstream expression signature, thereby providing functional robustness to NAPY class calls compared to schemes that rely solely on the expression of TFs. Based on this NAPY consensus classification of our SCLCs we describe the redetection of known and identification of novel associations of molecular and clinical patters across the four major molecular subtypes.
    DOI:  https://doi.org/10.1158/2767-9764.CRC-25-0512
  6. Nat Commun. 2026 Feb 24. pii: 2048. [Epub ahead of print]17(1):
    Eukaryote initiation factor 6 modulates small-cell lung carcinoma plasticity via the integrin-FAK signaling axis.
    Haoning Peng, Zhile Wang, Mengyao Wang, Zheyu Ding, Kaixiu Li, Yuqing Wang, Xuejiao Yu, Siyang Song, Yulan Deng, Yi Liu, Qiang Pu, Lu Li, Michael Cerezo, Weiya Wang, Lunxu Liu, Shensi Shen.
      Small cell lung carcinoma (SCLC) is an aggressive neuroendocrine cancer that rapidly develops resistance to platinum-based chemotherapy. A key feature of SCLC is its ability to switch between neuroendocrine (NE) and non-neuroendocrine (non-NE) states, a process linked to therapeutic failure, yet the underlying mechanisms driving this plasticity remain incompletely understood. Here, we show that the translation initiation factor eIF6 is a critical regulator of non-NE transdifferentiation in SCLC. eIF6 expression is consistently upregulated in non-NE states across cell lines, mouse models, and patient samples, accompanied by global remodelling of the translational landscape. Mechanistically, eIF6 dissociates from ribosomes and interacts with the CD104-FAK complex, leading to MAPK pathway activation. Intervening eIF6 suppresses non-NE transdifferentiation and enhances SCLC chemotherapy sensitivity in vitro and in vivo. These findings position the eIF6-CD104-FAK axis as a prognostic marker and therapeutic target, offering a potential strategy to mitigate SCLC resistance.
    DOI:  https://doi.org/10.1038/s41467-026-69899-8
  7. Res Sq. 2026 Feb 12. pii: rs.3.rs-8490828. [Epub ahead of print]
    A hierarchical Bayesian framework for inferring mitochondrial clonal selection from single-cell data.
    Qianqian Song, Xiaobo Zhou, Aoqi Wang, Yanfei Wang, Xiaona Liu, Qing Wang, Sen Guo, Jianguo Wen.
      Mitochondrial genetic heterogeneity arises from the accumulation of somatic mitochondrial DNA (mtDNA) mutations within individual cells, generating intracellular clonal populations whose selective dynamics in disease remain poorly characterized. Here, we present MitoBayes, a hierarchical Bayesian framework that jointly models mitochondrial clonal lineage structure, allele frequency variation, and single-cell disease-relevant phenotypic burdens to infer clone-specific selection pressures. Extensive benchmarking demonstrates that MitoBayes accurately recovers ground-truth selection coefficients across a wide range of genetic heterogeneity, data sparsity, and lineage complexity scenarios. Application of MitoBayes to single-cell atlases of Alzheimer's disease (AD) cortex, treatment-naïve non-small-cell lung cancer (NSCLC), and chemotherapy-resistant small-cell lung cancer (SCLC) revealed distinct, disease-specific patterns of mitochondrial clonal selection. These include selective expansion of high-risk mitochondrial clones associated with disruption of PVALB interneuron homeostasis in AD; disease-driven clonal remodeling in cycling T/NK cells from NSCLC tumors characterized by increased mitochondrial biogenesis and impaired immune regulatory programs; and preferential enrichment of a tumor-associated MT-ATP6 (m.8859A > G) clone linked to metabolic adaptation and platinum resistance in SCLC. Pan-cancer survival analyses further confirmed the clinical relevance of elevated MT-ATP6 activity, which was associated with inferior chemotherapy outcomes. Additionally, in hepatocellular carcinoma (HCC), a dominant m.2356C > G clone correlated with POLR2A activation and widespread transcriptional amplification, consistent with a mitochondria-nucleus signaling axis contributing to adverse prognosis in this cancer type. Collectively, these findings establish MitoBayes as a robust statistical framework linking mitochondrial genetic diversity to disease phenotypes and highlight mitochondrial clonal selection as a mechanistically and clinically actionable target for therapeutic and diagnostic development.
    DOI:  https://doi.org/10.21203/rs.3.rs-8490828/v1
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