Biology subjects

Haberecker, M.

Publications and source records attributed to Haberecker, M..

2 recordsLinked to original sources

bioRxiv2026

MTAP deficiency is a novel biomarker in neuroendocrine neoplasms of the lung

IntroductionMTAP emerges as potential predictive biomarker for MTA-cooperative PRMT5-inhibitors. Although MTAP attracts increasing attention in non-small cell lung cancer, its role in pulmonary neuroendocrine neoplasms (NENs) remains largely unexplored. MethodsHere, we assessed the prevalence of MTAP deficiency in 209 pulmonary NENs using immunohistochemistry (IHC). Additionally, we performed fluorescence in situ hybridization (FISH), whole exome sequencing (WES), deep proteomic profiling, transcriptomic, and methylation analyses of selected MTAP deficient and proficient carcinoids to further elucidate the underlying mechanisms of MTAP expression pattern. ResultsMTAP deficiency by IHC was detected in all neuroendocrine precursor lesions (n=17), 92% of typical carcinoids (n=51), 86% of atypical carcinoids (n=21), and 10% of large cell neuroendocrine carcinomas (LCNEC) (n=30). In contrast, all small cell lung cancers (SCLC) were MTAP proficient (n=90). In MTAP deficient carcinoids, FISH and WES did not detect homozygous 9p21 deletions, and methylation analysis showed no evidence of MTAP promoter hypermethylation. Comparing MTAP deficient and MTAP proficient carcinoids, proteomic data showed a clear separation between the two groups. Further, there was an inverse correlation between the expression of MTAP and OTP (orthopedia homeobox protein), which is known as a strong prognostic marker in pulmonary carcinoids. DiscussionMTAP deficiency is a novel hallmark of neuroendocrine precursors and most pulmonary carcinoids, clearly distinguishing the latter from SCLC and most LCNEC. It is neither caused by 9p21 deletion, nor by MTAP promoter hypermethylation. MTAP deficiency is a group-defining feature of carcinoids that might pave the way for new therapeutic approaches.

pathology↗

bioRxiv2025

Exploiting pair correlation function to describe biological tissue structure

Multiplexed imaging technologies now enable the simultaneous profiling of hundreds to thousands of molecular targets in intact tissues, providing unprecedented insight into cellular heterogeneity and spatial organization. While data generation has rapidly matured, the quantitative analysis of spatial structure remains challenging and poorly standardized, particularly across biological length scales. Existing approaches, such as distance-based metrics, neighborhood analyses and graph neural networks, either capture only local interactions or sacrifice interpretability for predictive power. Here we introduce PCF-SiM (Pair Correlation Function Sigmoid Modeling), a scalable and interpretable framework that leverages parametric modeling of the pair correlation function to quantify spatial organization in multiplexed imaging data. PCF-SiM compresses complex spatial patterns into a small set of biologically meaningful parameters, enabling robust comparisons across cell types, samples and conditions. Applying PCF-SiM to diverse public spatial transcriptomics datasets, we demonstrate its ability to detect condition-dependent tissue remodeling in a mouse colitis model. We further extend the framework with a co-scaling strategy that identifies cell types participating in shared spatial structures. Using newly generated clinical datasets from Hashimotos thyroiditis and uveal melanoma liver metastases, PCF-SiM reveals hierarchical organization of autoimmune infiltrates and coordinated spatial interactions between lymphatic endothelial cells and tumor-infiltrating lymphocytes. Finally, we show that reliable inference of tissue-scale architecture requires whole-slide imaging, exposing intrinsic limitations of tumor microarray-based spatial analyses. Together, PCF-SiM provides a principled and interpretable approach for spatial analysis of multiplexed imaging data.

bioinformatics↗