Biology subjects

Woldmar, N.

Publications and source records attributed to Woldmar, N..

4 recordsLinked to original sources

bioRxiv2026

From Routine Pathology to Precision Oncology: Automated FFPE Tissue Processing for Large-Scale Molecular Studies

Melanoma incidence continues to rise globally, with formalin-fixed paraffin-embedded (FFPE) tissue archives representing an invaluable resource for large-scale retrospective proteomic studies. However, inconsistent deparaffinization remains a critical pre-analytical bottleneck limiting protein yield, reproducibility, and downstream data quality. In this study, we developed and validated a fully automated FFPE deparaffinization workflow using the Fluent(R) 780 liquid handling workstation (Tecan (C)) and evaluated its performance against a conventional manual protocol in a cohort of 54 patients with primary cutaneous melanoma, predominantly at early AJCC 8th edition stage I-II. The automated workflow achieved superior protein identification (6,146 {+/-} 860 vs. 4,941 {+/-} 1,091 proteins; p < 0.0001) with lower technical variability, while maintaining highly comparable global proteomic profiles as confirmed by principal component analysis and hierarchical clustering. A total of 8,305 proteins (96.1%) were identified by both methods, supporting the reproducibility and equivalence of the automated approach. Patients were stratified by the presence (N=21) or absence (N=33) of histological regression in the primary tumor. Proteomic comparison revealed 97 upregulated and 226 downregulated proteins in regressing melanomas, with pathway enrichment analysis demonstrating elevated mitochondrial and translational activity alongside reduced innate immune and complement pathway activation in the regression group. No statistically significant differences in overall, disease-free, or progression-free survival were observed between groups, consistent with the early-stage composition of the cohort. Digital pathology validated tissue morphology preservation across processing conditions. These findings support the integration of automated FFPE processing with proteomic and digital pathology workflows as a scalable platform for precision melanoma research. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/744404v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1a3ea5dorg.highwire.dtl.DTLVardef@6fb8edorg.highwire.dtl.DTLVardef@d60a4dorg.highwire.dtl.DTLVardef@3e47b6_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2026

Proteomics-Enhanced AI-Digital Pathology in Metastatic Mucinous Colorectal Carcinoma: A Case Report

Mucinous colorectal carcinoma (CRC) is a distinct histomorphological subtype characterized by abundant extracellular mucin that may promote immune evasion and chemoresistance. We describe a metastatic mucinous CRC case integrating digital pathology and proteomics to investigate disease progression and therapy resistance. Formalin-fixed paraffin-embedded samples from the primary tumor, peritoneal metastasis, and hepatoduodenal ligament metastasis of a 56-year-old patient were analyzed. Whole-slide imaging with QuPath-based AI enabled detailed histological annotation, while data-independent acquisition mass spectrometry identified over 6,000 proteins. Digital pathology revealed extensive mucin pools, architectural evolution from heterogeneous glandular patterns in the primary tumor to cribriform morphology in advanced metastases, and immune cell exclusion from mucin-rich regions. Proteomics revealed metabolic reprogramming, suppressed antigen presentation, and stage-specific activation of inflammatory, angiogenic, EMT, and PI3K/AKT/mTOR-MYC signaling pathways, consistent with proliferative and therapy-resistant phenotypes.Integration of AI-assisted histopathology with spatial proteomics highlighted the mucin barrier as a key mediator of immune evasion and chemoresistance. These findings support a personalized therapeutic framework targeting mucin-associated mechanisms alongside pathway-directed inhibitors, suggesting that spatial multi-omics may guide precision management strategies for aggressive mucinous colorectal cancer.

pathology↗

bioRxiv2024

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making. O_FIG O_LINKSMALLFIG WIDTH=164 HEIGHT=200 SRC="FIGDIR/small/592032v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@754668org.highwire.dtl.DTLVardef@1616263org.highwire.dtl.DTLVardef@827009org.highwire.dtl.DTLVardef@1899ba7_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Identifying Ferroptosis Inducers, HDAC, and RTK Inhibitor Sensitivity in Melanoma Subtypes through Unbiased Drug Target Prediction

The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem. Here we mine large scale MM proteogenomic data integrating it with MM cell line dependency screen, and drug sensitivity data to identify druggable targets and forecast treatment efficacy and resistance. Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC and AKT1, across five distinct MM subtypes. These proteins serve as potential drug targets applicable to one or multiple MM subtypes. By analyzing transcriptomic data from 48 publicly accessible melanoma cell lines sourced from Achilles and CRISPR dependency screens, we forecasted 162 potentially targetable genes. We also identified genetic resistance in 260 genes across at least one melanoma subtype. In addition, we employed publicly available compound sensitivity data (Cancer Therapeutics Response Portal, CTRPv2) on the cell lines to assess the correlation of compound effectiveness within each subtype. We have identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype. Remarkably, employing this unbiased approach, we have uncovered compounds targeting ferroptosis, that demonstrate a striking 30x fold difference in sensitivity among different subtypes. This implies that the proteogenomic classification of melanoma has the potential to predict sensitivity to ferroptosis compounds. Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy. Highlights(1) Proteogenomic subtype classification can define the landscape of genetic dependencies in melanoma (2) Nine proteins from molecular subtypes were identified as potential drug targets for specified MM patients (3) 20 compounds identified that show potential effectiveness in at least one melanoma subtype (4) Proteogenomics can predict specific ferroptosis inducers, HDAC, and RTK Inhibitor sensitivity in melanoma subtypes O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/579424v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@73dd77org.highwire.dtl.DTLVardef@d0cb8eorg.highwire.dtl.DTLVardef@1e4117forg.highwire.dtl.DTLVardef@38b727_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

cancer biology↗