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Schnaubelt, M.

Publications and source records attributed to Schnaubelt, M..

3 recordsLinked to original sources

Spatial drivers and pre-cancer populations collaborate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is a lethal disease with limited treatment options and poor survival. We studied 73 samples from 21 patients (7 treatment-naive and 14 treated with neoadjuvant regimens), analyzing distinct spatial units and performing bulk proteogenomics, single cell sequencing, and cellular imaging. Spatial drivers, including mutant KRAS, SMAD4, and GNAQ, were associated with differential phosphosignaling and metabolic responses compared to wild type. Single cell subtyping discovered 12 of 21 tumors with mixed basal and classical features. Trefoil factor family members were upregulated in classical populations, while the basal populations showed enhanced expression of mesenchymal genes, including VIM and IGTB1. Acinar-ductal metaplasia (ADM) populations, present in 95% of patients, with 46% reduction of driver mutation fractions compared to tumor populations, exhibited suppressive and oncogenic features linked to morphologic states. We identified coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin receptor expression in tumor cells. Higher expression of angiogenic and stress response genes in dendritic cells compared to tumor cells suggests they have a pro-tumorigenic role in remodeling the microenvironment. Treated samples contain a three-fold enrichment of inflammatory CAFs when compared to untreated samples, while other CAF subtypes remain similar. A subset of tumor and/or ADM-specific biomarkers showed differential expression between treatment groups, and several known drug targets displayed potential cross-cell type reactivities. This resolution that spatially defined single cell omics provides reveals the diversity of tumor and microenvironment populations in PDAC. Such understanding may lead to more optimal treatment regimens for patients with this devastating disease. HIGHLIGHTSO_LIAcinar-ductal metaplasia (ADM) cells represent a genetic and morphologic transition state between acinar and tumor cells. C_LIO_LIInflammatory cancer associated fibroblasts (iCAFs) are a major component of the PDAC TME and are significantly higher in treated samples C_LIO_LIReceptor-ligand analysis reveals tumor cell-TME interactions through NECTIN4-TIGIT C_LIO_LITumor and ADM cell proteogenomics differ between treated and untreated samples, with unique and shared potential drug targets C_LI

cancer biology↗

Orthogonal proteomic platforms and their implications for the stable classification of high-grade serous ovarian cancer subtypes

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) has established a two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) workflow using isobaric tagging to compare protein abundance across samples. The workflow has been used for large-scale clinical proteomic studies with deep proteomic coverage within and outside of CPTAC. SWATH-MS, an instance of data-independent acquisition (DIA) proteomic methods, was recently developed as an alternate proteomic approach. In this study, we analyzed remaining aliquots of peptides using SWATH-MS from the original retrospective TCGA samples generated for the CPTAC ovarian cancer proteogenomic study (Zhang et al., 2016). The SWATH-MS results indicated that both methods confidently identified differentially expressed proteins in enriched pathways associated with the robust Mesenchymal subtype of high-grade serous ovarian cancer (HGSOC) and the homologous recombination deficient tumors also present in the original study. The results demonstrated that SWATH/DIA-MS presents a promising complementary or orthogonal alternative to the CPTAC harmonized proteomic method, with the advantages of simpler, faster, and cheaper workflows, as well as lower sample consumption. However, the SWATH/DIA-MS workflow resulted in shallower proteome coverage. Overall, we concluded that both analytical methods are suitable to characterize clinical samples such as in the high-grade serous ovarian cancer study, providing proteomic workflow alternatives for cancer researchers depending on the specific goals and context of the studies.

cancer biology↗

MS-PyCloud: An open-source, cloud computing-based pipeline for LC-MS/MS data analysis

Rapid development and wide adoption of mass spectrometry-based proteomics technologies have empowered scientists to study proteins and their modifications in complex samples on a large scale. This progress has also created unprecedented challenges for individual labs to store, manage and analyze proteomics data, both in the cost for proprietary software and high-performance computing, and the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI) support, for LC-MS/MS data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignment, false discovery rate estimation, protein inference, determination of protein post-translation modifications, and quantitation of specific (modified) peptides and proteins. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale iTRAQ/TMT LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at: https://bitbucket.org/mschnau/ms-pycloud/downloads/

bioinformatics↗