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Drissler, S.

Publications and source records attributed to Drissler, S..

5 recordsLinked to original sources

Integrated spatial proteomics of human PDAC uncovers an expanded tumour-immune-stroma spectrum with genomic associations

Distinctively, pancreatic ductal adenocarcinoma (PDAC) consists of sparse tumour lesions intertwined with extensive desmoplastic stroma. The complexity of tumour-microenvironment interactions within this desmoplasia poses a challenge for accurate tumour profiling and patient stratification, and characterizes a profoundly chemoresistant tumour. Here we mapped the spatial relationships between tumour, stroma, and immune cell compartments delineating tumour and microenvironment types that expand the classical to basal spectrum of human PDAC. We used imaging mass cytometry to profile the in situ multi-cellular organization of 81 cell types in resected cases with paired whole genome sequencing. Cell types, functions, and pathway activation were distributed as highly reproducible environments in discrete locations throughout these tumours, which we deep-profiled using laser-capture mass spectrometry. We show that the connections between tumour phenotypes, vascularization, immune response, and stromal biophysical state are reinforced by genomic aberrations, altered by treatment, and associated with patient outcome. Predictive machine-learning models showed that spatial single cell data outperformed genomic or clinical features but integrated multi-omics models provide the best prediction of patient survival with compressed models requiring only 10 non-redundant robust molecular measures associated with the phenotypic spectrum of PDAC. Together, these findings define a phenotypic and molecular framework of PDAC that captures tumour-microenvironment co-dependencies and offers a refined basis for patient stratification and therapeutic targeting.

cancer biology↗

Single-cell analysis of an engineered organoid-based model of pancreatic cancer identifies hypoxia as a contributing factor in the determination of transcriptional subtypes

Pancreatic ductal adenocarcinoma (PDAC) is a high-mortality cancer characterized by its aggressive, treatment-resistant phenotype and a complex tumour microenvironment (TME) featuring significant hypoxia. Bulk transcriptomic analysis has identified the "classical" and "basal-like" transcriptional subtypes which have prognostic value in PDAC; however, it remains unclear how microenvironmental heterogeneity contributes to the expression of these transcriptional signatures. Here, we used single cell transcriptome analysis of the organoid TRACER platform to explore the effect of oxygen and other microenvironmental gradients on PDAC organoid cells. We found that the microenvironmental gradients present in TRACER significantly impact the distribution of organoid transcriptional phenotypes and the enrichment of gene sets linked to cancer progression and treatment resistance. More significantly, we found that microenvironmental gradients drive changes in the expression of the classical and basal-like transcriptional subtype gene signatures. This effect is likely dominated by the oxygen gradients in TRACER, as hypoxia alone induced decreases in the expression of classical marker GATA6 at both the gene and protein level in PDAC cells. This work suggests that hypoxia contributes to determining transcriptional subtypes in PDAC and broadly underscores the importance of considering microenvironmental gradients in organoid-based transcriptomic studies of PDAC.

cancer biology↗

SOX9 is sufficient to drive endothelial cells towards a mesenchymal fate by altering the chromatin landscape.

The transcription factor SOX9 is expressed in multiple tissues during embryogenesis and directs developmental processes. SOX9 is activated upon endothelial-to-mesenchymal transition (EndMT) in the developing heart, but its role in regulating this process is less clear. Using human umbilical vein endothelial cells as an EndMT model, we show that SOX9 expression alone is sufficient to activate mesenchymal enhancers and steer endothelial cells towards a mesenchymal fate. By genome-wide mapping of the chromatin landscape, we show that SOX9 acts as a pioneer transcription factor, having the ability to open chromatin and lead to deposition of active histone marks at a specific subset of previously silent enhancers, guided by SOX motifs and H2A.Z enrichment. This leads to a switch in enhancer activity states resulting in activation of mesenchymal genes and concurrent suppression of endothelial genes to drive EndMT. Moreover, we show that SOX9 chromatin binding is dynamic, but induces stable changes in the chromatin landscape. Our data also show widespread SOX9 chromatin scanning in silent chromatin that is not associated with SOX motifs or H2A.Z enrichment. Our study highlights the crucial developmental role of SOX9 and provides new insight into key molecular functions of SOX9 in the chromatin landscape and mechanisms of EndMT. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/293993v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1248576org.highwire.dtl.DTLVardef@b6420forg.highwire.dtl.DTLVardef@1aaffc5org.highwire.dtl.DTLVardef@17de585_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation. Specifically, high-throughput phenotyping of 530 knockout mouse lines identified 140 monogenic \"hits\" (>25%), most of which had never hitherto been implicated in immunology. Furthermore, they were conspicuously enriched in genes for which humans show poor tolerance to loss-of-function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with one another and with specific physiologic traits. By limiting the freedom of individual immune parameters, such linkages impose genetically regulated \"immunological structures\", whose integrity was found to be associated with immunocompetence. Hence, our findings provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.

immunology↗

ConsensusDriver Improves Upon Individual Algorithms For Predicting Driver Alterations In Different Cancer Types And Individual Patients -- A Toolbox For Precision Oncology

BackgroundIn recent years, several large-scale cancer genomics studies have helped generate detailed molecular profiling datasets for many cancer types and thousands of patients. These datasets provide a unique resource for studying cancer driver prediction methods and their utility for precision oncology, both to predict driver genetic alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nMethodsWe performed the most comprehensive assessment to date of 18 driver gene prediction methods, on more than 3,400 tumour samples, from 15 cancer types, to determine their suitability in guiding precision medicine efforts. These methods have diverse approaches, which can be classified into five categories: functional impact on proteins in general (FI) or specific to cancer (FIC), cohort-based analysis for recurrent mutations (CBA), mutations with expression correlation (MEC) and methods that use gene interaction network-based analysis (INA).\n\nResultsThe performance of driver prediction methods varies considerably, with concordance with a gold-standard varying from 9% to 68%. FI methods show relatively poor performance (concordance <22%) while CBA methods provide conservative results, but require large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provide the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of drivers, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity). This tool can be applied to predict driver alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nConclusionExisting cancer driver prediction methods are based on very different assumptions and each of them can only detect a particular subset of driver events. Consensus-based methods, like ConsensusDriver, are thus a promising approach to harness the strengths of different driver prediction paradigms.

bioinformatics↗