Biology subjects

Suter, R. K.

Publications and source records attributed to Suter, R. K..

5 recordsLinked to original sources

bioRxiv2025

Multiple distinct metastatic cell states are induced by epithelial-mesenchymal plasticity

Epithelial-mesenchymal transition (EMT) enables epithelial cancer cells to acquire mesenchymal-associated traits that can promote invasion and metastasis. Although distinct EMT-associated states have been linked to invasive and metastatic behavior, it remains unclear when these states arise during primary tumor progression, how they diversify, and whether metastatic competence is restricted to a particular EMT phenotype. Using single-cell RNA sequencing in a genetically engineered mouse model of triple-negative breast cancer (TNBC), together with functional studies of tumor organoids, we reconstructed the emergence of EMT-associated heterogeneity during tumor progression. We found that early malignant cells first lost mammary lineage identity, generating lineage-altered epithelial states with increased intrinsic plasticity. Rather than progressing through a single EMT program, these plastic states diversified through ERK1/2-low and ERK1/2-high EMT-associated programs. These programs generated distinct hybrid epithelial-mesenchymal states in early tumors and more uniform mesenchymal-like subpopulations at later stages, with canonical EMT features, diminished plasticity, and highly invasive behavior. Importantly, metastatic competence was not restricted to a single EMT-associated state--both heterogeneous hybrid cells and more uniform mesenchymal-like cells initiated metastases, with metastatic lesions retaining features of their initiating populations. Together, our results show that EMT-associated heterogeneity in TNBC emerges through early lineage-state disruption followed by parallel regulatory programs that generate distinct metastatic cell states rather than converging on a single highly metastatic phenotype.

cancer biology↗

bioRxiv2025

In silico drug sensitivity predicts subgroup-specific therapeutics in medulloblastoma patients

BackgroundMedulloblastoma is the most common malignant pediatric brain tumor. Survival rates vary widely between subgroups, with an average overall survival of 70%. Recurrent medulloblastoma is highly aggressive, treatment-resistant, and usually fatal. In addition, current treatments are highly toxic to the developing brain and surviving patients suffer from lifelong side effects. Therefore, novel therapeutic options are urgently needed. MethodsTo inform risk-based, personalized therapy, we developed a novel platform called DrugSeq, which allows predictions of drug sensitivities in patients across medulloblastoma subgroups. We used a perturbagen-response dataset to calculate transcriptional response signatures for each drug and compared this to patient medulloblastoma tumor gene expression. We then stratified patients by molecular subgroup and used an ANOVA analysis to identify drugs that selectively targeted each subgroup. ResultsWe found distinct differences in transcriptional profiles and predicted drug sensitivity for each medulloblastoma subgroup. We identified several kinase inhibitors, epigenetic inhibitors, and several drugs that have been investigated in drug repositioning studies for cancer. ConclusionsWe posit that DrugSeq may identify novel therapies and facilitate patient stratification in clinical trials, leading to more successful targeted medulloblastoma therapies that improve tumor response while minimizing late toxicities. This computational tool can also be used for other cancers to stratify patients based on any clinical or molecular feature. Key points DrugSeq calculates drug sensitivity for medulloblastoma tumors stratified by subgroup. DrugSeq platform may inform patient stratification strategies in clinical trials. Importance of the StudyMedulloblastoma is the most common malignant pediatric brain tumor. Current standard-of-care typically includes surgical resection, multi-agent chemotherapy, and radiation. However, survival rates vary widely between subgroups, ranging from 45 to 90%, depending on age and molecular features. In addition, surviving children frequently suffer from debilitating late side effects of therapy including neurocognitive impairment, epilepsy, stroke, subsequent cancer, endocrinopathies, and early mortality. Therefore, novel therapeutic options are urgently needed. However, a one-size-fits-all approach for therapy is unlikely to be effective given the well-characterized intertumor heterogeneity of medulloblastoma.

cancer biology↗

bioRxiv2025

Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations

Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity. GBM tumors contain neoplastic cells within a spectrum of different transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we developed a novel framework, ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identified compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states. We validated the significance of these findings in vitro, ex vivo, and in vivo, and identified a novel combination of an OLIG2 inhibitor and Depatux-M for GBM. Our studies suggest that ISOSCELES identifies cell states sensitive and resistant to targeted therapies in GBM and that it can be applied to identify new synergistic combinations. HighlightsO_LIIntegration of GBM single-cell RNA sequencing data with L1000-derived drug response signatures facilitates clustering of tumor cells and small molecules on cell-drug connectivity. C_LIO_LICell-drug connectivity predicts the identities of drug-sensitive and resistant cell states. C_LIO_LIIn silico perturbation analysis using cell-drug connectivity predicts drug-induced changes in the cell-drug connectivity landscape in vivo. C_LIO_LIIn silico perturbation analysis to predict drug-induced changes in the tumor cell-drug connectivity landscape predicts drug combinations that synergize in vivo to extend survival. C_LI

cancer biology↗

bioRxiv2024

Identification of disease-specific vulnerability states at the single-cell level

Intratumor heterogeneity in glioblastoma (GBM) impedes successful treatment as it is not obvious which tumor cells should be targeted. Here, we posit that single-cell-resolution transcriptomic data can be integrated with loss-of-function screens to identify the most critical cells to target within a tumor. We parsed CRISPR screen data from the Dependency Map (DepMap) Consortium and identified a GBM Dependency Signature (GDS) - 168 genes that are essential for GBM cell viability in vitro. Through similarity scoring of GDS transcriptomic profiles in single-cell RNA-sequencing (scRNA-seq) data and iterative hierarchical clustering, we identify and report 3 single-cell vulnerability states (VS) characterized in 49 GBM tumors using both scRNA-seq and spatial transcriptomic data. These VS reflect single-cell gene dependencies and differ significantly in enrichment profiles and spatial distributions. Additionally, each VS is differently sensitive to cancer drugs, with VS2 solely responsive to temozolomide treatment. Importantly, the proportion of VS in each GBM tumor is variable, suggesting a means of stratifying patients in clinical trials. Collectively, we have developed a novel computational pipeline to identify unique vulnerability states in GBM and other cancers, which can be used to identify existing or novel drugs for incurable diseases.

cancer biology↗

bioRxiv2024

Natural killer cells associate with epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment

Pancreatic ductal adenocarcinoma (PDAC) exhibits dense fibrosis and immune exclusion. While fibrosis has been studied globally and at the region-of-interest level, its impact on stromal-ductal architecture and immune cell localization remains unknown. Here, we establish cancer-associated fibroblast (CAF)-stratified ductal spatial architecture as a fundamental determinant of immune exclusion in PDAC. Focusing on malignant PDAC epithelial ductal regions, the critical interface where immune cells must access tumor epithelium, we demonstrate that periductal fibroblast organization dictates leukocyte proximity. Through integrative analysis of treatment-naive patient samples from three independent cohorts - including imaging mass cytometry, multiplex immunohistochemistry, and single-cell RNA sequencing - we uncovered that activated, pro-inflammatory leukocytes preferentially localized near malignant ducts in regions with low fibroblast density. Stratifying epithelial-ductal regions by CAF abundance revealed a graded constraint: increasing fibroblast content corresponded to reduced leukocyte-epithelial proximity and elevated collagen I deposition. Despite their exclusion in high-CAF ducts, leukocytes in low-CAF ducts retained functional competence. Mechanistically, ligand-receptor inference implicated collagen-CD44 signaling as an adhesion axis anchoring immune cells within fibroblast-rich zones, with CD44 blockade enhancing natural killer cell invasion and motility in fibrotic spheroid models. Thus, by establishing ductal regions as critical spatial units of immune exclusion, these findings provide a framework for dissecting stromal-immune interactions and reveal targetable "stromal checkpoints" that can be leveraged to overcome CAF-driven barriers to leukocyte motility and infiltration in PDAC. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/593868v3_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@17df874org.highwire.dtl.DTLVardef@142c809org.highwire.dtl.DTLVardef@15af4a7org.highwire.dtl.DTLVardef@74183a_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗