Biology subjects

Fertig, E. J.

Publications and source records attributed to Fertig, E. J..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Cancer associated fibroblasts drive epithelial to mesenchymal transition and classical to basal change in pancreatic ductal adenocarcinoma cells with loss of IL-8 expression

Pancreatic ductal adenocarcinoma (PDAC) carries an extremely poor prognosis, in part resulting from cellular heterogeneity that supports overall tumorigenicity. Cancer associated fibroblasts (CAF) are key determinants of PDAC biology and response to systemic therapy. While CAF subtypes have been defined, the effects of patient-specific CAF heterogeneity and plasticity on tumor cell behavior remain unclear. Here, multi-omics was used to characterize the tumor microenvironment (TME) in tumors from patients undergoing curative-intent surgery for PDAC. In these same patients, matched tumor organoid and CAF lines were established to functionally validate the impact of CAFs on the tumor cells. CAFs were found to drive epithelial-mesenchymal transition (EMT) and a switch in tumor cell classificiaton from classical to basal subtype. Furthermore, we identified CAF-specific interleukin 8 (IL-8) as an important modulator of tumor cell subtype. Finally, we defined neighborhood relationships between tumor cell and T cell subsets. Statement of SignificanceThis multidimensional analysis highlights the diverse role CAFs have in influencing other cell types in the TME, including epithelial-derived tumor and infiltrating immune cells. Our methods provide a platform for evaluating emerging therapeutic approaches and for studying mechanisms that dictate tumor behavior in a manner that reflects patient-specific heterogeneity.

cancer biology↗

bioRxiv2024

Spatial multi-omics reveal intratumoral humoral immunity niches associated with tertiary lymphoid structures in pancreatic cancer immunotherapy pathologic responders

Pancreatic adenocarcinoma (PDAC) is a rapidly progressing cancer that responds poorly to immunotherapies. Intratumoral tertiary lymphoid structures (TLS) have been associated with rare long-term PDAC survivors, but the role of TLS in PDAC and their spatial relationships within the context of the broader tumor microenvironment remain unknown. We generated a spatial multi-omics atlas encompassing 26 PDAC tumors from patients treated with combination immunotherapies. Using machine learning-enabled H&E image classification models and unsupervised gene expression matrix factorization methods for spatial transcriptomics, we characterized cellular states within TLS niches spanning across distinct morphologies and immunotherapies. Unsupervised learning generated a TLS-specific spatial gene expression signature that significantly associates with improved survival in PDAC patients. These analyses demonstrate TLS-associated intratumoral B cell maturation in pathological responders, confirmed with spatial proteomics and BCR profiling. Our study also identifies spatial features of pathologic immune responses, revealing TLS maturation colocalizing with IgG/IgA distribution and extracellular matrix remodeling. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/613714v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@d5e1a5org.highwire.dtl.DTLVardef@1d14012org.highwire.dtl.DTLVardef@c57c6corg.highwire.dtl.DTLVardef@16baa56_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIIntegrated multi-modal spatial profiling of human PDAC tumors from neoadjuvant immunotherapy clinical trials reveal diverse spatial niches enriched in TLS. C_LIO_LITLS maturity is influenced by tumor location and the cellular neighborhoods in which TLS immune cells are recruited. C_LIO_LIUnsupervised machine learning of genome-wide signatures on spatial transcriptomics data characterizes the TLS-enriched TME and associates TLS transcriptomes with survival outcomes in PDAC. C_LIO_LIInteractions of spatially variable gene expression patterns showed TLS maturation is coupled with immunoglobulin distribution and ECM remodeling in pathologic responders. C_LIO_LIIntratumoral plasma cell and immunoglobin gene expression spatial dynamics demonstrate trafficking of TLS-driven humoral immunity in the PDAC TME. C_LI SignificanceWe report a spatial multi-omics atlas of PDAC tumors from a series of immunotherapy neoadjuvant clinical trials. Intratumorally, pathologic responders exhibit mature TLS that propagate plasma cells into malignant niches. Our findings offer insights on the role of TLS-associated humoral immunity and stromal remodeling during immunotherapy treatment.

cancer biology↗

bioRxiv2024

Identifying a gene signature of metastatic potential by linking pre-metastatic state to ultimate metastatic fate

Identifying the key molecular pathways that enable metastasis by analyzing the eventual metastatic tumor is challenging because the state of the founder subclone likely changes following metastatic colonization. To address this challenge, we labeled primary mouse pancreatic ductal adenocarcinoma (PDAC) subclones with DNA barcodes to characterize their pre-metastatic state using ATAC-seq and RNA-seq and determine their relative in vivo metastatic potential prospectively. We identified a gene signature separating metastasis-high and metastasis-low subclones orthogonal to the normal-to-PDAC and classical-to-basal axes. The metastasis-high subclones feature activation of IL-1 pathway genes and high NF-{kappa}B and Zeb/Snail family activity and the metastasis-low subclones feature activation of neuroendocrine, motility, and Wnt pathway genes and high CDX2 and HOXA13 activity. In a functional screen, we validated novel mediators of PDAC metastasis in the IL-1 pathway, including the NF-{kappa}B targets Fos and Il23a, and beyond the IL-1 pathway including Myo1b and Tmem40. We scored human PDAC tumors for our signature of metastatic potential from mouse and found that metastases have higher scores than primary tumors. Moreover, primary tumors with higher scores are associated with worse prognosis. We also found that our metastatic potential signature is enriched in other human carcinomas, suggesting that it is conserved across epithelial malignancies. This work establishes a strategy for linking cancer cell state to future behavior, reveals novel functional regulators of PDAC metastasis, and establishes a method for scoring human carcinomas based on metastatic potential.

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↗

bioRxiv2024

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

systems biology↗

bioRxiv2023

Immune landscape of tertiary lymphoid structures in hepatocellular carcinoma (HCC) treated with neoadjuvant immune checkpoint blockade

Neoadjuvant immunotherapy is thought to produce long-term remissions through induction of antitumor immune responses before removal of the primary tumor. Tertiary lymphoid structures (TLS), germinal center-like structures that can arise within tumors, may contribute to the establishment of immunological memory in this setting, but understanding of their role remains limited. Here, we investigated the contribution of TLS to antitumor immunity in hepatocellular carcinoma (HCC) treated with neoadjuvant immunotherapy. We found that neoadjuvant immunotherapy induced the formation of TLS, which were associated with superior pathologic response, improved relapse free survival, and expansion of the intratumoral T and B cell repertoire. While TLS in viable tumor displayed a highly active mature morphology, in areas of tumor regression we identified an involuted TLS morphology, which was characterized by dispersion of the B cell follicle and persistence of a T cell zone enriched for ongoing antigen presentation and T cell-mature dendritic cell interactions. Involuted TLS showed increased expression of T cell memory markers and expansion of CD8+ cytotoxic and tissue resident memory clonotypes. Collectively, these data reveal the circumstances of TLS dissolution and suggest a functional role for late-stage TLS as sites of T cell memory formation after elimination of viable tumor. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/562104v2_fig0.gif" ALT="Figure 0"> View larger version (30K): org.highwire.dtl.DTLVardef@859572org.highwire.dtl.DTLVardef@169ed2org.highwire.dtl.DTLVardef@11510f0org.highwire.dtl.DTLVardef@b39a4_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LSTHighlightsC_LSTO_LIIn patients with hepatocellular carcinoma (HCC), tertiary lymphoid structures (TLS) are induced by neoadjuvant immunotherapy and are associated with favorable clinical outcomes. C_LIO_LITLS within the same tumor demonstrate extensive sharing of expanded granzyme K and granzyme B-expressing CD8+ T effector memory clonotypes, but the B cell repertoires of individual TLS are almost wholly distinct, consistent with independent germinal center reactions. C_LIO_LIWithin areas of viable tumor, mature TLS are characterized by high expression of CD21 and CD23, BCL6+ germinal center B cells, and close interactions between DCLAMP+ mature dendritic cells and CXCR5-CXCR3+ CD4 T peripheral helper cells within a T cell zone adjacent to the B cell follicle. C_LIO_LIWithin areas of tumor regression, an involuted TLS morphology is identified that is notable for dissolution of the B cell germinal center, retention of the T cell zone, and increased T cell memory. C_LI

immunology↗

bioRxiv2023

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework--a cell behavior hypothesis grammar--that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

systems biology↗

bioRxiv2023

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGF{beta} in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

bioengineering↗

bioRxiv2023

Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention

A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scR-NAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNAs performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.

bioinformatics↗

bioRxiv2023

SpliceMutr enables pan-cancer analysis of splicing-derived neoantigen burden in tumors

Aberrant alternative splicing can generate neoantigens, which can themselves stimulate immune responses and surveillance. Previous methods for quantifying splicing-derived neoantigens are limited by independent references and potential batch effects. Here, we introduce SpliceMutr, a bioinformatics approach and pipeline for identifying splicing derived neoantigens from paired tumor normal data. SpliceMutr facilitates the identification of tumor-specific antigenic splice variants, predicts MHC-binding affinity, and estimates splicing antigenicity scores per gene. By applying this tool to genomic data from The Cancer Genome Atlas (TCGA), we generate splicing-derived neoantigens and neoantigenicity scores per sample and across all cancer types and find numerous correlations between splicing antigenicity and well-established biomarkers of anti-tumor immunity. Notably, carriers of mutations within splicing machinery genes have higher splicing antigenicity, which provides support for our approach. Further analysis of splicing antigenicity in cohorts of melanoma patients treated with mono- or combined immune checkpoint inhibition suggest that the abundance of splicing antigens is reduced post-treatment from baseline in patients who progress, likely because of an immunoediting process. We also observe increased splicing antigenicity in responders to immunotherapy, which may relate to an increased capacity to mount an immune response to splicing-derived antigens. This new computational tool provides novel analytical capabilities for splicing antigenicity and is openly available for further immuno-oncologic analysis.

cancer biology↗

bioRxiv2023

Latent transcriptional programs reveal histology-encoded tumor features spanning tissue origins

Precision medicine in cancer treatment depends on deciphering tumor phenotypes to reveal the underlying biological processes. Molecular profiles, including transcriptomics, provide an information-rich tumor view, but their high-dimensional features and assay costs can be prohibitive for clinical translation at scale. Recent studies have suggested jointly leveraging histology and genomics as a strategy for developing practical clinical biomarkers. Here, we use machine learning techniques to identify de novo latent transcriptional processes in squamous cell carcinomas (SCCs) and to accurately predict their activity levels directly from tumor histology images. In contrast to analyses focusing on pre-specified, individual genes or sample groups, our latent space analysis reveals sets of genes associated with both histologically detectable features and clinically relevant processes, including immune response, collagen remodeling, and fibrosis. The results demonstrate an approach for discovering clinically interpretable histological features that indicate complex, potentially treatment-informing biological processes.

cancer biology↗

bioRxiv2023

A mechanistic simulation of molecular cell states over time

Computer simulations of cell behaviors and dynamics allow for investigation of aspects of cellular biology with a ground truth that is currently difficult or impossible to generate from experimentally generated profiling data. Here, we present a mechanistic simulation of cell states that models the stochastic interactions of molecules revealing the DNA accessibility, RNA expression, and protein expression state of a simulated cell and how these states evolve over time. By designing each component to correspond to a specific biological molecule or parameter, the simulation becomes highly interpretable. From the simulated cells generated, we explore the importance of parameters such as splicing and degradation rates of genes on RNA and protein expression, demonstrating that perturbing these parameters leads to changes in long term gene and protein expression levels. We observe that the expression levels of corresponding RNA and proteins are not necessarily well correlated and identify mechanistic explanations that may help explain the similar phenomenon that has been observed in real cells. We evaluate whether the RNA data output from the simulation provides sufficient information to reconstruct the underlying regulatory relationships between genes. While predictive relationships can be inferred, direct causal regulatory relationships between genes cannot be reliably distinguished from other predictive relationships between genes arising independently from a direct regulatory mechanism. We observe the same inability to robustly distinguish causal gene regulatory relationships using simulated data from the simpler BoolODE model, suggesting this may be a limitation to the identifiability of network inference.

bioinformatics↗

bioRxiv2023

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients and recurrence can also occur. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy. We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response. ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell markers expression suggesting strong activity of these cells. Cancer-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic response, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by cancer-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient. These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

cancer biology↗

bioRxiv2022

MLL1 regulates cytokine-driven cell migration and metastasis

Cell migration is a critical requirement for cancer metastasis. Cytokine production and its role in cancer cell migration have been traditionally associated with immune cells in the tumor microenvironment. MLL1 is a histone methyltransferase that controls 3D cell migration via the secretion of cytokines, IL-6 and TGF-{beta}1, by the cancer cells themselves. In vivo, MLL1 depletion reduced metastatic burden and prolonged survival. MLL1 exerts its effects with its scaffold protein, Menin. Mechanistically, the MLL1-Menin interaction controls actin filament assembly via the IL-6/pSTAT3/Arp3 axis and acto-myosin contractility via the TGF-{beta}1/Gli2/ROCK1/2/pMLC2 axis, which regulate dynamic protrusion generation and 3D cell migration. MLL1 also regulates cell proliferation via mitosis-based and cell cycle-related pathways. Combining an MLL1-Menin inhibitor with Paclitaxel, a standard chemotherapeutic, abrogated tumor growth and metastasis in a syngeneic model. These results highlight the potential of targeting the MLL1 in metastasis prevention and its potential to be combined with currently administered chemotherapeutics. Statement of SignificanceWe identify MLL1 as being vital to metastasis, which causes the vast majority of cancer-related deaths. MLL1 controls cell migration, a requirement for metastasis, by regulating the secretion of cytokines. MLL1 inhibition lowers metastatic burden independent of its impact on primary tumor growth, highlighting its anti-metastatic potential in TNBC.

cancer biology↗

bioRxiv2022

Transcriptomic forecasting with neural ODEs

Single cell transcriptomics technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, understanding the dynamic cellular processes requires extending from inferring trajectories from snapshots of cellular states to estimating temporal changes in cellular gene expression. To address this challenge, we have developed a neural ordinary differential equation-based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps in an embedding-independent manner. We demonstrate that RNAForecaster can accurately predict future expression states in simulated single cell transcriptomic data with cellular tracking over time. We then show that using metabolic labeling scRNA-seq data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a three day period. Thus, RNAForecaster enables short term estimation of future expression states in biological systems from high-throughput datasets with temporal information.

bioinformatics↗

bioRxiv2022

Spatial transcriptomics of FFPE pancreatic intraepithelial neoplasias reveals cellular and molecular alterations of progression to pancreatic ductal carcinoma

Spatial transcriptomics (ST) is a powerful new approach to characterize the cellular and molecular architecture of the tumor microenvironment. Previous single-cell RNA-sequencing (scRNA-seq) studies of pancreatic ductal adenocarcinoma (PDAC) have revealed a complex immunosuppressive environment characterized by numerous cancer associated fibroblasts (CAFs) subtypes that contributes to poor outcomes. Nonetheless, the evolutionary processes yielding that microenvironment remain unknown. Pancreatic intraepithelial neoplasia (PanIN) is a premalignant lesion with potential to develop into PDAC, but the formalin-fixed and paraffin-embedded (FFPE) specimens required for PanIN diagnosis preclude scRNA-seq profiling. We developed a new experimental pipeline for FFPE ST analysis of PanINs that preserves clinical specimens for diagnosis. We further developed novel multi-omics analysis methods for threefold integration of imaging, ST, and scRNA-seq data to analyze the premalignant microenvironment. The integration of ST and imaging enables automated cell type annotation of ST spots at a single-cell resolution, enabling spot selection and deconvolution for unique cellular components of the tumor microenvironment (TME). Overall, this approach demonstrates that PanINs are surrounded by the same subtypes of CAFs present in invasive PDACs, and that the PanIN lesions are predominantly of the classical PDAC subtype. Moreover, this new experimental and computational protocol for ST analysis suggests a biological model in which CAF-PanIN interactions promote inflammatory signaling in neoplastic cells which transitions to proliferative signaling as PanINs progress to PDAC. SummaryPancreatic intraepithelial neoplasia (PanINs) are pre-malignant lesions that progress into pancreatic ductal adenocarcinoma (PDAC). Recent advances in single-cell technologies have allowed for detailed insights into the molecular and cellular processes of PDAC. However, human PanINs are stored as formalin-fixed and paraffin-embedded (FFPE) specimens limiting similar profiling of human carcinogenesis. Here, we describe a new analysis protocol that enables spatial transcriptomics (ST) analysis of PanINs while preserving the FFPE blocks required for clinical assessment. The matched H&E imaging for the ST data enables novel machine learning approaches to automate cell type annotations at a single-cell resolution and isolate neoplastic regions on the tissue. Transcriptional profiles of these annotated cells enable further refinement of imaging-based cellular annotations, showing that PanINs are predominatly of the classical subtype and surrounded by PDAC cancer associated fibroblast (CAF) subtypes. Applying transfer learning to integrate ST PanIN data with PDAC scRNA-seq data enables the analysis of cellular and molecular progression from PanINs to PDAC. This analysis identified a transition between inflammatory signaling induced by CAFs and proliferative signaling in PanIN cells as they become invasive cancers. Altogether, this integration of imaging, ST, and scRNA-seq data provides an experimental and computational approach for the analysis of cancer development and progression.

cancer biology↗

bioRxiv2022

Inflammatory Signaling and Fibroblast-Cancer Cell Interactions Transfer from a Harmonized Human Single-cell RNA Sequencing Atlas of Pancreatic Ductal Adenocarcinoma to Organoid Co-Culture

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a heterogeneous tumor microenvironment (TME) that is enriched with cancer associated fibroblasts (CAFs)1. Cell-cell interactions involving these CAFs promote an immunosuppressive phenotype with altered inflammatory gene expression. While single-cell transcriptomics provides a tool to dissect the complex intercellular pathways that regulate cancer-associated inflammation in human tumors, complementary experimental systems for mechanistic validation remain limited. This study integrated single-cell data from human tumors and novel organoid co-cultures to study the PDAC TME. We derived a comprehensive atlas of PDAC gene expression from six published human single-cell RNA sequencing (scRNA-seq) datasets2-7 to characterize intercellular signaling pathways between epithelial tumor cells and CAFs that regulate the inflammatory TME. Analysis of the epithelial cell compartment identified global gene expression pathways that modulate inflammatory signaling and are correlated with CAF composition. We then generated patient-derived organoid-CAF co-cultures to serve as a biological model of the cellular interactions learned from human tissue in the atlas. Transfer learning analysis to additional scRNA-seq data of this co-culture system and mechanistic experiments confirmed the epithelial response to fibroblast signaling. This bidirectional approach of complementary computational and in vitro applications provides a framework for future studies identifying important mechanisms of intercellular interactions in PDAC.

cancer biology↗

bioRxiv2022

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. Still, inferring biological processes requires additional post hoc statistics and annotation for interpretation of features learned from software packages developed for NMF implementation. Here, we aim to introduce a suite of computational tools that implement NMF and provide methods for accurate, clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations, and open questions in the field is followed by three vignettes for the Bayesian NMF algorithm CoGAPS (Coordinated Gene Activity across Pattern Subsets). Each vignette will demonstrate NMF analysis to quantify cell state transitions in public domain single-cell RNA-sequencing (scRNA-seq) data of malignant epithelial cells in 25 pancreatic ductal adenocarcinoma (PDAC) tumors and 11 control samples. The first uses PyCoGAPS, our new Python interface for CoGAPS that we developed to enhance runtime of Bayesian NMF for large datasets. The second vignette steps through the same analysis using our R CoGAPS interface, and the third introduces two new cloud-based, plug-and-play options for running CoGAPS using GenePattern Notebook and Docker. By providing Python support, cloud-based computing options, and relevant example workflows, we facilitate user-friendly interpretation and implementation of NMF for single-cell analyses.

systems biology↗