Biology subjects

Kagohara, L. T.

Publications and source records attributed to Kagohara, L. T..

At least 19 recordsLinked to original sources

bioRxiv2026

STAPLE: automating spatial transcriptomics analysis and AI interpretation

Spatial transcriptomics workflows often span separate tools for cell typing, neighborhoods, and cell-cell communication, yielding fragmented outputs that hinder scalability, interpretation, and reproducibility. STAPLE systematizes analyses across distinct methods into a modular framework, unifying data structures and cross-tool interoperability. End-to-end analyses are performed unassisted with a single invocation, fostering rigorous, reproducible spatial transcriptomics analysis. Its novel, AI-enabled reporting layer synthesizes quantitative results into summaries of biological findings, facilitating analysis interpretation.

bioinformatics↗

bioRxiv2026

Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer

Resistance to systemic therapy is a major unmet challenge in pancreatic cancer. To identify potential mechanisms of resistance, we developed a novel 3D pipeline in clinical samples that uses deep learning to classify sensitive and persistent tumor cell populations based on morphological features, enabling subsequent molecular characterization of intratumoral heterogeneity. We applied this automated 3D pipeline to a cohort of human pancreatic cancer samples treated with neoadjuvant chemotherapy, identifying heterogeneity in response to therapy both between and within tumors. Application of spatial proteomics to these sensitive and persistent regions identified enhanced epithelial-to-mesenchymal transition and non-classical cell states in persistent cells, confirming our morphological classification. Integration of spatial transcriptomics in multiple pancreatic cancer cohorts associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy. Our validated 3D multi-omic pipeline is now poised for application to clinical trials, enabling discovery of resistance mechanisms and design of new therapeutic combinations to circumvent resistance. Statement of significanceWe developed a novel 3D multi-omic pipeline to identify mechanisms of resistance to chemotherapy in clinical samples. This approach associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy and is poised for broader application in neoadjuvant clinical trials.

cancer biology↗

bioRxiv2026

Reconstructing clone-resolved transcriptional programs from bulk tumor sequencing

Tumor clones acquire distinct transcriptional programs as they evolve, but bulk RNA-seq averages over clonal mixtures and obscures the lineage-specific biology that DNA-sequencing reveals. We present PICTographPlus, the first method to infer clone-resolved transcriptional programs by integrating bulk DNA-derived clonal phylogenies and proportions with bulk RNA-seq alone, without single-cell data. Benchmarked against experimentally measured ground truth, scDNA/scRNA co-profiled cells from a wellDR-seq cancer dataset, across 320 pseudo-bulk replicates spanning four tumor purities and four sample counts and evaluated under seven regularization models, PICTographPlus recovers clone-level expression at mean Pearson r [≥] 0.92 and localizes pathway gains and losses to correct evolutionary branches (median F1 0.31-0.40, well above a no-deconvolution baseline). Applied to multi-region NSCLC, pancreatic precursor lesions, and rapid-autopsy PDAC, it localizes metabolic reprogramming, precursor-to-invasive transitions, and organ-adapted metastatic states to specific clonal branches. PICTographPlus turns standard bulk assays into clone-resolved transcriptional maps, enabling retrospective analyses where single-cell profiling is impractical.

genomics↗

bioRxiv2025

Quantitative Calibration of a Spatial QSP Model Identifies Fibroblast Impact on HCC Immunotherapy

AbstractComputational models are increasingly used to predict treatment response and optimize cancer therapy strategies. Among these, quantitative systems pharmacology (QSP) models mechanistically simulate tumor progression and pharmacological interventions, enabling virtual clinical trials, model-informed drug development, and biomarker identification. Coupling QSP with an agent-based model yields a spatial QSP (spQSP) platform that captures tissue-level spatial organization of the tumor microenvironment (TME). However, parameterizing such models to represent tumor biology remains an open problem. In this study, we developed a calibration framework using the Approximate Bayesian Computation - Sequential Monte Carlo (ABC-SMC) approach to calibrate the spQSP model with a combination of clinical and spatial molecular data, reflecting the TME characteristics of human tumors. This calibration framework matches tumor architectures between spQSP model predictions and patient spatial molecular data by fitting statistical summaries of cellular neighborhoods. We demonstrate that model calibration using CODEX data from untreated HCC patients enables prediction of TME spatial molecular states in an independent cohort receiving immune-checkpoint inhibitor (ICI) and tyrosine kinase inhibitor (TKI) combination therapy. Finally, we identify spatial and non-spatial pretreatment biomarkers and assess their predictive power for therapeutic response. This workflow demonstrates how integrating spatial-omics with multiscale mechanistic models enables quantitative calibration, biological insight, and in silico biomarker discovery, providing a framework for personalized cancer therapy across tumor types. SignificanceDigital twins and computational models are increasingly used to simulate disease and guide therapy, but they often struggle to capture the immense complexity driving the spatiotemporal evolution of the TME. The challenge is compounded by clinical sample limitations, which typically provide measurements from only static snapshots of the TME for parameter estimation. We demonstrate how mechanistic modeling frameworks can overcome this limitation by enabling inference of spatiotemporal model parameters from static spatial data - an intractable task for purely data-driven approaches. Ultimately, our work presents a workflow that integrates spatial-omics with multiscale mechanistic models, enabling quantitative calibration, deeper biological insight, and in silico biomarker discovery.

bioinformatics↗

bioRxiv2025

Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic mutation profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

bioinformatics↗

bioRxiv2025

3D multi-omic mapping of whole nondiseased human fallopian tubes at cellular resolution reveals a large incidence of ovarian cancer precursors

Uncovering the spatial and molecular landscape of precancerous lesions is essential for developing meaningful cancer prevention and early detection strategies. High-Grade Serous Carcinoma (HGSC), the most lethal gynecologic malignancy, often originates from Serous Tubal Intraepithelial Carcinomas (STICs) in the fallopian tubes, yet their minute size and our historical reliance on standard 2D histology contribute to their underreporting. Here, we present a spatially resolved, multi-omics framework that integrates whole-organ 3D imaging at cellular resolution with targeted proteomic, metabolomic, and transcriptomic profiling to detect and characterize microscopic tubal lesions. Using this platform, we identified a total of 99 STICs and their presumed precursors that harbor TP53 mutations in morphologically unremarkable tubal epithelium in all five specimens obtained from cancer-free organ donors with average-risk of developing ovarian cancer. Although these lesions comprised only 0.2% of the epithelial compartment, they displayed geographic diversity, immune exclusion, metabolic rewiring, and DNA copy number changes among lesions and normal fallopian tube epithelium discovered alterations in STIC-associated genes and the pathways they control. In sum, this platform provides a comprehensive 3D atlas of early neoplastic transformation, yielding mechanistic insights into tumor initiation and informing clinical screening strategies for detecting cancer precursors in whole organs at cellular resolution.

bioinformatics↗

bioRxiv2025

PIVOT: an open-source tool for multi-omic spatial data registration

Advances in spatial profiling have resulted in the generation of multi-omic atlases that span biological scales. In general, multiple workflows are required for image registration, coordinate registration, and spot deconvolution to integrate modalities. To improve the throughput of registration of multi-omic cohorts, we introduce PIVOT, a user-friendly and open-source interface for streamlined nonlinear registration. We demonstrate PIVOTs strengths through registration of three multi-omic datasets, and show comparison of its performance to existing workflows.

bioinformatics↗

bioRxiv2025

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal

Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring. We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. This new functionality includes compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems.

bioinformatics↗

bioRxiv2025

CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images

Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training of segmentation algorithms requires advanced programming skills and intricate workflows, limiting their accessibility to many investigators. Here, we present a protocol and software for automatic segmentation of medical images guided by a graphical user interface (GUI) using the CODAvision algorithm. This workflow simplifies the process of semantic segmentation of microanatomical structures by enabling users to train highly customizable deep learning models without extensive coding expertise. The protocol outlines best practices for creating robust training datasets, configuring model parameters, and optimizing performance across diverse biomedical image modalities. CODAvision enhances the usability of the CODA algorithm (Nature Methods, 2022) by streamlining parameter configuration, model training, and performance evaluation, automatically generating quantitative results and comprehensive reports. We expand beyond the original implementation of CODA to serial histology by demonstrating robust performance across numerous medical image modalities and diverse biological questions. We provide sample results in data types including histology, magnetic resonance imaging (MRI), and computed tomography (CT). We demonstrate the diverse use of this tool in applications including quantification of metastatic burden in in vivo models and deconvolution of spot-based spatial transcriptomics datasets. This protocol is designed for researchers with interest in rapid design of highly customizable semantic segmentation algorithms and a basic understanding of programming and anatomy.

bioinformatics↗

bioRxiv2025

Antigen-presenting cancer-associated fibroblasts in murine pancreatic tumors differentially control regulatory T cell phenotype and function via CXCL9 and CCL22

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a complex tumor microenvironment (TME) including stromal cells that influence resistance to therapy. Recent studies have revealed that stromal cancer-associated fibroblasts (CAFs) are heterogeneous in origin, gene expression, and function. Antigen-presenting CAFs (apCAFs), are defined by major histocompatibility complex (MHC)-II expression and can activate effector CD4+ T cells that have the potential to contribute to the anti-cancer immune response, but also can induce regulatory T cell (Treg) differentiation. Whether apCAFs promote or restrain the antitumor response remains uncertain. Using tumor clones of the KPC murine PDAC model differing in sensitivity to immune checkpoint blockade (ICB), we found that immunosensitive (sKPC) tumors were characterized by higher immune cell and apCAF infiltration than resistant (rKPC) tumors. IMC analysis showed proximity of apCAFs and CD4+ T cells in both sKPC and rKPC tumors implicating interaction within the TME. apCAF-depleted sKPC tumor-bearing mice had diminished sensitivity to ICB. apCAFs from both sKPC and rKPC tumors activated tumor-infiltrating CD4+ T cells and induced Treg differentiation. However, transcriptomic analysis showed that Tregs induced by apCAFs were overexpressed for immunosuppressive genes in rKPCs relative to sKPCs, and that this is associated with differential chemokine signaling from apCAFs depending on tumor origin. Together these data implicate apCAFs as important mediators of the antitumor immune response, modulation of which could facilitate the development of more effective anti-tumor immune based approaches for PDAC patients.

cancer biology↗

bioRxiv2025

An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulations

Thyroid cancer progression from curable well-differentiated thyroid carcinoma to highly lethal anaplastic thyroid carcinoma is distinguished by tumor cell de-differentiation and recruitment of a robust stromal infiltrate. Combining an integrated thyroid cancer single-cell sequencing atlas with spatial transcriptomics and bulk RNA-sequencing, we define stromal cell subpopulations and tumor-stromal cross-talk occurring across the histologic and mutational spectrum of thyroid cancer. We identify distinct inflammatory and myofibroblastic cancer-associated fibroblast (iCAF and myCAF) populations and perivascular-like populations. The myCAF population is only found in malignant samples and is associated with tumor cell invasion, BRAFV600E mutation, lymph node metastasis, and disease progression. Tumor-adjacent myCAFs abut invasive tumor cells with a partial epithelial-to-mesenchymal phenotype. Tumor-distant iCAFs infiltrate inflammatory autoimmune thyroid lesions and anaplastic tumors. In summary, our study provides an integrated atlas of thyroid cancer fibroblast subtypes and spatial characterization at sites of tumor invasion and de-differentiation, defining the stromal reorganization central to disease progression.

cancer biology↗

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

Mimicking the breast metastatic microenvironment: characterization of a novel syngeneic model of HER2+ breast cancer

Preclinical murine models in which primary tumors spontaneously metastasize to distant organs are valuable tools to study metastatic progression and novel cancer treatment combinations. Here, we characterize a novel syngeneic murine breast tumor cell line, NT2.5-lung metastasis (- LM), that provides a model of spontaneously metastatic neu-expressing breast cancer with quicker onset of widespread metastases after orthotopic mammary implantation in immune-competent NeuN mice. Within one week of orthotopic implantation of NT2.5-LM in NeuN mice, distant metastases can be observed in the lungs. Within four weeks, metastases are also observed in the bones, spleen, colon, and liver. Metastases are rapidly growing, proliferative, and responsive to HER2-directed therapy. We demonstrate altered expression of markers of epithelial-to-mesenchymal transition (EMT) and enrichment in EMT-regulating pathways, suggestive of their enhanced metastatic potential. The new NT2.5-LM model provides more rapid and spontaneous development of widespread metastases. Besides investigating mechanisms of metastatic progression, this new model may be used for the rationalized development of novel therapeutic interventions and assessment of therapeutic responses targeting distant visceral metastases SUMMARY STATEMENTWe characterize a new syngeneic, immune-competent murine model of breast cancer (NT2.5-LM) that yields rapid and widespread metastases, preserves spontaneous metastasis, and provides a model for studying novel therapeutic interventions.

cancer 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

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↗