Career trajectories of MD-PhD physician scientists: The loss of women investigators

Advances in biomedical research require a robust physician scientist workforce. Despite being equally successful at securing early career awards from the NIH as men, women MD-PhD physician scientists are less likely to serve as principal investigators on mid- and later careers awards. Here, we discuss the causes of gender disparities in academic medicine, the implications of losing highly trained women physician scientists, and the institutional and systemic changes needed to sustain this pool of talented investigators.

Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts (CAF). This study used a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid coculture experiments. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing data indicated that CAF density is associated with increased inflammation and epithelial-mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional data from patient-derived organoid and CAF cocultures provided in silico validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in cocultures demonstrated integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGFA) interactions with neuropilin-1 mediating CAF-epithelial cell cross-talk. Together, this study introduces transfer learning from human single-cell data to organoid coculture analyses for experimental validation of discoveries of cell-cell cross-talk and identifies fibroblast-mediated regulation of EMT and inflammation.

SIGNIFICANCE

Adaptation of transfer learning to relate human single-cell RNA sequencing data to organoid-CAF cocultures facilitates discovery of human pancreatic cancer intercellular interactions and uncovers cross-talk between CAFs and tumor cells through VEGFA and ITGB1.

Personalized neoantigen vaccine and pembrolizumab in advanced hepatocellular carcinoma: a phase 1/2 trial

Programmed cell death protein 1 (PD-1) inhibitors have modest efficacy as a monotherapy in hepatocellular carcinoma (HCC). A personalized therapeutic cancer vaccine (PTCV) may enhance responses to PD-1 inhibitors through the induction of tumor-specific immunity. We present results from a single-arm, open-label, phase 1/2 study of a DNA plasmid PTCV (GNOS-PV02) encoding up to 40 neoantigens coadministered with plasmid-encoded interleukin-12 plus pembrolizumab in patients with advanced HCC previously treated with a multityrosine kinase inhibitor. Safety and immunogenicity were assessed as primary endpoints, and treatment efficacy and feasibility were evaluated as secondary endpoints. The most common treatment-related adverse events were injection-site reactions, observed in 15 of 36 (41.6%) patients. No dose-limiting toxicities or treatment-related grade ≥3 events were observed. The objective response rate (modified intention-to-treat) per Response Evaluation Criteria in Solid Tumors 1.1 was 30.6% (11 of 36 patients), with 8.3% (3 of 36) of patients achieving a complete response. Clinical responses were associated with the number of neoantigens encoded in the vaccine. Neoantigen-specific T cell responses were confirmed in 19 of 22 (86.4%) evaluable patients by enzyme-linked immunosorbent spot assays. Multiparametric cellular profiling revealed active, proliferative and cytolytic vaccine-specific CD4+ and CD8+ effector T cells. T cell receptor β-chain (TCRβ) bulk sequencing results demonstrated vaccination-enriched T cell clone expansion and tumor infiltration. Single-cell analysis revealed posttreatment T cell clonal expansion of cytotoxic T cell phenotypes. TCR complementarity-determining region cloning of expanded T cell clones in the tumors following vaccination confirmed reactivity against vaccine-encoded neoantigens. Our results support the PTCV's mechanism of action based on the induction of antitumor T cells and show that a PTCV plus pembrolizumab has clinical activity in advanced HCC. ClinicalTrials.gov identifier: NCT04251117 .

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

MLL1 regulates cytokine-driven cell migration and metastasis

Cell migration is a critical contributor to metastasis. Cytokine production and its role in cancer cell migration have been traditionally associated with immune cells. We find that the histone methyltransferase Mixed-Lineage Leukemia 1 (MLL1) controls 3D cell migration via cytokines, IL-6, IL-8, and TGF-β1, secreted by the cancer cells themselves. MLL1, with its scaffold protein Menin, controls actin filament assembly via the IL-6/8/pSTAT3/Arp3 axis and myosin contractility via the TGF-β1/Gli2/ROCK1/2/pMLC2 axis, which together regulate dynamic protrusion generation and 3D cell migration. MLL1 also regulates cell proliferation via mitosis-based and cell cycle-related pathways. Mice bearing orthotopic MLL1-depleted tumors exhibit decreased lung metastatic burden and longer survival. MLL1 depletion leads to lower metastatic burden even when controlling for the difference in primary tumor growth rates. Combining MLL1-Menin inhibitor with paclitaxel abrogates tumor growth and metastasis, including preexistent metastasis. These results establish MLL1 as a potent regulator of cell migration and highlight the potential of targeting MLL1 in patients with metastatic disease.

Entinostat, nivolumab and ipilimumab for women with advanced HER2-negative breast cancer: a phase Ib trial

We report the results of 24 women, 50% (N = 12) with hormone receptor-positive breast cancer and 50% (N = 12) with advanced triple-negative breast cancer, treated with entinostat + nivolumab + ipilimumab from the dose escalation (N = 6) and expansion cohort (N = 18) of ETCTN-9844 ( NCT02453620 ). The primary endpoint was safety. Secondary endpoints were overall response rate, clinical benefit rate, progression-free survival and change in tumor CD8:FoxP3 ratio. There were no dose-limiting toxicities. Among evaluable participants (N = 20), the overall response rate was 25% (N = 5), with 40% (N = 4) in triple-negative breast cancer and 10% (N = 1) in hormone receptor-positive breast cancer. The clinical benefit rate was 40% (N = 8), and progression-free survival at 6 months was 50%. Exploratory analyses revealed that changes in myeloid cells may contribute to responses; however, no correlation was noted between changes in CD8:FoxP3 ratio, PD-L1 status and tumor mutational burden and response. These findings support further investigation of this treatment in a phase II trial.

Antibody dependent cell-mediated cytotoxicity selection pressure induces diverse mechanisms of resistance

Targeted monoclonal antibody therapy has emerged as a powerful therapeutic strategy for cancer. However, only a minority of patients have durable responses and the development of resistance remains a major clinical obstacle. Antibody-dependent cell-mediated cytotoxicity (ADCC) represents a crucial therapeutic mechanism of action; however, few studies have explored ADCC resistance. Using multiple in vitro models of ADCC selection pressure, we have uncovered both shared and distinct resistance mechanisms. Persistent ADCC selection pressure yielded ADCC-resistant cells that are characterized by a loss of NK cell conjugation and this shared resistance phenotype is associated with cell-line dependent modulation of cell surface proteins that contribute to immune synapse formation and NK cell function. We employed single-cell RNA sequencing and proteomic screens to interrogate molecular mechanisms of resistance. We demonstrate that ADCC resistance involves upregulation of interferon/STAT1 and DNA damage response signaling as well as activation of the immunoproteasome. Here, we identify pathways that modulate ADCC sensitivity and report strategies to enhance ADCC-mediated elimination of cancer cells. ADCC resistance could not be reversed with combinatorial treatment approaches. Hence, our findings indicate that tumor cells utilize multiple strategies to inhibit NK cell mediated-ADCC. Future research and development of NK cell-based immunotherapies must incorporate plans to address or potentially prevent the induction of resistance.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

Age-associated Senescent - T Cell Signaling Promotes Type 3 Immunity that Inhibits the Biomaterial Regenerative Response

Aging is associated with immunological changes that compromise response to infections and vaccines, exacerbate inflammatory diseases and can potentially mitigate tissue repair. Even so, age-related changes to the immune response to tissue damage and regenerative medicine therapies remain unknown. Here, it is characterized how aging induces changes in immunological signatures that inhibit tissue repair and therapeutic response to a clinical regenerative biological scaffold derived from extracellular matrix. Signatures of inflammation and interleukin (IL)-17 signaling increased with injury and treatment both locally and regionally in aged animals, and computational analysis uncovered age-associated senescent-T cell communication that promotes type 3 immunity in T cells. Local inhibition of type 3 immune activation using IL17-neutralizing antibodies improves healing and restores therapeutic response to the regenerative biomaterial, promoting muscle repair in older animals. These results provide insights into tissue immune dysregulation that occurs with aging that can be targeted to rejuvenate repair.

CD4 T cell-activating neoantigens enhance personalized cancer vaccine efficacy

Personalized cancer vaccines aim to activate and expand cytotoxic antitumor CD8+ T cells to recognize and kill tumor cells. However, the role of CD4+ T cell activation in the clinical benefit of these vaccines is not well defined. We previously established a personalized neoantigen vaccine (PancVAX) for the pancreatic cancer cell line Panc02, which activates tumor-specific CD8+ T cells but required combinatorial checkpoint modulators to achieve therapeutic efficacy. To determine the effects of neoantigen-specific CD4+ T cell activation, we generated a vaccine (PancVAX2) targeting both major histocompatibility complex class I- (MHCI-) and MHCII-specific neoantigens. Tumor-bearing mice vaccinated with PancVAX2 had significantly improved control of tumor growth and long-term survival benefit without concurrent administration of checkpoint inhibitors. PancVAX2 significantly enhanced priming and recruitment of neoantigen-specific CD8+ T cells into the tumor with lower PD-1 expression after reactivation compared with the CD8+ vaccine alone. Vaccine-induced neoantigen-specific Th1 CD4+ T cells in the tumor were associated with decreased Tregs. Consistent with this, PancVAX2 was associated with more proimmune myeloid-derived suppressor cells and M1-like macrophages in the tumor, demonstrating a less immunosuppressive tumor microenvironment. This study demonstrates the biological importance of prioritizing and including CD4+ T cell-specific neoantigens for personalized cancer vaccine modalities.

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. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

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.

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.

Barcoding intracellular reverse transcription enables high-throughput phenotype-coupled T cell receptor analyses

Assays linking cellular phenotypes with T cell or B cell antigen receptor sequences are crucial for characterizing adaptive immune responses. Existing methodologies are limited by low sample throughput and high cost. Here, we present INtraCEllular Reverse Transcription with Sorting and sequencing (INCERTS), an approach that combines molecular indexing of receptor repertoires within intact cells and fluorescence-activated cell sorting (FACS). We demonstrate that INCERTS enables efficient processing of millions of cells from pooled human peripheral blood mononuclear cell (PBMC) samples while retaining robust association between T cell receptor (TCR) sequences and cellular phenotypes. We used INCERTS to discover antigen-specific TCRs from patients with cancer immunized with a novel mutant KRAS peptide vaccine. After ex vivo stimulation, 28 uniquely barcoded samples were pooled prior to FACS into peptide-reactive and non-reactive CD4+ and CD8+ populations. Combining complementary patient-matched single-cell RNA sequencing (scRNA-seq) data enabled retrieval of full-length, paired TCR alpha and beta chain sequences for future validation of therapeutic utility.

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

BACKGROUND

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients. 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.

METHODS

We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a prospective 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 at the time of resection.

RESULTS

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 marker expression suggesting strong activity of these cells. HCC-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 the tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic responses, 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 HCC-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient.

CONCLUSIONS

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.

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 TGFβ 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.

Transcriptomic forecasting with neural ordinary differential equations

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 by using metabolic labeling single-cell RNA sequencing (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 3-day period. Thus, RNAForecaster enables short-term estimation of future expression states in biological systems from high-throughput datasets with temporal information.

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells' (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo-derived senescence signature (SenSig) using a foreign body response-driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and "cartilage-like" fibroblasts as senescent and defined cell type-specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34-CSF1R-TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.

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 scRNAseq 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. ScANNA's 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.

Converging on a Cure: The Roads to Predictive Immunotherapy

SUMMARY

Convergence science teams integrating clinical, biological, engineering, and computational expertise are inventing new forecast systems to monitor and predict evolutionary changes in tumor and immune interactions during early cancer progression and therapeutic response. The resulting methods should inform a new predictive medicine paradigm to select adaptive immunotherapeutic regimens personalized to patients' tumors at a given time during their cancer progression for durable patient response.

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

Morphology-guided transcriptomic analysis of human pancreatic cancer organoids reveals microenvironmental signals that enhance invasion

Pancreatic ductal adenocarcinoma (PDAC) frequently presents with metastasis, but the molecular programs in human PDAC cells that drive invasion are not well understood. Using an experimental pipeline enabling PDAC organoid isolation and collection based on invasive phenotype, we assessed the transcriptomic programs associated with invasion in our organoid model. We identified differentially expressed genes in invasive organoids compared with matched noninvasive organoids from the same patients, and we confirmed that the encoded proteins were enhanced in organoid invasive protrusions. We identified 3 distinct transcriptomic groups in invasive organoids, 2 of which correlated directly with the morphological invasion patterns and were characterized by distinct upregulated pathways. Leveraging publicly available single-cell RNA-sequencing data, we mapped our transcriptomic groups onto human PDAC tissue samples, highlighting differences in the tumor microenvironment between transcriptomic groups and suggesting that non-neoplastic cells in the tumor microenvironment can modulate tumor cell invasion. To further address this possibility, we performed computational ligand-receptor analysis and validated the impact of multiple ligands (TGF-β1, IL-6, CXCL12, MMP9) on invasion and gene expression in an independent cohort of fresh human PDAC organoids. Our results identify molecular programs driving morphologically defined invasion patterns and highlight the tumor microenvironment as a potential modulator of these programs.

Abstract 2270: Combination of CXCR4 antagonist and anti-PD1 therapy results in significant mobilization and increased infiltration of myeloid cells into the metastatic liver microenvironment of PDAC

Pancreatic adenocarcinoma (PDAC) is extremely lethal and resistant to checkpoint immunotherapy, characterized by an immunosuppressive tumor microenvironment consisting of stromal and myeloid cells. Prior studies have demonstrated the utility of targeting the chemokine signaling axis CXCR4-CXCL12 (SDF-1), a highlighted feature in PDACs, to overcome the CXCL12-driven immobilization of T cells and thus facilitate their antitumor role within the tumor. Based on these findings, we have conducted a phase 2 trial evaluating the effects of plerixafor, a CXCR4 antagonist, and cemiplimab, a PD1 inhibitor antibody, in patients with metastatic PDAC who have progressed after one line of systemic chemotherapy (NCT04177810). To determine the immunological responses to therapy, blood samples and tissue biopsies were obtained at baseline and during treatment. Hematological assessment confirmed the activity of CXCR4 antagonist in mobilizing hematopoietic precursors (CD34+), immature myeloid cells, and lymphoid cells, as well as monocytic and granulocytic cell populations. Suspension mass cytometry analysis of peripheral blood mononuclear cells revealed that mobilized monocytic subpopulations had high expressions of chemokine receptors CCR2, CCR5, and CXCR2. Histopathologic evaluation of the serial tissue biopsies from the liver metastatic PDAC revealed increased levels of inflammation upon treatment. To further characterize the cellular constituents of the observed inflammation, multiplexed immunohistochemistry by imaging mass cytometry was performed, demonstrating strong trends toward increased infiltration of not only effector T cells but also macrophages and granulocytic cells into the tumor microenvironment. Taken together, these findings suggest that mobilization of myeloid cells by CXCR4 antagonism results in the recruitment of additional myeloid cells from circulation and that alternative chemokine signaling pathways are sufficient for doing so. This implicates a potential mode of resistance against CXCR4-targeted therapies. Furthermore, these observations reinforce the value of ongoing research efforts in the field to subvert the recruitment or immunosuppressive function of myeloid cells, which would be particularly relevant in the setting of CXCR4 antagonism.

Citation Format: Sarah M. Shin, Alexei Hernandez, Erin Coyne, Zhehao Zhang, Sarah Mitchell, Jennifer Durham, Xuan Yuan, Hongqui Yang, Elana J. Fertig, Elizabeth M. Jaffee, Katherine M. Bever, Dung T. Le, Won Jin Ho. Combination of CXCR4 antagonist and anti-PD1 therapy results in significant mobilization and increased infiltration of myeloid cells into the metastatic liver microenvironment of PDAC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2270.

Abstract 2873: Evolution of immune cell composition and functionality as pancreatic intraepithelial neoplasia progresses to pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is most often diagnosed at an advanced stage. Newly diagnosed patients therefore have a dismal five-year survival rate of 11%. However, PDAC progresses from pre-invasive pancreatic intraepithelial neoplasia (PanIN) over at least a decade. Throughout this transition, the tissue microenvironment becomes increasingly immunosuppressive. Early PanINs may therefore be more amenable to immune-based interception strategies; however, little is known about the pre-malignant lesion immune microenvironment in PDAC. We hypothesized that the identification of the immune landscape of PanINs will elucidate the immuno-dynamic changes that occur during PanIN-to-PDAC progression and identify novel strategies for intercepting PDAC. Here, we use an inducible mouse model to study the evolution of immunosuppression from PanINs to PDAC. We pair spatial molecular profiling of our mouse model with profiling of human tissue in a cohort of patients with normal tissue, chronic pancreatitis, PanIN, and PDAC. To examine the evolution of the immune microenvironment throughout PanIN-to-PDAC progression, we first optimized a tamoxifen-inducible Pdx1-CreERT2 mouse that controls KRASG12D expression and knocks out p53. The impact of KRASG12D expression on the immune cell landscape in PanIN and PDAC lesions was examined by immunohistochemistry (IHC) and RNA in situ hybridization on mouse pancreas. We used imaging mass cytometry (IMC) of 35 immune markers to better classify and quantify the immune cell subtypes. Our analyses thus far reveal increased Tregs as PanINs progress to PDAC. Furthermore, although CD3+ T cells are recruited to tumors, these immune cells are strictly restricted to the immediate edge of the tumor and predominantly consist of Tregs. For our human analyses, FFPE pancreas sections from treatment naïve patients who had undergone surgical resection without neoadjuvant chemotherapy were evaluated. Each section contained regions of normal tissue, chronic pancreatitis, PanIN and PDAC. These were evaluated using IHC, IMC, and spatial transcriptomics to allow us to spatially evaluate the immune populations associated with lesions and PDAC. The relative density and localization of myeloid and lymphoid cell types in both PanIN and PDAC regions revealed an initial influx of CD8+ and CD4+ T cells to PanINs and a progressively immunosuppressive microenvironment in subsequent stages. While PanINs and PDAC both recruited immune cells, the phenotypes of the immune infiltrates were distinct and revealed unique immune pathways that could contribute to immunosuppression as PanINs develop into PDACs. Our proteomic and transcriptomic data from mouse and human pancreas show that mutant KRAS driven premalignant lesions recruit an evolving immune response that readily becomes immunosuppressive as progression to PDAC occurs.

Citation Format: Melissa R. Lyman, Jacob T. Mitchell, Luciane T. Kagohara, Benjamin Barrett, Amanda Huff, Sarah Shin, Gabriella Longway, Anuj Gupta, Lalitya Andaloori, Todd D. Armstrong, Daniel Haldar, Robert Anders, Elizabeth Thompson, Nilo Azad, Won Jin Ho, Elizabeth Jaffee, Elana J. Fertig, Neeha Zaidi. Evolution of immune cell composition and functionality as pancreatic intraepithelial neoplasia progresses to pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2873.

Abstract 5076: Combination PancVAX neo-epitope vaccine with anti-CTLA-4 and anti-PD-1 antibodies enhances infiltration of cytotoxic T cells and mitigates T cell exhaustion in a murine model of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a deadly cancer with a low tumor mutational burden and therefore few neoantigen targets that can be recognized by cytotoxic T cells. Most PDACs are thus insensitive to either single or dual immune checkpoint inhibitor (ICI) therapy. Personalized neoantigen vaccines can expand the number and repertoire of anti-tumor T cells that infiltrate the tumor and mediate cytotoxicity. To model a personalized neoantigen vaccine treatment strategy in PDAC, we previously developed PancVAX, a peptide-based vaccine targeting 12 neoantigens expressed in the murine pancreatic cell line Panc02 (Kinkead et al, JCI Insight 2018). Although we observed increased T cell infiltration present in the tumor post-vaccination, these cells expressed high levels of exhaustion markers. We therefore hypothesized that sequential administration of anti-CTLA-4 and anti-PD-1 would enhance the pool of T cells primed by the neoantigen vaccine and maintain activation of antigen-experienced T cells, respectively, to yield optimal and durable neoantigen-specific anti-tumor immunity in PDAC. To address this, mice bearing subcutaneous Panc02 tumors were vaccinated with two rounds of the PancVAX neoantigen vaccine followed by anti-CTLA-4 and anti-PD-1 3 days later. Anti-PD-1 maintenance was given twice weekly beginning at the first vaccine dose. Twelve days after the last peptide vaccine dose, tumors were harvested and dissociated into single-cell suspensions for paired single-cell RNA-sequencing and TCR-sequencing. Mice that were untreated or given ICIs without PancVAX had the highest proportions of CD8+ T cells expressing exhaustion markers. PancVAX-treated mice had more intratumoral cycling CD8 T cells and effector CD8+ T cells with high cytotoxic gene expression. Among mice treated with PancVAX, tumors from mice treated with PancVAX + anti-PD1 or PancVAX + anti-PD1 + anti-CTLA-4 had the highest proportions of effector CD8+ T cells. Ongoing analyses include differential gene expression and pathway analysis between treatment conditions in the T cell compartment in mice treated with combination ICI and PancVAX. Additionally, we will assess changes in T cell clonality and diversity within the tumors when mice are treated with single or combination therapy. These results will define a transcriptional signature associated with the generation of a productive anti-tumor immune response when neoantigen vaccines and ICI are used in combination. This work demonstrates how the addition of ICIs to personalized neo-epitope vaccines for PDAC can further enhance the quality of vaccine-induced T cell effector function in an otherwise immunologically cold tumor type and supports their inclusion in neoantigen vaccination strategies for patients with PDAC.

Citation Format: Jacob T. Mitchell, Amanda Huff, Emily Davis-Marcisak, Fangluo Chen, Todd D. Armstrong, Luciane T. Kagohara, James Leatherman, Rulin Wang, Srinivasan Yegnasubramanian, Elizabeth M. Jaffee, Elana J. Fertig, Neeha Zaidi. Combination PancVAX neo-epitope vaccine with anti-CTLA-4 and anti-PD-1 antibodies enhances infiltration of cytotoxic T cells and mitigates T cell exhaustion in a murine model of pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5076.

Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy

Advances in cancer immunotherapy, particularly immune checkpoint inhibitors, have dramatically improved the prognosis for patients with metastatic melanoma and other previously incurable cancers. However, patients with pancreatic ductal adenocarcinoma (PDAC) generally do not respond to these therapies. PDAC is exceptionally difficult to treat because of its often late stage at diagnosis, modest mutation burden, and notoriously complex and immunosuppressive tumor microenvironment. Simultaneously interrogating features of cancer, immune, and other cellular components of the PDAC tumor microenvironment is therefore crucial for identifying biomarkers of immunotherapeutic resistance and response. Notably, single-cell and multiomics technologies, along with the analytical tools for interpreting corresponding data, are facilitating discoveries of the systems-level cellular and molecular interactions contributing to the overall resistance of PDAC to immunotherapy. Thus, in this review, we will explore how multiomics and single-cell analyses provide the unprecedented opportunity to identify biomarkers of resistance and response to successfully sensitize PDAC to immunotherapy.

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.

A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy

Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone. Random forest classifiers were trained on publicly available anti-PD1 data (n = 104), and subsequently tested on an independent anti-PD1 cohort (n = 69). Both the training and test datasets included a range of cancer types such as non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), melanoma, and smaller numbers of patients from other tumor types. Features used include summaries such as TMB and number of frameshift mutations, as well as more gene-level features such as counts of mutations associated with immune checkpoint response and resistance. Both ML algorithms demonstrated area under the receiver-operator curves (AUC) that exceeded TMB alone (AUC 0.63 "human-guided," 0.64 "cluster," and 0.58 TMB alone). Mutations within oncogenes disproportionately modulate anti-PD1 responses relative to their overall contribution to tumor neoepitope burden. The use of a ML algorithm evaluating multiple proposed genomic determinants of anti-PD1 responses modestly improves performance over TMB alone, highlighting the need to integrate other biomarkers to further improve model performance.

Multi-omic analyses of changes in the tumor microenvironment of pancreatic adenocarcinoma following neoadjuvant treatment with anti-PD-1 therapy

Successful pancreatic ductal adenocarcinoma (PDAC) immunotherapy necessitates optimization and maintenance of activated effector T cells (Teff). We prospectively collected and applied multi-omic analyses to paired pre- and post-treatment PDAC specimens collected in a platform neoadjuvant study of granulocyte-macrophage colony-stimulating factor-secreting allogeneic PDAC vaccine (GVAX) vaccine ± nivolumab (anti-programmed cell death protein 1 [PD-1]) to uncover sensitivity and resistance mechanisms. We show that GVAX-induced tertiary lymphoid aggregates become immune-regulatory sites in response to GVAX + nivolumab. Higher densities of tumor-associated neutrophils (TANs) following GVAX + nivolumab portend poorer overall survival (OS). Increased T cells expressing CD137 associated with cytotoxic Teff signatures and correlated with increased OS. Bulk and single-cell RNA sequencing found that nivolumab alters CD4+ T cell chemotaxis signaling in association with CD11b+ neutrophil degranulation, and CD8+ T cell expression of CD137 was required for optimal T cell activation. These findings provide insights into PD-1-regulated immune pathways in PDAC that should inform more effective therapeutic combinations that include TAN regulators and T cell activators.

Engineered bispecific antibodies targeting the interleukin-6 and -8 receptors potently inhibit cancer cell migration and tumor metastasis

Simultaneous inhibition of interleukin-6 (IL-6) and interleukin-8 (IL-8) signaling diminishes cancer cell migration, and combination therapy has recently been shown to synergistically reduce metastatic burden in a preclinical model of triple-negative breast cancer. Here, we have engineered two novel bispecific antibodies that target the IL-6 and IL-8 receptors to concurrently block the signaling activity of both ligands. We demonstrate that a first-in-class bispecific antibody design has promising therapeutic potential, with enhanced selectivity and potency compared with monoclonal antibody and small-molecule drug combinations in both cellular and animal models of metastatic triple-negative breast cancer. Mechanistic characterization revealed that our engineered bispecific antibodies have no impact on cell viability, but profoundly reduce the migratory potential of cancer cells; hence they constitute a true anti-metastatic treatment. Moreover, we demonstrate that our antibodies can be readily combined with standard-of-care anti-proliferative drugs to develop effective anti-cancer regimens. Collectively, our work establishes an innovative metastasis-focused direction for cancer drug development.

Integrated T cell cytometry metrics for immune-monitoring applications in immunotherapy clinical trials

Mass cytometry, or cytometry by TOF (CyTOF), provides a robust means of determining protein-level measurements of more than 40 markers simultaneously. While the functional states of immune cells occur along continuous phenotypic transitions, cytometric studies surveying cell phenotypes often rely on static metrics, such as discrete cell-type abundances, based on canonical markers and/or restrictive gating strategies. To overcome this limitation, we applied single-cell trajectory inference and nonnegative matrix factorization methods to CyTOF data to trace the dynamics of T cell states. In the setting of cancer immunotherapy, we showed that patient-specific summaries of continuous phenotypic shifts in T cells could be inferred from peripheral blood-derived CyTOF mass cytometry data. We further illustrated that transfer learning enabled these T cell continuous metrics to be used to estimate patient-specific cell states in new sample cohorts from a reference patient data set. Our work establishes the utility of continuous metrics for CyTOF analysis as tools for translational discovery.

Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer

The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

Triple-negative breast cancer metastasis involves complex epithelial-mesenchymal transition dynamics and requires vimentin

Triple-negative breast cancer (TNBC) is an aggressive subtype associated with early metastatic recurrence and worse patient outcomes. TNBC tumors express molecular markers of the epithelial-mesenchymal transition (EMT), but its requirement during spontaneous TNBC metastasis in vivo remains incompletely understood. We demonstrated that spontaneous TNBC tumors from a genetically engineered mouse model (GEMM), multiple patient-derived xenografts, and archival patient samples exhibited large populations in vivo of hybrid E/M cells that lead invasion ex vivo while expressing both epithelial and mesenchymal characteristics. The mesenchymal marker vimentin promoted invasion and repressed metastatic outgrowth. We next tested the requirement for five EMT transcription factors and observed distinct patterns of utilization during invasion and colony formation. These differences suggested a sequential activation of multiple EMT molecular programs during the metastatic cascade. Consistent with this model, our longitudinal single-cell RNA analysis detected three different EMT-related molecular patterns. We observed cancer cells progressing from epithelial to hybrid E/M and strongly mesenchymal patterns during invasion and from epithelial to a hybrid E/M pattern during colony formation. We next investigated the relative epithelial versus mesenchymal state of cancer cells in both GEMM and patient metastases. In both contexts, we observed heterogeneity between and within metastases in the same individual. We observed a complex spectrum of epithelial, hybrid E/M, and mesenchymal cell states within metastases, suggesting that there are multiple successful molecular strategies for distant organ colonization. Together, our results demonstrate an important and complex role for EMT programs during TNBC metastasis.

Abstract 1970: Single cell proteomic quantification of T cell states using mass cytometry for applications in monitoring immune responses in cancer immunotherapy clinical trials

Although immunotherapies have been established as a successful treatment option for several types of cancers, additional treatment strategies are necessary to broaden the cohorts of patients who may receive clinical benefit. Common immunological profiling for immunotherapy response depends on assessment of the composition of immune cell types in tissue samples and the periphery. Still, the function and state of each of these cells further contributes to the ultimate clinical benefit of immunotherapeutic response. Additionally, our ability to comprehensively study treatment strategies depends on elucidating the temporal effects different immunotherapy regimens have on the immune system of patients. Single-cell proteomics profiling with CyTOF mass cytometry enables robust protein-level profiling of cell types and is also high-dimensional enough to characterize functional states of immune cells. To construct an immunological framework that may feasibly serve to empower functional assessment of immune cell states for clinical trial monitoring strategies using CyTOF, we are employing single cell trajectory inference methods and non-negative matrix factorization (NMF). We demonstrate this approach using peripheral blood derived CyTOF mass cytometry data to model T cell states at the proteomic level. First, we extract T helper (Th) and T cytotoxic (Tc) cell integrated metrics from CyTOF mass cytometry data obtained from diverse cell marker panel designs, immunological states, and diseases to benchmark that these computational pipelines can accurately reflect Th and Tc activation and exhaustion. Next, we develop a new pipeline that enables the use of continuous functional cell-state metrics from continuous pseudotime and NMF pattern weights as immunological profiling markers from CyTOF that can be applied to cancer immunotherapy clinical trials. For instance, we find CyTOF Tc pseudotime and memory pattern weights are higher in PDAC patients with stable disease treated with ipilimumab and a pancreatic cancer-specific vaccine. Thus, our pipeline has the ability to integrate metrics from mass cytometry data to empower translational analyses for determining patient responses in cancer immunotherapy, such as adaptive immune responses to checkpoint inhibitors and cancer vaccines. Moreover, applying this pipeline to peripheral blood samples obtained before and after treatment also enables us to map further temporal cell state transitions in Tc and Th cells resulting from that treatment. Future work will include leveraging CyTOF T cell integrated metrics in larger scale survival analyses to test the predictive potential in a clinical proof of concept.

Citation Format: Dimitrios N. Sidiropoulos, Zaw Phyo, Nicole Gross, Soren Charmsaz, Stephanie Xavier, James Leatherman, Mark Yarchoan, Elizabeth M. Jaffee, Elana J. Fertig, Won Jin Ho. Single cell proteomic quantification of T cell states using mass cytometry for applications in monitoring immune responses in cancer immunotherapy clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1970.

Abstract 1480: miR-21 as a post-transcriptional regulator of pancreatic ductal adenocarcinoma (PDAC) tumorigenesis

Introduction: MicroRNAs are short non-coding RNAs that are frequently dysregulated across cancers. Specifically, microRNA-21 (miR-21) is a known oncomir overexpressed in pancreatic adenocarcinoma (PDAC) that regulates multiple gene targets downstream of KRAS, the site of the primary driver mutation in PDAC. Past efforts to target mutant KRAS have been limited by compensatory activation of other growth pathways and treatment-related toxicity. Inhibiting miR-21 expression is a novel therapeutic strategy to target KRAS effector function through post-transcriptional regulation. We previously demonstrated that systemic inhibition of miRNA-21 (miR-21) intercepts tumorigenesis in the transgenic KrasG12D/+;Trp53R172H/+;Pdx-1-Cre (KPC) mice without causing overt toxicity. Our major goal was to verify the translation of the previous findings to human models and examine the mechanistic implications of miR-21 inhibition in PDAC.

Experimental Procedures: Using publicly available data from a cohort of patients with PDAC in The Cancer Genome Atlas (TCGA), we performed differential expression analysis of miR-21 and KRAS-related gene targets as well as gene set enrichment analysis of oncogenic pathways identified by the Molecular Signatures Database (MSigDB). Concurrently, de-novo patient-derived organoid (PDO) models were generated from core biopsies and surgical resection specimens. To evaluate the effects of miR-21 inhibition on KRAS pathway activity in a human model system, we selected 6 PDO cell lines and determined miR-21 gene expression by quantitative PCR at baseline and after knockdown using a lentiviral construct.

Results: Analysis of TCGA PDAC cohort identified heterogeneous endogenous expression of miR-21, which was validated ex vivo in our PDO model system. Gene set enrichment analysis revealed enrichment of gene sets associated with KRAS dependency, MEK, AKT, and MTOR signaling in patients with higher endogenous miR-21 expression. MiR-21 knockdown in PDO cell lines was stable at multiple intervals following lentiviral transduction. Further, expression of PDCD4, a tumor suppressor gene and target of miR-21 downstream of KRAS, was enhanced in PDO lines following miR-21 inhibition.

Conclusions: We previously demonstrated that miR-21 appears to be an early and reliable molecular marker of pancreatic neoplasia and that systemic inhibition in a murine model intercepts PDAC tumorigenesis. We now demonstrate in human models that higher miR-21 expression is associated with enrichment of gene sets downstream of KRAS. Additionally, knocking down miR-21 expression enhances the expression of gene targets with tumor suppressor function, notably PDCD4. This suggests that modulating miR-21 expression subsequently modulates the expression of gene targets with critical cell regulatory functions and provides additional insight into novel therapeutic targets.

Citation Format: Jacquelyn W. Zimmerman, Daniel H. Shu, Richard A. Burkhart, Joseph Tandurella, Elana J. Fertig, Elizabeth M. Jaffee. miR-21 as a post-transcriptional regulator of pancreatic ductal adenocarcinoma (PDAC) tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1480.

Abstract 1459: Identification of transcription factor enrichments in HPV+ head and neck cancer using ChIP-seq and cistrome analysis

The subset of head and neck squamous cell carcinomas (HNSCC) that are driven by infection with the Human Papilloma Virus (HPV+ HNSCC) are increasing in prevalence and are defined by distinct biological signatures and mechanisms, such as abnormal transcriptional regulation. Here, we use optimized protocols to perform ChIP-seq and RNA-seq on patient surgical samples, which we use to define promoter, typical-enhancer, and super-enhancer (SE) regulatory regions with differential chromatin states (i.e. H3K27ac signal) between tumor and normal samples. We then leverage publicly available transcription factor (TF) cistrome data to test 372 TFs for tumor or normal specific enrichment across these differential regulatory regions. We identify 31 tumor-specific TF enrichments, such as TP53/63/73, NKX2-1, JUNB/D, FOSL1/2, E2F1/3, KLF3/4/5, SMAD3/4, TEAD1/4, TFAP2A/C, HIF1A, PPARG, GRHL2, EPAS1, and SNAI2. A number of these enriched TFs, including TP63, TP73, FOSL1, and E2F1, show both significantly increased expression in tumors and significant transcriptional reductions after JQ1 treatment. These results provide novel insight into the regulatory biology of HPV+ HNSCC, and suggest that the transcriptional networks and feedback loops governing TF expression and dysregulation in HPV+ HNSCC can be targeted using JQ1 treatment.

Citation Format: Fernando Zamuner, Spencer S. Chan, Michael Kessler, Ilya Vorontsov, Ludmila Danilova, Rossin Erbe, Dylan Kelley, Theresa Guo, Eddie Imada, Elana J. Fertig, Ivan Kulakovskiy, Alexander Favorov, Daria A. Gaykalova. Identification of transcription factor enrichments in HPV+ head and neck cancer using ChIP-seq and cistrome analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1459.

Abstract 2130: Uncovering the spatial landscape of tumor-immune interactions using latent spaces from spatial transcriptomics

Recent advances in spatial transcriptomics (ST) enable us to measure gene expression from cancer tissues while retaining their spatial context. We present a novel bioinformatics pipeline to infer molecular changes from tumor and immune cell interactions in the tumor microenvironment (TME) from ST data. Latent space methods enable inference of biological patterns from ST without the need for spot deconvolution into cell-based spatial features. While linear latent space methods yield interpretable biological patterns, interactions between tumor and immune cells can be nonlinear. To enable comprehensive inference of the pathways in the TME, we developed novel algorithms to characterize biological patterns from ST data using linear latent space methods and further nonlinear effects from their interactions. For any given set of genes, the patternSpotter tool visualizes the spatial variation in the relative contribution of individual patterns to the aggregate expression at each location in the tumor sample. Application of this tool to latent features identified using CoGAPS non-negative matrix factorization on a Visium ST (10x Genomics) data from a lymph node with pancreatic cancer metastasis confirms its known immune cell architecture. Furthermore, we develop a patternMarker algorithm to identify sets of coexpressed genes associated with the patterns, which help us to pinpoint the underlying biological processes and cell types. Further analyzing a breast cancer sample with invasive carcinoma and multiple precursor lesions demonstrates that this approach can uncover tumor and immune regions without prior reliance on pathology annotations from H&E imaging. In this case, an ensemble-based factorization of multiple dimensions enhances our resolution of intra-tumor heterogeneity and identifies distinct hormone receptor pathways in different precursor lesions with the patternMarker algorithm. Additional latent features are associated with immune cells, revealing further heterogeneity in immune infiltration between the invasive carcinoma and distinct precursor lesions. Still, the molecular interactions resulting from this infiltration induce a further non-linear alteration to transcription not captured through the inferred latent spaces. To resolve this, we develop a further interactionMarker statistic to identify regions of inter-pattern interaction and the associated genes. We apply this approach to detect additional intra-tumor heterogeneity in immune signaling from infiltration suggestive of differences in immune attack of invasive lesions. Altogether, our pipeline for latent space analysis of ST can identify the location and context-specific molecular interactions within the TME, broadly applicable to a better understanding of the key drivers of tumorigenesis and resistance to immune attack in cancer.

Citation Format: Atul Deshpande, Melanie Loth, Dimitrios Sidiropoulos, QingFeng Zhu, Genevieve Stein-O'Brien, NIkhil Rao, Cedric Uytingco, Stephen Williams, Cesar Santa-Maria, Daniele M. Gilkes, Lei Zhang, Elizabeth Jaffee, Robert Anders, Ludmila Danilova, Luciane T. Kagohara, Elana J. Fertig. Uncovering the spatial landscape of tumor-immune interactions using latent spaces from spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2130.

Abstract 3820: Spatial transcriptomics of advanced hepatocellular carcinomas distinguishes intercellular interactions in responders and non-responders to cabozantinib and nivolumab neoadjuvant therapy

The recent development of genome-wide spatial transcriptomics (ST) approaches enable near single-cell gene expression profiling to infer cellular composition and intercellular interactions that drive cancer development and responses to therapy. This study applied ST on 10 surgical biospecimens from a clinical trial with neoadjuvant therapy with cabozantinib (multi-kinase inhibitor) and nivolumab (anti-PD-1 monoclonal antibody) with advanced hepatocellular carcinoma (HCC). Within our cohort, 6 of the samples were obtained from non-responders and 4 with demonstrated pathological response were previously associated with immune infiltration using spatial proteomics technologies. Our analysis with ST was performed to determine the specific pathways that drive immune infiltration in responders and to map intercellular interactions relevant for response and resistance to the combined therapy.Analysis of these data uncovered three main differences between responders and non-responders. First, to better understand the tumor mechanisms of response and resistance, we performed differential expression and pathway analysis only in the subset of tumor clusters from responders versus non-responders. In responders, we observed enrichment for pathways associated with immune response (TNF-alpha, IFN-gamma, T cell differentiation), while in non-responders the deregulated pathways are associated with cell growth, transcriptional activity and hypoxia (Myc, E2F, oxidative phosphorylation). Second, the intercellular interaction analyses indicate that CD8-HLA interactions are more abundant in responders, while interactions activating VEGFR, the main target of cabozantinib, are enriched in non-responders. The interaction profiles are evidence that in responders the tumor cells express tumor specific antigens that are recognized by the cytotoxic cells which activity is enhanced by nivolumab. In non-responders, the activation of the VEGF pathway is an indication that the tumor cells have developed mechanism of resistance to cabozantinib. Third, responding tumors have higher densities of immune and stromal cells, and the immune cells are enriched with aggregates composed of both B and T cells. The regions surrounded by these immune aggregates are transcriptionally distinct from regions enriched for stromal cells, suggesting that tumor gene expression profile drives immune infiltration.Overall, the ST analysis of neoadjuvant HCC treated samples detects tumor induced immune cell immune infiltration in responders compared to non-responders with enrichment of cytotoxic interactions to eliminate the tumor cells. It also identifies intercellular interactions suggestive of resistance to anti-VEGF blockade.

Citation Format: Luciane T. Kagohara, Shuming Zhang, Long Yuan, Qingfeng Zhu, Robert Anders, Daniel Shu, Aleksander S. Popel, Elizabeth M. Jaffee, Mark Yarchoan, Elana J. Fertig. Spatial transcriptomics of advanced hepatocellular carcinomas distinguishes intercellular interactions in responders and non-responders to cabozantinib and nivolumab neoadjuvant therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3820.

Abstract 637: Spatial transcriptomics for FFPE characterizes the molecular and cellular architecture of malignant changes in pancreatic pre-malignant lesions

We have optimized an experimental and computational pipeline to adapt spatial transcriptomics (ST) approaches based upon the Visium (10x Genomics) technology to infer cellular composition and intercellular interactions of FFPE clinical specimens. We apply this technology to deliver an approach to examine pancreatic intraepithelial neoplasia (PanIN) to identify intrinsic and extrinsic mechanisms that are associated with the progression of these pre-malignant lesions to invasive carcinoma. Currently, most pancreatic cancers are diagnosed at an advanced stage that reflects in dismal survival rates and a better understanding of PanINs biology will provide valuable insights for early therapeutic interventions. Thus, we used PanINs as our model system to implement the FFPE ST workflow. Our workflow for FFPE ST analysis facilitates sectioning of small regions (5mm in diameter) from a paraffin block that are stained and imaged with H&E and concurrently measured for genome-wide transcriptional profiling. Subsequently, the image is used for automated cell annotation using an algorithm, CODA, trained to identify normal and neoplastic pancreatic cell types. CODA identified the normal pancreatic histological regions (ducts, acini, islets of Langerhans, stroma), as well as the neoplastic cells. This automated analysis enables isolation of specific spots for differential expression analysis to pinpoint the transcriptional changes that occur within neoplastic cells along ducts in PanIn and their changes between high-grade and low-grade lesions. The spatial gene expression analysis identified clusters that mapped to the cell types annotated by CODA and the marker genes of each cluster matched known markers for the correspondent cell type. Although PanINs are very small in size (< 1mm), we found specific clusters accurately mapped to these lesions in each sample. Overall, the spatial sequencing data presented enough depth and complexity to allow differential expression and pathway analysis. We observed a significant number of deregulated genes in PanINs compared to normal ducts. Some deregulated genes are known PanIN markers, but potential new markers were also identified. Moreover, the integration of CODA with gene expression changes enables us to verify that unique stromal regions annotated with CODA and associated with PanIns are in fact heterogeneous and formed by distinct cell subtypes. Altogether, our workflow combining automated cell annotation with STA from the same section provides a methodology to precisely examine the sample architecture while measuring heterogeneity at the transcriptional level. This combined approach can be applied to different FFPE tumor types to leverage the use of large bioarchives of samples not previously accessible to genome-wide spatial methods.

Citation Format: Alexander T. Bell, Kohei Fujikura, Jacob Stern, Rena Chan, James Chell, Stephen Williams, Ashley Kiemen, Elizabeth M. Jaffee, Denis Wirtz, Laura D. Wood, Elana J. Fertig, Luciane T. Kagohara. Spatial transcriptomics for FFPE characterizes the molecular and cellular architecture of malignant changes in pancreatic pre-malignant lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 637.

Entinostat decreases immune suppression to promote anti-tumor responses in a HER2+ breast tumor microenvironment

Therapeutic combinations to alter immunosuppressive, solid tumor microenvironments (TMEs), such as in breast cancer, are essential to improve responses to immune checkpoint inhibitors (ICIs). Entinostat, an oral histone deacetylase inhibitor (HDACi), has been shown to improve responses to ICIs in various tumor models with immunosuppressive TMEs. The precise and comprehensive alterations to the TME induced by entinostat remain unknown. Here, we employed single-cell RNA-sequencing on HER2-overexpressing breast tumors from mice treated with entinostat and ICIs in order to fully characterize changes across multiple cell types within the TME. This analysis demonstrates that treatment with entinostat induced a shift from a pro-tumor to an anti-tumor TME signature, characterized predominantly by changes in myeloid cells. We confirmed myeloid-derived suppressor cells (MDSCs) within entinostat-treated tumors associated with a less suppressive granulocytic (G)-MDSC phenotype and exhibited altered suppressive signaling that involved the NFkB and STAT3 pathways. In addition to MDSCs, tumor-associated macrophages were epigenetically reprogrammed from a pro-tumor M2-like phenotype toward an anti-tumor M1-like phenotype, which may be contributing to a more sensitized TME. Overall, our in-depth analysis suggests that entinostat-induced changes on multiple myeloid cell types reduce immunosuppression and increase anti-tumor responses, which, in turn, improve sensitivity to ICIs. Sensitization of the TME by entinostat could ultimately broaden the population of patients with breast cancer who could benefit from ICIs.

A tumor-derived type III collagen-rich ECM niche regulates tumor cell dormancy

Cancer cells disseminate and seed in distant organs, where they can remain dormant for many years before forming clinically detectable metastases. Here we studied how disseminated tumor cells sense and remodel the extracellular matrix (ECM) to sustain dormancy. ECM proteomics revealed that dormant cancer cells assemble a type III collagen-enriched ECM niche. Tumor-derived type III collagen is required to sustain tumor dormancy, as its disruption restores tumor cell proliferation through DDR1-mediated STAT1 signaling. Second-harmonic generation two-photon microscopy further revealed that the dormancy-to-reactivation transition is accompanied by changes in type III collagen architecture and abundance. Analysis of clinical samples revealed that type III collagen levels were increased in tumors from patients with lymph node-negative head and neck squamous cell carcinoma compared to patients who were positive for lymph node colonization. Our data support the idea that the manipulation of these mechanisms could serve as a barrier to metastasis through disseminated tumor cell dormancy induction.

Generating colorblind-friendly scatter plots for single-cell data

Reduced-dimension or spatial in situ scatter plots are widely employed in bioinformatics papers analyzing single-cell data to present phenomena or cell-conditions of interest in cell groups. When displaying these cell groups, color is frequently the only graphical cue used to differentiate them. However, as the complexity of the information presented in these visualizations increases, the usefulness of color as the only visual cue declines, especially for the sizable readership with color-vision deficiencies (CVDs). In this paper, we present scatterHatch, an R package that creates easily interpretable scatter plots by redundant coding of cell groups using colors as well as patterns. We give examples to demonstrate how the scatterHatch plots are more accessible than simple scatter plots when simulated for various types of CVDs.

Abstract PO-111: A human single-cell RNA sequencing atlas of pancreatic ductal adenocarcinoma enables harmonized cell type calling and comprehensive analyses of potential intercellular signaling

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dismal prognosis, low ductal cancer cellularity and a dominant tumor microenvironment (TME) in response to malignant degeneration. Modern single-cell RNA sequencing (scRNA-seq) platforms fundamentally improved the opportunities to analyze PDAC biology through isolation of diverse cell types including ductal, mesenchymal, myeloid and lymphoid populations. Published scRNA-seq data of PDAC patients provide innovative and astounding insights, but are limited by cohort size and intrinsically vulnerable to internal biases. Herein, we present a human single-cell PDAC atlas which links the previous sequencing efforts aiming for increasing depth and robustness with the combined analysis of transcriptomes. Methods: We selected scRNA-seq data of all patients with PDAC from six publicly available datasets (published between 2019-2020). Altogether, 61 different human samples with 142,807 cells were integrated into one dataset leveraging 15,219 genes, which were conclusively identical between all datasets based on the utilized nomenclature in the provided raw data. In addition, we extracted 16 samples with 31,587 cells from control pancreas specimens which were included in three out of the six datasets. The analyses were performed using the R statistics and Python environments utilizing established software including Monocle3 and Seurat. Results: After computational preprocessing of the integrated dataset, cell types were identified based on differentially expressed and canonical markers. The generated PDAC atlas consists of 26% ductal cancer, 2% ductal normal, 12% mesenchymal (stellate cells and cancer-associated fibroblasts), 18% myeloid, 19% lymphoid and 23% other cells (including acinar and endocrine cells). Copy number variation analyses confirmed the discrimination between cancer and normal ductal cells. Certain subpopulations within cell types were mapped based on the expression of supervised gene sets. Within the ductal cancer cell population, the Classical and Basal-like Moffitt signatures coexisted in the majority of patients with distinct ratios and predominance, which were associated with differences in the TME composition. Furthermore, the presence of myofibroblasts and inflammatory fibroblasts could be quantified at the patient-level. The reconstruction of intercellular signaling between ductal cancer cells and several TME components revealed potential ligands, receptors and transcription factors that may functionally define routes and polarity of cross-talk in PDAC. Conclusion: This human scRNA-seq atlas is the largest available dataset of patients with PDAC while harmonizing previously published data. It is engineered to analyze current knowledge gaps with increased rigor and, most importantly, overcomes obstacles related to bulk transcriptome sequencing data. Molecular characteristics of the ductal cancer cells and TME components inferred from the presented framework are promising to identify disease- and patient-specific signaling, key regulators, and therapeutic targets.

Citation Format: Benedict Kinny-Köster, Melissa R. Lyman, Dimitrios N. Sidiropoulos, Melanie Loth, Alexandra B. Puscek, Laura D. Wood, Jin He, Jun Yu, Richard A. Burkhart, Elizabeth M. Jaffee, Jacquelyn W. Zimmerman, Elana J. Fertig. A human single-cell RNA sequencing atlas of pancreatic ductal adenocarcinoma enables harmonized cell type calling and comprehensive analyses of potential intercellular signaling [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-111.

DPP inhibition alters the CXCR3 axis and enhances NK and CD8+ T cell infiltration to improve anti-PD1 efficacy in murine models of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer death in the USA by 2030. Immune checkpoint inhibitors fail to control most PDAC tumors because of PDAC’s extensive immunosuppressive microenvironment and poor immune infiltration, a phenotype also seen in other non-inflamed (ie, ‘cold’) tumors. Identifying novel ways to enhance immunotherapy efficacy in PDAC is critical. Dipeptidyl peptidase (DPP) inhibition can enhance immunotherapy efficacy in other cancer types; however, the impact of DPP inhibition on PDAC tumors remains unexplored.

Methods

We examined the effects of an oral small molecule DPP inhibitor (BXCL701) on PDAC tumor growth using mT3-2D and Pan02 subcutaneous syngeneic murine models in C57BL/6 mice. We explored the effects of DPP inhibition on the tumor immune landscape using RNAseq, immunohistochemistry, cytokine evaluation and flow cytometry. We then tested if BXCL701 enhanced anti-programmed cell death protein 1 (anti-PD1) efficacy and performed immune cell depletion and rechallenged studies to explore the relevance of cytotoxic immune cells to combination treatment efficacy.

Results

In both murine models of PDAC, DPP inhibition enhanced NK and T cell immune infiltration and reduced tumor growth. DPP inhibition also enhanced the efficacy of anti-PD1. The efficacy of dual anti-PD1 and BXCL701 therapy was dependent on both CD8+ T cells and NK cells. Mice treated with this combination therapy developed antitumor immune memory that cleared some tumors after re-exposure. Lastly, we used The Cancer Genome Atlas (TCGA) to demonstrate that increased NK cell content, but not T cell content, in human PDAC tumors is correlated with longer overall survival. We propose that broad DPP inhibition enhances antitumor immune response via two mechanisms: (1) DPP4 inhibition increases tumor content of CXCL9/10, which recruits CXCR3+ NK and T cells, and (2) DPP8/9 inhibition activates the inflammasome, resulting in proinflammatory cytokine release and Th1 response, further enhancing the CXCL9/10-CXCR3 axis.

Conclusions

These findings show that DPP inhibition with BXCL701 represents a pharmacologic strategy to increase the tumor microenvironment immune cell content to improve anti-PD1 efficacy in PDAC, suggesting BXCL701 can enhance immunotherapy efficacy in ‘cold’ tumor types. These findings also highlight the potential importance of NK cells along with T cells in regulating PDAC tumor growth.

Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response

Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.

Spatial correlation statistics enable transcriptome-wide characterization of RNA structure binding

Molecular interactions at identical transcriptomic locations or at proximal but non-overlapping sites can mediate RNA modification and regulation, necessitating tools to uncover these spatial relationships. We present nearBynding, a flexible algorithm and software pipeline that models spatial correlation between transcriptome-wide tracks from diverse data types. nearBynding can process and correlate interval as well as continuous data and incorporate experimentally derived or in silico predicted transcriptomic tracks. nearBynding offers visualization functions for its statistics to identify colocalizations and adjacent features. We demonstrate the application of nearBynding to correlate RNA-binding protein (RBP) binding preferences with other RBPs, RNA structure, or RNA modification. By cross-correlating RBP binding and RNA structure data, we demonstrate that nearBynding recapitulates known RBP binding to structural motifs and provides biological insights into RBP binding preference of G-quadruplexes. nearBynding is available as an R/Bioconductor package and can run on a personal computer, making correlation of transcriptomic features broadly accessible.