Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multiplexed proteomic data set from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcomes. We designed a multiplex immunohistochemistry antibody panel to compare T-cell functionality and spatial localization in resected tumors from treatment-naïve patients with localized pancreatic ductal adenocarcinoma (PDAC) with resected tumors from a second cohort of patients treated with neoadjuvant agonistic CD40 (anti-CD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both cohorts were assayed, and over 1,000 tumor microenvironment (TME) features were quantified. We then trained ML models to accurately predict anti-CD40 treatment status and disease-free survival (DFS) following anti-CD40 therapy based on TME features. Through downstream interpretation of the ML models' predictions, we found anti-CD40 therapy reduced canonical aspects of T-cell exhaustion within the TME, as compared with treatment-naïve TMEs. Using automated clustering approaches, we found improved DFS following anti-CD40 therapy correlated with an increased presence of CD44+CD4+ Th1 cells located specifically within cellular neighborhoods characterized by increased T-cell proliferation, antigen experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of ML in molecular cancer immunology applications, highlight the impact of anti-CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for anti-CD40-treated patients with PDAC.

Metabolic abnormalities in the bone marrow cells of young offspring born to obese mothers.

Intrauterine metabolic reprogramming occurs in obese mothers during gestation, putting the offspring at high risk of developing obesity and associated metabolic disorders even before birth. We have generated a mouse model of maternal high-fat diet-induced obesity that recapitulates the metabolic changes seen in humans. Here, we profiled and compared the metabolic characteristics of bone marrow cells of newly weaned 3-week-old offspring of dams fed either a high-fat (Off-HFD) or a regular diet (Off-RD). We utilized a state-of-the-art targeted metabolomics approach coupled with a Seahorse metabolic analyzer. We revealed significant metabolic perturbation in the offspring of HFD-fed vs. RD-fed dams, including utilization of glucose primarily via oxidative phosphorylation, and reduction in levels of amino acids, a phenomenon previously linked to aging. Furthermore, in the bone marrow of three-week-old offspring of high-fat diet-fed mothers, we identified a unique B cell population expressing CD19 and CD11b, and found increased expression of Cyclooxygenase-2 (COX-2) on myeloid CD11b, and on CD11b hi B cells, with all the populations being significantly more abundant in offspring of dams fed HFD but not a regular diet. Altogether, we demonstrate that the offspring of obese mothers show metabolic and immune changes in the bone marrow at a very young age and prior to any symptomatic metabolic disease.

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer.

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (αCD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict αCD40 treatment status and disease-free survival (DFS) following αCD40 therapy based upon TME features. Through downstream interpretation of the machine learning models' predictions, we found αCD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following αCD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of αCD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for αCD40-treated patients with PDAC.

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.

Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole-slide imaging and accurate segmentation.

Imaging mass cytometry (IMC) is a powerful technique capable of detecting over 30 markers on a single slide. It has been increasingly used for single-cell-based spatial phenotyping in a wide range of samples. However, it only acquires a rectangle field of view (FOV) with a relatively small size and low image resolution, which hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dimensional IMC on the same tissue slide. Our computational pipeline uses the whole-slide image (WSI) of IF as a spatial reference and integrates small-FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images, and demonstrated the advantage of the dual-modality imaging strategy.

MYC Deregulation and PTEN Loss Model Tumor and Stromal Heterogeneity of Aggressive Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) patients have a poor prognosis and few treatment options. Mouse models of TNBC are important for development of new therapies, however, few mouse models represent the complexity of TNBC. Here, we develop a female TNBC murine model by mimicking two common TNBC mutations with high co-occurrence: amplification of the oncogene MYC and deletion of the tumor suppressor PTEN. This Myc;Ptenfl model develops heterogeneous triple-negative mammary tumors that display histological and molecular features commonly found in human TNBC. Our research involves deep molecular and spatial analyses on Myc;Ptenfl tumors including bulk and single-cell RNA-sequencing, and multiplex tissue-imaging. Through comparison with human TNBC, we demonstrate that this genetic mouse model develops mammary tumors with differential survival and therapeutic responses that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC, providing a pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response.

An open protocol for modeling T Cell Clonotype repertoires using TCRß CDR3 sequences.

T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.

Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole slide imaging with accurate single-cell segmentation.

Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for singlecell-based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dimensional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as a spatial reference and integrates small FOVs IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images, and demonstrated the advantage of the dual-modality imaging strategy. Motivation: Highly multiplexed tissue imaging allows visualization of the spatially resolved expression of multiple proteins at the single-cell level. Although imaging mass cytometry (IMC) using metal isotope-conjugated antibodies has a significant advantage of low background signal and absence of autofluorescence or batch effect, it has a low resolution that hampers accurate cell segmentation and results in inaccurate feature extraction. In addition, IMC only acquires mm 2 -sized rectangle regions, which limits its application and efficiency when studying larger clinical samples with non-rectangle shapes. To maximize the research output of IMC, we developed the dual-modality imaging method based on a highly practical and technical improvement requiring no extra specialized equipment or agents and proposed a comprehensive computational pipeline that combines IF and IMC. The proposed method greatly improves the accuracy of cell segmentation and downstream analysis and is able to obtain whole slide image IMC to capture the comprehensive cellular landscape of large tissue sections.

Supervised learning of high-confidence phenotypic subpopulations from single-cell data.

Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here, by deploying a learning with rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to select informative features and identify cell subpopulations simultaneously, which enables the accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel ability for supervised phenotypic trajectory learning of subpopulations from single-cell data. We conducted comprehensive simulations to evaluate PENCILs versatility in simultaneous gene selection, subpopulation identification and phenotypic trajectory prediction. PENCIL is fast and scalable to analyze 1 million cells within 1 hour. Using the classification mode, PENCIL detected T-cell subpopulations associated with melanoma immunotherapy outcomes. Moreover, when applied to scRNA-seq of a mantle cell lymphoma patient with drug treatment across multiple time points, the regression mode of PENCIL revealed a transcriptional treatment response trajectory. Collectively, our work introduces a scalable and flexible infrastructure to accurately identify phenotype-associated subpopulations from single-cell data.

Challenges and opportunities for modeling aging and cancer.

Age is among the main risk factors for cancer, and any cancer study in adults is faced with an aging tissue and organism. Yet, pre-clinical studies are carried out using young mice and are not able to address the impact of aging and associated comorbidities on disease biology and treatment outcomes. Here, we discuss the limitations of current mouse cancer models and suggest strategies for developing novel models to address these major gaps in knowledge and experimental approaches.

Alignment, segmentation and neighborhood analysis in cyclic immunohistochemistry data using CASSATT.

Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. Additionally, cycIHC benefits from the speed and simplicity of brightfield microscopy, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the log odds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact-or do not interact-at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT is a fully open source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

T cell egress via lymphatic vessels is tuned by antigen encounter and limits tumor control.

Antigen-specific CD8+ T cell accumulation in tumors is a prerequisite for effective immunotherapy, and yet the mechanisms of lymphocyte transit are not well defined. Here we show that tumor-associated lymphatic vessels control T cell exit from tumors via the chemokine CXCL12, and intratumoral antigen encounter tunes CXCR4 expression by effector CD8+ T cells. Only high-affinity antigen downregulates CXCR4 and upregulates the CXCL12 decoy receptor, ACKR3, thereby reducing CXCL12 sensitivity and promoting T cell retention. A diverse repertoire of functional tumor-specific CD8+ T cells, therefore, exit the tumor, which limits the pool of CD8+ T cells available to exert tumor control. CXCR4 inhibition or loss of lymphatic-specific CXCL12 boosts T cell retention and enhances tumor control. These data indicate that strategies to limit T cell egress might be an approach to boost the quantity and quality of intratumoral T cells and thereby response to immunotherapy.

An open protocol for modeling T Cell Clonotype repertoires using TCRß CDR3 sequences.

T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.

Modulation of myeloid and T cells in vivo by Bruton's tyrosine kinase inhibitor ibrutinib in patients with metastatic pancreatic ductal adenocarcinoma.

BACKGROUND: In preclinical studies of pancreatic ductal adenocarcinoma (PDAC), ibrutinib improved the antitumor efficacy of the standard of care chemotherapy. This led to a phase 1b clinical trial to determine the safety, tolerability, and immunologic effects of ibrutinib treatment in patients with advanced PDAC. METHODS: Previously untreated patients with PDAC were enrolled in a phase 1b clinical trial (ClinicalTrials.gov) to determine the safety, toxicity, and maximal tolerated dose of ibrutinib when administered with the standard regimen of gemcitabine and nab-paclitaxel. To study the immune response to ibrutinib alone, the trial included an immune response arm where patients were administered with ibrutinib daily for a week followed by ibrutinib combined with gemcitabine and nab-paclitaxel. Endoscopic ultrasonography-guided primary PDAC tumor biopsies and blood were collected before and after ibrutinib monotherapy. Changes in abundance and functional state of immune cells in the blood was evaluated by mass cytometry by time of flight and statistical scaffold analysis, while that in the local tumor microenvironment (TME) were assessed by multiplex immunohistochemistry. Changes in B-cell receptor and T-cell receptor repertoire were assessed by sequencing and analysis of clonality. RESULTS: In the blood, ibrutinib monotherapy significantly increased the frequencies of activated inducible T cell costimulator+(ICOS+) CD4+ T cells and monocytes. Within the TME, ibrutinib monotherapy led to a trend in decreased B-cell abundance but increased interleukin-10+ B-cell frequency. Monotherapy also led to a trend in increased mature CD208+dendritic cell density, increased late effector (programmed cell death protein 1 (PD-1-) eomesodermin (EOMES+)) CD8+ T-cell frequency, with a concomitantly decreased dysfunctional (PD-1+ EOMES+) CD8+ T-cell frequency. When ibrutinib was combined with chemotherapy, most of these immune changes were not observed. Patients with partial clinical responses had more diverse T and B cell receptor repertoires prior to therapy initiation. CONCLUSION: Ibrutinib monotherapy skewed the immune landscape both in the circulation and TME towards activated T cells, monocytes and DCs. These effects were not observed when combining ibrutinib with standard of care chemotherapy. Future studies may focus on other therapeutic combinations that augment the immunomodulatory effects of ibrutinib in solid tumors. TRIAL REGISTRATION NUMBER: NCT02562898.

Axatilimab for Chronic Graft-Versus-Host Disease After Failure of at Least Two Prior Systemic Therapies: Results of a Phase I/II Study.

PURPOSE: Chronic graft-versus-host disease (cGVHD) remains the major cause of late morbidity after allogeneic hematopoietic cell transplantation. Colony-stimulating factor 1 receptor (CSF-1R)-dependent macrophages promote cGVHD fibrosis, and their elimination in preclinical studies ameliorated cGVHD. Axatilimab is a humanized monoclonal antibody that inhibits CSF-1R signaling and restrains macrophage development. PATIENTS AND METHODS: This phase I (phI)/phase II (phII) open-label study (ClinicalTrials.gov identifier: NCT03604692) evaluated safety, tolerability, and efficacy of axatilimab in patients age ≥ 6 years with active cGVHD after ≥ 2 prior systemic therapy lines. Primary objectives in phI were to identify the optimal biologic and recommended phII dose and in phII to evaluate the overall (complete and partial) response rate (ORR) at the start of treatment cycle 7. RESULTS: Forty enrolled patients (17 phI; 23 phII) received at least one axatilimab dose. In phI, a dose of 3 mg/kg given once every 4 weeks met the optimal biologic dose definition. Two dose-limiting toxicities occurred at the 3 mg/kg dose given once every 2 weeks. At least one treatment-related adverse event (TRAE) was observed in 30 patients with grade ≥ 3 TRAEs in eight patients, the majority known on-target effects of CSF-1R inhibition. No cytomegalovirus reactivations occurred. With the 50% ORR at cycle 7 day 1, the phII cohort met the primary efficacy end point. Furthermore, the ORR in the first six cycles, an end point supporting regulatory approvals, was 82%. Responses were seen in all affected organs regardless of prior therapy. Fifty-eight percent of patients reported significant improvement in cGVHD-related symptoms using the Lee Symptom Scale. On-target activity of axatilimab was suggested by the decrease in skin CSF-1R-expressing macrophages. CONCLUSION: Targeting profibrotic macrophages with axatilimab is a therapeutically promising novel strategy with a favorable safety profile for refractory cGVHD.

Therapeutics That Promote Sympathetic Reinnervation Modulate the Inflammatory Response After Myocardial Infarction.

Myocardial infarction (MI) triggers an inflammatory response that transitions from pro-inflammatory to reparative over time. Restoring sympathetic nerves in the heart after MI prevents arrhythmias. This study investigated if reinnervation altered the immune response after MI. This study used quantitative multiplex immunohistochemistry to identify the immune cells present in the heart 2 weeks after ischemia-reperfusion. Two therapeutics stimulated reinnervation, preventing arrhythmias and shifting the immune response from inflammatory to reparative, with fewer pro-inflammatory macrophages and more regulatory T cells and reparative macrophages. Treatments did not alter macrophage phenotype in vitro, which suggested reinnervation contributed to the altered immune response.

Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with poor 5-year survival rates, necessitating identification of novel therapeutic targets. Elucidating the biology of the tumor immune microenvironment (TiME) can provide vital insights into mechanisms of tumor progression. In this study, we developed a quantitative image processing platform to analyze sequential multiplexed IHC data from archival PDAC tissue resection specimens. A 27-plex marker panel was employed to simultaneously phenotype cell populations and their functional states, followed by a computational workflow to interrogate the immune contextures of the TiME in search of potential biomarkers. The PDAC TiME reflected a low-immunogenic ecosystem with both high intratumoral and intertumoral heterogeneity. Spatial analysis revealed that the relative distance between IL10+ myelomonocytes, PD-1+ CD4+ T cells, and granzyme B+ CD8+ T cells correlated significantly with survival, from which a spatial proximity signature termed imRS was derived that correlated with PDAC patient survival. Furthermore, spatial enrichment of CD8+ T cells in lymphoid aggregates was also linked to improved survival. Altogether, these findings indicate that the PDAC TiME, generally considered immuno-dormant or immunosuppressive, is a spatially nuanced ecosystem orchestrated by ordered immune hierarchies. This new understanding of spatial complexity may guide novel treatment strategies for PDAC. SIGNIFICANCE: Quantitative image analysis of PDAC specimens reveals intertumoral and intratumoral heterogeneity of immune populations and identifies spatial immune architectures that are significantly associated with disease prognosis.

A multiplex implantable microdevice assay identifies synergistic combinations of cancer immunotherapies and conventional drugs.

Systematically identifying synergistic combinations of targeted agents and immunotherapies for cancer treatments remains difficult. In this study, we integrated high-throughput and high-content techniques-an implantable microdevice to administer multiple drugs into different sites in tumors at nanodoses and multiplexed imaging of tumor microenvironmental states-to investigate the tumor cell and immunological response signatures to different treatment regimens. Using a mouse model of breast cancer, we identified effective combinations from among numerous agents within days. In vivo studies in three immunocompetent mammary carcinoma models demonstrated that the predicted combinations synergistically increased therapeutic efficacy. We identified at least five promising treatment strategies, of which the panobinostat, venetoclax and anti-CD40 triple therapy was the most effective in inducing complete tumor remission across models. Successful drug combinations increased spatial association of cancer stem cells with dendritic cells during immunogenic cell death, suggesting this as an important mechanism of action in long-term breast cancer control.