Biology subjects

Coussens, L. M.

Publications and source records attributed to Coussens, L. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Plectin promotes an aggressive phenotype and represses cytotoxic T cell activity in pancreatic cancer

BackgroundPancreatic adenocarcinoma (PDAC) is an abysmal disease, with a poor clinical outcome, largely due to limited life-extending treatments for patients. Notoriously, PDAC displays a T cell-suppressive tumor microenvironment where underlying molecular mechanisms that lead to this phenotype remain poorly understood. To unravel specific mechanisms, we utilized bioinformatic analyses with functional studies and revealed the cytolinker protein plectin (PLEC) as a novel player in regulating the T cell-suppressive tumor microenvironment of PDAC. MethodsUtilizing the TCGA-PAAD dataset, tumor samples were separated by PLEC expression to evaluate patient survival, and pathway analyses associated with increased tumorigenesis. Evaluation of immune infiltration and subsequent immune deconvolution was performed using tidyestimate and CIBERSORTx R packages. Single-cell RNA-seq (scRNA-seq) analysis from 229 PDAC patients was analyzed to investigate signaling dynamics and immune cell infiltration in PLECHigh patients. Functional validation was provided using a monoclonal antibody (mAb) against cell surface plectin (CSP) in two murine PDAC models to examine changes in tumor growth and immune cell subset abundance. ResultsOur studies revealed that high plectin expression results in an overall worse survival associated with activation of pro-tumorigenic pathways and decreased anti-tumor immune signature in PDAC patients. Analysis via GSEA indicates PLECHigh patients display an aggressive phenotype and suppressed pro-inflammatory signaling pathways. Immune ESTIMATE scores were significantly decreased in PLECHigh patients, and scRNA-seq analysis revealed that PLECHigh tumors display a decrease in anti-tumor CD8+ T cells. In vivo analyses using an anti-CSP mAb revealed a reduction in tumor growth kinetics compared to IgG control corresponding with a significant increase in proliferating and activated cytotoxic CD8+ T cells. Anti-CSP-mediated tumor suppression was inhibited when CD8+ T cells were depleted, indicating that anti-CSP treatment is contingent on cytotoxic T cell functionality. ConclusionOur findings identify plectin as a biomarker of aggressive disease in PDAC, with high plectin expression associated with decreased T cell infiltration, and that treatment with anti-CSP mAb reinstates anti-tumor immunity and decreases tumor volume in vivo. These findings position plectin as a high-priority therapeutic target, with the potential to fundamentally reshape immune responses in PDAC and improve outcomes for patients with few remaining options.

cancer biology↗

bioRxiv2026

ImmunoPheno: A Computational Framework for Data-Driven Design and Analysis of Immunophenotyping Experiments

Immunophenotyping is fundamental to characterizing tissue cellular composition, pathogenic processes, and immune infiltration, yet its accuracy and reproducibility remain constrained by heuristic antibody panel design and manual gating. Here, we present ImmunoPheno, an open-source computational platform that repurposes large-scale single-cell proteo-transcriptomic data to guide immunophenotyping experimental design and analysis. ImmunoPheno integrates existing datasets to automate the design of optimal antibody panels, gating strategies, and cell identity annotation. We used ImmunoPheno to construct a harmonized reference (HICAR) comprising 390 monoclonal antibodies and 93 human immune cell populations. Leveraging this resource, we algorithmically designed minimal panels to isolate rare populations, such as MAIT cells and pDCs, which we validated experimentally. We further demonstrate accurate cell identity annotation across publicly available and newly generated cytometry datasets spanning diverse technologies, including spatial platforms like CODEX. ImmunoPheno complements expert curation and supports continual expansion, providing a scalable framework to enhance the accuracy, reproducibility, and resolution of immunophenotyping.

bioinformatics↗

bioRxiv2025

A window trial in metastatic pancreatic ductal adenocarcinoma reveals resistance mechanisms to targeting the KRAS-MEK pathway

Copy number alterations of KRAS, mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), and MYC occur in 30-40% of PDAC. Here we demonstrate that KRAS and MYC are frequently co-gained and accompanied with worse prognosis in PDAC. In a Window-of-Opportunity clinical trial for metastatic PDAC, serial biopsies and deep multi-omics analyses were utilized to explore resistance mechanisms to MEK inhibition, as a surrogate for KRAS inhibition. Tumors from four of 14 patients showed Ki-67/CA19-9-based biomarker response (BR). Non-BR tumors were enriched for KRAS/MYC co-gain and KRASG12D variant. A transcriptomic signature of BR tumors was inversely correlated with KRASG12D/MYC co-gain in a large PDAC dataset and predictive for KRAS inhibitor response in multiple models. Finally, co-targeting KRAS and MYC was synergistic in KRASG12D/MYC co-gain PDAC. Together, this study provides insight into KRAS inhibitor resistance and supports MYC as an important target to improve patient outcomes in this deadly disease.

cancer biology↗

bioRxiv2025

Myc and Kras cooperate in adult acinar cells to drive phenotypic heterogeneity, metastasis, and therapeutic resistance in a novel pancreatic cancer mouse model

Pancreatic ductal adenocarcinoma (PDAc) is a deadly malignancy, most commonly diagnosed in advanced stages when no curative treatments are available. The development of new models that aid ongoing investigation into the mechanisms by which it initiates, disseminates, and evades treatment is of the utmost importance. In vivo models that accurately recapitulate the features and spectrum of human pancreatic cancer are paramount to make a dent in this disease as two decades of the standard-of-care have failed to substantially improve survival. Here, we take advantage of our finding that post-translational stabiliziation of MYC downstream of the canonical PDAc driver, mutant KRAS, is an early event in PDAc progression to design a novel mouse model of PDAc progression based on deregulated, constituitive expression of Myc and mutant Kras in adult pancreatic acinar cells. Tumors from this KMC model histologically and molecularly recapitulate heterogeneity seen in human PDAc, with a high rate of metastasis to the liver. Cell lines derived from KMC autochthonous PDAc provide new models for orthotopic primary tumors that reliably metastasize to the liver and lung, providing important new tools to efficiently study the metastatic cascade and aid in the develoment of new therapeutics addressing metastatic disease. Cell lines represent distinct molecular subtypes with corresponding differential drug sensitivity. Toghether, this model provides a new and additional tool in the study of pancreatic cancer and the means by which it so deftly evades our best efforts at treatment.

cancer biology↗

bioRxiv2025

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

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

bioinformatics↗

bioRxiv2025

mRNA stability factor HuR promotes immune evasion in pancreatic ductal adenocarcinoma

The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by a limited infiltration of tumor-specific T cells and anti-tumor T cell activity. Extracellular factors in the PDAC TME have been widely reported to mediate immune suppression, but the contribution from tumor-intrinsic factors is not well understood. The RNA-binding protein, HuR (ELAVL1), is enriched in PDAC and negatively correlates with T cell infiltration. In an immunocompetent Kras-p53-Cre (KPC) orthotopic model of PDAC, we found that genetic disruption of HuR impaired tumor growth due to a novel role of HuR inducing T-cell suppression. Importantly, we found that HuR depletion in tumors enhanced both T cell number and activation states and diminished myeloid phenotypes by comprehensive spatial profiling of the PDAC TME. Mechanistically, HuR mediated the stabilization of mTOR pathway transcripts, and inhibition of mTOR activity rescued the impaired function of local T cells. Translating these findings, we demonstrated that HuR depletion sensitized PDAC tumors to immune checkpoint blockade, while isogenic, wildtype tumors are resistant. For the first time, we show that HuR facilitates tumor immune suppression in PDAC by inhibiting T cell infiltration and function and implicate targeting HuR as a potential therapeutic strategy in combination with immunotherapy. SIGNIFICANCEThis study identified a novel mechanism that HuR supports pancreatic tumor growth by restricting T cell infiltration, promoting immune evasion. Our work supports targeting strategies against HuR in PDAC with the goal of enhancing PDAC sensitivity to immune-based cancer therapies, such as checkpoint blockade and T cell transfer.

cancer biology↗

bioRxiv2025

Neoplastic immune mimicry is a generalizable phenomenon in breast cancer and epithelial CD69 enables early tumor progression

Dedifferentiation programs are commonly enacted during breast cancer progression to enhance tumor cell fitness. Increased cellular plasticity within the neoplastic compartment of tumors correlates with disease aggressiveness, often culminating in greater resistance to cytotoxic therapies or augmented metastatic potential. Here we report that subpopulations of dedifferentiated neoplastic breast epithelial cells express canonical leukocyte cell surface receptor proteins and have thus named this cellular program "immune mimicry." We document neoplastic cells engaging in immune mimicry within public human breast tumor single-cell RNA-seq datasets, histopathological breast tumor specimens, breast cancer cell lines, as well as in murine transgenic and cell line-derived mammary cancer models. Immune-mimicked neoplastic cells harbor hallmarks of dedifferentiation and are enriched in treatment-resistant and high-grade breast tumors. We corroborated these observations in aggressive breast cancer cell lines where anti-proliferative cytotoxic chemotherapies drove epithelial cells toward immune mimicry. Moreover, in subsequent proof-of-concept studies, we demonstrate that expression of the CD69 leukocyte activation protein by neoplastic cells confers a proliferative advantage that facilitates early tumor growth and therefore conclude that neoplastic breast epithelial cells upregulating leukocyte surface receptors potentiate malignancy. Moving forward, neoplastic immune mimicry should be evaluated for prognostic utility in additional breast cancer cohorts to determine its potential for patient stratification. Future research should evaluate correlates with distal metastases, progression-free survival, overall survival, and therapeutic response/resistance. Statement of SignificanceNeoplastic breast epithelial cells express surface receptors canonically attributed to leukocytes and are associated with therapy resistance and aggressive tumor behavior.

cancer biology↗

bioRxiv2023

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 CD11bhi 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.

physiology↗

bioRxiv2023

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 (CD40) 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 CD40 treatment status and disease-free survival (DFS) following CD40 therapy based upon TME features. Through downstream interpretation of the machine learning models predictions, we found CD40 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 CD40 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 CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for CD40-treated patients with PDAC.

cancer biology↗

bioRxiv2023

Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers

CDK4/6 inhibitors (CDK4/6i) have transformed the treatment of hormone receptor-positive (HR+), HER2-negative (HR+) breast cancers as they are effective across all clinicopathological, age, and ethnicity subgroups for metastatic HR+ breast cancer. In metastatic ER+ breast cancer, CDK4/6i lead to strong and consistent improvement in survival across different lines of therapy. To improve understanding of how metastatic HR+ breast cancers become refractory to CDK4/6i, we have created a multimodal and longitudinal tumor atlas to investigate therapeutic adaptations in malignant cells and in the tumor immune microenvironment. This atlas is part of the NCI Cancer Moonshot Human Tumor Atlas Network and includes seven pairs of pre- and on-progression biopsies from five metastatic HR+ breast cancer patients treated with CDK4/6i. Biopsies were profiled with bulk genomics, transcriptomics, and proteomics as well as single-cell ATAC-seq and multiplex tissue imaging for spatial, single-cell resolution. These molecular datasets were then linked with detailed clinical metadata to create an atlas for understanding tumor adaptations during therapy. Analysis of our atlas datasets suggests a diverse set of tumor adaptations to CDK4/6i therapy. Malignant cells may adapt to therapy via mTORC1 activation, cell cycle bypass, and increased replication stress. The tumor immune microenvironment displayed evidence of both immune activation and immune suppression during therapy. Together, our metastatic ER+ breast cancer atlas represents a rich multimodal resource to better understand HR+ breast cancer tumor therapeutic adaptations to CDK4/6i therapy.

cancer biology↗

bioRxiv2023

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.

bioinformatics↗

bioRxiv2023

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. MotivationHighly 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 mm2-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.

bioinformatics↗

bioRxiv2022

A Novel Mouse Model that Recapitulates the Heterogeneity of Human 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 targeted therapies, but few TNBC mouse models exist. Here, we developed a novel 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 murine model develops TN mammary tumors that display histological and molecular features commonly found in human TNBC. We performed deep omic analyses on Myc;Ptenfl tumors including machine learning for morphologic features, bulk and single-cell RNA-sequencing, multiplex immunohistochemistry and single-cell phenotyping. Through comparison with human TNBC, we demonstrated that this new genetic mouse model develops mammary tumors with differential survival that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC; providing a unique pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response. Statement of significanceThe development of cancer models that mimic triple-negative breast cancer (TNBC) microenvironment complexities is critical to develop effective drugs and enhance disease understanding. This study addresses a critical need in the field by identifying a murine model that faithfully mimics human TNBC heterogeneity and establishing a foundation for translating preclinical findings into effective human clinical trials.

cancer biology↗

bioRxiv2022

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. In addition, cycIHC benefits from the speed and simplicity of brightfield microscopy for data collection, 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 (greater than 50 GB) 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 logodds 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 was first tested using a newly generated cycIHC dataset consisting of six GBM tissue sections processed through eight cycles of AEC based IHC staining. Further validation was completed on a previously published lung cancer tissue microarray dataset consisting of 107 cores processed through eighteen cycles of staining and imaging. CASSATT is a fully open-source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

bioinformatics↗

bioRxiv2022

A Web-based Software Resource for Interactive Analysis of Multiplex Tissue Imaging Datasets

Highly multiplexed tissue imaging (MTI) are powerful spatial proteomics technologies that enable in situ single-cell characterization of tissues. However, analysis and visualization of MTI datasets remains challenging, and we developed the Galaxy-ME software hub to address this challenge. Galaxy-ME is a web-based, interactive software hub that enables end-to-end analysis and visualization of MTI datasets and is accessible to everyone. To demonstrate its utility, Galaxy-ME was used to analyze datasets obtained from multiple MTI assays in both normal and cancerous tissues. Galaxy-ME is a publicly available web resource.

bioinformatics↗

bioRxiv2022

T cell Egress via Lymphatic Vessels Limits the Intratumoral T cell Repertoire in Melanoma

Antigen-specific CD8+ T cell accumulation in tumors is a prerequisite for effective immunotherapy, and yet, the mechanisms of lymphocyte transit remain poorly defined. We find that tumor-associated lymphatic vessels control T cell exit from tumors via the chemokine CXCL12, and intratumoral antigen encounter tunes CXCR4 expression on 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 exit the tumor, thereby limiting tumor control. CXCR4 inhibition and loss of lymphatic-specific CXCL12 boosts T cell retention and enhances response to therapeutic immune checkpoint blockade. Strategies that limit T cell egress, therefore, provide a new tool to boost immunotherapy response. One-Sentence SummaryLymphatic vessel-mediated, antigen-dependent CD8+ T cell egress limits T cell accumulation in melanomas and impairs anti-tumor immunity.

immunology↗

bioRxiv2022

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

T cell receptor (TCR) repertoires can be profiled using next generation sequencing (NGS) to monitor dynamical changes in response to disease and other perturbations. Several strategies for profiling TCRs have been recently developed with different benefits and drawbacks. Genomic DNA-based bulk sequencing, however, remains the most cost-effective method to profile TCRs. The major disadvantage of this method is the need for multiplex target amplification with a large set of primer pairs with potentially very different amplification efficiencies. One approach addressing this problem is by iteratively adjusting the concentrations of the primers based on their efficiencies, and then computationally correcting any remaining bias. Yet there are no standard, publicly available protocols to process and analyze raw sequencing data generated by this method. 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 approaches, we show that the concordance between bulk clonality metrics obtained from using the commercial kits and that developed herein is high. Therefore, we suggest the method presented here as an inexpensive and non-commercial alternative for measuring and monitoring adaptive dynamics in TCR clonotype repertoire.

bioinformatics↗

bioRxiv2021

Multiplex spatial systems analysis of local nanodose drug responses predicts effective treatment combinations of immunotherapies and targeted agents in mammary carcinoma

Better methods are needed to identify effective combinations of immunotherapies with chemotherapies and targeted anti-cancer agents. Here we present a Multiplex Implantable Microdevice Assay (MIMA) system for rapid in vivo assessment of the effects of multiple, spatially separate anticancer drugs directly in the complex tumor microenvironment. In prototypic experiments, olaparib, lenvatinib, palbociclib, venetoclax, panobinostat, doxorubicin, and paclitaxel and combinations thereof were administered simultaneously to murine mammary tumor models. Quantitative multiplex immunohistochemistry and spatial systems analyses of each local drug response defined cellular relations of fibroblasts, endothelial cells, immune lineages, immunogenic cell death, tumor proliferation and/or cancer stem cells that were used to predict effective drug combinations. A predicted combination of panobinostat, venetoclax and anti-CD40 showed long-term anti-tumor efficacy in multiple mouse models with no observable toxicity when administered systemically. Future MIMA use promises to design effective drug combinations for tumor cell control and immune activation on a personalized basis.

cancer biology↗