Biology subjects

Cooper, L. A. D.

Publications and source records attributed to Cooper, L. A. D..

8 recordsLinked to original sources

bioRxiv2026

Interstitial macrophages drive chronic lung allograft dysfunction

Despite immunosuppressive regimens targeting adaptive immunity, chronic lung allograft dysfunction (CLAD) remains the major obstacle to durable lung allograft survival. Here, we identify colony-stimulating factor 1 receptor (CSF1R)-expressing interstitial macrophages as critical orchestrators of CLAD. Using lung tissue from patients with CLAD and a mouse model of mismatched lung transplantation, we show that both donor-derived tissue-resident and recipient- monocyte-derived interstitial macrophages spatially co-localize within peribronchial immune aggregates in patients with CLAD. These interstitial macrophages express distinct cytokine programs that include those implicated in the recruitment of T and B cells. Pharmacological inhibition of CSF1R after lung transplantation in mice reduced interstitial macrophage abundance and attenuated CLAD pathology. Our findings identify donor- and recipient-derived interstitial macrophages as upstream regulators of CLAD and suggest CSF1R as a therapeutic target for its prevention and treatment.

immunology↗

bioRxiv2026

AI-enabled Spatial Profiling of Circulating Tumor-Immune Ecosystems Predicts Patient Outcomes Across Cancers

Circulating tumor cells (CTCs) and immune cells form dynamic multicellular ecosystems in blood, but their spatial organization and clinical relevance have not been systematically characterized. We developed the Cell and Cluster Identification Program (CCIP), an artificial intelligence-based framework that analyzes routine multiplex immunofluorescence blood scans to segment cells, identify CTCs and five immune lineages with high accuracy, and quantify multicellular clusters and tumor-immune interactions. Applying CCIP to 2,693 blood scans from 1,399 patients, we profiled over 60 million cells (>7 million multi-cell clusters) and linked imaging-derived features to patient outcomes. Correlated with circulating-tumor DNA mutation burdens, a 14-feature image model predicted overall survival in breast cancer, outperformed clinicopathologic variables and CTC enumeration, and generalized to prostate cancer. Prognostic imaging signatures were also associated with therapy response-related progression-free survival as well as with single-cell RNA sequencing-derived immune suppression states, connecting circulating tumor-immune architecture with systemic immune dysfunction.

cancer biology↗

bioRxiv2023

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry

Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models which make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1,820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia [AUROC 0.961] and accurately differentiates AML versus B- and T- lymphoblastic leukemia [AUROC 0.965]. Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) [AUROC 0.814], and NPM1 variants [AUROC 0.807]. Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

pathology↗

bioRxiv2022

A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research; however, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedure and produces massive data that capture tumor histological details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nuclei segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nuclei segmentation and TME quantification within image patches; however, existing algorithms are computationally intensive and time-consuming for WSI analysis. In this study, we present Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nuclei segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing methods for WSI analysis in nuclei detection and classification accuracy, as well as computation time.

pathology↗

bioRxiv2022

An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears

Pathologic diagnosis of bone marrow disorders relies in part on microscopic analysis of bone marrow aspirate (BMA) smears and manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including analysis of only a small subset of optimal slide areas and nucleated cells, and inter-observer variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline utilizes a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into one of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produces 11-component DCCs that demonstrate high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we show that automated analysis can reduce intra-slide variance in DCCs by analyzing the whole slide and marrow nucleated cells within optimal regions. Finally, pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully-automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.

pathology↗

bioRxiv2021

Systematic discovery of mutation-directed neo-protein-protein interactions in cancer

Comprehensive sequencing of patient tumors reveals numerous genomic mutations across tumor types that enable tumorigenesis and progression. A subset of oncogenic driver mutations results in neomorphic activity where the mutant protein mediates functions not engaged by the parental molecule. Here, we identify prevalent variant-enabled neomorph-protein-protein interactions (neoPPI) with a quantitative High Throughput differential Screening (qHT-dS) platform. Coupling of highly sensitive BRET biosensors with miniaturized co-expression in an ultra-HTS format allows large-scale monitoring of interactions of wild-type and mutant variant counterparts with a library of cancer-associated proteins in live cells. Screening of 13,392 interactions with 1,474,560 data points revealed a landscape of gain-of-interactions encompassing both oncogenic and tumor suppressor mutations. For example, the recurrent BRAF V600E lesion mediates KEAP1 neoPPI, rewiring a BRAFV600E-KEAP1 signaling axis and creating collateral vulnerability to NQO1 substrates, offering a combination therapeutic strategy. Thus, cancer genomic alterations can create neo-interactions, informing variant-directed therapeutic approaches for precision medicine.

cancer biology↗

bioRxiv2017

Predicting cancer outcomes from histology and genomics using convolutional networks

Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

bioinformatics↗

bioRxiv2017

Interactive Phenotyping Of Large-Scale Histology Imaging Data With HistomicsML

Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe histologic elements, yielding measurements for hundreds of millions of objects. A central challenge in utilizing this data is enabling investigators to train and evaluate classification rules for identifying objects related to processes like angiogenesis or immune response. Here we present HistomicsML, an interactive machine-learning framework for large whole-slide imaging data. HistomicsML uses active learning direct user feedback, making classifier training efficient and scalable in datasets containing 108+ histologic objects. We demonstrate how HistomicsML can be used to phenotype microvascular structures in gliomas to predict survival, and to explore the molecular pathways associated with these phenotypes. Our approach enables researchers to unlock phenotypic information from digital pathology datasets to investigate prognostic image biomarkers and genotype-phenotype associations.

bioinformatics↗