Biology subjects

Douglass, E. F.

Publications and source records attributed to Douglass, E. F..

12 recordsLinked to original sources

bioRxiv2026

Spatial Logic Reconciles Gene-signature Methods in Triple Negative Breast Cancer

Triple-Negative Breast Cancer (TNBC) presents a significant clinical challenge due to its heterogeneity and lack of targeted treatment options, with chemotherapy and immunotherapy combinations currently serving as the main therapeutic strategy. Efforts to address TNBC heterogeneity have largely focused on classifying intrinsic cancer subtypes based on differential tumor mRNA expression, a strategy that has proven effective in hormone receptor-positive breast cancers but has yet to yield a clinically useful predictor of survival or treatment response in TNBC. We hypothesize that both the intrinsic characteristics of TNBC and the surrounding immune microenvironment influence treatment outcomes and that immune cell infiltration affects TNBC subtype classification and response variability. To explore this hypothesis, we compared the predictive and prognostic capabilities of cancer subtype-based (TNBC-type) gene signatures and immune cell deconvolution methods (CIBERSORT) within the same TNBC datasets. We found that immune cell abundance outperformed TNBC subtype-signatures and multicellular immune cell aggregates showed the highest performance of all. More specifically, aggregate immune cells associated with tertiary lymphoid structures and tumor associated macrophages/monocytes demonstrated statistically significant predictive value. These findings were confirmed in an independent cohort of 67 TNBC patients treated with neoadjuvant chemotherapy. Further, single-cell RNA sequencing analysis revealed that the predictive power of cancer-subtype could be partially explained by immune- and stromal features. Examination of single-cell resolution spatial transcriptomic data confirmed presence of TLS-like, TAM- and cancer-stromal niches within TNBC biopsy samples that were associated with treatment response. Overall, our results highlight that immune cell aggregates, which capture the spatial organization of the TME, outperform cell-type specific gene signatures in predicting TNBC outcomes. Our novel approach provides a robust framework for interpreting spatial relationships in bulk RNA-seq data, offering a pathway for reconciling past data with current advancements in spatial profiling technologies. This work paves the way for future studies to leverage the multi-cellular complexity of TNBC, enhancing diagnostic precision and facilitating the development of therapies that strategically modulate the tumor microenvironment for improved anti-cancer responses.

cancer biology↗

bioRxiv2026

Convergent biology, divergent drivers: a cross-species comparison of human and canine invasive urothelial carcinoma

Traditional animal models are often inbred and genetically uniform. This makes them powerful for controlled experiments, but it limits how well they represent the patient-to-patient variation seen in real-world disease. Comparative oncology seeks to address this gap by studying naturally occurring cancers in outbred companion animals, especially dogs. Canine medicine offers two important advantages: first, prospective trials can often be completed faster than in humans and second, dogs are already part of the translational pipeline through pharmacokinetic and toxicology studies. Here, we assessed the transcriptional fidelity of human and canine invasive urothelial carcinoma in primary tumors and patient-derived organoids. We then used single-cell and spatial data to resolve the underlying cellular organization. Despite strong species and platform differences, human and canine tumors preserved the same major luminal-basal structure and a similar tumor microenvironment. The two species reached this shared biology through different recurrent mutations. These included FGFR3 alterations in humans and BRAF alterations in dogs, which converged on overlapping pathways and a luminal phenotype. Human and canine organoids also underwent a similar shift in culture. Both became more proliferative and metabolic while losing inflammatory programs. Thus, organoids preserved important tumor biology while introducing predictable platform effects. Single-cell and spatial analyses showed that the luminal-basal axis reflects a gradient of cell states organized around the tumor-stroma boundary, rather than two discrete tumor types. This helps explain why bulk RNA-sequencing subtypes are reproducible but coarse. Together, these findings define where canine and human bladder cancer agree, where they differ, and how dogs can support parallel therapeutic and diagnostic development.

bioinformatics↗

bioRxiv2026

AnchorR: A QuPath and R interface for collaborative exploration of spatial transcriptomics and histology

Single-cell spatial transcriptomics can connect molecular cell states with tissue morphology, but this promise depends on accurate registration to histopathology. In serial sections, however, tissue borders often differ because of sectioning artifacts, staining variability, and field-of-view acquisition, limiting conventional area-based registration. We developed AnchorR, an expert-guided workflow for coarse-grained alignment of hematoxylin and eosin (H&E) images with CosMx Spatial Molecular Imaging data. Bioinformaticians first define and color-code cell types in Seurat, and pathologists then identify corresponding internal landmarks using QuPath overlays. AnchorR combines these paired landmarks to estimate affine transformations, quantify residual error, and support visual quality control and anchor refinement. Using six oral pre-cancerous tissue sections, we identified 60 cross-modal landmarks. Fitting each section independently reduced mean landmark error from 121.5 m with a single whole-slide transformation to 14.6 m. Cross-validation further showed that increasing the number of anchors improved robustness, with nine-anchor fits achieving approximately 20 m error, or about one cell diameter. AnchorR is designed to complement automated computer-vision methods by providing reliable tissue-level alignment when border mismatch makes global registration difficult. By creating a shared workspace for pathologists and bioinformaticians, it operationalizes an expert-in-the-loop approach and makes feature-based multimodal registration accessible without specialized computer-vision expertise or high-performance computing.

bioinformatics↗

bioRxiv2026

A Comprehensive Mathematical Model of Avidity in Cytokine Signaling

Cytokine sensitivity varies substantially across cell types and cellular states, in part through differences in the abundance of individual receptor subunits. Yet for multicomponent receptors, the quantitative relationship between receptor abundance, binding affinity, and functional potency remains poorly defined. Here, we derive closed-form expressions for EC50 in multivalent ligand-receptor systems at cell surfaces. Unlike monovalent models, these equations predict that potency depends on both binding constants and receptor abundance, with individual receptor subunits playing asymmetric roles in controlling maximal complex formation and cellular sensitivity. We validate these predictions using quantitative antibody-binding, cytokine-binding, and signaling data, and extend the equilibrium framework to steady-state signaling conditions in which downstream kinetic processes can impose additional limits on functional potency. We then derive a regression-compatible formulation and test its predicted ligand-receptor-response relationships across an in vivo murine cytokine perturbation atlas, human spatial transcriptomic data, and an independent cohort of 510 patients with lung adenocarcinoma. Multivariable cytokine relationships were conserved across human datasets and associated with clinical outcome. In addition, quantitative proteomics from primary human immune cells supported receptor mRNA abundance as an informative but imperfect proxy for protein abundance. Together, this framework connects multivalent receptor biophysics with cell-type-specific signaling across molecular, cellular, tissue, and patient scales.

systems biology↗

bioRxiv2025

Development of a Time-based Drug Screening Platform for Improved Clinical Translation

In 2022, the average cost for a single drugs development increased by 15%, reaching $2.3 billion. In drug development, oncology has the highest attrition rate with 95% of new drugs failing Phase 2 clinical trials alone. Consequently, innovative approaches are needed to assess clinical utility prior to proceeding with human trials. Current drug development follows the theory that "Best Potency = Best Drug" which is a concentration-centric paradigm focused on optimizing drug-target interaction (Kd) to improve the concentration of drug necessary to elicit its therapeutic effect (IC50). However, both drug concentration and exposure time contribute to clinical efficacy, yet most laboratory studies focus on IC50 alone. This manuscript characterizes drug potency and kinetics of ten oncology drugs in three different cancer cell lines and integrates in vivo pharmacokinetic-pharmacodynamic models to identify how these parameters relate to real-world clinical outcomes. Our analyses revealed that Cmax normalization is necessary for concentration response data as IC50 alone does not account for drugs that are studied at sub-therapeutic concentrations. Additionally, the temporal effect of drug efficacy varies between cell lines and dose, where some drugs are unable to overcome the proliferation rate of the cells to induce a decrease in disease progression. This work aims to enhance the design and implementation of drug regimens by understanding the time-dependence of clinical efficacy and cytotoxicity.

pharmacology and toxicology↗

bioRxiv2025

Clinical Exposure Normalization Restores Translational Predictiveness of In Vitro Drug Response

Single-agent clinical response data for widely used chemotherapies have remained difficult to analyze because they are scattered across decades of print literature. We consolidated these sources into NCI1970-Meta, a 49,002-patient dataset covering 30 drugs across 18 cancers, enabling the first quantitative comparison of clinical outcomes, laboratory potency metrics, clinical exposure, and literature-derived biomarkers. Clinical patterns were strongly lineage-driven: cancer type explained far more ORR variability than drug identity (37.3% vs 15.6%; F = 13.43 vs 3.29). FDA approvals reflected these same patterns where ORR strongly predicted indication status (F = 98.3-104.1). In contrast, laboratory efficacy metrics did not track clinical activity. Raw in vitro AUC showed no association with ORR (R2 = 0.00; 95% CI: 0.00-0.01) and was dominated by drug identity rather than cancer lineage (85.2% vs 8.6%; F = 212.2 vs 21.6). Instead, AUC correlated with clinical exposure: unbound Cmax (R2 = 0.21 [0.21-0.34]) and therapeutic minimum concentrations (R2 = 0.69 [0.64-0.73]). This indicates that standard assay ranges capture exposure requirements rather than true efficacy. Normalizing potency by exposure restored the expected clinical relationships and resolved drug-specific anomalies such as gemcitabine. Biomarkers showed consistent behavior across clinical and laboratory settings. Among 314 biomarker-drug pairs, correlation directions were significantly conserved (R2 = 0.17 [0.10-0.25]; p = 2.8x10-14). Literature-defined sensitivity and resistance annotations were enriched in vitro (OR = 3.7; p = 2.36x10-8) and in the clinic (OR = 3.9; p = 6.52x10-9), with stronger performance for correlations >0.1 (OR = 13.3 in vitro; OR = 8.4 clinically). Simple biomarker-sum models performed well across drugs, consistent with multi-pathway pseudo-first-order behavior. Overall, NCI1970-Meta provides a quantitative framework linking laboratory pharmacology to real-world clinical efficacy. Biological signal is reliably preserved within drugs, while cross-drug comparisons require explicit exposure normalization. This resource offers a statistical foundation for improving drug prioritization, biomarker development, and translational pharmacodynamic modeling.

pharmacology and toxicology↗

bioRxiv2024

A Quantitative Approach for Assessing Multidrug Resistance in Cancer

Multidrug resistance (MDR) is a primary barrier to successful cancer treatment with small molecule drugs. One mechanism of resistance is through transporters which pump drug out of cancer cells before they exert any therapeutic effect. The most studied transporter is P-glycoprotein (Pgp), a member of the ATP Binding Cassette (ABC) superfamily of drug pumps. Prior research has focused on determining whether drugs are substrates of Pgp. Pgp specificity for FDA approved drugs is currently unclear due to technical variability in assays that quantify enzyme kinetics using non-cellular experimental models. Unfortunately, there is extreme variability in enzyme parameters for drugs that are characterized and gaps in literature for drugs that have not been characterized. Thus, our overall goal is to develop live cell methods to obtain quantitative scores for substrates in cells and fill gaps in the literature. Studying Pgp in the context of MDR, we improve Pgp specificity scores by leveraging new Pgp expression (cell lines, tissues) and function (drug screening) datasets. We experimentally and computationally integrate functional dataset information to better understand Pgp specificity using an approach based on underlying Michaelis-Menten enzyme kinetics. We obtain consensus scores for Pgp specificity across [~]150 FDA approved oncology drugs and validate them experimentally in a subset of 76 substrates selected to represent the spectrum of drugs for Pgp specificity. These scores can be used to calibrate clinical diagnostics (Pgp expression), and our experimental platform can be used to quantify Pgp function in clinical samples. Overall, we develop a parallel computational and experimental procedure to estimate Pgp selectivity in live cells. This procedure can be expanded to other drug transporters which contribute to MDR to further characterize this phenotype quantitatively. Significance StatementMDR is facilitated through the action of drug transporters which are upregulated in cancer. Even though selective inhibitors have been designed to decrease drug efflux, they failed clinically. So, current clinical practice is to select non-substrates. However, non-substrates are difficult to define. Here, we have developed a quantitative platform for characterizing MDR in cancer that factors in gene expression and enzyme kinetics. Our computational and experimental platform successfully identified the spectrum of substrates for Pgp from the FDA approved oncology drug library. This quantitative approach reflects the multigene phenotype of cancer drug resistance and can be applied to other MDR transporter genes to optimize drug selection clinically.

biochemistry↗

bioRxiv2022

The OncoLoop network-based precision oncology framework

At present, prioritizing cancer treatments at the individual patient level remains challenging, and performing co-clinical studies using patient-derived models in real-time is often not feasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework to predict and validate drug sensitivity in human tumors and their pre-existing high-fidelity (cognate) model(s) by leveraging perturbational profiles of clinically-relevant oncology drugs. As proof-of-concept, we applied OncoLoop to prostate cancer (PCa) using a series of genetically-engineered mouse models (GEMMs) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of published cohorts using Master Regulator (MR) conservation analysis revealed that most patients were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated, including in two cognate allografts and one cognate patient-derived xenograft (PDX). OncoLoop is a highly generalizable framework that can be extended to other cancers and potentially other diseases. Significance StatementOncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround co-clinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available.

cancer biology↗

bioRxiv2020

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

cancer biology↗

bioRxiv2020

The Host Cell ViroCheckpoint: Identification and Pharmacologic Targeting of Novel Mechanistic Determinants of Coronavirus-Mediated Hijacked Cell States

Most antiviral agents are designed to target virus-specific proteins and mechanisms rather than the host cell proteins that are critically dysregulated following virus-mediated reprogramming of the host cell transcriptional state. To overcome these limitations, we propose that elucidation and pharmacologic targeting of host cell Master Regulator proteins--whose aberrant activities govern the reprogramed state of infected-coronavirus cells--presents unique opportunities to develop novel mechanism-based therapeutic approaches to antiviral therapy, either as monotherapy or as a complement to established treatments. Specifically, we propose that a small module of host cell Master Regulator proteins (ViroCheckpoint) is hijacked by the virus to support its efficient replication and release. Conventional methodologies are not well suited to elucidate these potentially targetable proteins. By using the VIPER network-based algorithm, we successfully interrogated 12h, 24h, and 48h signatures from Calu-3 lung adenocarcinoma cells infected with SARS-CoV, to elucidate the time-dependent reprogramming of host cells and associated Master Regulator proteins. We used the NYS CLIA-certified Darwin OncoTreat algorithm, with an existing database of RNASeq profiles following cell perturbation with 133 FDA-approved and 195 late-stage experimental compounds, to identify drugs capable of virtually abrogating the virus-induced Master Regulator signature. This approach to drug prioritization and repurposing can be trivially extended to other viral pathogens, including SARS-CoV-2, as soon as the relevant infection signature becomes available.

systems biology↗

bioRxiv2019

A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells

Despite considerable pan-cancer efforts, the link between genomics and transcriptomics in cancer remains relatively weak and mostly based on statistical rather than mechanistic principles. By performing integrative analysis of transcriptomic and mutational profiles on a sample-by-sample basis, via regulatory/signaling networks, we identified a repertoire of 407 Master-Regulator proteins responsible for canalizing the genetics of 20 TCGA cohorts into 112 transcriptionally-distinct tumor subtypes. Further analysis highlighted a highly-recurrent regulatory architecture (oncotecture) with Master-Regulators organized into 24 modular MR-Blocks, regulating highly-specific tumor-hallmark functions and predictive of patient outcome. Critically, >50% of the somatic alterations identified in individual samples were in proteins affecting Master-Regulator activity, thus yielding novel insight into mechanisms linking tumor genetics and transcriptional identity and establishing novel non-oncogene dependencies. Experimental validation of functional mutations upstream of the most conserved MR-Block confirmed their ability to affect MR-protein activity, suggesting that the proposed methodology may effectively complement and extend current pan-cancer knowledge.

bioinformatics↗

bioRxiv2018

Quantitative Assessment of Protein Activity in Orphan Tissues and Single Cells Using the metaVIPER Algorithm

We and others have shown that transition and maintenance of biological states is controlled by master regulator protein, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, a novel algorithm designed to assess protein activity in tissue-independent by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactome. We confirmed the algorithms value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

systems biology↗