Biology subjects

Jordan, K. R.

Publications and source records attributed to Jordan, K. R..

11 recordsLinked to original sources

bioRxiv2026

Stromal innervation is prognostic in non-muscle-invasive bladder cancer and predicted by a distinct microenvironmental transcriptomic signature

Peripheral nerves are increasingly recognized as active components of the tumor microenvironment (TME). Yet, their role in non-muscle-invasive bladder cancer (NMIBC) and response to Bacillus Calmette-Guerin (BCG) remains poorly defined. Using multispectral imaging, we characterized autonomic and sensory nerve populations within the tumor-adjacent stroma of 241 treatment-naive high-risk NMIBC tumor regions from 141 patients. Stromal tumor-associated nerve (STaN) abundance varied substantially across patients and was associated with aggressive clinicopathological features, BCG failure, and disease progression. Unsupervised clustering resolved distinct STaN phenotypic groups with differential clinical and spatial associations. Noradrenergic and a neurochemically heterogeneous STaN populations showed the strongest associations with adverse outcomes. In contrast, a mixed population of nerves with high expression of the synaptic vesicle protein synaptophysin exhibited prominent spatial remodeling associated with BCG failure, despite limited prognostic value by abundance alone. Integration with an independent single-cell atlas nominated cancer-associated fibroblasts and mast cells as candidate cellular intermediaries of neural communication within the TME. Matched transcriptomic profiling revealed coordinated neuronal, extracellular matrix, lipid metabolism, and immune programs in STaN-high tumors, whereas STaN-low tumors preferentially enriched proliferative and hypoxic stress-response pathways. Leveraging these transcriptional features, we developed a transcriptomic classifier that distinguished STaN-high from STaN-low tumors in an independent test cohort (AUC = 0.87) and stratified clinical outcomes in additional patient cohorts. Together, these findings establish tumor innervation as a heterogeneous and clinically relevant feature of the NMIBC microenvironment, with neural abundance, phenotype, and spatial organization capturing distinct aspects of tumor behavior and clinical outcome.

cancer biology↗

bioRxiv2026

Cross-assay RNA modeling reveals cancer biomarkers

The clinical heterogeneity of cancer poses a major challenge for precision medicine. Limited cohort sizes across evolving assay platforms impede reliable biomarker discovery. Here, we systematically evaluate how to integrate data from four transcriptomics platforms: bulk and single-cell (sc) RNA sequencing (RNA-seq), NanoString, and microarray for predictive modeling in cancer. We use high-grade serous carcinoma (HGSC) of tube-ovarian origin as a model system, as it is highly heterogeneous in both biology and assay data. We find that using fold-change of gene expression in patients with matched pre- and post-neoadjuvant chemotherapy samples reduces inter-patient and inter-assay variability but is insufficient to overcome platform-specific biases. Microarray and scRNA-seq data exhibit systematic biases, while RNA-seq and NanoString show the most promise for combination into a single training cohort. To mitigate inter-assay limitations, we generate a new data set of HGSC tumor samples profiled with both RNA-seq and NanoString, and use it to identify the limits of detection and optimal harmonization strategies. Our approaches enable integration of cohorts for separate and combined RNA-seq and NanoString predictive models of disease recurrence (test-set AUROCs > 0.8), validated in external microarray cohorts. We leverage single-cell and bulk RNA-seq network-based analyses to provide mechanistic context for genes in the predictive models. Our models indicate that GBP4 expression is a key predictor of recurrence and marks immune remodeling towards cytotoxicity. We provide an interactive web portal to facilitate exploration of data and results. These findings guide cross-assay harmonization of transcriptomic data and enable improved predictive modeling in heterogeneous cancers. Statement of SignificanceWe present a framework for integrating RNA-seq, NanoString, microarray, and single-cell transcriptomic data for predictive modeling, enabling robust biomarker discovery in heterogeneous cancers and identifying GBP4 as a marker of immune remodeling.

bioinformatics↗

bioRxiv2026

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data

In the treatment of high grade serous ovarian cancer (HGSC), patients initially diagnosed with unresectable tumors are first treated with neoadjuvant chemotherapy (NACT) to reduce tumor burden prior to surgery. Analysis of matched pre- and post-NACT samples from the same patients enables the investigation of chemotherapy impacts and the biomarkers of progression. Although the tumor immune microenvironment (TIME) has increasingly been recognized as critical in shaping the development and progression of HGSC, we lack a comprehensive understanding of how chemotherapy remodels the TIME. Previous studies have found evidence for a general inflammatory response post-NACT, despite inconsistencies regarding which differentially expressed genes and pathways are implicated. We combine matched NanoString gene expression data from multiple sources to create a large dataset of matched pre- and post- NACT samples (N=83, with 29 novel to this study) and investigate reproducibility. Further, we use machine learning methods to investigate whether patient progression-free survival (PFS) can be predicted from the observed impact of chemotherapy on the TIME as represented by the comprehensive set of NanoString features. We find overall low predictability of PFS from all NanoString features, suggesting that previous results may have been limited by small sample size effects and that larger datasets are needed to identify more generalizable and translatable findings. We identify a set of differential expression features that are the most important for predicting patient outcomes that can be validated in future computational and biological studies. Author summaryA subset of patients with high grade serous ovarian cancer are treated with chemotherapy before surgery to reduce tumor burden. We investigate a large dataset of samples taken before and after chemotherapy. These matched samples enable an investigation of how the environment around tumors, for example immune cell infiltration, reacts to chemotherapy, providing insights into biomarkers for treatment response and treatments that could complement chemotherapy. This larger dataset only partially replicates results from previous studies, while also providing new insights. Machine learning models designed to predict the time to patient recurrence from available biomarkers indicate that they are not strongly predictive of patient outcomes, in contrast to past studies. These results suggest that larger datasets are needed. We identify a set of genes that change with chemotherapy and are indicative of and potentially useful for predicting time to disease recurrence and can be further investigated.

cancer biology↗

bioRxiv2025

Characterization of an immunodeficiency-associated EZH2 variant

Regulation of gene expression is central to the development of immune cells and their ability to respond to infection. As part of a clinical evaluation, we identified two sisters with recurrent infections, hypogammaglobulinemia, and memory B cell deficiency, diagnosed as common variable immunodeficiency. Whole exome sequencing identified a heterozygous variant (Leu50Ser, L50S) in a conserved region of EZH2, the catalytic subunit of the epigenetic gene repressor Polycomb Repressive Complex 2 (PRC2). EZH2-catalyzed histone H3 lysine 27 methylation (H3K27me) in bulk was not overall significantly disrupted by this variant, in patient samples or cell lines expressing EZH2-L50S. EZH2-L50S protein is expressed similar to wild-type and can form PRC2. However, we find that specific genomic regions that normally have high wild-type levels of H3K27me3 are deficient in the L50S context, particularly around gene promoters. EZH2-L50S is still recruited to these sites, but is not as active. Using recombinant purified PRC2, we determine that L50S affects methylation of nucleosomes and disrupts allosteric stimulation that normally amplifies H3K27me3, consistent with the location of L50 in the allosteric regulatory region of PRC2. Thus, variation of EZH2 L50, occurring at low frequency in the population may interfere with normal B cell gene expression patterns, contributing to immunodeficiency. This study has implications for genetic variation in PRC2 in the general population.

molecular biology↗

bioRxiv2024

Ablation of hematopoietic stem cell derived adipocytes reduces tumor burden in syngeneic mouse models of high-grade serous carcinoma

Hematopoietic stem cell-derived adipocytes (HSCDAs) are an adipose subtype derived from myeloid precursors that are distinct from conventional mesenchymal adipocytes (CMAs). We hypothesized that HSCDAs promote high grade serous carcinoma (HGSC), the most common form of ovarian cancer. Despite similar rates of differentiation, primary human HSCDAs from female donors showed marked transcriptional differences from CMAs, including downregulation of cell cycle and upregulation of lipid metabolic pathways. HSCDAs secreted greater amounts of inflammatory cytokines than CMAs. We also conducted two independent tumor studies using ID8 and SO syngeneic HGSC murine models in immunocompetent mice that were either HSCDA Proficient (HSCDA-Pro; can make both adipocyte subtypes) or Deficient (HSCDA-Def; can only make CMAs). Tumor burden trended lower in HSCDA-Def mice in both models. Relative to HSCDA-Pro mice, omental ID8 tumors from HSCDA-Def mice downregulated transcription of multiple metabolic pathways that were enriched in human HSCDA cells in vitro, suggesting that ablation of HSCDAs altered the tumor metabolic environment. Compared to HSCDA-Pro mice, tumors from HSCDA-Def mice had lower densities of dendritic cells (DC) and natural killer (NK) cells, as well as fewer DCs, NKs, and B-cells in proximity to tumor cells. Our data suggest that HSCDAs alter the peritoneal immune and metabolic environment to support HGSC progression. ONE SENTENCE SUMMARYHematopoietic stem cell derived adipocytes may alter the peritoneal metabolic and immune environment to establish a metastatic niche and support ovarian cancer progression.

cancer biology↗

bioRxiv2023

Combination CDC-like kinase inhibition (CLK)/Dual-specificity tyrosine-regulated kinase (DYRK) and taxane therapy in CTNNB1-mutated endometrial cancer

SM08502 (cirtuvivint) is a novel pan CDC-like kinase (CLK) and Dual specificity tyrosine kinase (DYRK) inhibitor that targets mRNA splicing and is optimized for Wnt pathway inhibition. Previous evaluation of single agent CLK/DYRK inhibition (SM04690) demonstrated inhibition of tumor progression and {beta}-catenin/TCF transcriptional activity in CTNNB1-mutant endometrial cancer (EC). In-vitro analysis of SM08502 similarly decreases Wnt transcriptional activity and cellular proliferation while increasing cellular apoptosis. SM08502 is an active single-agent therapy with IC50s in the nanomolar range for all EC cell lines evaluated. Combination of SM08502 with paclitaxel has synergistic effect in vitro, as demonstrated by Combination Index <1, and inhibits tumor progression in four endometrial cancer models (HEC265, Ishikawa, Ishikawa-S33Y, and SNGM). In our in vivo mouse models, Ishikawa demonstrated significantly lower tumor volumes of combination vs SM08502 alone (Repeated Measures one-way ANOVA, p = 0.04), but not vs paclitaxel alone. HEC265, SNGM, and Ishikawa-S33Y tumors all had significantly lower tumor volumes with combination SM08502 and paclitaxel compared to single-agent paclitaxel (Repeated Measures one-way ANOVA, p = 0.01, 0.004, and 0.0008, respectively) or single-agent SM08502 (Repeated Measures one-way ANOVA, p = 0.002, 0.005, and 0.01, respectively) alone. Mechanistically, treatment with SM08502 increases alternative splicing (AS) events compared to treatment with paclitaxel. AS regulation is an important post-transcriptional mechanism associated with the oncogenic process in many cancers, including EC. Results from these studies have led to a Phase I evaluation of this combination in recurrent EC.

cancer biology↗

bioRxiv2023

Combinatory EHMT and PARP inhibition induces an interferon response and a CD8 T cell-dependent tumor regression in PARP inhibitor-resistant models

Euchromatic histone lysine methyltransferases 1 and 2 (EHMT1/2), which catalyze demethylation of histone H3 lysine 9 (H3K9me2), contribute to tumorigenesis and therapy resistance through unknown mechanisms of action. In ovarian cancer, EHMT1/2 and H3K9me2 are directly linked to acquired resistance to poly-ADP-ribose polymerase (PARP) inhibitors and are correlated with poor clinical outcomes. Using a combination of experimental and bioinformatic analyses in several PARP inhibitor resistant ovarian cancer models, we demonstrate that combinatory inhibition of EHMT and PARP is effective in treating PARP inhibitor resistant ovarian cancers. Our in vitro studies show that combinatory therapy reactivates transposable elements, increases immunostimulatory dsRNA formation, and elicits several immune signaling pathways. Our in vivo studies show that both single inhibition of EHMT and combinatory inhibition of EHMT and PARP reduces tumor burden, and that this reduction is dependent on CD8 T cells. Together, our results uncover a direct mechanism by which EHMT inhibition helps to overcome PARP inhibitor resistance and shows how an epigenetic therapy can be used to enhance anti-tumor immunity and address therapy resistance.

cancer biology↗

bioRxiv2022

A Platform-Independent Framework for Phenotyping of Multiplex Tissue Imaging Data

Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-accurate representations of the input data, but exhibit signifi-cantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

bioinformatics↗

bioRxiv2021

SPF: A Spatial and Functional Data Analytic Approach to cell Imaging data

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subjects clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available Multiplexed Ion beam Imaging (MIBI) triple-negative breast cancer dataset.

bioinformatics↗

bioRxiv2020

Expression and T cell Regulatory Action of the PD-1 Immune Checkpoint in the Ovary and Fallopian Tube

The Programmed Cell Death Protein-1 (PD-1/PDCD-1/CD279) checkpoint has powerful immunomodulatory action, including in the context of cancer. PD-1 receptor activation by its ligands (PD-L1/2) is associated with downregulated immune response, and tumor cells can avoid surveillance via PD-1 and/or ligand expression. While receptor expression is largely limited to lymphoid, myeloid, and tumor cells, we show that membrane bound and soluble variants of PD-1 and ligands are also expressed by permanent constituent cell types of the human ovary and fallopian tube, including granulosa cells and oocytes. PD-1 and soluble ligands were highly enriched in exosome fractions in human follicular fluid at bioactive levels that can control T cell PD-1 activation. PD-1 checkpoint signaling may be involved in physiological ovarian functions including follicle, and ultimately, germline and embryo immune-privilege.Competing Interest StatementThe authors have declared no competing interest.View Full Text

physiology↗

bioRxiv2020

The capacity of the ovarian cancer tumor microenvironment to integrate inflammation signaling conveys a shorter disease-free interval

Ovarian cancer has one of the highest deaths to incidence ratios across all cancers. Initial chemotherapy is typically effective, but most patients will develop chemo-resistant disease. Mechanisms driving clinical chemo-response and -resistance in ovarian cancer are not well understood. However, achieving optimal surgical cytoreduction improves survival, and cytoreduction is improved by neoadjuvant platinum/taxane-based chemotherapy (NACT). NACT offers a window to profile pre-versus post-therapy tumor specimens, which we used to identify chemotherapy-induced changes to the tumor microenvironment. We hypothesized changes in the immune microenvironment correlate with tumor chemo-response and disease progression. We obtained matched pre- and post-NACT archival tumor tissues from patients with high-grade serous ovarian cancer (patient n=6). We measured mRNA levels of 770 genes (NanoString), and performed reverse phase protein array (RPPA) on a subset of matched tumors. We examined cytokine levels in additional pre-NACT ascites samples (n=39) by multiplex ELISA. A tissue microarray with 128 annotated ovarian tumors expanded the transcriptional, RPPA, and cytokine data by multi-spectral immunohistochemistry. In NanoString analyses, transcriptional profiles segregated based on pre- and post-NACT status. The most upregulated gene post-NACT was IL6 (17.1-fold, adjusted p = 0.045). RPPA data were highly concordant with mRNA, consistent with elevated immune infiltration. Elevated IL-6 in pre-NACT ascites specimens correlated with a shorter time to recurrence. Integrating NanoString, RPPA, and cytokine studies identified an activated inflammatory signaling network and induced IL6 and IER3 (Immediate Early Response 3) post-NACT, associated with poor chemo-response and decreased time to recurrence. Taken together, multi-omic profiling of ovarian tumor samples before and after NACT provides unique insight into chemo-induced changes to the tumor and microenvironment. We integrated transcriptional, proteomic, and cytokine data and identified a novel IL-6/IER3 signaling axis through increased inflammatory signaling which may drive ovarian cancer chemo-resistance.

cancer biology↗