Biology subjects

Behbakht, K.

Publications and source records attributed to Behbakht, K..

7 recordsLinked to original sources

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↗

bioRxiv2024

Claudin-4 remodeling of nucleus-cell cycle crosstalk maintains ovarian tumor genome stability and drives resistance to genomic instability-inducing agents.

During cancer development, the interplay between the nucleus and the cell cycle leads to a state of genomic instability, often accompanied by observable morphological aberrations. These aberrations can be controlled by tumor cells to evade cell death, either by preventing or eliminating genomic instability. In epithelial ovarian cancer (EOC), overexpression of the multifunctional protein claudin-4 is a key contributor to therapy resistance through mechanisms associated with genomic instability. However, the molecular mechanisms underlying claudin-4 overexpression in EOC remain poorly understood. Here, we altered claudin-4 expression and employed a unique claudin-4 targeting peptide (CMP) to manipulate the function of claudin-4. We found that claudin-4 facilitates genome maintenance by linking the nuclear envelope and cytoskeleton dynamics with cell cycle progression. Claudin-4 caused nuclei constriction by excluding lamin B1 and promoting perinuclear F-actin accumulation, associated with remodeling nuclear architecture, thus altering nuclear envelope dynamics. Consequently, cell cycle modifications due to claudin-4 overexpression resulted in fewer cells entering the S-phase and reduced genomic instability. Importantly, disrupting biological interactions of claudin-4 using CMP and forskolin altered oxidative stress cellular response and increased the efficacy of PARP inhibitor treatment. Our data indicate that claudin-4 protects tumor genome integrity by remodeling the crosstalk between the nuclei and the cell cycle, leading to resistance to genomic instability formation and the effects of genomic instability-inducing agents. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/611120v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@7ed420org.highwire.dtl.DTLVardef@98949eorg.highwire.dtl.DTLVardef@1453a68org.highwire.dtl.DTLVardef@1fdab87_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Claudin-4 plays a crucial role in remodeling the cytoskeleton, particularly influencing nuclear architecture. This remodeling appears to act as a regulatory mechanism, limiting the progression of ovarian cancer cells into the S-phase and correlating with reduced genomic instability in ovarian tumors. Moreover, since olaparib treatment triggered a cellular oxidative response, it is likely that this claudin-4-mediated remodeling contributes to resistance against the effects of the PARP inhibitor. C_FIG

cancer biology↗

bioRxiv2024

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors -- one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled -- and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

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↗

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↗

bioRxiv2018

Targeting Wnt Signaling To Overcome PARP Inhibitor Resistance

Epithelial ovarian cancer (EOC) has one of the highest deaths to incidence ratios. High grade serous ovarian carcinoma (HGSOC) is the most common and deadliest EOC histotype because of the lack of secondary therapeutic options following debulking surgery and platinum/taxane-based chemotherapies. For recurrent chemosensitive HGSOC, poly(ADP)-ribose polymerase inhibitors (PARPi; olaparib, rucaparib, or niraparib) represent an emerging treatment strategy. While PARPi are most effective in homologous recombination DNA repair-deficient (HRD) HGSOCs, more recent studies have observed a significant clinical benefit in non-HRD HGSOCs. However, all HGSOC patients are likely to acquire resistance to PARPi. Therefore, there is an urgent clinical need to better understand PARPi resistance, and to introduce novel combinatorial therapies to overcome PARPi resistance and extend HGSOC disease-free intervals. Utilizing a two BRCA2-mutated and one BRCA-wildtype HGSOC cell lines that are olaparib sensitive, we established resistant cells. Transcriptome analysis of the matched olaparib-sensitive versus resistant cells did not detect BRCA2 reversion mutations, but revealed activation of Wnt/TCF signaling pathway, as TCF transcriptional activity was significantly increased in PARPi-resistant cells. In parallel, forced activation of Wnt signaling in PARPi-sensitive cells via WNT3A stimulation reduced response to PARPi. In a recurrent-HGSOC PARPi insensitive patient-derived xenograft model there was an increase in a Wnt/TCF transcriptional target. PARPi resistant cells were sensitive to inhibition of Wnt signaling using the FDA-approved compound, pyrvinium pamoate, which has been shown to inhibit Wnt signaling. We observed that combining pyrvinium pamoate with olaparib resulted in a significant decrease in tumor burden and number of tumor nodules. This study demonstrates that Wnt signaling can mediate PARPi resistance in HGSOC and provides a clinical rationale for combining PARPi and Wnt inhibitors.

cancer biology↗