AXL-initiated paracrine activation of pSTAT3 enhances mesenchymal and vasculogenic supportive features of tumor-associated macrophages.

Tumor-associated macrophages (TAMs) are integral to the development of complex tumor microenvironments (TMEs) and can execute disparate cellular programs in response to extracellular cues. However, upstream signaling processes underpinning this phenotypic plasticity remain to be elucidated. Here, we report that concordant AXL-STAT3 signaling in TAMs is triggered by lung cancer cells or cancer-associated fibroblasts in the cytokine milieu. This paracrine action drives TAM differentiation toward a tumor-promoting "M2-like" phenotype with upregulation of CD163 and putative mesenchymal markers, contributing to TAM heterogeneity and diverse cellular functions. One of the upregulated markers, CD44, mediated by AXL-IL-11-pSTAT3 signaling cascade, enhances macrophage ability to interact with endothelial cells and facilitate formation of primitive vascular networks. We also found that AXL-STAT3 inhibition can impede the recruitment of TAMs in a xenograft mouse model, thereby suppressing tumor growth. These findings suggest the potential application of AXL-STAT3-related markers to quantitatively assess metastatic potential and inform therapeutic strategies in lung cancer.

PRC2-Mediated Epigenetic Suppression of Type I IFN-STAT2 Signaling Impairs Antitumor Immunity in Luminal Breast Cancer.

The immunosuppressive tumor microenvironment in some cancer types, such as luminal breast cancer, supports tumor growth and limits therapeutic efficacy. Identifying approaches to induce an immunostimulatory environment could help improve cancer treatment. Here, we demonstrate that inhibition of cancer-intrinsic EZH2 promotes antitumor immunity in estrogen receptor α-positive (ERα+) breast cancer. EZH2 is a component of the polycomb-repressive complex 2 (PRC2) complex, which catalyzes trimethylation of histone H3 at lysine 27 (H3K27me3). A 53-gene PRC2 activity signature was closely associated with the immune responses of ERα+ breast cancer cells. The stimulatory effects of EZH2 inhibition on immune surveillance required specific activation of type I IFN signaling. Integrative analysis of PRC2-repressed genes and genome-wide H3K27me3 landscape revealed that type I IFN ligands are epigenetically silenced by H3K27me3. Notably, the transcription factor STAT2, but not STAT1, mediated the immunostimulatory functions of type I IFN signaling. Following EZH2 inhibition, STAT2 was recruited to the promoters of IFN-stimulated genes even in the absence of the cytokines, suggesting the formation of an autocrine IFN-STAT2 axis. In patients with luminal breast cancer, high levels of EZH2 and low levels of STAT2 were associated with the worst antitumor immune responses. Collectively, this work paves the way for the development of an effective therapeutic strategy that may reverse immunosuppression in cancer. SIGNIFICANCE: Inhibition of EZH2 activates a type I IFN-STAT2 signaling axis and provides a therapeutic strategy to stimulate antitumor immunity and therapy responsiveness in immunologically cold luminal breast cancer.

A First-in-Class Inhibitor of ER Coregulator PELP1 Targets ER+ Breast Cancer.

Most patients with estrogen receptor alpha-positive (ER+) breast cancers initially respond to treatment but eventually develop therapy resistance with disease progression. Overexpression of oncogenic ER coregulators, including proline, glutamic acid, and leucine-rich protein 1 (PELP1), are implicated in breast cancer progression. The lack of small molecules that inhibits PELP1 represents a major knowledge gap. Here, using a yeast-two-hybrid screen, we identified novel peptide inhibitors of PELP1 (PIP). Biochemical assays demonstrated that one of these peptides, PIP1, directly interacted with PELP1 to block PELP1 oncogenic functions. Computational modeling of PIP1 revealed key residues contributing to its activity and facilitated the development of a small-molecule inhibitor of PELP1, SMIP34, and further analyses confirmed that SMIP34 directly bound to PELP1. In breast cancer cells, SMIP34 reduced cell growth in a dose-dependent manner. SMIP34 inhibited proliferation of not only wild-type (WT) but also mutant (MT) ER+ and therapy-resistant breast cancer cells, in part by inducing PELP1 degradation via the proteasome pathway. RNA sequencing analyses showed that SMIP34 treatment altered the expression of genes associated with estrogen response, cell cycle, and apoptosis pathways. In cell line-derived and patient-derived xenografts of both WT and MT ER+ breast cancer models, SMIP34 reduced proliferation and significantly suppressed tumor progression. Collectively, these results demonstrate SMIP34 as a first-in-class inhibitor of oncogenic PELP1 signaling in advanced breast cancer. SIGNIFICANCE: Development of a novel inhibitor of oncogenic PELP1 provides potential therapeutic avenues for treating therapy-resistant, advanced ER+ breast cancer.

Phosphorylated MED1 links transcription recycling and cancer growth.

Mediator activates RNA polymerase II (Pol II) function during transcription, but it remains unclear whether Mediator is able to travel with Pol II and regulate Pol II transcription beyond the initiation and early elongation steps. By using in vitro and in vivo transcription recycling assays, we find that human Mediator 1 (MED1), when phosphorylated at the mammal-specific threonine 1032 by cyclin-dependent kinase 9 (CDK9), dynamically moves along with Pol II throughout the transcribed genes to drive Pol II recycling after the initial round of transcription. Mechanistically, MED31 mediates the recycling of phosphorylated MED1 and Pol II, enhancing mRNA output during the transcription recycling process. Importantly, MED1 phosphorylation increases during prostate cancer progression to the lethal phase, and pharmacological inhibition of CDK9 decreases prostate tumor growth by decreasing MED1 phosphorylation and Pol II recycling. Our results reveal a novel role of MED1 in Pol II transcription and identify phosphorylated MED1 as a targetable driver of dysregulated Pol II recycling in cancer.

Deadenylase-dependent mRNA decay of GDF15 and FGF21 orchestrates food intake and energy expenditure.

Hepatokines, secretory proteins from the liver, mediate inter-organ communication to maintain a metabolic balance between food intake and energy expenditure. However, molecular mechanisms by which hepatokine levels are rapidly adjusted following stimuli are largely unknown. Here, we unravel how CNOT6L deadenylase switches off hepatokine expression after responding to stimuli (e.g., exercise and food) to orchestrate energy intake and expenditure. Mechanistically, CNOT6L inhibition stabilizes hepatic Gdf15 and Fgf21 mRNAs, increasing corresponding serum protein levels. The resulting upregulation of GDF15 stimulates the hindbrain to suppress appetite, while increased FGF21 affects the liver and adipose tissues to induce energy expenditure and lipid consumption. Despite the potential of hepatokines to treat metabolic disorders, their administration therapies have been challenging. Using small-molecule screening, we identified a CNOT6L inhibitor enhancing GDF15 and FGF21 hepatokine levels, which dramatically improves diet-induced metabolic syndrome. Our discovery, therefore, lays the foundation for an unprecedented strategy to treat metabolic syndrome.

Single-cell RNA profiling identifies diverse cellular responses to EWSR1/FLI1 downregulation in Ewing sarcoma cells.

BACKGROUND: The EWSR1/FLI1 gene fusion is the most common rearrangement leading to cell transformation in Ewing sarcoma (ES). Previous studies have indicated that expression at the cellular level is heterogeneous, and that levels of expression may oscillate, conferring different cellular characteristics. In ES the role of EWSR1/FLI1 in regulating subpopulation dynamics is currently unknown. METHODS: We used siRNA to transiently suppress EWSR1/FLI1 expression and followed population dynamics using both single cell expression profiling, CyTOF and functional assays to define characteristics of exponentially growing ES cells and of ES cells in which EWSR1/FLI1 had been downregulated. Novel transcriptional states with distinct features were assigned using random forest feature selection in combination with machine learning. Cells isolated from ES xenografts in immune-deficient mice were interrogated to determine whether characteristics of specific subpopulations of cells in vitro could be identified. Stem-like characteristics were assessed by primary and secondary spheroid formation in vitro, and invasion/motility was determined for each identified subpopulation. Autophagy was determined by expression profiling, cell sorting and immunohistochemical staining. RESULTS: We defined a workflow to study EWSR1/FLI1 driven transcriptional states and phenotypes. We tracked EWSR1/FLI1 dependent proliferative activity over time to discover sources of intra-tumoral diversity. Single-cell RNA profiling was used to compare expression profiles in exponentially growing populations (si-Control) or in two dormant populations (D1, D2) in which EWSR1/FLI1 had been suppressed. Three distinct transcriptional states were uncovered contributing to ES intra-heterogeneity. Our predictive model identified ~1% cells in a dormant-like state and ~ 2-4% cells with stem-like and neural stem-like features in an exponentially proliferating ES cell line and in ES xenografts. Following EWSR1/FLI1 knockdown, cells re-entering the proliferative cycle exhibited greater stem-like properties, whereas for those cells remaining quiescent, FAM134B-dependent dormancy may provide a survival mechanism. CONCLUSIONS: We show that time-dependent changes induced by suppression of oncogenic EWSR1/FLI1 expression induces dormancy, with different subpopulation dynamics. Cells re-entering the proliferative cycle show enhanced stem-like characteristics, whereas those remaining dormant for prolonged periods appear to survive through autophagy. Cells with these characteristics identified in exponentially growing cell populations and in tumor xenografts may confer drug resistance and could potentially contribute to metastasis.

2-Hydroxyglutarate destabilizes chromatin regulatory landscape and lineage fidelity to promote cellular heterogeneity.

The epigenome delineates lineage-specific transcriptional programs and restricts cell plasticity to prevent non-physiological cell fate transitions. Although cell diversification fosters tumor evolution and therapy resistance, upstream mechanisms that regulate the stability and plasticity of the cancer epigenome remain elusive. Here we show that 2-hydroxyglutarate (2HG) not only suppresses DNA repair but also mediates the high-plasticity chromatin landscape. A combination of single-cell epigenomics and multi-omics approaches demonstrates that 2HG disarranges otherwise well-preserved stable nucleosome positioning and promotes cell-to-cell variability. 2HG induces loss of motif accessibility to the luminal-defining transcriptional factors FOXA1, FOXP1, and GATA3 and a shift from luminal to basal-like gene expression. Breast tumors with high 2HG exhibit enhanced heterogeneity with undifferentiated epigenomic signatures linked to adverse prognosis. Further, ascorbate-2-phosphate (A2P) eradicates heterogeneity and impairs growth of high 2HG-producing breast cancer cells. These findings suggest 2HG as a key determinant of cancer plasticity and provide a rational strategy to counteract tumor cell evolution.

Construction and Evaluation of Robust Interpretation Models for Breast Cancer Metastasis Prediction.

Interpretability of machine learning (ML) models represents the extent to which a model's decision-making process can be understood by model developers and/or end users. Transcriptomics-based cancer prognosis models, for example, while achieving good accuracy, are usually hard to interpret, due to the high-dimensional feature space and the complexity of models. As interpretability is critical for the transparency and fairness of ML models, several algorithms have been proposed to improve the interpretability of arbitrary classifiers. However, evaluation of these algorithms often requires substantial domain knowledge. Here, we propose a breast cancer metastasis prediction model using a very small number of biologically interpretable features, and a simple yet novel model interpretation approach that can provide personalized interpretations. In addition, we contributed, to the best of our knowledge, the first method to quantitatively compare different interpretation algorithms. Experimental results show that our model not only achieved competitive prediction accuracy, but also higher inter-classifier interpretation consistency than state-of-the-art interpretation methods. Importantly, our interpretation results can improve the generalizability of the prediction models. Overall, this work provides several novel ideas to construct and evaluate interpretable ML models that can be valuable to both the cancer machine learning community and related application domains.

Toward uncharted territory of cellular heterogeneity: advances and applications of single-cell RNA-seq.

Among single-cell analysis technologies, single-cell RNA-seq (scRNA-seq) has been one of the front runners in technical inventions. Since its induction, scRNA-seq has been well received and undergone many fast-paced technical improvements in cDNA synthesis and amplification, processing and alignment of next generation sequencing reads, differentially expressed gene calling, cell clustering, subpopulation identification, and developmental trajectory prediction. scRNA-seq has been exponentially applied to study global transcriptional profiles in all cell types in humans and animal models, healthy or with diseases, including cancer. Accumulative novel subtypes and rare subpopulations have been discovered as potential underlying mechanisms of stochasticity, differentiation, proliferation, tumorigenesis, and aging. scRNA-seq has gradually revealed the uncharted territory of cellular heterogeneity in transcriptomes and developed novel therapeutic approaches for biomedical applications. This review of the advancement of scRNA-seq methods provides an exploratory guide of the quickly evolving technical landscape and insights of focused features and strengths in each prominent area of progress.

Contacts with Macrophages Promote an Aggressive Nanomechanical Phenotype of Circulating Tumor Cells in Prostate Cancer.

Aggressive tumors of epithelial origin shed cells that intravasate and become circulating tumor cells (CTC). The CTCs that are able to survive the stresses encountered in the bloodstream can then seed metastases. We demonstrated previously that CTCs isolated from the blood of prostate cancer patients display specific nanomechanical phenotypes characteristic of cell endurance and invasiveness and patient sensitivity to androgen deprivation therapy. Here we report that patient-isolated CTCs are nanomechanically distinct from cells randomly shed from the tumor, with high adhesion as the most distinguishing biophysical marker. CTCs uniquely coisolated with macrophage-like cells bearing the markers of tumor-associated macrophages (TAM). The presence of these immune cells was indicative of a survival-promoting phenotype of "mechanical fitness" in CTCs based on high softness and high adhesion as determined by atomic force microscopy. Correlations between enumeration of macrophages and mechanical fitness of CTCs were strong in patients before the start of hormonal therapy. Single-cell proteomic analysis and nanomechanical phenotyping of tumor cell-macrophage cocultures revealed that macrophages promoted epithelial-mesenchymal plasticity in prostate cancer cells, manifesting in their mechanical fitness. The resulting softness and adhesiveness of the mechanically fit CTCs confer resistance to shear stress and enable protective cell clustering. These findings suggest that selected tumor cells are coached by TAMs and accompanied by them to acquire intermediate epithelial/mesenchymal status, thereby facilitating survival during the critical early stage leading to metastasis. SIGNIFICANCE: The interaction between macrophages and circulating tumor cells increases the capacity of tumor cells to initiate metastasis and may constitute a new set of blood-based targets for pharmacologic intervention.

PAI-1-Dependent Inactivation of SMAD4-Modulated Junction and Adhesion Complex in Obese Endometrial Cancer.

While plasminogen activator inhibitor-1 (PAI-1) is known to potentiate cellular migration via proteolytic regulation, this adipokine is implicated as an oncogenic ligand in the tumor microenvironment. To understand the underlying paracrine mechanism, here, we conduct transcriptomic analysis of 1,898 endometrial epithelial cells (EECs) exposed and unexposed to PAI-1-secreting adipose stromal cells. The PAI-1-dependent action deregulates crosstalk among tumor-promoting and tumor-repressing pathways, including transforming growth factor ß (TGF-ß). When PAI-1 is tethered to lipoprotein receptor-related protein 1 (LRP1), the internalized signaling causes downregulation of SMAD4 at the transcriptional and post-translational levels that attenuates TGF-ß-related transcription programs. Repression of genes encoding the junction and adhesion complex preferentially occurs in SMAD4-underexpressed EECs of persons with obesity. The findings highlight a role of PAI-1 signaling that renders ineffective intercellular communication for the development of adiposity-associated endometrial cancer.

Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops.

Current computational methods on Hi-C analysis focused on identifying Mb-size domains often failed to unveil the underlying functional and mechanistic relationship of chromatin structure and gene regulation. We developed a novel computational method HiSIF to identify genome-wide interacting loci. We illustrated HiSIF outperformed other tools for identifying chromatin loops. We applied it to Hi-C data in breast cancer cells and identified 21 genes with gained loops showing worse relapse-free survival in endocrine-treated patients, suggesting the genes with enhanced loops can be used for prognostic signatures for measuring the outcome of the endocrine treatment. HiSIF is available at https://github.com/yufanzhouonline/HiSIF .

Age-associated genes in human mammary gland drive human breast cancer progression.

BACKGROUND: Aging is a comorbidity of breast cancer suggesting that aging-associated transcriptome changes may promote breast cancer progression. However, the mechanism underlying the age effect on breast cancer remains poorly understood. METHOD: We analyzed transcriptomics of the matched normal breast tissues from the 82 breast cancer patients in The Cancer Genome Atlas (TCGA) dataset with linear regression for genes with age-associated expression that are not associated with menopause. We also analyzed differentially expressed genes between the paired tumor and non-tumor breast tissues in TCGA for the identification of age and breast cancer (ABC)-associated genes. A few of these genes were selected for further investigation of their malignancy-regulating activities with in vitro and in vivo assays. RESULTS: We identified 148 upregulated and 189 downregulated genes during aging. Overlapping of tumor-associated genes between normal and tumor tissues with age-dependent genes resulted in 14 upregulated and 24 downregulated genes that were both age and breast cancer associated. These genes are predictive in relapse-free survival, indicative of their potential tumor promoting or suppressive functions, respectively. Knockdown of two upregulated genes (DYNLT3 and P4HA3) or overexpression of the downregulated ALX4 significantly reduced breast cancer cell proliferation, migration, and clonogenicity. Moreover, knockdown of P4HA3 reduced growth and metastasis whereas overexpression of ALX4 inhibited the growth of xenografted breast cancer cells in mice. CONCLUSION: Our study suggests that transcriptome alterations during aging may contribute to breast tumorigenesis. DYNLT3, P4HA3, and ALX4 play significant roles in breast cancer progression.

ERα-related chromothripsis enhances concordant gene transcription on chromosome 17q11.1-q24.1 in luminal breast cancer.

BACKGROUND: Chromothripsis is an event of genomic instability leading to complex chromosomal alterations in cancer. Frequent long-range chromatin interactions between transcription factors (TFs) and targets may promote extensive translocations and copy-number alterations in proximal contact regions through inappropriate DNA stitching. Although studies have proposed models to explain the initiation of chromothripsis, few discussed how TFs influence this process for tumor progression. METHODS: This study focused on genomic alterations in amplification associated regions within chromosome 17. Inter-/intra-chromosomal rearrangements were analyzed using whole genome sequencing data of breast tumors in the Cancer Genome Atlas (TCGA) cohort. Common ERα binding sites were defined based on MCF-7, T47D, and MDA-MB-134 breast cancer cell lines using univariate K-means clustering methods. Nanopore sequencing technology was applied to validate frequent rearrangements detected between ATC loci on 17q23 and an ERα hub on 20q13. The efficacy of pharmacological inhibition of a potentially druggable target gene on 17q23 was evaluated using breast cancer cell lines and patient-derived circulating breast tumor cells. RESULTS: There are five adjoining regions from 17q11.1 to 17q24.1 being hotspots of chromothripsis. Inter-/intra-chromosomal rearrangements of these regions occurred more frequently in ERα-positive tumors than in ERα-negative tumors. In addition, the locations of the rearrangements were often mapped within or close to dense ERα binding sites localized on these five 17q regions or other chromosomes. This chromothriptic event was linked to concordant upregulation of 96 loci that predominantly regulate cell-cycle machineries in advanced luminal tumors. Genome-editing analysis confirmed that an ERα hub localized on 20q13 coordinately regulates a subset of these loci localized on 17q23 through long-range chromosome interactions. One of these loci, Tousled Like Kinase 2 (TLK2) known to participate in DNA damage checkpoint control, is an actionable target using phenothiazine antipsychotics (PTZs). The antiproliferative effect of PTZs was prominent in high TLK2-expressing cells, compared to low expressing cells. CONCLUSION: This study demonstrates a new approach for identifying tumorigenic drivers from genomic regions highly susceptible to ERα-related chromothripsis. We found a group of luminal breast tumors displaying 17q-related chromothripsis for which antipsychotics can be repurposed as treatment adjuncts.

Comparative evaluation of network features for the prediction of breast cancer metastasis.

BACKGROUND: Discovering a highly accurate and robust gene signature for the prediction of breast cancer metastasis from gene expression profiling of primary tumors is one of the most challenging tasks to reduce the number of deaths in women. Due to the limited success of gene-based features in achieving satisfactory prediction accuracy, many methodologies have been proposed in recent years to develop network-based features by integrating network information with gene expression. However, evaluation results are inconsistent to confirm the effectiveness of network-based features, because of many confounding factors involved in classification model learning process, such as data normalization, dimension reduction, and feature selection. An unbiased comparative evaluation is essential for uncovering the strength of network-based features. METHODS: In this study, we compared several types of network-based features obtained using different mathematical operators (Mean, Maximum, Minimum, Median, Variance) on geneset (i.e., a gene and its' neighbors in the network) in protein-protein interaction network and gene co-expression network for their ability in predicting breast cancer metastasis using gene expression data from more than 10 patient cohorts. RESULTS: While network-based features are usually statistically more significant than gene-based feature, a consistent improvement of prediction performance using network-based features requires a substantial number of patients in the dataset. In contrary to many previous reports, no evidence was found to support the robustness of network-based features and we argue some of the robustness may be due to the inherent bias associated with node degree in the network. In addition, different types of network features seem to cover different pathways and are complementary to each other. Consequently, an ensemble classifier combining different network features was proposed and was found to significantly outperform classifiers based on gene-based feature or any single type of network-based features. CONCLUSIONS: Network-based features and their combination show promise for improving the prediction of breast cancer metastasis but may require a large amount of training data. Robustness claim of network-based features needs to be re-examined with network node degree and other confounding factors in consideration.

Single-Cell Proteomic Profiling Identifies Combined AXL and JAK1 Inhibition as a Novel Therapeutic Strategy for Lung Cancer.

Cytometry by time-of-flight (CyTOF) simultaneously measures multiple cellular proteins at the single-cell level and is used to assess intertumor and intratumor heterogeneity. This approach may be used to investigate the variability of individual tumor responses to treatments. Herein, we stratified lung tumor subpopulations based on AXL signaling as a potential targeting strategy. Integrative transcriptome analyses were used to investigate how TP-0903, an AXL kinase inhibitor, influences redundant oncogenic pathways in metastatic lung cancer cells. CyTOF profiling revealed that AXL inhibition suppressed SMAD4/TGFß signaling and induced JAK1-STAT3 signaling to compensate for the loss of AXL. Interestingly, high JAK1-STAT3 was associated with increased levels of AXL in treatment-naïve tumors. Tumors with high AXL, TGFß, and JAK1 signaling concomitantly displayed CD133-mediated cancer stemness and hybrid epithelial-to-mesenchymal transition features in advanced-stage patients, suggesting greater potential for distant dissemination. Diffusion pseudotime analysis revealed cell-fate trajectories among four different categories that were linked to clinicopathologic features for each patient. Patient-derived organoids (PDO) obtained from tumors with high AXL and JAK1 were sensitive to TP-0903 and ruxolitinib (JAK inhibitor) treatments, supporting the CyTOF findings. This study shows that single-cell proteomic profiling of treatment-naïve lung tumors, coupled with ex vivo testing of PDOs, identifies continuous AXL, TGFß, and JAK1-STAT3 signal activation in select tumors that may be targeted by combined AXL-JAK1 inhibition. SIGNIFICANCE: Single-cell proteomic profiling of clinical samples may facilitate the optimal selection of novel drug targets, interpretation of early-phase clinical trial data, and development of predictive biomarkers valuable for patient stratification.

Detection of DNA Methylation by MeDIP and MBDCap Assays: An Overview of Techniques.

DNA methylation has been characterized as the representative example of epigenetic modifications and implicated in numerous biological processes, such as genomic imprinting and X chromosome inactivation. It primarily occurs at CpG dinucleotides in mammals and plays a critical role in transcriptional regulations. Examination of DNA methylation patterns in gene(s) or across a genome is vital to understand the role of epigenetic modulation in the progress of development and tumorigenesis. Currently, lots of approaches have been developed to investigate DNA methylation patterns for either limited regions or genome-scale studies, but some of them rely on using restriction enzymes. In this chapter, we describe two commonly used protocols to detect enrichment of methylated DNA regions, namely methylated immunoprecipitation (MeDIP) and capture of methylated DNA by methyl-CpG binding domain-based (MBD) proteins (MBDCap). They are the most economical and effective methods to study DNA methylation in either single locus or genome-wide scale.

Attenuation of NAD[P]H:quinone oxidoreductase 1 aggravates prostate cancer and tumor cell plasticity through enhanced TGFß signaling.

NAD[P]H:quinone oxidoreductase 1 (NQO1) regulates cell fate decisions in response to stress. Oxidative stress supports cancer maintenance and progression. Previously we showed that knockdown of NQO1 (NQO1low) prostate cancer cells upregulate pro-inflammatory cytokines and survival under hormone-deprived conditions. Here, we tested the ability of NQO1low cells to form tumors. We found NQO1low cells form aggressive tumors compared with NQO1high cells. Biopsy specimens and circulating tumor cells showed biochemical recurrent prostate cancer was associated with low NQO1. NQO1 silencing was sufficient to induce SMAD-mediated TGFß signaling and mesenchymal markers. TGFß treatment decreased NQO1 levels and induced molecular changes similar to NQO1 knockdown cells. Functionally, NQO1 depletion increased migration and sensitivity to oxidative stress. Collectively, this work reveals a possible new gatekeeper role for NQO1 in counteracting cellular plasticity in prostate cancer cells. Further, combining NQO1 with TGFß signaling molecules may serve as a better signature to predict biochemical recurrence.

Spatial EGFR Dynamics and Metastatic Phenotypes Modulated by Upregulated EphB2 and Src Pathways in Advanced Prostate Cancer.

Advanced prostate cancer is a very heterogeneous disease reflecting in diverse regulations of oncogenic signaling pathways. Aberrant spatial dynamics of epidermal growth factor receptor (EGFR) promote their dimerization and clustering, leading to constitutive activation in oncogenesis. The EphB2 and Src signaling pathways are associated with the reorganization of the cytoskeleton leading to malignancy, but their roles in regulating EGFR dynamics and activation are scarcely reported. Using single-particle tracking techniques, we found that highly phosphorylated EGFR in the advanced prostate cancer cell line, PC3, was associated with higher EGFR diffusivity, as compared with LNCaP and less aggressive DU145. The increased EGFR activation and biophysical dynamics were consistent with high proliferation, migration, and invasion. After performing single-cell RNA-seq on prostate cancer cell lines and circulating tumor cells from patients, we identified that upregulated gene expression in the EphB2 and Src pathways are associated with advanced malignancy. After dasatinib treatment or siRNA knockdowns of EphB2 or Src, the PC3 cells exhibited significantly lower EGFR dynamics, cell motility, and invasion. Partial inhibitory effects were also found in DU145 cells. The upregulation of parts of the EphB2 and Src pathways also predicts poor prognosis in the prostate cancer patient cohort of The Cancer Genome Atlas. Our results provide evidence that overexpression of the EphB2 and Src signaling pathways regulate EGFR dynamics and cellular aggressiveness in some advanced prostate cancer cells.