Phenotype prediction from single-cell RNA-seq data using attention-based neural networks.

MOTIVATION: A patient's disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models. RESULTS: Here, we propose the method ScRAT for phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as coronavirus disease (COVID) and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies. AVAILABILITY AND IMPLEMENTATION: The code of our proposed method ScRAT is published at https://github.com/yuzhenmao/ScRAT.

Establishment and validation of preclinical models of SMARCA4-inactivated and ARID1A/ARID1B co-inactivated dedifferentiated endometrial carcinoma.

OBJECTIVE: Dedifferentiated endometrial cancer (DDEC) is an uncommon and clinically highly aggressive subtype of endometrial cancer characterized by genomic inactivation of SWItch/Sucrose Non-Fermentable (SWI/SNF) complex protein. It responds poorly to conventional systemic treatment and its rapidly progressive clinical course limits the therapeutic windows to trial additional lines of therapies. This underscores a pressing need for biologically accurate preclinical tumor models to accelerate therapeutic development. METHODS: DDEC tumor from surgical samples were implanted into immunocompromised mice for patient-derived xenograft (PDX) and cell line development. The histologic, immunophenotypic, genetic and epigenetic features of the patient tumors and the established PDX models were characterized. The SMARCA4-deficienct DDEC model was evaluated for its sensitivity toward a KDM6A/B inhibitor (GSK-J4) that was previously reported to be effective therapy for other SMARCA4-deficient cancer types. RESULTS: All three DDEC models exhibited rapid growth in vitro and in vivo, with two PDX models showing spontaneous development of metastases in vivo. The PDX tumors maintained the same undifferentiated histology and immunophenotype, and exhibited identical genomic and methylation profiles as seen in the respective parental tumors, including a mismatch repair (MMR)-deficient DDEC with genomic inactivation of SMARCA4, and two MMR-deficient DDECs with genomic inactivation of both ARID1A and ARID1B. Although the SMARCA4-deficient cell line showed low micromolecular sensitivity to GSK-J4, no significant tumor growth inhibition was observed in the corresponding PDX model. CONCLUSIONS: These established patient tumor-derived models accurately depict DDEC and represent valuable preclinical tools to gain therapeutic insights into this aggressive tumor type.

Establishment and characterization of a novel treatment-related neuroendocrine prostate cancer cell line KUCaP13.

The prevalence of neuroendocrine prostate cancer (NEPC) arising from adenocarcinoma (AC) upon potent androgen receptor (AR) pathway inhibition is increasing. Deeper understanding of NEPC biology and development of novel therapeutic agents are needed. However, research is hindered by the paucity of research models, especially cell lines developed from NEPC patients. We established a novel NEPC cell line, KUCaP13, from tissue of a patient initially diagnosed with AC which later recurred as NEPC. The cell line has been maintained permanently in vitro under regular cell culture conditions and is amenable to gene engineering with lentivirus. KUCaP13 cells lack the expression of AR and overexpress NEPC-associated genes, including SOX2, EZH2, AURKA, PEG10, POU3F2, ENO2, and FOXA2. Importantly, the cell line maintains the homozygous deletion of CHD1, which was confirmed in the primary AC of the index patient. Loss of heterozygosity of TP53 and PTEN, and an allelic loss of RB1 with a transcriptomic signature compatible with Rb pathway aberration were revealed. Knockdown of PEG10 using shRNA significantly suppressed growth in vivo. Introduction of luciferase allowed serial monitoring of cells implanted orthotopically or in the renal subcapsule. Although H3K27me was reduced by EZH2 inhibition, reversion to AC was not observed. KUCaP13 is the first patient-derived, treatment-related NEPC cell line with triple loss of tumor suppressors critical for NEPC development through lineage plasticity. It could be valuable in research to deepen the understanding of NEPC.

CircMiner: accurate and rapid detection of circular RNA through splice-aware pseudo-alignment scheme.

MOTIVATION: The ubiquitous abundance of circular RNAs (circRNAs) has been revealed by performing high-throughput sequencing in a variety of eukaryotes. circRNAs are related to some diseases, such as cancer in which they act as oncogenes or tumor-suppressors and, therefore, have the potential to be used as biomarkers or therapeutic targets. Accurate and rapid detection of circRNAs from short reads remains computationally challenging. This is due to the fact that identifying chimeric reads, which is essential for finding back-splice junctions, is a complex process. The sensitivity of discovery methods, to a high degree, relies on the underlying mapper that is used for finding chimeric reads. Furthermore, all the available circRNA discovery pipelines are resource intensive. RESULTS: We introduce CircMiner, a novel stand-alone circRNA detection method that rapidly identifies and filters out linear RNA sequencing reads and detects back-splice junctions. CircMiner employs a rapid pseudo-alignment technique to identify linear reads that originate from transcripts, genes or the genome. CircMiner further processes the remaining reads to identify the back-splice junctions and detect circRNAs with single-nucleotide resolution. We evaluated the efficacy of CircMiner using simulated datasets generated from known back-splice junctions and showed that CircMiner has superior accuracy and speed compared to the existing circRNA detection tools. Additionally, on two RNase R treated cell line datasets, CircMiner was able to detect most of consistent, high confidence circRNAs compared to untreated samples of the same cell line. AVAILABILITY AND IMPLEMENTATION: CircMiner is implemented in C++ and is available online at https://github.com/vpc-ccg/circminer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA.

MOTIVATION: Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the 'liquid biopsy' possible, which involves sampling a patient's blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable. RESULTS: Here we introduce SViCT , a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT was able to detect all known SVs in two real cfDNA reference datasets (at 0.6-5% ctDNA) and predict a novel structural variant in a prostate cancer cohort. AVAILABILITY: SViCT is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca.

Computational identification of micro-structural variations and their proteogenomic consequences in cancer.

Motivation: Rapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. We introduce a computational framework, ProTIE, to integratively analyze all three types of omics data for a complete molecular profile of a tissue sample. Our framework features MiStrVar, a novel algorithmic method to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can accurately profile structurally aberrant transcripts in tumors. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures. Observing structural aberrations in all three types of omics data validates their presence in the tumor samples. Results: We have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq datasets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations, fusions in particular, were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Availability and implementation: MiStrVar is available for download at https://bitbucket.org/compbio/mistrvar, and ProTIE is available at https://bitbucket.org/compbio/protie. Contact: cenksahi@indiana.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions.

Motivation: Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nt that do not get translated into proteins. Often these transcripts are processed (spliced, capped and polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA. Results: Here, we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined P-value. We applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29 and ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays. Availability and implementation: MechRNA is available for download at https://bitbucket.org/compbio/mechrna. Supplementary information: Supplementary data are available at Bioinformatics online.

Discovery and genotyping of novel sequence insertions in many sequenced individuals.

MOTIVATION: Despite recent advances in algorithms design to characterize structural variation using high-throughput short read sequencing (HTS) data, characterization of novel sequence insertions longer than the average read length remains a challenging task. This is mainly due to both computational difficulties and the complexities imposed by genomic repeats in generating reliable assemblies to accurately detect both the sequence content and the exact location of such insertions. Additionally, de novo genome assembly algorithms typically require a very high depth of coverage, which may be a limiting factor for most genome studies. Therefore, characterization of novel sequence insertions is not a routine part of most sequencing projects. RESULT: Here, we present Pamir, a new algorithm to efficiently and accurately discover and genotype novel sequence insertions using either single or multiple genome sequencing datasets. Pamir is able to detect breakpoint locations of the insertions and calculate their zygosity (i.e. heterozygous versus homozygous) by analyzing multiple sequence signatures, matching one-end-anchored sequences to small-scale de novo assemblies of unmapped reads, and conducting strand-aware local assembly. We test the efficacy of Pamir on both simulated and real data, and demonstrate its potential use in accurate and routine identification of novel sequence insertions in genome projects. AVAILABILITY AND IMPLEMENTATION: Pamir is available at https://github.com/vpc-ccg/pamir . CONTACT: fhach@{sfu.ca, prostatecentre.com } or calkan@cs.bilkent.edu.tr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TEMPO-Oxidized Bacterial Cellulose Pellicle with Silver Nanoparticles for Wound Dressing.

Biocompatible bacterial cellulose pellicle (BCP) is a candidate for biomedical material such as wound dressing. However, due to lack of antibacterial activity, to grant BCP with the property is crucial for its biomedical application. In the present study, BCP was modified by 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)-mediated oxidation using TEMPO/NaClO/NaBr system at pH 10 to form TEMPO-oxidized BCP (TOBCP) with anionic C6 carboxylate groups. The TOBCP was subsequently ion-exchanged in AgNO3 solution and silver nanoparticles (AgNP) with diameter of ∼16.5 nm were in situ synthesized on TOBC nanofiber surfaces by thermal reduction without using a reducing agent. Field emission scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectra, Fourier transform infrared spectroscopy, and thermogravimetric analysis were carried out to confirm morphology and structure of the pellicles with AgNP. The AgNP continuously released Ag+ with a rate of 12.2%/day at 37 °C in 3 days. The TOBCP/AgNP exhibited high biocompatibility according to the result of in vitro cytotoxicity test (cell viability >95% after 48 h of incubation) and showed significant antibacterial activities of 100% and 99.2% against E. coli and S. aureus, respectively. Hence, the highly biocompatible and highly antibacterial TOBCP/AgNP prepared in the present study is a promising candidate for wound dressing.

ORMAN: optimal resolution of ambiguous RNA-Seq multimappings in the presence of novel isoforms.

MOTIVATION: RNA-Seq technology is promising to uncover many novel alternative splicing events, gene fusions and other variations in RNA transcripts. For an accurate detection and quantification of transcripts, it is important to resolve the mapping ambiguity for those RNA-Seq reads that can be mapped to multiple loci: >17% of the reads from mouse RNA-Seq data and 50% of the reads from some plant RNA-Seq data have multiple mapping loci. In this study, we show how to resolve the mapping ambiguity in the presence of novel transcriptomic events such as exon skipping and novel indels towards accurate downstream analysis. We introduce ORMAN ( O ptimal R esolution of M ultimapping A mbiguity of R N A-Seq Reads), which aims to compute the minimum number of potential transcript products for each gene and to assign each multimapping read to one of these transcripts based on the estimated distribution of the region covering the read. ORMAN achieves this objective through a combinatorial optimization formulation, which is solved through well-known approximation algorithms, integer linear programs and heuristics. RESULTS: On a simulated RNA-Seq dataset including a random subset of transcripts from the UCSC database, the performance of several state-of-the-art methods for identifying and quantifying novel transcripts, such as Cufflinks, IsoLasso and CLIIQ, is significantly improved through the use of ORMAN. Furthermore, in an experiment using real RNA-Seq reads, we show that ORMAN is able to resolve multimapping to produce coverage values that are similar to the original distribution, even in genes with highly non-uniform coverage. AVAILABILITY: ORMAN is available at http://orman.sf.net

A diversity of novel type-2 innate lymphoid cell subpopulations revealed during tumour expansion.

Type 2 innate lymphoid cells (ILC2s) perform vital functions in orchestrating humoral immune responses, facilitating tissue remodelling, and ensuring tissue homeostasis. Additionally, in a role that has garnered considerably less attention, ILC2s can also enhance Th1-related cytolytic T lymphocyte immune responses against tumours. Studies have thus far generally failed to address the mystery of how one ILC2 cell-type can participate in a multiplicity of functions. Here we utilized single cell RNA sequencing analysis to create the first comprehensive atlas of naïve and tumour-associated lung ILC2s and discover multiple unique subtypes of ILC2s equipped with developmental gene programs that become skewed during tumour expansion favouring inflammation, antigen processing, immunological memory and Th1-related anti-tumour CTL responses. The discovery of these new subtypes of ILC2s challenges current paradigms of ILC2 biology and provides an explanation for their diversity of function.

Deciphering the Transcription Factor Landscape in Neuroendocrine Prostate Cancer Progression: A Novel Approach to Understand NE Transdifferentiation.

Background and Objective: Prostate cancer (PCa) is a leading cause of cancer mortality in men, with neuroendocrine prostate cancer (NEPC) representing a particularly resistant subtype. The role of transcription factors (TFs) in the progression from prostatic adenocarcinoma (PRAD) to NEPC is poorly understood. This study aims to identify and analyze lineage-specific TF profiles in PRAD and NEPC and illustrate their dynamic shifts during NE transdifferentiation. Methods: A novel algorithmic approach was developed to evaluate the weighted expression of TFs within patient samples, enabling a nuanced understanding of TF landscapes in PCa progression and TF dynamic shifts during NE transdifferentiation. Results: unveiled TF profiles for PRAD and NEPC, identifying 126 shared TFs, 46 adenocarcinoma-TFs, and 56 NEPC-TFs. Enrichment analysis across multiple clinical cohorts confirmed the lineage specificity and clinical relevance of these lineage-TFs signatures. Functional analysis revealed that lineage-TFs are implicated in pathways critical to cell development, differentiation, and lineage determination. Novel lineage-TF candidates were identified, offering potential targets for therapeutic intervention. Furthermore, our longitudinal study on NE transdifferentiation highlighted dynamic TF expression shifts and delineated a three-phase hypothesis for the process comprised of de-differentiation, dormancy, and re-differentiation. and proposing novel insights into the mechanisms of PCa progression. Conclusion: The lineage-specific TF profiles in PRAD and NEPC reveal a dynamic shift in the TF landscape during PCa progression, highlighting three distinct phases of NE transdifferentiation.

Influence of ADT on B7-H3 expression during CRPC progression from hormone-naïve prostate cancer.

Androgen deprivation therapy (ADT) is the standard care for advanced prostate cancer (PCa) patients. Unfortunately, although tumors respond well initially, they enter dormancy and eventually progress to fatal/incurable castration-resistant prostate cancer (CRPC). B7-H3 is a promising new target for PCa immunotherapy. CD276 (B7-H3) gene has a presumptive androgen receptor (AR) binding site, suggesting potential AR regulation. However, the relationship between B7-H3 and AR is controversial. Meanwhile, the expression pattern of B7-H3 following ADT and during CRPC progression is largely unknown, but critically important for identifying patients and determining the optimal timing of B7-H3 targeting immunotherapy. In this study, we performed a longitudinal study using our unique PCa patient-derived xenograft (PDX) models and assessed B7-H3 expression during post-ADT disease progression. We further validated our findings at the clinical level in PCa patient samples. We found that B7-H3 expression was negatively regulated by AR during the early phase of ADT treatment, but positively associated with PCa proliferation during the remainder of disease progression. Our findings suggest its use as a biomarker for diagnosis, prognosis, and ADT treatment response, and the potential of combining ADT and B7-H3 targeting immunotherapy for hormone-naïve PCa treatment to prevent fatal CRPC relapse.

TEAD4 as an Oncogene and a Mitochondrial Modulator.

TEAD4 (TEA Domain Transcription Factor 4) is well recognized as the DNA-anchor protein of YAP transcription complex, which is modulated by Hippo, a highly conserved pathway in Metazoa that controls organ size through regulating cell proliferation and apoptosis. To acquire full transcriptional activity, TEAD4 requires co-activator, YAP (Yes-associated protein) or its homolog TAZ (transcriptional coactivator with PDZ-binding motif) the signaling hub that relays the extracellular stimuli to the transcription of target genes. Growing evidence suggests that TEAD4 also exerts its function in a YAP-independent manner through other signal pathways. Although TEAD4 plays an essential role in determining that differentiation fate of the blastocyst, it also promotes tumorigenesis by enhancing metastasis, cancer stemness, and drug resistance. Upregulation of TEAD4 has been reported in several cancers, including colon cancer, gastric cancer, breast cancer, and prostate cancer and serves as a valuable prognostic marker. Recent studies show that TEAD4, but not other members of the TEAD family, engages in regulating mitochondrial dynamics and cell metabolism by modulating the expression of mitochondrial- and nuclear-encoded electron transport chain genes. TEAD4's functions including oncogenic activities are tightly controlled by its subcellular localization. As a predominantly nuclear protein, its cytoplasmic translocation is triggered by several signals, such as osmotic stress, cell confluency, and arginine availability. Intriguingly, TEAD4 is also localized in mitochondria, although the translocation mechanism remains unclear. In this report, we describe the current understanding of TEAD4 as an oncogene, epigenetic regulator and mitochondrial modulator. The contributing mechanisms will be discussed.

Framework of Intrinsic Immune Landscape of Dormant Prostate Cancer.

Androgen deprivation therapy (ADT) is the standard therapy for men with advanced prostate cancer (PCa). PCa often responds to ADT and enters a dormancy period, which can be recognized clinically as a minimal residual disease. However, the majority of these patients will eventually experience a relapse in the form of castration-resistant PCa with poor survival. Therefore, ADT-induced dormancy is a unique time window for treatment that can provide a cure. The study of this well-recognized phase of prostate cancer progression is largely hindered by the scarcity of appropriate clinical tissue and clinically relevant preclinical models. Here, we report the utility of unique and clinically relevant patient-derived xenograft models in the study of the intrinsic immune landscape of dormant PCa. Using data from RNA sequencing, we have reconstructed the immune evasion mechanisms that can be utilized by dormant PCa cells. Since dormant PCa cells need to evade the host immune surveillance for survival, our results provide a framework for further study and for devising immunomodulatory mechanisms that can eliminate dormant PCa cells.

Typhoid toxin sorting and exocytic transport from Salmonella Typhi-infected cells.

Typhoid toxin is an essential virulence factor for Salmonella Typhi, the cause of typhoid fever in humans. This toxin has an unusual biology in that it is produced by Salmonella Typhi only when located within host cells. Once synthesized, the toxin is secreted to the lumen of the Salmonella-containing vacuole from where it is transported to the extracellular space by vesicle carrier intermediates. Here, we report the identification of the typhoid toxin sorting receptor and components of the cellular machinery that packages the toxin into vesicle carriers, and exports it to the extracellular space. We found that the cation-independent mannose-6-phosphate receptor serves as typhoid toxin sorting receptor and that the coat protein COPII and the GTPase Sar1 mediate its packaging into vesicle carriers. Formation of the typhoid toxin carriers requires the specific environment of the Salmonella Typhi-containing vacuole, which is determined by the activities of specific effectors of its type III protein secretion systems. We also found that Rab11B and its interacting protein Rip11 control the intracellular transport of the typhoid toxin carriers, and the SNARE proteins VAMP7, SNAP23, and Syntaxin 4 their fusion to the plasma membrane. Typhoid toxin's cooption of specific cellular machinery for its transport to the extracellular space illustrates the remarkable adaptation of an exotoxin to exert its function in the context of an intracellular pathogen.

Modeling Androgen Deprivation Therapy-Induced Prostate Cancer Dormancy and Its Clinical Implications.

Treatment-induced tumor dormancy is a state in cancer progression where residual disease is present but remains asymptomatic. Dormant cancer cells are treatment-resistant and responsible for cancer recurrence and metastasis. Prostate cancer treated with androgen-deprivation therapy (ADT) often enters a dormant state. ADT-induced prostate cancer dormancy remains poorly understood due to the challenge in acquiring clinical dormant prostate cancer cells and the lack of representative models. In this study, we aimed to develop clinically relevant models for studying ADT-induced prostate cancer dormancy. Dormant prostate cancer models were established by castrating mice bearing patient-derived xenografts (PDX) of hormonal naïve or sensitive prostate cancer. Dormancy status and tumor relapse were monitored and evaluated. Paired pre- and postcastration (dormant) PDX tissues were subjected to morphologic and transcriptome profiling analyses. As a result, we established eleven ADT-induced dormant prostate cancer models that closely mimicked the clinical courses of ADT-treated prostate cancer. We identified two ADT-induced dormancy subtypes that differed in morphology, gene expression, and relapse rates. We discovered transcriptomic differences in precastration PDXs that predisposed the dormancy response to ADT. We further developed a dormancy subtype-based, predisposed gene signature that was significantly associated with ADT response in hormonal naïve prostate cancer and clinical outcome in castration-resistant prostate cancer treated with ADT or androgen-receptor pathway inhibitors. IMPLICATIONS: We have established highly clinically relevant PDXs of ADT-induced dormant prostate cancer and identified two dormancy subtypes, leading to the development of a novel predicative gene signature that allows robust risk stratification of patients with prostate cancer to ADT or androgen-receptor pathway inhibitors.

Novel Lignin-Capped Silver Nanoparticles against Multidrug-Resistant Bacteria.

The emergence of bacteria resistant to antibiotics and the resulting infections are increasingly becoming a public health issue. Multidrug-resistant (MDR) bacteria are responsible for infections leading to increased morbidity and mortality in hospitals, prolonged time of hospitalization, and additional burden to financial costs. Therefore, there is an urgent need for novel antibacterial agents that will both treat MDR infections and outsmart the bacterial evolutionary mechanisms, preventing further resistance development. In this study, a green synthesis employing nontoxic lignin as both reducing and capping agents was adopted to formulate stable and biocompatible silver-lignin nanoparticles (NPs) exhibiting antibacterial activity. The resulting silver-lignin NPs were approximately 20 nm in diameter and did not agglomerate after one year of storage at 4 °C. They were able to inhibit the growth of a panel of MDR clinical isolates, including Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii, at concentrations that did not affect the viability of a monocyte-derived THP-1 human cell line. Furthermore, the exposure of silver-lignin NPs to the THP-1 cells led to a significant increase in the secretion of the anti-inflammatory cytokine IL-10, demonstrating the potential of these particles to act as an antimicrobial and anti-inflammatory agent simultaneously. P. aeruginosa genes linked with efflux, heavy metal resistance, capsular biosynthesis, and quorum sensing were investigated for changes in gene expression upon sublethal exposure to the silver-lignin NPs. Genes encoding for membrane proteins with an efflux function were upregulated. However, all other genes were membrane proteins that did not efflux metals and were downregulated.

Multiomics Characterization of Low-Grade Serous Ovarian Carcinoma Identifies Potential Biomarkers of MEK Inhibitor Sensitivity and Therapeutic Vulnerability.

Low-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates in patients with metastatic disease. There is a pressing need to develop effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. Here, we use multiomics integration of whole-exome sequencing, RNA sequencing, and mass spectrometry-based proteomics on 14 LGSOC cell lines to elucidate novel biomarkers and therapeutic vulnerabilities. Comparison of LGSOC cell line data with LGSOC tumor data enabled predictive biomarker identification of MEK inhibitor (MEKi) efficacy, with KRAS mutations found exclusively in MEKi-sensitive cell lines and NRAS mutations found mostly in MEKi-resistant cell lines. Distinct patterns of Catalogue of Somatic Mutations in Cancer mutational signatures were identified in MEKi-sensitive and MEKi-resistant cell lines. Deletions of CDKN2A/B and MTAP genes were more frequent in cell lines than tumor samples and possibly represent key driver events in the absence of KRAS/NRAS/BRAF mutations. These LGSOC cell lines were representative models of the molecular aberrations found in LGSOC tumors. For prediction of in vitro MEKi efficacy, proteomic data provided better discrimination than gene expression data. Condensin, minichromosome maintenance, and replication factor C protein complexes were identified as potential treatment targets in MEKi-resistant cell lines. This study suggests that CDKN2A/B or MTAP deficiency may be exploited using synthetically lethal treatment strategies, highlighting the importance of using proteomic data as a tool for molecular drug prediction. Multiomics approaches are crucial to improving our understanding of the molecular underpinnings of LGSOC and applying this information to develop new therapies. SIGNIFICANCE: These findings highlight the utility of global multiomics to characterize LGSOC cell lines as research models, to determine biomarkers of MEKi resistance, and to identify potential novel therapeutic targets.

The Antiproliferation Activity of Ganoderma formosanum Extracts on Prostate Cancer Cells.

Androgen-independent prostate cancer accounts for mortality in the world. In this study, various extracts of a medical fungus dubbed Ganoderma formosanum were screened for inhibition of DU145 cells, an androgen-independent prostate cancer cell line. Results demonstrated that both hexane (GF-EH) and butanol (GF-EB) fraction of G. formosanum ethanol extract inhibited DU145 cell viability in a dose-dependent manner. GF-EH induced cell-cycle arrest in G1 phase of DU145 cells via downregulation of cyclin E2 protein expression. In addition, GF-EB triggered extrinsic apoptosis of DU145 cells by activating caspase 3 gene expression resulting in programed cell death. Above all, both GF-EH and GF-EB show lower toxicity to normal human fibroblast cell line compared to DU145 cell, implying that they possess specific drug action on cancer cells. This study provides a molecular basis of G. formosanum extract as a potential ingredient for treatment of androgen-independent prostate cancer.