Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer.

While mutations affecting protein-coding regions have been examined across many cancers, structural variants at the genome-wide level are still poorly defined. Through integrative deep whole-genome and -transcriptome analysis of 101 castration-resistant prostate cancer metastases (109X tumor/38X normal coverage), we identified structural variants altering critical regulators of tumorigenesis and progression not detectable by exome approaches. Notably, we observed amplification of an intergenic enhancer region 624 kb upstream of the androgen receptor (AR) in 81% of patients, correlating with increased AR expression. Tandem duplication hotspots also occur near MYC, in lncRNAs associated with post-translational MYC regulation. Classes of structural variations were linked to distinct DNA repair deficiencies, suggesting their etiology, including associations of CDK12 mutation with tandem duplications, TP53 inactivation with inverted rearrangements and chromothripsis, and BRCA2 inactivation with deletions. Together, these observations provide a comprehensive view of how structural variations affect critical regulators in metastatic prostate cancer.

PACT: a pipeline for analysis of circulating tumor DNA.

MOTIVATION: Detection of genomic alterations in circulating tumor DNA (ctDNA) is currently used for active clinical monitoring of cancer progression and treatment response. While methods for analysis of small mutations are more developed, strategies for detecting structural variants (SVs) in ctDNA are limited. Additionally, reproducibly calling small-scale mutations, copy number alterations, and SVs in ctDNA is challenging due to the lack to unified tools for these different classes of variants. RESULTS: We developed a unified pipeline for the analysis of ctDNA [Pipeline for the Analysis of ctDNA (PACT)] that accurately detects SVs and consistently outperformed similar tools when applied to simulated, cell line, and clinical data. We provide PACT in the form of a Common Workflow Language pipeline which can be run by popular workflow management systems in high-performance computing environments. AVAILABILITY AND IMPLEMENTATION: PACT is freely available at https://github.com/ChrisMaherLab/PACT.

Chemerin triggers migration of a CD8 T cell subset with natural killer cell functions.

The recruitment of cells with effector functions into the tumor microenvironment holds potential for delaying cancer progression. We show that subsets of human CD28-effector CD8 T cells, CCR7- CD45RO+ effector memory, and CCR7- CD45RO- effector memory RA phenotypes, express the chemerin receptor CMKLR1 and bind chemerin via the receptor. CMKLR1-expressing human CD8 effector memory T cells present gene, protein, and cytotoxic features of NK cells. Active chemerin promotes chemotaxis of CMKLR1-expressing CD8 effector memory cells and triggers activation of the α4ß1 integrin. In an experimental prostate tumor mouse model, chemerin expression is downregulated in the tumor microenvironment, which is associated with few tumor-infiltrating CD8+ T cells, while forced overexpression of chemerin by mouse prostate cancer cells leads to an accumulation of intra-tumor CD8+ T cells. Furthermore, α4 integrin blockade abrogated the chemerin-dependent recruitment of CD8+ T effector memory cells into implanted prostate tumors in vivo. The results identify a role for chemerin:CMKLR1 in defining a specialized NK-like CD8 T cell, and suggest the use of chemerin-dependent modalities to target effector CMKLR1-expressing T cells to the tumor microenvironment for immunotherapeutic purposes.

Visualizing tumor evolution with the fishplot package for R.

BACKGROUND: Massively-parallel sequencing at depth is now enabling tumor heterogeneity and evolution to be characterized in unprecedented detail. Tracking these changes in clonal architecture often provides insight into therapeutic response and resistance. In complex cases involving multiple timepoints, standard visualizations, such as scatterplots, can be difficult to interpret. Current data visualization methods are also typically manual and laborious, and often only approximate subclonal fractions. RESULTS: We have developed an R package that accurately and intuitively displays changes in clonal structure over time. It requires simple input data and produces illustrative and easy-to-interpret graphs suitable for diagnosis, presentation, and publication. CONCLUSIONS: The simplicity, power, and flexibility of this tool make it valuable for visualizing tumor evolution, and it has potential utility in both research and clinical settings. The fishplot package is available at https://github.com/chrisamiller/fishplot .

The Alternaria genomes database: a comprehensive resource for a fungal genus comprised of saprophytes, plant pathogens, and allergenic species.

BACKGROUND: Alternaria is considered one of the most common saprophytic fungal genera on the planet. It is comprised of many species that exhibit a necrotrophic phytopathogenic lifestyle. Several species are clinically associated with allergic respiratory disorders although rarely found to cause invasive infections in humans. Finally, Alternaria spp. are among the most well known producers of diverse fungal secondary metabolites, especially toxins. DESCRIPTION: We have recently sequenced and annotated the genomes of 25 Alternaria spp. including but not limited to many necrotrophic plant pathogens such as A. brassicicola (a pathogen of Brassicaceous crops like cabbage and canola) and A. solani (a major pathogen of Solanaceous plants like potato and tomato), and several saprophytes that cause allergy in human such as A. alternata isolates. These genomes were annotated and compared. Multiple genetic differences were found in the context of plant and human pathogenicity, notably the pro-inflammatory potential of A. alternata. The Alternaria genomes database was built to provide a public platform to access the whole genome sequences, genome annotations, and comparative genomics data of these species. Genome annotation and comparison were performed using a pipeline that integrated multiple computational and comparative genomics tools. Alternaria genome sequences together with their annotation and comparison data were ported to Ensembl database schemas using a self-developed tool (EnsImport). Collectively, data are currently hosted using a customized installation of the Ensembl genome browser platform. CONCLUSION: Recent efforts in fungal genome sequencing have facilitated the studies of the molecular basis of fungal pathogenicity as a whole system. The Alternaria genomes database provides a comprehensive resource of genomics and comparative data of an important saprophytic and plant/human pathogenic fungal genus. The database will be updated regularly with new genomes when they become available. The Alternaria genomes database is freely available for non-profit use at http://alternaria.vbi.vt.edu .

Allerdictor: fast allergen prediction using text classification techniques.

MOTIVATION: Accurately identifying and eliminating allergens from biotechnology-derived products are important for human health. From a biomedical research perspective, it is also important to identify allergens in sequenced genomes. Many allergen prediction tools have been developed during the past years. Although these tools have achieved certain levels of specificity, when applied to large-scale allergen discovery (e.g. at a whole-genome scale), they still yield many false positives and thus low precision (even at low recall) due to the extreme skewness of the data (allergens are rare). Moreover, the most accurate tools are relatively slow because they use protein sequence alignment to build feature vectors for allergen classifiers. Additionally, only web server implementations of the current allergen prediction tools are publicly available and are without the capability of large batch submission. These weaknesses make large-scale allergen discovery ineffective and inefficient in the public domain. RESULTS: We developed Allerdictor, a fast and accurate sequence-based allergen prediction tool that models protein sequences as text documents and uses support vector machine in text classification for allergen prediction. Test results on multiple highly skewed datasets demonstrated that Allerdictor predicted allergens with high precision over high recall at fast speed. For example, Allerdictor only took ∼6 min on a single core PC to scan a whole Swiss-Prot database of ∼540 000 sequences and identified <1% of them as allergens. AVAILABILITY AND IMPLEMENTATION: Allerdictor is implemented in Python and available as standalone and web server versions at http://allerdictor.vbi.vt.edu CONTACT: lawrence@vbi.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Pan-cancer transcriptome analysis reveals long noncoding RNAs with conserved function.

A growing number of gene-centric studies have highlighted the emerging significance of lncRNAs in cancer. However, these studies primarily focus on a single cancer type. Therefore, we conducted a pan-cancer analysis of lncRNAs comparing tumor and matched normal expression levels using RNA-Seq data from ∼ 3,000 patients in 8 solid tumor types. While the majority of differentially expressed lncRNAs display tissue-specific expression we discovered 229 lncRNAs with outlier or differential expression across multiple cancers, which we refer to as 'onco-lncRNAs'. Due to their consistent altered expression, we hypothesize that these onco-lncRNAs may have conserved oncogenic and tumor suppressive functions across cancers. To address this, we associated the onco-lncRNAs in biological processes based on their co-expressed protein coding genes. To validate our predictions, we experimentally confirmed cell growth dependence of 2 novel oncogenic lncRNAs, onco-lncRNA-3 and onco-lncRNA-12, and a previously identified lncRNA CCAT1. Overall, we discovered lncRNAs that may have broad oncogenic and tumor suppressor roles that could significantly advance our understanding of cancer lncRNA biology.

Single cell-transcriptomic analysis informs the lncRNA landscape in metastatic castration resistant prostate cancer.

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal form of prostate cancer. Although long-noncoding RNAs (lncRNAs) have been implicated in mCRPC, past studies have relied on bulk sequencing methods with low depth and lack of single-cell resolution. Hence, we performed a lncRNA-focused analysis of single-cell RNA-sequencing data (n = 14) from mCRPC biopsies followed by integration with bulk multi-omic datasets. This yielded 389 cell-enriched lncRNAs in prostate cancer cells and the tumor microenvironment (TME). These lncRNAs demonstrated enrichment with regulatory elements and exhibited alterations during prostate cancer progression. Prostate-lncRNAs were correlated with AR mutational status and response to treatment with enzalutamide, while TME-lncRNAs were associated with RB1 deletions and poor prognosis. Finally, lncRNAs identified between prostate adenocarcinomas and neuroendocrine tumors exhibited distinct expression and methylation profiles. Our findings demonstrate the ability of single-cell analysis to refine our understanding of lncRNAs in mCRPC and serve as a resource for future mechanistic studies.

Integrative analysis of ultra-deep RNA-seq reveals alternative promoter usage as a mechanism of activating oncogenic programmes during prostate cancer progression.

Transcription factor (TF) proteins regulate gene activity by binding to regulatory regions, most importantly at gene promoters. Many genes have alternative promoters (APs) bound by distinct TFs. The role of differential TF activity at APs during tumour development is poorly understood. Here we show, using deep RNA sequencing in 274 biopsies of benign prostate tissue, localized prostate tumours and metastatic castration-resistant prostate cancer, that AP usage increases as tumours progress and APs are responsible for a disproportionate amount of tumour transcriptional activity. Expression of the androgen receptor (AR), the key driver of prostate tumour activity, is correlated with elevated AP usage. We identified AR, FOXA1 and MYC as potential drivers of AP activation. DNA methylation is a likely mechanism for AP activation during tumour progression and lineage plasticity. Our data suggest that prostate tumours activate APs to magnify the transcriptional impact of tumour drivers, including AR and MYC.

Single-cell transcriptomics reveals long noncoding RNAs associated with tumor biology and the microenvironment in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is highly heterogeneous and lethal. Long noncoding RNAs (lncRNAs) are an important class of genes regulating tumorigenesis and progression. Prior bulk transcriptomic studies in PDAC have revealed the dysregulation of lncRNAs but lack single-cell resolution to distinguish lncRNAs in tumor-intrinsic biology and the tumor microenvironment (TME). We analyzed single-cell transcriptome data from 73 multiregion samples in 21 PDAC patients to evaluate lncRNAs associated with intratumoral heterogeneity and the TME in PDAC. We found 111 cell-specific lncRNAs that reflected tumor, immune and stromal cell contributions, associated with outcomes, and validated across orthogonal datasets. Single-cell analysis of tumor cells revealed lncRNAs associated with TP53 mutations and FOLFIRINOX treatment that were obscured in bulk tumor analysis. Lastly, tumor subcluster analysis revealed widespread intratumor heterogeneity and intratumoral lncRNAs associated with cancer hallmarks and tumor processes such as angiogenesis, epithelial-mesenchymal transition, metabolism and immune signaling. Intratumoral subclusters and lncRNAs were validated across six datasets and showed clinically relevant associations with patient outcomes. Our study provides the first comprehensive assessment of the lncRNA landscape in PDAC using single-cell transcriptomic data and can serve as a resource, PDACLncDB (accessible at https://www.maherlab.com/pdaclncdb-overview), to guide future functional studies.

Characterization of cell-type specific circular RNAs associated with colorectal cancer metastasis.

Colorectal cancer (CRC) is the most common gastrointestinal malignancy and a leading cause of cancer deaths in the United States. More than half of CRC patients develop metastatic disease (mCRC) with an average 5-year survival rate of 13%. Circular RNAs (circRNAs) have recently emerged as important tumorigenesis regulators; however, their role in mCRC progression remains poorly characterized. Further, little is known about their cell-type specificity to elucidate their functions in the tumor microenvironment (TME). To address this, we performed total RNA sequencing (RNA-seq) on 30 matched normal, primary and metastatic samples from 14 mCRC patients. Additionally, five CRC cell lines were sequenced to construct a circRNA catalog in CRC. We detected 47 869 circRNAs, with 51% previously unannotated in CRC and 14% novel candidates when compared to existing circRNA databases. We identified 362 circRNAs differentially expressed in primary and/or metastatic tissues, termed circular RNAs associated with metastasis (CRAMS). We performed cell-type deconvolution using published single-cell RNA-seq datasets and applied a non-negative least squares statistical model to estimate cell-type specific circRNA expression. This predicted 667 circRNAs as exclusively expressed in a single cell type. Collectively, this serves as a valuable resource, TMECircDB (accessible at https://www.maherlab.com/tmecircdb-overview), for functional characterization of circRNAs in mCRC, specifically in the TME.

Genomic and epigenomic analysis of plasma cell-free DNA identifies stemness features associated with worse survival in AR -altered lethal prostate cancer.

Metastatic castration-resistant prostate cancer (mCRPC) resistant to androgen receptor (AR)-targeted agents is often lethal. Unfortunately, biomarkers for this deadly disease remain under investigation, and underpinning mechanisms are ill-understood. Here, we applied deep sequencing to ∼100 mCRPC patients prior to the initiation of first-line AR-targeted therapy, which detected AR /enhancer alterations in over a third of patients, which correlated with lethality. To delve into the mechanism underlying why these patients with cell-free AR /enhancer alterations developed more lethal prostate cancer, we next performed genome-wide cell-free DNA epigenomics. Strikingly, we found that binding sites for transcription factors associated with developmental stemness were nucleosomally more accessible. These results were corroborated using cell-free DNA methylation data, as well as tumor RNA sequencing from a held-out cohort of mCRPC patients. Thus, we validated the importance of AR /enhancer alterations as a prognostic biomarker in lethal mCRPC, and showed that the underlying mechanism for lethality involves reprogramming developmental states toward increased stemness.

Loss of Long Noncoding RNA NXTAR in Prostate Cancer Augments Androgen Receptor Expression and Enzalutamide Resistance.

Androgen receptor (AR) signaling continues to play a dominant role in all stages of prostate cancer, including castration-resistant prostate cancers (CRPC) that have developed resistance to second generation AR antagonists such as enzalutamide. In this study, we identified a long noncoding RNA (lncRNA), NXTAR (LOC105373241) that is located convergent with the AR gene and is repressed in human prostate tumors and cell lines. NXTAR bound upstream of the AR promoter and promoted EZH2 recruitment, causing significant loss of AR (and AR-V7) expression. Paradoxically, AR bound the NXTAR promoter, and inhibition of AR by the ACK1/TNK2 small molecule inhibitor (R)-9b excluded AR from the NXTAR promoter. The histone acetyltransferase GCN5 bound and deposited H3K14 acetylation marks, enhancing NXTAR expression. Application of an oligonucleotide derived from NXTAR exon 5 (NXTAR-N5) suppressed AR/AR-V7 expression and prostate cancer cell proliferation, indicating the translational relevance of the negative regulation of AR. In addition, pharmacologic restoration of NXTAR using (R)-9b abrogated enzalutamide-resistant prostate xenograft tumor growth. Overall, this study uncovers a positive feedback loop, wherein NXTAR acts as a novel prostate tumor-suppressing lncRNA by inhibiting AR/AR-V7 expression, which in turn upregulates NXTAR levels, compromising enzalutamide-resistant prostate cancer. The restoration of NXTAR could serve as a new therapeutic modality for patients who have acquired resistance to second generation AR antagonists. SIGNIFICANCE: This study identifies NXTAR as a tumor suppressive lncRNA that can epigenetically downregulate AR/AR-V7 expression and provides a therapeutic strategy to reinstate NXTAR expression for treating recurrent CRPC.

LINC00355 regulates p27KIP expression by binding to MENIN to induce proliferation in late-stage relapse breast cancer.

Late-stage relapse (LSR) in patients with breast cancer (BC) occurs more than five years and up to 10 years after initial treatment and has less than 30% 5-year relative survival rate. Long non-coding RNAs (lncRNAs) play important roles in BC yet have not been studied in LSR BC. Here, we identify 1127 lncRNAs differentially expressed in LSR BC via transcriptome sequencing and analysis of 72 early-stage and 24 LSR BC patient tumors. Decreasing expression of the most up-regulated lncRNA, LINC00355, in BC and MCF7 long-term estrogen deprived cell lines decreases cellular invasion and proliferation. Subsequent mechanistic studies show that LINC00355 binds to MENIN and changes occupancy at the CDKN1B promoter to decrease p27Kip. In summary, this is a key study discovering lncRNAs in LSR BC and LINC00355 association with epigenetic regulation and proliferation in BC.

Long noncoding RNAs in cancer metastasis.

Metastasis is a major contributor to cancer-associated deaths. It is characterized by a multistep process that occurs through the acquisition of molecular and phenotypic changes enabling cancer cells from a primary tumour to disseminate and colonize at distant organ sites. Over the past decade, the discovery and characterization of long noncoding RNAs (lncRNAs) have revealed the diversity of their regulatory roles, including key contributions throughout the metastatic cascade. Here, we review how lncRNAs promote metastasis by functioning in discrete pro-metastatic steps including the epithelial-mesenchymal transition, invasion and migration and organotrophic colonization, and by influencing the metastatic tumour microenvironment, often by interacting within ribonucleoprotein complexes or directly with other nucleic acid entities. We discuss well-characterized lncRNAs with in vivo phenotypes and highlight mechanistic commonalities such as convergence with the TGFß-ZEB1/ZEB2 axis or the nuclear factor-κB pathway, in addition to lncRNAs with controversial mechanisms and the influence of methodologies on mechanistic interpretation. Furthermore, some lncRNAs can help identify tumours with increased metastatic risk and spur novel therapeutic strategies, with several lncRNAs having shown potential as novel targets for antisense oligonucleotide therapy in animal models. In addition to well-characterized examples of lncRNAs functioning in metastasis, we discuss controversies and ongoing challenges in lncRNA biology. Finally, we present areas for future study for this rapidly evolving field.

Targeted Therapy to ß3 Integrin Reduces Chemoresistance in Breast Cancer Bone Metastases.

Breast cancer bone metastases are common and incurable. Tumoral integrin ß3 (ß3) expression is induced through interaction with the bone microenvironment. Although ß3 is known to promote bone colonization, its functional role during therapy of established bone metastases is not known. We found increased numbers of ß3+ tumor cells in murine bone metastases after docetaxel chemotherapy. ß3+ tumor cells were present in 97% of post-neoadjuvant chemotherapy triple-negative breast cancer patient samples (n = 38). High tumoral ß3 expression was associated with worse outcomes in both pre- and postchemotherapy triple-negative breast cancer groups. Genetic deletion of tumoral ß3 had minimal effect in vitro, but significantly enhanced in vivo docetaxel activity, particularly in the bone. Rescue experiments confirmed that this effect required intact ß3 signaling. Ultrastructural, transcriptomic, and functional analyses revealed an alternative metabolic response to chemotherapy in ß3-expressing cells characterized by enhanced oxygen consumption, reactive oxygen species generation, and protein production. We identified mTORC1 as a candidate for therapeutic targeting of this ß3-mediated, chemotherapy-induced metabolic response. mTORC1 inhibition in combination with docetaxel synergistically attenuated murine bone metastases. Furthermore, micelle nanoparticle delivery of mTORC1 inhibitor to cells expressing activated αvß3 integrins enhanced docetaxel efficacy in bone metastases. Taken together, we show that ß3 integrin induction by the bone microenvironment promotes resistance to chemotherapy through an altered metabolic response that can be defused by combination with αvß3-targeted mTORC1 inhibitor nanotherapy. Our work demonstrates the importance of the metastatic microenvironment when designing treatments and presents new, bone-specific strategies for enhancing chemotherapeutic efficacy.

Pan-cancer proteogenomic analysis reveals long and circular noncoding RNAs encoding peptides.

Recent studies show that annotated long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) encode for stable, functional peptides that contribute to human development and disease. To systematically discover lncRNAs and circRNAs encoding peptides, we performed a comprehensive integrative analysis of mass spectrometry-based proteomic and transcriptomic sequencing data from >900 patients across nine cancer types. This enabled us to identify 19,871 novel peptides derived from 8,903 lncRNAs. Further, we exploited open reading frames overlapping the backspliced region of circRNAs to identify 3,238 peptides that are uniquely derived from 2,834 circRNAs and not their corresponding linear RNAs. Collectively, our pan-cancer proteogenomic analysis will serve as a resource for evaluating the coding potential of lncRNAs and circRNAs that could aid future mechanistic studies exploring their function in cancer.

Heparin-based hydrogel scaffolding alters the transcriptomic profile and increases the chemoresistance of MDA-MB-231 triple-negative breast cancer cells.

The tumor microenvironment plays a critical role in the proliferation and chemoresistance of cancer cells. Growth factors (GFs) are known to interact with the extracellular matrix (ECM) via heparin binding sites, and these associations influence cell behavior. In the present study, we demonstrate the ability to define signals presented by the scaffold by pre-mixing growth factors, such as epidermal growth factor, into the heparin-based (HP-B) hydrogel prior to gelation. In the 3D biomimetic microenvironment, breast cancer cells formed spheroids within 24 hours of initial seeding. Despite higher number of proliferating cells in 2D cultures, 3D spheroids exhibited a higher degree of chemoresistance after 72 hours. Further, our RNA sequencing results highlighted the phenotypic changes influenced by solid-phase GF presentation. Wnt/ß-catenin and TGF-ß signaling were upregulated in the cells grown in the hydrogel, while apoptosis, IL2-STAT5 and PI3K-AKT-mTOR signaling were downregulated. With emerging technologies for precision medicine in cancer, this nature of fine-tuning the microenvironment is paramount for cultivation and downstream characterization of primary cancer cells and rare circulating tumor cells (CTCs), and effective screening of chemotherapeutic agents.

SV-HotSpot: detection and visualization of hotspots targeted by structural variants associated with gene expression.

Whole genome sequencing (WGS) has enabled the discovery of genomic structural variants (SVs), including those targeting intergenic and intronic non-coding regions that eluded previous exome focused strategies. However, the field currently lacks an automated tool that analyzes SV candidates to identify recurrent SVs and their targeted sites (hotspot regions), visualizes these genomic events within the context of various functional elements, and evaluates their potential effect on gene expression. To address this, we developed SV-HotSpot, an automated tool that integrates SV candidates, copy number alterations, gene expression, and genome annotations (e.g. gene and regulatory elements) to discover, annotate, and visualize recurrent SVs and their targeted hotspot regions that may affect gene expression. We applied SV-HotSpot to WGS and matched transcriptome data from metastatic castration resistant prostate cancer patients and rediscovered recurrent SVs targeting coding and non-coding functional elements known to promote prostate cancer progression and metastasis. SV-HotSpot provides a valuable resource to integrate SVs, gene expression, and genome annotations for discovering biologically relevant SVs altering coding and non-coding genome. SV-HotSpot is available at https://github.com/ChrisMaherLab/SV-HotSpot .