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.

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.

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.

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.

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

Motivation

The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: (i) in the input space, the gene expression data due to difference in the basic biology, and (ii) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution.

Results

We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.

Availability and implementation

https://github.com/hosseinshn/AITL.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of conserved evolutionary trajectories in tumors

MOTIVATION

As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics).

RESULTS

In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories.

AVAILABILITY AND IMPLEMENTATION

CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

GRB10 sustains AR activity by interacting with PP2A in prostate cancer cells

Prostate cancer (PCa) progression is driven by androgen receptor (AR) signaling. Unfortunately, androgen-deprivation therapy and the use of even more potent AR pathway inhibitors (ARPIs) cannot bring about a cure. ARPI resistance (ie, castration-resistant PCa, CRPC) will inevitably develop. Previously, we demonstrated that GRB10 is an AR transcriptionally repressed gene that functionally contributes to CRPC development and ARPI resistance. GRB10 expression is elevated prior to CRPC development in our patient-derived xenograft models and is significantly upregulated in clinical CRPC samples. Here, we analyzed transcriptomic data from GRB10 knockdown in PCa cells and found that AR signaling is downregulated. While the mRNA expression of AR target genes decreased upon GRB10 knockdown, AR expression was not affected at the mRNA or protein level. We further found that phosphorylation of AR serine 81 (S81), which is critical for AR transcriptional activity, is decreased by GRB10 knockdown and increased by its overexpression. Luciferase assay using GRB10-knockdown cells also indicate reduced AR activity. Immunoprecipitation coupled with mass spectrometry revealed an interaction between GRB10 and the PP2A complex, which is a known phosphatase of AR. Further validations and analyses showed that GRB10 binds to the PP2Ac catalytic subunit with its PH domain. Mechanistically, GRB10 knockdown increased PP2Ac protein stability, which in turn decreased AR S81 phosphorylation and reduced AR activity. Our findings indicate a reciprocal feedback between GRB10 and AR signaling, implying the importance of GRB10 in PCa progression.

Paternally Expressed Gene 10 (PEG10) Promotes Growth, Invasion, and Survival of Bladder Cancer

Paternally expressed gene 10 (PEG10) has been associated with neuroendocrine muscle-invasive bladder cancer (MIBC), a subtype of the disease with the poorest survival. In this work, we further characterized the expression pattern of PEG10 in The Cancer Genome Atlas database of 412 patients with MIBC, and found that, compared with other subtypes, PEG10 mRNA level was enhanced in neuroendocrine-like MIBC and highly correlated with other neuroendocrine markers. PEG10 protein level also associated with neuroendocrine markers in a tissue microarray of 82 cases. In bladder cancer cell lines, PEG10 expression was induced in drug-resistant compared with parental cells, and knocking down of PEG10 resensitized cells to chemotherapy. Loss of PEG10 increased protein levels of cell-cycle regulators p21 and p27 and delayed G₁-S-phase transition, while overexpression of PEG10 enhanced cancer cell proliferation. PEG10 silencing also lowered levels of SLUG and SNAIL, leading to reduced invasion and migration. In an orthotopic bladder cancer model, systemic treatment with PEG10 antisense oligonucleotide delayed progression of T24 xenografts. In summary, elevated expression of PEG10 in MIBC may contribute to the disease progression by promoting survival, proliferation, and metastasis. Targeting PEG10 is a novel potential therapeutic approach for a subset of bladder cancers.

Alignment-free clustering of UMI tagged DNA molecules

MOTIVATION

Next-Generation Sequencing has led to the availability of massive genomic datasets whose processing raises many challenges, including the handling of sequencing errors. This is especially pertinent in cancer genomics, e.g. for detecting low allele frequency variations from circulating tumor DNA. Barcode tagging of DNA molecules with unique molecular identifiers (UMI) attempts to mitigate sequencing errors; UMI tagged molecules are polymerase chain reaction (PCR) amplified, and the PCR copies of UMI tagged molecules are sequenced independently. However, the PCR and sequencing steps can generate errors in the sequenced reads that can be located in the barcode and/or the DNA sequence. Analyzing UMI tagged sequencing data requires an initial clustering step, with the aim of grouping reads sequenced from PCR duplicates of the same UMI tagged molecule into a single cluster, and the size of the current datasets requires this clustering process to be resource-efficient.

RESULTS

We introduce Calib, a computational tool that clusters paired-end reads from UMI tagged sequencing experiments generated by substitution-error-dominant sequencing platforms such as Illumina. Calib clusters are defined as connected components of a graph whose edges are defined in terms of both barcode similarity and read sequence similarity. The graph is constructed efficiently using locality sensitive hashing and MinHashing techniques. Calib's default clustering parameters are optimized empirically, for different UMI and read lengths, using a simulation module that is packaged with Calib. Compared to other tools, Calib has the best accuracy on simulated data, while maintaining reasonable runtime and memory footprint. On a real dataset, Calib runs with far less resources than alignment-based methods, and its clusters reduce the number of tentative false positive in downstream variation calling.

AVAILABILITY AND IMPLEMENTATION

Calib is implemented in C++ and its simulation module is implemented in Python. Calib is available at https://github.com/vpc-ccg/calib.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Conditionally Reprogrammed Cells from Patient-Derived Xenograft to Model Neuroendocrine Prostate Cancer Development

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer. It develops mainly via NE transdifferentiation of prostate adenocarcinoma in response to androgen receptor (AR)-inhibition therapy. The study of NEPC development has been hampered by a lack of clinically relevant models. We previously established a unique and first-in-field patient-derived xenograft (PDX) model of adenocarcinoma (LTL331)-to-NEPC (LTL331R) transdifferentiation. In this study, we applied conditional reprogramming (CR) culture to establish a LTL331 PDX-derived cancer cell line named LTL331_CR_Cell. These cells retain the same genomic mutations as the LTL331 parental tumor. They can be continuously propagated in vitro and can be genetically manipulated. Androgen deprivation treatment on LTL331_CR_Cells had no effect on cell proliferation. Transcriptomic analyses comparing the LTL331_CR_Cell to its parental tumor revealed a profound downregulation of the androgen response pathway and an upregulation of stem and basal cell marker genes. The transcriptome of LTL331_CR_Cells partially resembles that of post-castrated LTL331 xenografts in mice. Notably, when grafted under the renal capsules of male NOD/SCID mice, LTL331_CR_Cells spontaneously gave rise to NEPC tumors. This is evidenced by the histological expression of the NE marker CD56 and the loss of adenocarcinoma markers such as PSA. Transcriptomic analyses of the newly developed NEPC tumors further demonstrate marked enrichment of NEPC signature genes and loss of AR signaling genes. This study provides a novel research tool derived from a unique PDX model. It allows for the investigation of mechanisms underlying NEPC development by enabling gene manipulations ex vivo and subsequent functional evaluations in vivo.

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.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Metagenomic analysis reveals a rich bacterial content in high-risk prostate tumors from African men

BACKGROUND

Inflammation is a hallmark of prostate cancer (PCa), yet no pathogenic agent has been identified. Men from Africa are at increased risk for both aggressive prostate disease and infection. We hypothesize that pathogenic microbes may be contributing, at least in part, to high-risk PCa presentation within Africa and in turn the observed ethnic disparity.

METHODS

Here we reveal through metagenomic analysis of host-derived whole-genome sequencing data, the microbial content within prostate tumor tissue from 22 men. What is unique about this study is that patients were separated by ethnicity, African vs European, and environments, Africa vs Australia.

RESULTS

We identified 23 common bacterial genera between the African, Australian, and Chinese prostate tumor samples, while nonbacterial microbes were notably absent. While the most abundant genera across all samples included: Escherichia, Propionibacterium, and Pseudomonas, the core prostate tumor microbiota was enriched for Proteobacteria. We observed a significant increase in the richness of the bacterial communities within the African vs Australian samples (t = 4.6-5.5; P = .0004-.001), largely driven by eight predominant genera. Considering core human gut microbiota, African prostate tissue samples appear enriched for Escherichia and Acidovorax, with an abundance of Eubacterium associated with host tumor hypermutation.

CONCLUSIONS

Our study provides suggestive evidence for the presence of a core, bacteria-rich, prostate microbiome. While unable to exclude for fecal contamination, the observed increased bacterial content and richness within the African vs non-African samples, together with elevated tumor mutational burden, suggests the possibility that bacterially-driven oncogenic transformation within the prostate microenvironment may be contributing to aggressive disease presentation in Africa.

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.

The evolution of long noncoding RNA acceptance in prostate cancer initiation, progression, and its clinical utility in disease management

CONTEXT

It is increasingly evident that non-protein-coding regions of the genome can give rise to transcripts that form functional layers of the cancer genome. One of most abundant classes in these regions is long noncoding RNAs (lncRNAs). They have gained increasing attention in prostate cancer (PCa) and paved the way for a greater understanding of these cryptic regulators in cancer.

OBJECTIVE

To review current research exploring the functional biology of lncRNAs in PCa over the past three decades.

EVIDENCE ACQUISITION

A systematic review was performed using PubMed to search for reports with terms "long noncoding RNA", "prostate", and "cancer" over the past 30 yr (1988-2018).

EVIDENCE SYNTHESIS

We comprehensively surveyed the literature collected and summarise experiments leading to the characterisation of lncRNAs in PCa. A historical timeline of lncRNA identification is described, where each lncRNA is categorised mechanistically and within the primary areas of carcinogenesis: tumour risk and initiation, tumour promotion, tumour suppression, and tumour treatment resistance. We describe select lncRNAs that exemplify these areas. We also review whether these lncRNAs have a clinical utility in PCa diagnosis, prognosis, and prediction, and as therapeutic targets.

CONCLUSIONS

The biology of lncRNA is multifaceted, demonstrating a complex array of molecular and cellular functions. These studies reveal that lncRNAs are involved in every stage of PCa. Their clinical utility for diagnosis, prognosis, and prediction of PCa is well supported, but further evaluation for their therapeutic candidacy is needed. We provide a detailed resource and view inside the lncRNA landscape for other cancer biologists, oncologists, and clinicians.

PATIENT SUMMARY

In this study, we review current knowledge of the non-protein-coding genome in prostate cancer (PCa). We conclude that many of these regions are functional and a source of accurate biomarkers in PCa. With a strong research foundation, they hold promise as future therapeutic targets, yet clinical trials are necessary to determine their intrinsic value to PCa disease management.

MOLI: multi-omics late integration with deep neural networks for drug response prediction

MOTIVATION

Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance.

RESULTS

We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI's performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI's high predictive power suggests it may have utility in precision oncology.

AVAILABILITY AND IMPLEMENTATION

https://github.com/hosseinshn/MOLI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Abstract 3698: Conditionally reprogrammed cells from patient-derived xenograft to model neuroendocrine prostate cancer development

Treatment-emergent neuroendocrine prostate cancer (t-NEPC) is a lethal subtype of advanced prostate cancer that occurs via NE transdifferentiation of prostate adenocarcinomas in response to androgen receptor (AR)-inhibition therapy. Study of t-NEPC has been hampered by a lack of clinically relevant models. We previously established a unique and first-in-field patient-derived xenograft (PDX) model of adenocarcinoma (LTL331)-to-NEPC (LTL331R) transdifferentiation. In this study, we established conditionally reprogrammed (CR) cells from the adenocarcinoma PDX tumor line LTL331. These LTL331-derived CR (LTL331-CR) cells retained the same genomic mutations of the parental tumor and, can be genetically manipulated and continuously propagated in vitro. Further androgen deprivation treatment on LTL331-CR cells showed no effect on cell proliferation. Transcriptomic analyses of the LTL331-CR cells revealed profound downregulation of androgen response pathway, and enrichment of stem/progenitor-like marker genes, compared with the parental tumor LTL331. Notably, when grafted back into the subrenal capsule of male NOD/SCID mice, these LTL331-CR cells gave rise to NEPC tumors directly as manifested by histological expression of NE markers. Transcriptomic analyses of the newly developed NEPC tumors also demonstrated marked enrichment of NEPC signature genes and loss of AR signaling genes. This study provides a novel strategy to investigate the mechanisms underlying t-NEPC development with a unique PDX by enabling gene manipulation ex vivo and subsequent functional evaluation in vivo.

Citation Format: Xinpei Ci, Jun Hao, Xin Dong, Hui Xue, Rebecca Wu, Anne M. Haegert, Colin C. Collins, Dong Lin, Yuzhuo Wang. Conditionally reprogrammed cells from patient-derived xenograft to model neuroendocrine prostate cancer development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3698.

Characterization of transcriptomic signature of primary prostate cancer analogous to prostatic small cell neuroendocrine carcinoma

Prostatic small cell neuroendocrine carcinoma (SC/NE) is well studied in metastatic castration‐resistant prostate cancer; however, it is not well characterized in the primary setting. Herein, we used gene expression profiling of SC/NE prostate cancer (PCa) to develop a 212 gene signature to identify treatment‐naïve primary prostatic tumors that are molecularly analogous to SC/NE (SC/NE‐like PCa). The 212 gene signature was tested in several cohorts confirming similar molecular profile between prostatic SC/NE and small cell lung carcinoma. The signature was then translated into a genomic score (SCGScore) using modularized logistic regression modeling and validated in four independent cohorts achieving an average AUC >0.95. The signature was evaluated in more than 25,000 primary adenocarcinomas to characterize the biology, prognosis and potential therapeutic response of predicted SC/NE‐like tumors. Assessing SCGScore in a prospective cohort of 17,967 RP and 6,697 biopsy treatment‐naïve primary tumors from the Decipher Genomic Resource Information Database registry, approximately 1% of the patients were found to have a SC/NE‐like transcriptional profile, whereas 0.5 and 3% of GG1 and GG5 patients respectively showed to be SC/NE‐like. More than 80% of these patients are genomically high‐risk based on Decipher score. Interrogating in vitro drug sensitivity analyses, SC/NE‐like prostatic tumors showed higher response to PARP and HDAC inhibitors.

What's new?

While genomic/transcriptomic data analysis has revolutionized cancer biology, this analysis is frequently only available late in the cancer history, often after years of therapy. Here the authors built a single sample genomic classifier to predict primary prostate cancer tumors with early small cell neuroendocrine differentiation. They show in three independent cohorts that small cell neuroendocrine tumors of the prostate are similar to small cell tumors of the lung and predict the specific prostate tumors to be responsive to inhibitors of poly ADP ribose polymerase and histone deacetylases, underscoring the use of these drugs in this subtype of prostate cancer.

Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks

BACKGROUND

Advances in large-scale tumor sequencing have led to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging.

FINDINGS

We introduce a novel combinatorial method, cd-CAP (combinatorial detection of conserved alteration patterns), for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not) and simultaneously detects multiple alteration profile conserved subnetworks.

CONCLUSIONS

In a number of The Cancer Genome Atlas datasets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples.

The Proteogenomic Landscape of Curable Prostate Cancer

DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.

The long noncoding RNA HORAS5 mediates castration-resistant prostate cancer survival by activating the androgen receptor transcriptional program

Prostate cancer (PCa) is driven by the androgen receptor (AR)‐signaling axis. Hormonal therapy often mitigates PCa progression, but a notable number of cases progress to castration‐resistant PCa (CRPC). CRPC retains AR activity and is incurable. Long noncoding RNA (lncRNA) represent an uncharted region of the transcriptome. Several lncRNA have been recently described to mediate oncogenic functions, suggesting that these molecules can be potential therapeutic targets. Here, we identified CRPC‐associated lncRNA by analyzing patient‐derived xenografts (PDXs) and clinical data. Subsequently, we characterized one of the CRPC‐promoting lncRNA,HORAS5, in vitro and in vivo. We demonstrated that HORAS5 is a stable, cytoplasmic lncRNA that promotes CRPC proliferation and survival by maintaining AR activity under androgen‐depleted conditions. Most strikingly, knockdown of HORAS5 causes a significant reduction in the expression of AR itself and oncogenic AR targets such as KIAA0101. Elevated expression of HORAS5 is also associated with worse clinical outcomes in patients. Our results from HORAS5 inhibition in in vivo models further confirm that HORAS5 is a viable therapeutic target for CRPC. Thus, we posit that HORAS5 is a novel, targetable mediator of CRPC through its essential role in the maintenance of oncogenic AR activity. Overall, this study adds to our mechanistic understanding of how lncRNA function in cancer progression.

Metagenomic and metatranscriptomic analysis of human prostate microbiota from patients with prostate cancer

Background

Prostate cancer (PCa) is the most common malignant neoplasm among men in many countries. Since most precancerous and cancerous tissues show signs of inflammation, chronic bacterial prostatitis has been hypothesized to be a possible etiology. However, establishing a causal relationship between microbial inflammation and PCa requires a comprehensive analysis of the prostate microbiome. The aim of this study was to characterize the microbiome in prostate tissue of PCa patients and investigate its association with tumour clinical characteristics as well as host expression profiles.

Results

The metagenome and metatranscriptome of tumour and the adjacent benign tissues were assessed in 65 Chinese radical prostatectomy specimens. Escherichia, Propionibacterium, Acinetobacter and Pseudomonas were abundant in both metagenome and metatranscriptome, thus constituting the core of the prostate microbiome. The biodiversity of the microbiomes could not be differentiated between the matched tumour/benign specimens or between the tumour specimens of low and high Gleason Scores. The expression profile of ten Pseudomonas genes was strongly correlated with that of eight host small RNA genes; three of the RNA genes may negatively associate with metastasis. Few viruses could be identified from the prostate microbiomes.

Conclusions

This is the first study of the human prostate microbiome employing an integrated metagenomics and metatranscriptomics approach. In this Chinese cohort, both metagenome and metatranscriptome analyses showed a non-sterile microenvironment in the prostate of PCa patients, but we did not find links between the microbiome and local progression of PCa. However, the correlated expression of Pseudomonas genes and human small RNA genes may provide tantalizing preliminary evidence that Pseudomonas infection may impede metastasis.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5457-z) contains supplementary material, which is available to authorized users.

BAP1 haploinsufficiency predicts a distinct immunogenic class of malignant peritoneal mesothelioma

Background

Malignant peritoneal mesothelioma (PeM) is a rare and fatal cancer that originates from the peritoneal lining of the abdomen. Standard treatment of PeM is limited to cytoreductive surgery and/or chemotherapy, and no effective targeted therapies for PeM exist. Some immune checkpoint inhibitor studies of mesothelioma have found positivity to be associated with a worse prognosis.

Methods

To search for novel therapeutic targets for PeM, we performed a comprehensive integrative multi-omics analysis of the genome, transcriptome, and proteome of 19 treatment-naïve PeM, and in particular, we examined BAP1 mutation and copy number status and its relationship to immune checkpoint inhibitor activation.

Results

We found that PeM could be divided into tumors with an inflammatory tumor microenvironment and those without and that this distinction correlated with haploinsufficiency of BAP1. To further investigate the role of BAP1, we used our recently developed cancer driver gene prioritization algorithm, HIT’nDRIVE, and observed that PeM with BAP1 haploinsufficiency form a distinct molecular subtype characterized by distinct gene expression patterns of chromatin remodeling, DNA repair pathways, and immune checkpoint receptor activation. We demonstrate that this subtype is correlated with an inflammatory tumor microenvironment and thus is a candidate for immune checkpoint blockade therapies.

Conclusions

Our findings reveal BAP1 to be a potential, easily trackable prognostic and predictive biomarker for PeM immunotherapy that refines PeM disease classification. BAP1 stratification may improve drug response rates in ongoing phases I and II clinical trials exploring the use of immune checkpoint blockade therapies in PeM in which BAP1 status is not considered. This integrated molecular characterization provides a comprehensive foundation for improved management of a subset of PeM patients.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0620-3) contains supplementary material, which is available to authorized users.

Markers of MEK inhibitor resistance in low-grade serous ovarian cancer: EGFR is a potential therapeutic target

BACKGROUND

Although low-grade serous ovarian cancer (LGSC) is rare, case-fatality rates are high as most patients present with advanced disease and current cytotoxic therapies are not overly effective. Recognizing that these cancers may be driven by MAPK pathway activation, MEK inhibitors (MEKi) are being tested in clinical trials. LGSC respond to MEKi only in a subgroup of patients, so predictive biomarkers and better therapies will be needed.

METHODS

We evaluated a number of patient-derived LGSC cell lines, previously classified according to their MEKi sensitivity. Two cell lines were genomically compared against their matching tumors samples. MEKi-sensitive and MEKi-resistant lines were compared using whole exome sequencing and reverse phase protein array. Two treatment combinations targeting MEKi resistance markers were also evaluated using cell proliferation, cell viability, cell signaling, and drug synergism assays.

RESULTS

Low-grade serous ovarian cancer cell lines recapitulated the genomic aberrations from their matching tumor samples. We identified three potential predictive biomarkers that distinguish MEKi sensitive and resistant lines: KRAS mutation status, and EGFR and PKC-alpha protein expression. The biomarkers were validated in three newly developed LGSC cell lines. Sub-lethal combination of MEK and EGFR inhibition showed drug synergy and caused complete cell death in two of four MEKi-resistant cell lines tested.

CONCLUSIONS

KRAS mutations and the protein expression of EGFR and PKC-alpha should be evaluated as predictive biomarkers in patients with LGSC treated with MEKi. Combination therapy using a MEKi with EGFR inhibition may represent a promising new therapy for patients with MEKi-resistant LGSC.

Markers of MEK inhibitor resistance in low-grade serous ovarian cancer: EGFR is a potential therapeutic target

Background

Although low-grade serous ovarian cancer (LGSC) is rare, case-fatality rates are high as most patients present with advanced disease and current cytotoxic therapies are not overly effective. Recognizing that these cancers may be driven by MAPK pathway activation, MEK inhibitors (MEKi) are being tested in clinical trials. LGSC respond to MEKi only in a subgroup of patients, so predictive biomarkers and better therapies will be needed.

Methods

We evaluated a number of patient-derived LGSC cell lines, previously classified according to their MEKi sensitivity. Two cell lines were genomically compared against their matching tumors samples. MEKi-sensitive and MEKi-resistant lines were compared using whole exome sequencing and reverse phase protein array. Two treatment combinations targeting MEKi resistance markers were also evaluated using cell proliferation, cell viability, cell signaling, and drug synergism assays.

Results

Low-grade serous ovarian cancer cell lines recapitulated the genomic aberrations from their matching tumor samples. We identified three potential predictive biomarkers that distinguish MEKi sensitive and resistant lines: KRAS mutation status, and EGFR and PKC-alpha protein expression. The biomarkers were validated in three newly developed LGSC cell lines. Sub-lethal combination of MEK and EGFR inhibition showed drug synergy and caused complete cell death in two of four MEKi-resistant cell lines tested.

Conclusions

KRAS mutations and the protein expression of EGFR and PKC-alpha should be evaluated as predictive biomarkers in patients with LGSC treated with MEKi. Combination therapy using a MEKi with EGFR inhibition may represent a promising new therapy for patients with MEKi-resistant LGSC.

Markers of MEK inhibitor resistance in low-grade serous ovarian cancer: EGFR is a potential therapeutic target

Background

Although low-grade serous ovarian cancer (LGSC) is rare, case-fatality rates are high as most patients present with advanced disease and current cytotoxic therapies are not overly effective. Recognizing that these cancers may be driven by MAPK pathway activation, MEK inhibitors (MEKi) are being tested in clinical trials. LGSC respond to MEKi only in a subgroup of patients, so predictive biomarkers and better therapies will be needed.

Methods

We evaluated a number of patient-derived LGSC cell lines, previously classified according to their MEKi sensitivity. Two cell lines were genomically compared against their matching tumors samples. MEKi-sensitive and MEKi-resistant lines were compared using whole exome sequencing and reverse phase protein array. Two treatment combinations targeting MEKi resistance markers were also evaluated using cell proliferation, cell viability, cell signaling, and drug synergism assays.

Results

Low-grade serous ovarian cancer cell lines recapitulated the genomic aberrations from their matching tumor samples. We identified three potential predictive biomarkers that distinguish MEKi sensitive and resistant lines: KRAS mutation status, and EGFR and PKC-alpha protein expression. The biomarkers were validated in three newly developed LGSC cell lines. Sub-lethal combination of MEK and EGFR inhibition showed drug synergy and caused complete cell death in two of four MEKi-resistant cell lines tested.

Conclusions

KRAS mutations and the protein expression of EGFR and PKC-alpha should be evaluated as predictive biomarkers in patients with LGSC treated with MEKi. Combination therapy using a MEKi with EGFR inhibition may represent a promising new therapy for patients with MEKi-resistant LGSC.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0725-1) contains supplementary material, which is available to authorized users.

The long noncoding RNA landscape of neuroendocrine prostate cancer and its clinical implications

Background

Treatment-induced neuroendocrine prostate cancer (tNEPC) is an aggressive variant of late-stage metastatic castrate-resistant prostate cancer that commonly arises through neuroendocrine transdifferentiation (NEtD). Treatment options are limited, ineffective, and, for most patients, result in death in less than a year. We previously developed a first-in-field patient-derived xenograft (PDX) model of NEtD. Longitudinal deep transcriptome profiling of this model enabled monitoring of dynamic transcriptional changes during NEtD and in the context of androgen deprivation. Long non-coding RNA (lncRNA) are implicated in cancer where they can control gene regulation. Until now, the expression of lncRNAs during NEtD and their clinical associations were unexplored.

Results

We implemented a next-generation sequence analysis pipeline that can detect transcripts at low expression levels and built a genome-wide catalogue (n = 37,749) of lncRNAs. We applied this pipeline to 927 clinical samples and our high-fidelity NEtD model LTL331 and identified 821 lncRNAs in NEPC. Among these are 122 lncRNAs that robustly distinguish NEPC from prostate adenocarcinoma (AD) patient tumours. The highest expressed lncRNAs within this signature are H19, LINC00617, and SSTR5-AS1. Another 742 are associated with the NEtD process and fall into four distinct patterns of expression (NEtD lncRNA Class I, II, III, and IV) in our PDX model and clinical samples. Each class has significant (z-scores >2) and unique enrichment for transcription factor binding site (TFBS) motifs in their sequences. Enriched TFBS include (1) TP53 and BRN1 in Class I, (2) ELF5, SPIC, and HOXD1 in Class II, (3) SPDEF in Class III, (4) HSF1 and FOXA1 in Class IV, and (5) TWIST1 when merging Class III with IV. Common TFBS in all NEtD lncRNA were also identified and include E2F, REST, PAX5, PAX9, and STAF. Interrogation of the top deregulated candidates (n = 100) in radical prostatectomy adenocarcinoma samples with long-term follow-up (median 18 years) revealed significant clinicopathological associations. Specifically, we identified 25 that are associated with rapid metastasis following androgen deprivation therapy (ADT). Two of these lncRNAs (SSTR5-AS1 and LINC00514) stratified patients undergoing ADT based on patient outcome.

Discussion

To date, a comprehensive characterization of the dynamic landscape of lncRNAs during the NEtD process has not been performed. A temporal analysis of the PDX-based NEtD model has for the first time provided this dynamic landscape. TFBS analysis identified NEPC-related TF motifs present within the NEtD lncRNA sequences, suggesting functional roles for these lncRNAs in NEPC pathogenesis. Furthermore, select NEtD lncRNAs appear to be associated with metastasis and patients receiving ADT. Treatment-related metastasis is a clinical consequence of NEPC tumours. Top candidate lncRNAs FENDRR, H19, LINC00514, LINC00617, and SSTR5-AS1 identified in this study are implicated in the development of NEPC. We present here for the first time a genome-wide catalogue of NEtD lncRNAs that characterize the transdifferentiation process and a robust NEPC lncRNA patient expression signature. To accomplish this, we carried out the largest integrative study that applied a PDX NEtD model to clinical samples. These NEtD and NEPC lncRNAs are strong candidates for clinical biomarkers and therapeutic targets and warrant further investigation.

Proteogenomic Characterization of Patient-Derived Xenografts Highlights the Role of REST in Neuroendocrine Differentiation of Castration-Resistant Prostate Cancer

PURPOSE

An increasing number of castration-resistant prostate cancer (CRPC) tumors exhibit neuroendocrine (NE) features. NE prostate cancer (NEPC) has poor prognosis, and its development is poorly understood.Experimental Design: We applied mass spectrometry-based proteomics to a unique set of 17 prostate cancer patient-derived xenografts (PDX) to characterize the effects of castration in vivo, and the proteome differences between NEPC and prostate adenocarcinomas. Genome-wide profiling of REST-occupied regions in prostate cancer cells was correlated to the expression changes in vivo to investigate the role of the transcriptional repressor REST in castration-induced NEPC differentiation.

RESULTS

An average of 4,881 proteins were identified and quantified from each PDX. Proteins related to neurogenesis, cell-cycle regulation, and DNA repair were found upregulated and elevated in NEPC, while the reduced levels of proteins involved in mitochondrial functions suggested a prevalent glycolytic metabolism of NEPC tumors. Integration of the REST chromatin bound regions with expression changes indicated a direct role of REST in regulating neuronal gene expression in prostate cancer cells. Mechanistically, depletion of REST led to cell-cycle arrest in G₁, which could be rescued by p53 knockdown. Finally, the expression of the REST-regulated gene secretagogin (SCGN) correlated with an increased risk of suffering disease relapse after radical prostatectomy.

CONCLUSIONS

This study presents the first deep characterization of the proteome of NEPC and suggests that concomitant inhibition of REST and the p53 pathway would promote NEPC. We also identify SCGN as a novel prognostic marker in prostate cancer.

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.

Pre-clinical Models for Malignant Mesothelioma Research: From Chemical-Induced to Patient-Derived Cancer Xenografts

Malignant mesothelioma (MM) is a rare disease often associated with environmental exposure to asbestos and other erionite fibers. MM has a long latency period prior to manifestation and a poor prognosis. The survival post-diagnosis is often less than a year. Although use of asbestos has been banned in the United States and many European countries, asbestos is still being used and extracted in many developing countries. Occupational exposure to asbestos, mining, and migration are reasons that we expect to continue to see growing incidence of mesothelioma in the coming decades. Despite improvements in survival achieved with multimodal therapies and cytoreductive surgeries, less morbid, more effective interventions are needed. Thus, identifying prognostic and predictive biomarkers for MM, and developing novel agents for targeted therapy, are key unmet needs in mesothelioma research and treatment. In this review, we discuss the evolution of pre-clinical model systems developed to study MM and emphasize the remarkable capability of patient-derived xenograft (PDX) MM models in expediting the pre-clinical development of novel therapeutic approaches. PDX disease model systems retain major characteristics of original malignancies with high fidelity, including molecular, histopathological and functional heterogeneities, and as such play major roles in translational research, drug development, and precision medicine.

Abstract 773: A heterochromatin gene signature unveils HP1α mediating neuroendocrine prostate cancer development and aggressiveness

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer (PCa) arising mostly from adenocarcinoma via NE transdifferentiation following androgen deprivation therapy. However, mechanisms contributing to both NEPC development and its aggressiveness remain elusive. In light of the fact that hyperchromatic nuclei is a distinguishing histopathological feature of NEPC, we identified 36 heterochromatin-related genes that are significantly enriched in NEPC by transcriptomic analyses of our patient-derived xenograft (PDX) models, multiple clinical cohorts, and genetically engineered mouse models. Longitudinal analysis using our unique, first-in-field PDX model of adenocarcinoma-to-NEPC transdifferentiation revealed that, among those 36 heterochromatin-related genes, heterochromatin protein 1α (HP1α) expression is increased early and steadily during NEPC development and remain elevated in the developed NEPC tumor. Its elevated expression is further confirmed in multiple PDX and clinical NEPC samples. Functional studies showed that HP1α knockdown in the NCI-H660 NEPC cell line dramatically inhibits proliferation, completely ablates colony formation, and induces apoptotic cell death, ultimately leading to tumor growth arrest. Its ectopic expression significantly promotes NE transdifferentiation in adenocarcinoma cells subjected to androgen deprivation treatment. Mechanistically, HP1α reduces expression of androgen receptor (AR) and RE1 silencing transcription factor (REST), two crucial transcription factors silenced in NEPC, and enriches the repressive histone mark trimethylation of histone H3 at Lys9 (H3K9me3) on their respective gene promoters. These observations indicate a novel mechanism underlying NEPC development mediated by abnormally expressed heterochromatin genes, with HP1α as an early functional mediator and a potential therapeutic target for NEPC prevention and/or management.

Citation Format: Xinpei Ci, Jun Hao, Xin Dong, Stephen Y. Choi, Hui Xue, Sifeng Qu, Ladan Fazli, Francesco Crea, Christopher Ong, Amina Zoubeidi, Housheng H. He, Martin E. Gleave, Colin C. Collins, Dong Lin, Yuzhuo Wang. A heterochromatin gene signature unveils HP1α mediating neuroendocrine prostate cancer development and aggressiveness [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 773.

Targeting MCT4 to reduce lactic acid secretion and glycolysis for treatment of neuroendocrine prostate cancer

Development of neuroendocrine prostate cancer (NEPC) is emerging as a major problem in clinical management of advanced prostate cancer (PCa). As increasingly potent androgen receptor (AR)‐targeting antiandrogens are more widely used, PCa transdifferentiation into AR‐independent NEPC as a mechanism of treatment resistance becomes more common and precarious, since NEPC is a lethal PCa subtype urgently requiring effective therapy. Reprogrammed glucose metabolism of cancers, that is elevated aerobic glycolysis involving increased lactic acid production/secretion, plays a key role in multiple cancer‐promoting processes and has been implicated in therapeutics development. Here, we examined NEPC glucose metabolism using our unique panel of patient‐derived xenograft PCa models and patient tumors. By calculating metabolic pathway scores using gene expression data, we found that elevated glycolysis coupled to increased lactic acid production/secretion is an important metabolic feature of NEPC. Specific inhibition of expression of MCT4 (a plasma membrane lactic acid transporter) by antisense oligonucleotides led to reduced lactic acid secretion as well as reduced glucose metabolism and NEPC cell proliferation. Taken together, our results indicate that elevated glycolysis coupled to excessive MCT4‐mediated lactic acid secretion is clinically relevant and functionally important to NEPC. Inhibition of MCT4 expression appears to be a promising therapeutic strategy for NEPC.

Heterochromatin Protein 1α Mediates Development and Aggressiveness of Neuroendocrine Prostate Cancer

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer arising mostly from adenocarcinoma via neuroendocrine transdifferentiation following androgen deprivation therapy. Mechanisms contributing to both NEPC development and its aggressiveness remain elusive. In light of the fact that hyperchromatic nuclei are a distinguishing histopathologic feature of NEPC, we utilized transcriptomic analyses of our patient-derived xenograft (PDX) models, multiple clinical cohorts, and genetically engineered mouse models to identify 36 heterochromatin-related genes that are significantly enriched in NEPC. Longitudinal analysis using our unique, first-in-field PDX model of adenocarcinoma-to-NEPC transdifferentiation revealed that, among those 36 heterochromatin-related genes, heterochromatin protein 1α (HP1α) expression increased early and steadily during NEPC development and remained elevated in the developed NEPC tumor. Its elevated expression was further confirmed in multiple PDX and clinical NEPC samples. HP1α knockdown in the NCI-H660 NEPC cell line inhibited proliferation, ablated colony formation, and induced apoptotic cell death, ultimately leading to tumor growth arrest. Its ectopic expression significantly promoted NE transdifferentiation in adenocarcinoma cells subjected to androgen deprivation treatment. Mechanistically, HP1α reduced expression of androgen receptor and RE1 silencing transcription factor and enriched the repressive trimethylated histone H3 at Lys9 mark on their respective gene promoters. These observations indicate a novel mechanism underlying NEPC development mediated by abnormally expressed heterochromatin genes, with HP1α as an early functional mediator and a potential therapeutic target for NEPC prevention and management.Significance: Heterochromatin proteins play a fundamental role in NEPC, illuminating new therapeutic targets for this aggressive disease. Cancer Res; 78(10); 2691-704. ©2018 AACR.

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.

Alternative RNA splicing of the MEAF6 gene facilitates neuroendocrine prostate cancer progression

Although potent androgen receptor pathway inhibitors (ARPI) improve overall survival of metastatic prostate cancer patients, treatment-induced neuroendocrine prostate cancer (t-NEPC) as a consequence of the selection pressures of ARPI is becoming a more common clinical issue. Improved understanding of the molecular biology of t-NEPC is essential for the development of new effective management approaches for t-NEPC. In this study, we identify a splice variant of the MYST/Esa1-associated factor 6 (MEAF6) gene, MEAF6-1, that is highly expressed in both t-NEPC tumor biopsies and neuroendocrine cell lines of prostate and lung cancers. We show that MEAF6-1 splicing is stimulated by neuronal RNA splicing factor SRRM4. Rather than inducing neuroendocrine trans-differentiation of cells in prostate adenocarcinoma, MEAF6-1 upregulation stimulates cell proliferation, anchorage-independent cell growth, invasion and xenograft tumor growth. Gene microarray identifies that these MEAF6-1 actions are in part mediated by the ID1 and ID3 genes. These findings suggest that the MEAF6-1 variant does not induce neuroendocrine differentiation of prostate cancer cells, but rather facilitates t-NEPC progression by increasing the proliferation rate of cells that have acquired neuroendocrine phenotypes.

Aneustat (OMN54) has aerobic glycolysis-inhibitory activity and also immunomodulatory activity as indicated by a first-generation PDX prostate cancer model

Aneustat (OMN54) is a multivalent, botanical anticancer candidate therapeutic. A recent Phase-I clinical trial has indicated that it is well tolerated by patients and has immunomodulatory activity. In our study, using in vitro and in vivo prostate cancer models, we investigated Aneustat with regard to effects on (i) cancer-generated immunosuppression based on aerobic glycolysis leading to acidification of the tumor microenvironment, and (ii) immune-related processes such as macrophage differentiation and shifts in the intratumoral levels of host immune cells. Aneustat markedly reduced glucose consumption, lactic acid secretion, glycolysis-related gene expressions and proliferation of human LNCaP prostate cancer cells. In addition, Aneustat induced differentiation of RAW264.7 macrophages to the M1 anticancer phenotype. Treatment of LNCaP xenografts and first-generation patient-derived prostate cancer tissue xenografts with Aneustat in both cases led to a marked shift in intratumoral host (mouse/patient) immune cell levels: a higher ratio of cytotoxic CD8⁺ T/Treg cells, higher numbers of NK cells, lower numbers of Treg cells and MDSCs, i.e. changes favoring the host anticancer immune response. Taken together, the data indicate that Aneustat has immunomodulatory activity based on inhibition of aerobic glycolysis which in patients may lead to reduction of cancer-induced immunosuppression. Furthermore, first-generation patient-derived cancer tissue xenograft models may be used for screening compounds for immunomodulatory activity.

The retinoblastoma protein regulates hypoxia-inducible genetic programs, tumor cell invasiveness and neuroendocrine differentiation in prostate cancer cells

Loss of tumor suppressor proteins, such as the retinoblastoma protein (Rb), results in tumor progression and metastasis. Metastasis is facilitated by low oxygen availability within the tumor that is detected by hypoxia inducible factors (HIFs). The HIF1 complex, HIF1α and dimerization partner the aryl hydrocarbon receptor nuclear translocator (ARNT), is the master regulator of the hypoxic response. Previously, we demonstrated that Rb represses the transcriptional response to hypoxia by virtue of its association with HIF1. In this report, we further characterized the role Rb plays in mediating hypoxia-regulated genetic programs by stably ablating Rb expression with retrovirally-introduced short hairpin RNA in LNCaP and 22Rv1 human prostate cancer cells. DNA microarray analysis revealed that loss of Rb in conjunction with hypoxia leads to aberrant expression of hypoxia-regulated genetic programs that increase cell invasion and promote neuroendocrine differentiation. For the first time, we have established a direct link between hypoxic tumor environments, Rb inactivation and progression to late stage metastatic neuroendocrine prostate cancer. Understanding the molecular pathways responsible for progression of benign prostate tumors to metastasized and lethal forms will aid in the development of more effective prostate cancer therapies.

Impact of Therapy on Genomics and Transcriptomics in High-Risk Prostate Cancer Treated with Neoadjuvant Docetaxel and Androgen Deprivation Therapy

Purpose: The combination of docetaxel chemotherapy and androgen deprivation therapy (ADT) has become a standard treatment for patients with metastatic prostate cancer. The recently accrued phase III CALGB 90203 trial was designed to investigate the clinical effectiveness of this treatment approach earlier in the disease. Specimens from this trial offer a unique opportunity to interrogate the acute molecular response to docetaxel and ADT and identify potential biomarkers.Experimental Design: We evaluated baseline clinical data, needle biopsies, and radical prostatectomy (RP) specimens from 52 (of 788) patients enrolled on CALGB 90203 at one high volume center. Pathology review, tumor and germline-targeted DNA sequencing (n = 72 genes), and expression profiling using NanoString platform (n = 163 genes) were performed to explore changes in critical prostate cancer pathways linked to aggression and resistance.Results: Three of 52 patients had only microfocal residual cancer at prostatectomy. The most common alterations included TMPRSS2-ERG fusion (n = 32), TP53 mutation or deletion (n = 11), PTEN deletion (n = 6), FOXA1 (n = 6), and SPOP (n = 4) mutation, with no significant enrichment in posttreated specimens. We did not observe AR amplification or mutations. The degree of AR signaling suppression varied among treated tumors and there was upregulation of both AR and AR-V7 expression as well as a subset of neuroendocrine and plasticity genes.Conclusions: These data support the feasibility of targeted and temporal genomic and transcriptome profiling of neoadjuvant-treated prostate cancer with limited formalin-fixed paraffin embedded tissue requirement. Characterization of the heterogeneity of treatment response and molecular outliers that arise posttreatment provides new insight into potential early markers of resistance. Clin Cancer Res; 23(22); 6802-11. ©2017 AACR.

Mutational Analysis of Gene Fusions Predicts Novel MHC Class I-Restricted T-Cell Epitopes and Immune Signatures in a Subset of Prostate Cancer

Purpose: Gene fusions are frequently found in prostate cancer and may result in the formation of unique chimeric amino acid sequences (CASQ) that span the breakpoint of two fused gene products. This study evaluated the potential for fusion-derived CASQs to be a source of tumor neoepitopes, and determined their relationship to patterns of immune signatures in prostate cancer patients.Experimental Design: A computational strategy was used to identify CASQs and their corresponding predicted MHC class I epitopes using RNA-Seq data from The Cancer Genome Atlas of prostate tumors. In vitro peptide-specific T-cell expansion was performed to identify CASQ-reactive T cells. A multivariate analysis was used to relate patterns of in silico-predicted tumor-infiltrating immune cells with prostate tumors harboring these mutational events.Results: Eighty-seven percent of tumors contained gene fusions with a mean of 12 per tumor. In total, 41% of fusion-positive tumors were found to encode CASQs. Within these tumors, 87% gave rise to predicted MHC class I-binding epitopes. This observation was more prominent when patients were stratified into low- and intermediate/high-risk categories. One of the identified CASQ from the recurrent TMPRSS2:ERG type VI fusion contained several high-affinity HLA-restricted epitopes. These peptides bound HLA-A*02:01 in vitro and were recognized by CD8⁺ T cells. Finally, the presence of fusions and CASQs were associated with expression of immune cell infiltration.Conclusions: Mutanome analysis of gene fusion-derived CASQs can give rise to patient-specific predicted neoepitopes. Moreover, these fusions predicted patterns of immune cell infiltration within a subgroup of prostate cancer patients. Clin Cancer Res; 23(24); 7596-607. ©2017 AACR.

HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology

Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the "random walk facility location" (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: "multihitting time," the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients' survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.

Abstract 3395: Alternative splicing of the MEAF6 gene promotes neuroendocrine prostate cancer development

In response to the selection pressures exerted by potent androgen receptor (AR) pathway inhibitors (ARPI), adenocarcinoma prostate cancer (PCa) cells can undergo an adaptive process of cellular phenotype reprogramming termed neuroendocrine trans-differentiation. With this AR-bypass mechanism of survival emerges a lethal treatment-resistant PCa subtype called treatment-induced neuroendocrine PCa (t-NEPC). t-NEPC is becoming a major clinical issue as it is estimated to affect >25% of advanced-stage PCa patients with the level of incidences predicted to rise as a result of the extensive applications of ARPI in the clinic. This underscores the gravity of our aims to delineate the molecular underpinnings of t-NEPC to inform future therapies that prevent or mitigate t-NEPC development.

In this study, we have identified a splice variant of the MYST/Esa1-associated factor 6 (MEAF6) gene, MEAF6-1, that is highly expressed in t-NEPC tumor biopsies as well as neuroendocrine cell lines of prostate and lung cancers. We show that the neuronal RNA splicing factor, SRRM4, stimulates MEAF6-1 splicing. Enhanced MEAF6-1 expression in prostate adenocarcinoma cell lines does not induce neuroendocrine trans-differentiation of these cells. Rather, it stimulates cell proliferation, anchorage-independent cell growth, invasion, and xenograft tumor growth. Gene microarray identified that these MEAF6-1 actions are in part mediated by the ID1 and ID3 genes. These findings suggest that the MEAF6-1 variant does not induce neuroendocrine differentiation of prostate cancer cells, but facilitates t-NEPC progression through accelerating proliferation of cells that have acquired neuroendocrine phenotypes.

Citation Format: Ahn R. Lee, Yinan Li, Ning Xie, Colin C. Collins, Xuesen Dong. Alternative splicing of the MEAF6 gene promotes neuroendocrine prostate cancer development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3395. doi:10.1158/1538-7445.AM2017-3395

Abstract 4420: Elevated glycolytic gene signature in patient-derived neuroendocrine prostate cancer xenograft models and its clinical relevance

Neuroendocrine prostate cancer (NEPC) is a highly aggressive subtype of prostate cancer (PCa) that is becoming increasingly common in the clinic. While the vast majority of PCa presents as androgen-dependent adenocarcinoma, recent uses of increasingly potent therapeutics targeting the androgen receptor signaling axis has resulted in the promotion of NEPC transdifferentiation as a mechanism of treatment resistance. Unfortunately, there is currently no effective treatment option for NEPC. Altered cancer metabolism is now recognized as a hallmark of cancer and a crucial factor for promoting tumour growth and spread. In particular, altered glucose metabolism and the resultant acidification of the tumor microenvironment via increased lactic acid production has been shown to play an important role in multiple cancer-promoting processes, including tissue invasion/metastasis, angiogenesis, and suppression of local anticancer immunity. While increased glycolysis is not generally considered a phenomenon relevant to primary treatment-naive PCa, we have recently demonstrated its relavance to castration-resistant prostate cancer (CRPC) and thus suspect that it is also relevant to the more aggressive NEPC. Our laboratory has also developed a number of unique serially transplantable patient-derived xenograft (PDX) models of NEPC that are histologically highly similar to the donor tissues and retain important genetic and epigenetic features. In particular, we have developed the first spontaneous NEPC transdifferentiation model in the field (LTL331/331R). The gene expression profiles of these NEPC PDX models were compared to that of PCa adenocarcinoma PDX models. To determine whether certain metabolic pathway alterations were specific to NEPC, genes from a number of key metabolic pathways were compiled and overall pathway scores were generated using average expression z-scores. Furthermore, publically available gene expression data from NEPC patient tumors were used to validate our findings. From our analysis, we found that genes in the glycolysis pathway were signficiantlly upregulatied in both our PDX models and also in patient NEPC samples. Of particular interest is the upregulation of genes involved in the production and secretion of lactic acid, such as LDHA and MCT4. As such, our results suggest that elevated glycolysis and production of lactic acid could be a clinically important NEPC phenotype. Furthermore, the inhibition of glycolysis and particularly the inhibition of lactic acid secretion via MCT4 could be a potentially viable therapeutic strategy for NEPC.

Citation Format: Stephen Y. Choi, Susan L. Ettinger, Dong Lin, Hui Xue, Robert H. Bell, Fan Mo, Michael Pollak, Colin C. Collins, Yuzhuo Wang. Elevated glycolytic gene signature in patient-derived neuroendocrine prostate cancer xenograft models and its clinical relevance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4420. doi:10.1158/1538-7445.AM2017-4420

Metabolic heterogeneity signature of primary treatment-naïve prostate cancer

To avoid over- or under-treatment of primary prostate tumours, there is a critical need for molecular signatures to discriminate indolent from aggressive, lethal disease. Reprogrammed energy metabolism is an important hallmark of cancer, and abnormal metabolic characteristics of cancers have been implicated as potential diagnostic/prognostic signatures. While genomic and transcriptomic heterogeneity of prostate cancer is well documented and associated with tumour progression, less is known about metabolic heterogeneity of the disease. Using a panel of high fidelity patient-derived xenograft (PDX) models derived from hormone-naïve prostate cancer, we demonstrated heterogeneity of expression of genes involved in cellular energetics and macromolecular biosynthesis. Such heterogeneity was also observed in clinical, treatment-naïve prostate cancers by analyzing the transcriptome sequencing data. Importantly, a metabolic gene signature of increased one-carbon metabolism or decreased proline degradation was identified to be associated with significantly decreased biochemical disease-free patient survival. These results suggest that metabolic heterogeneity of hormone-naïve prostate cancer is of biological and clinical importance and motivate further studies to determine the heterogeneity in metabolic flux in the disease that may lead to identification of new signatures for tumour/patient stratification and the development of new strategies and targets for therapy of prostate cancer.

Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer

BACKGROUND

Clinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa.

OBJECTIVE

To identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa).

DESIGN, SETTING, AND PARTICIPANTS

To develop the signature, whole-genome and whole-transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures.

RESULTS AND LIMITATIONS

A close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures.

CONCLUSIONS

Profiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors.

PATIENT SUMMARY

Our stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancer patients. Furthermore, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors.

Genomic Alterations in Cell-Free DNA and Enzalutamide Resistance in Castration-Resistant Prostate Cancer

Importance

The molecular landscape underpinning response to the androgen receptor (AR) antagonist enzalutamide in patients with metastatic castration-resistant prostate cancer (mCRPC) is undefined. Consequently, there is an urgent need for practical biomarkers to guide therapy selection and elucidate resistance. Although tissue biopsies are impractical to perform routinely in the majority of patients with mCRPC, the analysis of plasma cell-free DNA (cfDNA) has recently emerged as a minimally invasive method to explore tumor characteristics.

Objective

To reveal genomic characteristics from cfDNA associated with clinical outcomes during enzalutamide treatment.

Design, Setting, and Participants

Plasma samples were obtained from August 4, 2013, to July 31, 2015, at a single academic institution (British Columbia Cancer Agency) from 65 patients with mCRPC. We collected temporal plasma samples (at baseline, 12 weeks, end of treatment) for circulating cfDNA and performed array comparative genomic hybridization copy number profiling and deep AR gene sequencing. Samples collected at end of treatment were also subjected to targeted sequencing of 19 prostate cancer-associated genes.

Exposure

Enzalutamide, 160 mg, daily orally.

Main Outcomes and Measures

Prostate-specific antigen response rate (decline ≥50% from baseline confirmed ≥3 weeks later). Radiographic (as per Prostate Cancer Working Group 2 Criteria) and/or clinical progression (defined as worsening disease-related symptoms necessitating a change in anticancer therapy and/or deterioration in Eastern Cooperative Group performance status ≥2 levels).

Results

The 65 patients had a median (interquartile range) age of 74 (68-79) years. Prostate-specific antigen response rate to enzalutamide treatment was 38% (25 of 65), while median clinical/radiographic progression-free survival was 3.5 (95% CI, 2.1-5.0) months. Cell-free DNA was isolated from 122 of 125 plasma samples, and targeted sequencing was successful in 119 of 122. AR mutations and/or copy number alterations were robustly detected in 48% (31 of 65) and 60% (18 of 30) of baseline and progression samples, respectively. Detection of AR amplification, heavily mutated AR (≥2 mutations), and RB1 loss were associated with worse progression-free survival, with hazard ratios of 2.92 (95% CI, 1.59-5.37), 3.94 (95% CI, 1.46-10.64), and 4.46 (95% CI, 2.28-8.74), respectively. AR mutations exhibited clonal selection during treatment, including an increase in glucocorticoid-sensitive AR L702H and promiscuous AR T878A in patients with prior abiraterone treatment. At the time of progression, cfDNA sequencing revealed mutations or copy number changes in all patients tested, including clinically actionable alterations in DNA damage repair genes and PI3K pathway genes, and a high frequency (4 of 14) of activating CTNNB1 mutations.

Conclusions and Relevance

Clinically informative genomic profiling of cfDNA was feasible in nearly all patients with mCRPC and can provide important insights into enzalutamide response and resistance.

Clonality Inference from Single Tumor Samples Using Low-Coverage Sequence Data

Inference of intra-tumor heterogeneity can provide valuable insight into cancer evolution. Somatic mutations detected by sequencing can help estimate the purity of a tumor sample and reconstruct its subclonal composition. Although several methods have been developed to infer intra-tumor heterogeneity, the majority of these tools rely on variant allele frequencies as estimated via ultra-deep sequencing from multiple samples of the same tumor. In practice, obtaining sequencing data from a large number of samples per patient is only feasible in a few cancer types such as liquid tumors, or in rare cases involving solid tumors selected for research. We introduce CTPsingle, which aims at inferring the subclonal composition by using low-coverage sequencing data from a single tumor sample. We show that CTPsingle is able to infer the purity and the clonality of single-sample tumors with high accuracy, even restricted to a coverage depth of ∼30 × .

Switching off malignant mesothelioma: exploiting the hypoxic microenvironment

Malignant mesotheliomas are aggressive, asbestos-related cancers with poor patient prognosis, typically arising in the mesothelial surfaces of tissues in pleural and peritoneal cavity. The relative unspecific symptoms of mesotheliomas, misdiagnoses, and lack of precise targeted therapies call for a more critical assessment of this disease. In the present review, we categorize commonly identified genomic aberrations of mesotheliomas into their canonical pathways and discuss targeting these pathways in the context of tumor hypoxia, a hallmark of cancer known to render solid tumors more resistant to radiation and most chemo-therapy. We then explore the concept that the intrinsic hypoxic microenvironment of mesotheliomas can be Achilles' heel for targeted, multimodal therapeutic intervention.