Author Correction: The evolutionary history of lethal metastatic prostate cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

APOBEC3 mutational signatures are associated with extensive and diverse genomic instability across multiple tumour types.

BACKGROUND: The APOBEC3 (apolipoprotein B mRNA editing enzyme catalytic polypeptide 3) family of cytidine deaminases is responsible for two mutational signatures (SBS2 and SBS13) found in cancer genomes. APOBEC3 enzymes are activated in response to viral infection, and have been associated with increased mutation burden and TP53 mutation. In addition to this, it has been suggested that APOBEC3 activity may be responsible for mutations that do not fall into the classical APOBEC3 signatures (SBS2 and SBS13), through generation of double strand breaks.Previous work has mainly focused on the effects of APOBEC3 within individual tumour types using exome sequencing data. Here, we use whole genome sequencing data from 2451 primary tumours from 39 different tumour types in the Pan-Cancer Analysis of Whole Genomes (PCAWG) data set to investigate the relationship between APOBEC3 and genomic instability (GI). RESULTS AND CONCLUSIONS: We found that the number of classical APOBEC3 signature mutations correlates with increased mutation burden across different tumour types. In addition, the number of APOBEC3 mutations is a significant predictor for six different measures of GI. Two GI measures (INDELs attributed to INDEL signatures ID6 and ID8) strongly suggest the occurrence and error prone repair of double strand breaks, and the relationship between APOBEC3 mutations and GI remains when SNVs attributed to kataegis are excluded.We provide evidence that supports a model of cancer genome evolution in which APOBEC3 acts as a causative factor in the development of diverse and widespread genomic instability through the generation of double strand breaks. This has important implications for treatment approaches for cancers that carry APOBEC3 mutations, and challenges the view that APOBECs only act opportunistically at sites of single stranded DNA.

The architecture of clonal expansions in morphologically normal tissue from cancerous and non-cancerous prostates.

BACKGROUND: Up to 80% of cases of prostate cancer present with multifocal independent tumour lesions leading to the concept of a field effect present in the normal prostate predisposing to cancer development. In the present study we applied Whole Genome DNA Sequencing (WGS) to a group of morphologically normal tissue (n = 51), including benign prostatic hyperplasia (BPH) and non-BPH samples, from men with and men without prostate cancer. We assess whether the observed genetic changes in morphologically normal tissue are linked to the development of cancer in the prostate. RESULTS: Single nucleotide variants (P = 7.0 × 10-03, Wilcoxon rank sum test) and small insertions and deletions (indels, P = 8.7 × 10-06) were significantly higher in morphologically normal samples, including BPH, from men with prostate cancer compared to those without. The presence of subclonal expansions under selective pressure, supported by a high level of mutations, were significantly associated with samples from men with prostate cancer (P = 0.035, Fisher exact test). The clonal cell fraction of normal clones was always higher than the proportion of the prostate estimated as epithelial (P = 5.94 × 10-05, paired Wilcoxon signed rank test) which, along with analysis of primary fibroblasts prepared from BPH specimens, suggests a stromal origin. Constructed phylogenies revealed lineages associated with benign tissue that were completely distinct from adjacent tumour clones, but a common lineage between BPH and non-BPH morphologically normal tissues was often observed. Compared to tumours, normal samples have significantly less single nucleotide variants (P = 3.72 × 10-09, paired Wilcoxon signed rank test), have very few rearrangements and a complete lack of copy number alterations. CONCLUSIONS: Cells within regions of morphologically normal tissue (both BPH and non-BPH) can expand under selective pressure by mechanisms that are distinct from those occurring in adjacent cancer, but that are allied to the presence of cancer. Expansions, which are probably stromal in origin, are characterised by lack of recurrent driver mutations, by almost complete absence of structural variants/copy number alterations, and mutational processes similar to malignant tissue. Our findings have implications for treatment (focal therapy) and early detection approaches.

The evolutionary history of lethal metastatic prostate cancer.

Cancers emerge from an ongoing Darwinian evolutionary process, often leading to multiple competing subclones within a single primary tumour. This evolutionary process culminates in the formation of metastases, which is the cause of 90% of cancer-related deaths. However, despite its clinical importance, little is known about the principles governing the dissemination of cancer cells to distant organs. Although the hypothesis that each metastasis originates from a single tumour cell is generally supported, recent studies using mouse models of cancer demonstrated the existence of polyclonal seeding from and interclonal cooperation between multiple subclones. Here we sought definitive evidence for the existence of polyclonal seeding in human malignancy and to establish the clonal relationship among different metastases in the context of androgen-deprived metastatic prostate cancer. Using whole-genome sequencing, we characterized multiple metastases arising from prostate tumours in ten patients. Integrated analyses of subclonal architecture revealed the patterns of metastatic spread in unprecedented detail. Metastasis-to-metastasis spread was found to be common, either through de novo monoclonal seeding of daughter metastases or, in five cases, through the transfer of multiple tumour clones between metastatic sites. Lesions affecting tumour suppressor genes usually occur as single events, whereas mutations in genes involved in androgen receptor signalling commonly involve multiple, convergent events in different metastases. Our results elucidate in detail the complex patterns of metastatic spread and further our understanding of the development of resistance to androgen-deprivation therapy in prostate cancer.

Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer.

BACKGROUND: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. METHODS: Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out-of-bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). RESULTS: As ExoMeth risk score (range, 0-1) increased, the likelihood of high-grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78-2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver-operator characteristic curve (AUC) = 0.89 (95% CI: 0.84-0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87-0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. CONCLUSION: Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed.

A novel stratification framework for predicting outcome in patients with prostate cancer.

BACKGROUND: Unsupervised learning methods, such as Hierarchical Cluster Analysis, are commonly used for the analysis of genomic platform data. Unfortunately, such approaches ignore the well-documented heterogeneous composition of prostate cancer samples. Our aim is to use more sophisticated analytical approaches to deconvolute the structure of prostate cancer transcriptome data, providing novel clinically actionable information for this disease. METHODS: We apply an unsupervised model called Latent Process Decomposition (LPD), which can handle heterogeneity within individual cancer samples, to genome-wide expression data from eight prostate cancer clinical series, including 1,785 malignant samples with the clinical endpoints of PSA failure and metastasis. RESULTS: We show that PSA failure is correlated with the level of an expression signature called DESNT (HR = 1.52, 95% CI = [1.36, 1.7], P = 9.0 × 10-14, Cox model), and that patients with a majority DESNT signature have an increased metastatic risk (X2 test, P = 0.0017, and P = 0.0019). In addition, we develop a stratification framework that incorporates DESNT and identifies three novel molecular subtypes of prostate cancer. CONCLUSIONS: These results highlight the importance of using more complex approaches for the analysis of genomic data, may assist drug targeting, and have allowed the construction of a nomogram combining DESNT with other clinical factors for use in clinical management.

A four-group urine risk classifier for predicting outcomes in patients with prostate cancer.

OBJECTIVES: To develop a risk classifier using urine-derived extracellular vesicle (EV)-RNA capable of providing diagnostic information on disease status prior to biopsy, and prognostic information for men on active surveillance (AS). PATIENTS AND METHODS: Post-digital rectal examination urine-derived EV-RNA expression profiles (n = 535, multiple centres) were interrogated with a curated NanoString panel. A LASSO-based continuation ratio model was built to generate four prostate urine risk (PUR) signatures for predicting the probability of normal tissue (PUR-1), D'Amico low-risk (PUR-2), intermediate-risk (PUR-3), and high-risk (PUR-4) prostate cancer. This model was applied to a test cohort (n = 177) for diagnostic evaluation, and to an AS sub-cohort (n = 87) for prognostic evaluation. RESULTS: Each PUR signature was significantly associated with its corresponding clinical category (P < 0.001). PUR-4 status predicted the presence of clinically significant intermediate- or high-risk disease (area under the curve = 0.77, 95% confidence interval [CI] 0.70-0.84). Application of PUR provided a net benefit over current clinical practice. In an AS sub-cohort (n = 87), groups defined by PUR status and proportion of PUR-4 had a significant association with time to progression (interquartile range hazard ratio [HR] 2.86, 95% CI 1.83-4.47; P < 0.001). PUR-4, when used continuously, dichotomized patient groups with differential progression rates of 10% and 60% 5 years after urine collection (HR 8.23, 95% CI 3.26-20.81; P < 0.001). CONCLUSION: Urine-derived EV-RNA can provide diagnostic information on aggressive prostate cancer prior to biopsy, and prognostic information for men on AS. PUR represents a new and versatile biomarker that could result in substantial alterations to current treatment of patients with prostate cancer.

Benzo pyrene-induced DNA adducts and gene expression profiles in target and non-target organs for carcinogenesis in mice.

BACKGROUND: Gene expression changes induced by carcinogens may identify differences in molecular function between target and non-target organs. Target organs for benzo[a]pyrene (BaP) carcinogenicity in mice (lung, spleen and forestomach) and three non-target organs (liver, colon and glandular stomach) were investigated for DNA adducts by 32P-postlabelling, for gene expression changes by cDNA microarray and for miRNA expression changes by miRNA microarray after exposure of animals to BaP. RESULTS: BaP-DNA adduct formation occurred in all six organs at levels that did not distinguish between target and non-target. cDNA microarray analysis showed a variety of genes modulated significantly by BaP in the six organs and the overall gene expression patterns were tissue specific. Gene ontology analysis also revealed that BaP-induced bioactivities were tissue specific; eight genes (Tubb5, Fos, Cdh1, Cyp1a1, Apc, Myc, Ctnnb1 and Cav) showed significant expression difference between three target and three non-target organs. Additionally, several gene expression changes, such as in Trp53 activation and Stat3 activity suggested some similarities in molecular mechanisms in two target organs (lung and spleen), which were not found in the other four organs. Changes in miRNA expression were generally tissue specific, involving, in total, 21/54 miRNAs significantly up- or down-regulated. CONCLUSIONS: Altogether, these findings showed that DNA adduct levels and early gene expression changes did not fully distinguish target from non-target organs. However, mechanisms related to early changes in p53, Stat3 and Wnt/ß-catenin pathways may play roles in defining BaP organotropism.

Poly(amidoamine) dendrimer nanocarriers and their aerosol formulations for siRNA delivery to the lung epithelium.

Small interfering RNA (siRNA)-based therapies have great promise in the treatment of a number of prevalent pulmonary disorders including lung cancer, asthma and cystic fibrosis. However, progress in this area has been hindered due to the lack of carriers that can efficiently deliver siRNA to lung epithelial cells, and also due to challenges in developing oral inhalation (OI) formulations for the regional administration of siRNA and their carriers to the lungs. In this work we report the ability of generation four, amine-terminated poly(amidoamine) (PAMAM) dendrimer (G4NH2)-siRNA complexes (dendriplexes) to silence the enhanced green fluorescent protein (eGFP) gene on A549 lung alveolar epithelial cells stably expressing eGFP. We also report the formulation of the dendriplexes and their aerosol characteristics in propellant-based portable OI devices. The size and gene silencing ability of the dendriplexes was seen not to be a strong function of the N/P ratio. Silencing efficiencies of up to 40% are reported. Stable dispersions of the dendriplexes encapsulated in mannitol and also in a biodegradable and water-soluble co-oligomer were prepared in hydrofluoroalkane (HFA)-based pressurized metered-dose inhalers (pMDIs). Their aerosol characteristics were very favorable, and conducive to deep lung deposition, with respirable fractions of up to 77%. Importantly, siRNA formulated as dendriplexes in pMDIs was shown to keep its integrity after the particle preparation processes, and also after long-term exposures to HFA. The relevance of this study stems from the fact that this is the first work to report the formulation of inhalable siRNA with aerosol properties suitable to deep lung deposition using pMDIs devices that are the least expensive and most widely used portable inhalers. This study is relevant because, also for the first time, it shows that siRNA-G4NH2 dendriplexes can efficiently target lung alveolar epithelial A549 cells and silence genes even after siRNA has been exposed to the propellant environment.

Cancer invasion and anaerobic bacteria: new insights into mechanisms.

There is growing evidence that altered microbiota abundance of a range of specific anaerobic bacteria are associated with cancer, including Peptoniphilus spp., Porphyromonas spp., Fusobacterium spp., Fenollaria spp., Prevotella spp., Sneathia spp., Veillonella spp. and Anaerococcus spp. linked to multiple cancer types. In this review we explore these pathogenic associations. The mechanisms by which bacteria are known or predicted to interact with human cells are reviewed and we present an overview of the interlinked mechanisms and hypotheses of how multiple intracellular anaerobic bacterial pathogens may act together to cause host cell and tissue microenvironment changes associated with carcinogenesis and cancer cell invasion. These include combined effects on changes in cell signalling, DNA damage, cellular metabolism and immune evasion. Strategies for early detection and eradication of anaerobic cancer-associated bacterial pathogens that may prevent cancer progression are proposed.

Applications of Urinary Extracellular Vesicles in the Diagnosis and Active Surveillance of Prostate Cancer.

Prostate cancer is the most common non-cutaneous cancer among men in the UK, causing significant health and economic burdens. Diagnosis and risk prognostication can be challenging due to the genetic and clinical heterogeneity of prostate cancer as well as uncertainties in our knowledge of the underlying biology and natural history of disease development. Urinary extracellular vesicles (EVs) are microscopic, lipid bilayer defined particles released by cells that carry a variety of molecular cargoes including nucleic acids, proteins and other molecules. Urine is a plentiful source of prostate-derived EVs. In this narrative review, we summarise the evidence on the function of urinary EVs and their applications in the evolving field of prostate cancer diagnostics and active surveillance. EVs are implicated in the development of all hallmarks of prostate cancer, and this knowledge has been applied to the development of multiple diagnostic tests, which are largely based on RNA and miRNA. Common gene probes included in multi-probe tests include PCA3 and ERG, and the miRNAs miR-21 and miR-141. The next decade will likely bring further improvements in the diagnostic accuracy of biomarkers as well as insights into molecular biological mechanisms of action that can be translated into opportunities in precision uro-oncology.

Genomic evolution shapes prostate cancer disease type.

The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Alternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression.

Improved risk stratification in myeloma using a microRNA-based classifier.

Multiple myeloma (MM) is a heterogeneous disease. International Staging System/fluorescence hybridization (ISS/FISH)-based model and gene expression profiles (GEP) are effective approaches to define clinical outcome, although yet to be improved. The discovery of a class of small non-coding RNAs (micro RNAs, miRNAs) has revealed a new level of biological complexity underlying the regulation of gene expression. In this work, 163 presenting samples from MM patients were analysed by global miRNA profiling, and distinct miRNA expression characteristics in molecular subgroups with prognostic relevance (4p16, MAF and 11q13 translocations) were identified. Furthermore we developed an "outcome classifier", based on the expression of two miRNAs (MIR17 and MIR886-5p), which is able to stratify patients into three risk groups (median OS 19.4, 40.6 and 65.3 months, P = 0.001). The miRNA-based classifier significantly improved the predictive power of the ISS/FISH approach (P = 0.0004), and was independent of GEP-derived prognostic signatures (P < 0.002). Through integrative genomics analysis, we outlined the potential biological relevance of the miRNAs included in the classifier and their putative roles in regulating a large number of genes involved in MM biology. This is the first report showing that miRNAs can be built into molecular diagnostic strategies for risk stratification in MM.

Prognostic value of blood mRNA expression signatures in castration-resistant prostate cancer: a prospective, two-stage study.

BACKGROUND: Biomarkers are urgently needed to dissect the heterogeneity of prostate cancer between patients to improve treatment and accelerate drug development. We analysed blood mRNA expression arrays to identify patients with metastatic castration-resistant prostate cancer with poorer outcome. METHODS: Whole blood was collected into PAXgene tubes from patients with castration-resistant prostate cancer and patients with prostate cancer selected for active surveillance. In stage I (derivation set), patients with castration-resistant prostate cancer were used as cases and patients under active surveillance were used as controls. These patients were recruited from The Royal Marsden Hospital NHS Foundation Trust (Sutton, UK) and The Beatson West of Scotland Cancer Centre (Glasgow, UK). In stage II (validation-set), patients with castration-resistant prostate cancer recruited from the Memorial Sloan-Kettering Cancer Center (New York, USA) were assessed. Whole-blood RNA was hybridised to Affymetrix U133plus2 microarrays. Expression profiles were analysed with Bayesian latent process decomposition (LPD) to identify RNA expression profiles associated with castration-resistant prostate cancer subgroups; these profiles were then confirmed by quantative reverse transcriptase (qRT) PCR studies and correlated with overall survival in both the test-set and validation-set. FINDINGS: LPD analyses of the mRNA expression data divided the evaluable patients in stage I (n=94) into four groups. All patients in LPD1 (14 of 14) and most in LPD2 (17 of 18) had castration-resistant prostate cancer. Patients with castration-resistant prostate cancer and those under active surveillance comprised LPD3 (15 of 31 castration-resistant prostate cancer) and LDP4 (12 of 21 castration-resistant prostate cancer). Patients with castration-resistant prostate cancer in the LPD1 subgroup had features associated with worse prognosis and poorer overall survival than patients with castration-resistant prostate cancer in other LPD subgroups (LPD1 overall survival 10·7 months [95% CI 4·1-17·2] vs non-LPD1 25·6 months [18·0-33·4]; p<0·0001). A nine-gene signature verified by qRT-PCR classified patients into this LPD1 subgroup with a very low percentage of misclassification (1·2%). The ten patients who were initially unclassifiable by the LPD analyses were subclassified by this signature. We confirmed the prognostic utility of this nine-gene signature in the validation castration-resistant prostate cancer cohort, where LPD1 membership was also associated with worse overall survival (LPD1 9·2 months [95% CI 2·1-16·4] vs non-LPD1 21·6 months [7·5-35·6]; p=0·001), and remained an independent prognostic factor in multivariable analyses for both cohorts. INTERPRETATION: Our results suggest that whole-blood gene profiling could identify gene-expression signatures that stratify patients with castration-resistant prostate cancer into distinct prognostic groups. FUNDING: AstraZeneca, Experimental Cancer Medicine Centre, Prostate Cancer Charity, Prostate Cancer Foundation.

Improving Communication to Neonatal Resuscitation Team Members During High-Risk Births.

OBJECTIVE: To determine whether implementation of a written communication tool in labor and delivery during high-risk births improves communication, preparation, and satisfaction of responding neonatal resuscitation team members. DESIGN: Quality improvement project with a pretest and posttest design. SETTING/LOCAL PROBLEM: Two labor and delivery units and a third labor-delivery-recovery-postpartum unit within a health care system in the southeastern United States. PARTICIPANTS: Nurses, nurse practitioners, respiratory therapists, and physicians who are part of the neonatal resuscitation team. INTERVENTIONS/MEASUREMENTS: A researcher-designed, written communication tool titled the High-Risk Delivery Communication Tool was implemented in the settings. A researcher-designed measurement tool titled the Neonatal High-Risk Delivery Communication Scale was used as a before-and-after survey to measure communication to the neonatal resuscitation team, preparation for high-risk births, and satisfaction with communication from labor and delivery nurses. RESULTS: Findings from all portions of the Neonatal High-Risk Delivery Communication Scale indicated statistically significant improvements in communication, preparation, and neonatal resuscitation team member neonatal resuscitation team satisfaction while attending high-risk births. Scores improved on every item regarding hand-off, risk factor communication, preparation, and satisfaction. CONCLUSION: Implementing a communication tool for use in high-risk births may improve communication to neonatal resuscitation team members, enhance preparation for neonatal care, and increase team members' satisfaction with interprofessional communication.

Caution regarding the specificities of pan-cancer microbial structure.

Results published in an article by Poore et al. (Nature. 2020;579:567-574) suggested that machine learning models can almost perfectly distinguish between tumour types based on their microbial composition using machine learning models. Whilst we believe that there is the potential for microbial composition to be used in this manner, we have concerns with the paper that make us question the certainty of the conclusions drawn. We believe there are issues in the areas of the contribution of contamination, handling of batch effects, false positive classifications and limitations in the machine learning approaches used. This makes it difficult to identify whether the authors have identified true biological signal and how robust these models would be in use as clinical biomarkers. We commend Poore et al. on their approach to open data and reproducibility that has enabled this analysis. We hope that this discourse assists the future development of machine learning models and hypothesis generation in microbiome research.

Quality Control in Metagenomics Data.

Experiments involving metagenomics data are become increasingly commonplace. Processing such data requires a unique set of considerations. Quality control of metagenomics data is critical to extracting pertinent insights. In this chapter, we outline some considerations in terms of study design and other confounding factors that can often only be realized at the point of data analysis.In this chapter, we outline some basic principles of quality control in metagenomics, including overall reproducibility and some good practices to follow. The general quality control of sequencing data is then outlined, and we introduce ways to process this data by using bash scripts and developing pipelines in Snakemake (Python).A significant part of quality control in metagenomics is in analyzing the data to ensure you can spot relationships between variables and to identify when they might be confounded. This chapter provides a walkthrough of analyzing some microbiome data (in the R statistical language) and demonstrates a few days to identify overall differences and similarities in microbiome data. The chapter is concluded by discussing remarks about considering taxonomic results in the context of the study and interrogating sequence alignments using the command line.

Gene-Transcript Expression in Urine Supernatant and Urine Cell-Sediment Are Different but Equally Useful for Detecting Prostate Cancer.

There is considerable interest in urine as a non-invasive liquid biopsy to detect prostate cancer (PCa). PCa-specific transcripts such as the TMPRSS2:ERG fusion gene can be found in both urine extracellular vesicles (EVs) and urine cell-sediment (Cell) but the relative usefulness of these and other genes in each fraction in PCa detection has not been fully elucidated. Urine samples from 76 men (PCa n = 40, non-cancer n = 36) were analysed by NanoString for 154 PCa-associated genes-probes, 11 tissue-specific, and six housekeeping. Comparison to qRT-PCR data for four genes (PCA3, OR51E2, FOLH1, and RPLP2) was strong (r = 0.51-0.95, Spearman p < 0.00001). Comparing EV to Cells, differential gene expression analysis found 57 gene-probes significantly more highly expressed in 100 ng of amplified cDNA products from the EV fraction, and 26 in Cells (p < 0.05; edgeR). Expression levels of prostate-specific genes (KLK2, KLK3) measured were ~20× higher in EVs, while PTPRC (white-blood Cells) was ~1000× higher in Cells. Boruta analysis identified 11 gene-probes as useful in detecting PCa: two were useful in both fractions (PCA3, HOXC6), five in EVs alone (GJB1, RPS10, TMPRSS2:ERG, ERG_Exons_4-5, HPN) and four from Cell (ERG_Exons_6-7, OR51E2, SPINK1, IMPDH2), suggesting that it is beneficial to fractionate whole urine prior to analysis. The five housekeeping genes were not significantly differentially expressed between PCa and non-cancer samples. Expression signatures from Cell, EV and combined data did not show evidence for one fraction providing superior information over the other.

Whole exome sequencing study identifies candidate loss of function variants and locus heterogeneity in familial cholesteatoma.

Cholesteatoma is a rare progressive disease of the middle ear. Most cases are sporadic, but some patients report a positive family history. Identifying functionally important gene variants associated with this disease has the potential to uncover the molecular basis of cholesteatoma pathology with implications for disease prevention, surveillance, or management. We performed an observational WES study of 21 individuals treated for cholesteatoma who were recruited from ten multiply affected families. These family studies were complemented with gene-level mutational burden analysis. We also applied functional enrichment analyses to identify shared properties and pathways for candidate genes and their products. Filtered data collected from pairs and trios of participants within the ten families revealed 398 rare, loss of function (LOF) variants co-segregating with cholesteatoma in 389 genes. We identified six genes DENND2C, DNAH7, NBEAL1, NEB, PRRC2C, and SHC2, for which we found LOF variants in two or more families. The parallel gene-level analysis of mutation burden identified a significant mutation burden for the genes in the DNAH gene family, which encode products involved in ciliary structure. Functional enrichment analyses identified common pathways for the candidate genes which included GTPase regulator activity, calcium ion binding, and degradation of the extracellular matrix. The number of candidate genes identified and the locus heterogeneity that we describe within and between multiply affected families suggest that the genetic architecture for familial cholesteatoma is complex.

Major data analysis errors invalidate cancer microbiome findings.

We re-analyzed the data from a recent large-scale study that reported strong correlations between microbial organisms and 33 different cancer types, and that created machine learning predictors with near-perfect accuracy at distinguishing among cancers. We found at least two fundamental flaws in the reported data and in the methods: (1) errors in the genome database and the associated computational methods led to millions of false positive findings of bacterial reads across all samples, largely because most of the sequences identified as bacteria were instead human; and (2) errors in transformation of the raw data created an artificial signature, even for microbes with no reads detected, tagging each tumor type with a distinct signal that the machine learning programs then used to create an apparently accurate classifier. Each of these problems invalidates the results, leading to the conclusion that the microbiome-based classifiers for identifying cancer presented in the study are entirely wrong. These flaws have subsequently affected more than a dozen additional published studies that used the same data and whose results are likely invalid as well.