Metastatic site influences driver gene function in pancreatic cancer.

Driver gene mutations can increase the metastatic potential of the primary tumor1-3, but their role in sustaining tumor growth at metastatic sites is poorly understood. A paradigm of such mutations is inactivation of SMAD4 - a transcriptional effector of TGFß signaling - which is a hallmark of multiple gastrointestinal malignancies4,5. SMAD4 inactivation mediates TGFß's remarkable anti- to pro-tumorigenic switch during cancer progression and can thus influence both tumor initiation and metastasis6-14. To determine whether metastatic tumors remain dependent on SMAD4 inactivation, we developed a mouse model of pancreatic ductal adenocarcinoma (PDAC) that enables Smad4 depletion in the pre-malignant pancreas and subsequent Smad4 reactivation in established metastases. As expected, Smad4 inactivation facilitated the formation of primary tumors that eventually colonized the liver and lungs. By contrast, Smad4 reactivation in metastatic disease had strikingly opposite effects depending on the tumor's organ of residence: suppression of liver metastases and promotion of lung metastases. Integrative multiomic analysis revealed organ-specific differences in the tumor cells' epigenomic state, whereby the liver and lungs harbored chromatin programs respectively dominated by the KLF and RUNX developmental transcription factors, with Klf4 depletion being sufficient to reverse Smad4's tumor-suppressive activity in liver metastases. Our results show how epigenetic states favored by the organ of residence can influence the function of driver genes in metastatic tumors. This organ-specific gene-chromatin interplay invites consideration of anatomical site in the interpretation of tumor genetics, with implications for the therapeutic targeting of metastatic disease.

Profiling salivary miRNA expression levels in Fanconi anemia patients - a pilot study.

The overarching goal of this study is to predict the risk of developing oral squamous cell carcinoma (OSCC) in Fanconi anemia (FA) patients. We have compared the microRNA (miRNA, miR) expression levels in saliva samples from FA patients (n = 50) who are at a low-moderate and/or high risk of developing OSCC to saliva samples from healthy controls (n = 16). The miRNA expression levels in saliva samples were quantified using qPCR. We observed that miR-744, miR-150-5P, and miR-146B-5P had the best discriminatory capacity between FA patients and controls, with an area under the curve (AUC) of 94.0%, 92.9% and 85.3%, respectively. Our data suggest that miR-1, miR-146B-5P, miR-150-5P, miR-155-5P, and miR-744 could be used as panel to predict the risk of developing OSCC in FA patients, with a 89.3% sensitivity and a 68.2% specificity (AUC = 81.5%). Our preliminary data support the notion that the expression levels of salivary miRNAs have the potential to predict the risk of developing OSCC in FA patients and in the future may reduce deaths associated with OSCC.

The MHC Class II Antigen-Processing and Presentation Pathway Is Dysregulated in Type 1 Diabetes.

Peptide loading of MHC class II (MHCII) molecules is facilitated by HLA-DM (DM), which catalyzes CLIP release, stabilizes empty MHCII, and edits the MHCII-bound peptide repertoire. HLA-DO (DO) binds to DM and modulates its activity, resulting in an altered set of peptides presented at the cell surface. MHCII-peptide presentation in individuals with type 1 diabetes (T1D) is abnormal, leading to a breakdown in tolerance; however, no direct measurement of the MHCII pathway activity in T1D patients has been performed. In this study, we measured MHCII Ag-processing pathway activity in humans by determining MHCII, MHCII-CLIP, DM, and DO levels by flow cytometry for peripheral blood B cells, dendritic cells, and monocytes from 99 T1D patients and 97 controls. Results showed that MHCII levels were similar for all three APC subsets. In contrast, MHCII-CLIP levels, independent of sex, age at blood draw, disease duration, and diagnosis age, were significantly increased for all three APCs, with B cells showing the largest increase (3.4-fold). DM and DO levels, which usually directly correlate with MHCII-CLIP levels, were unexpectedly identical in T1D patients and controls. Gene expression profiling on PBMC RNA showed that DMB mRNA was significantly elevated in T1D patients with residual C-peptide. This resulted in higher levels of DM protein in B cells and dendritic cells. DO levels were also increased, suggesting that the MHCII pathway maybe differentially regulated in individuals with residual C-peptide. Collectively, these studies show a dysregulation of the MHCII Ag-processing pathway in patients with T1D.

Detecting salivary host and microbiome RNA signature for aiding diagnosis of oral and throat cancer.

OBJECTIVE: Oral squamous cell carcinoma (OSCC) and oropharyngeal squamous cell carcinoma (OPSCC) can go undetected resulting in late detection and poor outcomes. We describe the development and validation of CancerDetect for Oral & Throat cancer™ (CDOT), to detect markers of OSCC and/or OPSCC within a high-risk population. MATERIAL AND METHODS: We collected saliva samples from 1,175 individuals who were 50 years or older, or adults with a tobacco use history. 945 of those were used to train a classifier using machine learning methods, resulting in a salivary microbial and human metatranscriptomic signature. The classifier was then independently validated on the 230 remaining samples prospectively collected and unseen by the classifier, consisting of 20 OSCC (all stages), 76 OPSCC (all stages), and 134 negatives (including 14 pre-malignant). RESULTS: On the validation cohort, the specificity of the CDOT test was 94 %, sensitivity was 90 % for participants with OSCC, and 84.2 % for participants with OPSCC. Similar classification results were observed among people in early stage (stages I & II) vs late stage (stages III & IV). CONCLUSIONS: CDOT is a non-invasive test that can be easily administered in dentist offices, primary care centres and specialised cancer clinics for early detection of OPSCC and OSCC. This test, having received FDA's breakthrough designation for accelerated review, has the potential to enable early diagnosis, saving lives and significantly reducing healthcare expenditure.

Ordered and deterministic cancer genome evolution after p53 loss.

Although p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours.

The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer.

Despite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing.

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse.

Cells in the body remain healthy by tightly preventing and repairing random changes, or mutations, in their genetic material. In cancer cells, however, these mechanisms can break down. When these cells grow and multiply, they can then go on to accumulate many mutations. As a result, cancer cells in the same tumor can each contain a unique combination of genetic changes. This genetic heterogeneity has the potential to affect how cancer responds to treatment, and is increasingly becoming appreciated clinically. For example, if a drug only works against cancer cells carrying a specific mutation, any cells lacking this genetic change will keep growing and cause a relapse. However, it is still difficult to quantify and understand genetic heterogeneity in cancer. Copy number alterations (or CNAs) are a class of mutation where large and small sections of genetic material are gained or lost. This can result in cells that have an abnormal number of copies of the genes in these sections. Here, Baslan et al. set out to explore how CNAs might vary between individual cancer cells within the same tumor. To do so, thousands of individual cancer cells were isolated from human breast tumors, and a technique called single-cell genome sequencing used to screen the genetic information of each of them. These experiments confirmed that CNAs did differ ­ sometimes dramatically ­ between patients and among cells taken from the same tumor. For example, many of the cells carried extra copies of well-known cancer genes important for treatment, but the exact number of copies varied between cells. This heterogeneity existed for individual genes as well as larger stretches of DNA: this was the case, for instance, for an entire section of chromosome 8, a region often affected in breast and other tumors. The work by Baslan et al. captures the sheer extent of genetic heterogeneity in cancer and in doing so, highlights the power of single-cell genome sequencing. In the future, a finer understanding of the genetic changes present at the level of an individual cancer cell may help clinicians to manage the disease more effectively.

Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution.

A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy.IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.

Integrated Genome-Wide Analysis of an Isogenic Pair of Pseudomonas aeruginosa Clinical Isolates with Differential Antimicrobial Resistance to Ceftolozane/Tazobactam, Ceftazidime/Avibactam, and Piperacillin/Tazobactam.

Multidrug-resistant (MDR) Pseudomonas aeruginosa is one of the main causes of morbidity and mortality in hospitalized patients and the leading cause of nosocomial infections. We investigated, here, two MDR P. aeruginosa clinical isolates from a hospitalized patient with differential antimicrobial resistance to ceftazidime/avibactam (CZA), ceftolozane/tazobactam (C/T), and piperacillin/tazobactam (P/T). Their assembled complete genomes revealed they belonged to ST235, a widespread MDR clone; and were isogenic with only a single nucleotide variant, causing G183D mutation in AmpC ß-lactamase, responsible for a phenotypic change from susceptible to resistant to CZA and C/T. Further epigenomic profiling uncovered two conserved DNA methylation motifs targeted by two distinct putative methyltransferase-containing restriction-modification systems, respectively; more intriguingly, there was a significant difference between the paired isolates in the pattern of genomic DNA methylation and modifications. Moreover, genome-wide gene expression profiling demonstrated the inheritable genomic methylation and modification induced 14 genes being differentially regulated, of which only toxR (downregulated), a regulatory transcription factor, had its promoter region differentially methylate and modified. Since highly expressed opdQ encodes an OprD porin family protein, therefore, we proposed an epigenetic regulation of opdQ expression pertinent to the phenotypic change of P. aeruginosa from resistant to susceptible to P/T. The disclosed epigenetic mechanism controlling phenotypic antimicrobial resistance deserves further experimental investigation.

Optimizing a Whole-Genome Sequencing Data Processing Pipeline for Precision Surveillance of Health Care-Associated Infections.

The surveillance of health care-associated infection (HAI) is an essential element of the infection control program. While whole-genome sequencing (WGS) has widely been adopted for genomic surveillance, its data processing remains to be improved. Here, we propose a three-level data processing pipeline for the precision genomic surveillance of microorganisms without prior knowledge: species identification, multi-locus sequence typing (MLST), and sub-MLST clustering. The former two are closely connected to what have widely been used in current clinical microbiology laboratories, whereas the latter one provides significantly improved resolution and accuracy in genomic surveillance. Comparing to a broadly used reference-dependent alignment/mapping method and an annotation-dependent pan-/core-genome analysis, we implemented our reference- and annotation-independent, k-mer-based, simplified workflow to a collection of Acinetobacter and Enterococcus clinical isolates for tests. By taking both single nucleotide variants and genomic structural changes into account, the optimized k-mer-based pipeline demonstrated a global view of bacterial population structure in a rapid manner and discriminated the relatedness between bacterial isolates in more detail and precision. The newly developed WGS data processing pipeline would facilitate WGS application to the precision genomic surveillance of HAI. In addition, the results from such a WGS-based analysis would be useful for the precision laboratory diagnosis of infectious microorganisms.

A Domain Knowledge-Enhanced LSTM-CRF Model for Disease Named Entity Recognition.

Disease named entity recognition (NER) is a critical task for most biomedical natural language processing (NLP) applications. For example, extracting diseases from clinical trial text can be helpful for patient profiling and other downstream applications such as matching clinical trials to eligible patients. Similarly, disease annotation in biomedical articles can help information search engines to accurately index them such that clinicians can easily find relevant articles to enhance their knowledge. In this paper, we propose a domain knowledge-enhanced long short-term memory network-conditional random field (LSTM-CRF) model for disease named entity recognition, which also augments a character-level convolutional neural network (CNN) and a character-level LSTM network for input embedding. Experimental results on a scientific article dataset show the effectiveness of our proposed models compared to state-of-the-art methods in disease recognition.

Optimizing a Metatranscriptomic Next-Generation Sequencing Protocol for Bronchoalveolar Lavage Diagnostics.

Compared with conventional serologic, culture-based, and molecular-based diagnostic tests, next-generation sequencing (NGS) provides sequence-evidenced detection of various microbes, without prior knowledge, and thus is becoming a novel diagnostic approach. Herein we describe an RNA-based metatranscriptomic NGS (mtNGS) protocol for culture-independent detection of potential infectious pathogens, using clinical bronchoalveolar lavage specimens as an example. We present both an optimized workflow for experimental sequence data collection and a simplified pipeline for bioinformatics sequence data processing. As shown, the whole protocol takes approximately 24 to 36 hours to detect a wide range of Gram-positive and -negative bacteria and possibly other viral and/or fungal pathogens. In particular, we introduce a spike-in RNA mix as an internal control, which plays a critical role in mitigating false-positive and false-negative results of clinical diagnostic tests. Moreover, our mtNGS method can detect antibiotic resistance genes and virulence factors; although it may not be comprehensive, such information is imperative and helpful for the clinician to make better treatment decisions. Results from our preliminary testing suggest that the mtNGS approach is a useful alterative in diagnostic detection of emerging infectious pathogens in clinical laboratories. However, further improvements are needed to achieve better sensitivity and accuracy.

Evolution and mutations predisposing to daptomycin resistance in vancomycin-resistant Enterococcus faecium ST736 strains.

We recently identified a novel vancomycin-resistant Enterococcus faecium (VREfm) clone ST736 with reduced daptomycin susceptibility. The objectives of this study were to assess the population dynamics of local VREfm strains and genetic alterations predisposing to daptomycin resistance in VREfm ST736 strains. Multilocus sequence typing and single nucleotide variant data were derived from whole-genome sequencing of 250 E. faecium isolates from 1994-1995 (n = 43), 2009-2012 (n = 115) and 2013 (n = 92). A remarkable change was noticed in the clonality and antimicrobial resistance profiles of E. faecium strains between 1994-1995 and 2013. VREfm sequence type 17 (ST17), the prototype strain of clade A1, was the dominant clone (76.7%) recognized in 1994-1995. By contrast, clone ST736 accounted for 46.7% of VREfm isolates, followed by ST18 (26.1%) and ST412 (20.7%) in 2013. Bayesian evolutionary analysis suggested that clone ST736 emerged between 1996 and 2009. Co-mutations (liaR.W73C and liaS.T120A) of the liaFSR system were identified in all ST736 isolates (n = 111, 100%) examined. Thirty-eight (34.2%) ST736 isolates exhibited daptomycin-resistant phenotype, of which 13 isolates had mutations in both the liaFSR and cardiolipin synthase (cls) genes and showed high level of resistance with a daptomycin MIC50 of 32 µg/mL. The emergence of ST736 strains with mutations predisposing to daptomycin resistance and subsequent clonal spread among inpatients contributed to the observed high occurrence of daptomycin resistance in VREfm at our institution. The expanding geographic distribution of ST736 strains in other states and countries raises concerns about its global dissemination.

Complete Genome Sequences of Four Toxigenic Clostridium difficile Clinical Isolates from Patients of the Lower Hudson Valley, New York, USA.

Complete genome sequences of four toxigenic Clostridium difficile isolates from patients in the lower Hudson Valley, New York, USA, were achieved. These isolates represent four common sequence types (ST1, ST2, ST8, and ST42) belonging to two distinct phylogenetic clades. All isolates have a 4.0- to 4.2-Mb circular chromosome, and one carries a phage.

Complete Genome Sequence of a Colistin-Resistant Escherichia coli Strain Harboring mcr-1 on an IncHI2 Plasmid in the United States.

We report here the incidental detection and complete genome sequence of a urinary Escherichia coli strain harboring mcr-1 and resistant to colistin in a New York patient returning from Portugal in 2016. This strain, with sequence type 1485 (ST1485), was a non-extended-spectrum beta-lactamase (ESBL) and non-carbapenemase producer and carried the mcr-1 gene on an IncHI2 plasmid.

Emergence and Evolution of Multidrug-Resistant Klebsiella pneumoniae with both blaKPC and blaCTX-M Integrated in the Chromosome.

The extended-spectrum-ß-lactamase (ESBL)- and Klebsiella pneumoniae carbapenemase (KPC)-producing Enterobacteriaceae represent serious and urgent threats to public health. In a retrospective study of multidrug-resistant K. pneumoniae, we identified three clinical isolates, CN1, CR14, and NY9, carrying both blaCTX-M and blaKPC genes. The complete genomes of these three K. pneumoniae isolates were de novo assembled by using both short- and long-read whole-genome sequencing. In CR14 and NY9, blaCTX-M and blaKPC were carried on two different plasmids. In contrast, CN1 had one copy of blaKPC-2 and three copies of blaCTX-M-15 integrated in the chromosome, for which the blaCTX-M-15 genes were linked to an insertion sequence, ISEcp1, whereas the blaKPC-2 gene was in the context of a Tn4401a transposition unit conjugated with a PsP3-like prophage. Intriguingly, downstream of the Tn4401a-blaKPC-2-prophage genomic island, CN1 also carried a clustered regularly interspaced short palindromic repeat (CRISPR)-cas array with four spacers targeting a variety of K. pneumoniae plasmids harboring antimicrobial resistance genes. Comparative genomic analysis revealed that there were two subtypes of type I-E CRISPR-cas in K. pneumoniae strains and suggested that the evolving CRISPR-cas, with its acquired novel spacer, induced the mobilization of antimicrobial resistance genes from plasmids into the chromosome. The integration and dissemination of multiple copies of blaCTX-M and blaKPC from plasmids to chromosome depicts the complex pandemic scenario of multidrug-resistant K. pneumoniae Additionally, the implications from this study also raise concerns for the application of a CRISPR-cas strategy against antimicrobial resistance.

Enterovirus D68 Subclade B3 Strain Circulating and Causing an Outbreak in the United States in 2016.

In 2014 the United States experienced a nationwide outbreak of Enterovirus D68 (EV-D68) infection. There were no confirmed cases of EV-D68 in 2015 and CDC was only aware of limited sporadic EV-D68 detection in the US in 2016. In this report, we analyzed 749 nasopharyngeal (NP) specimens collected in 2015 and 2016 from patients in the Lower Hudson Valley, New York using a previously validated EV-D68-specific rRT-PCR assay. EV-D68 was detected in none of 199 NP specimens collected in 2015, and in one of 108 (0.9%) samples from January to May and 159 of 442 (36.0%) samples from July to October 2016. Complete EV-D68 genome sequences from 22 patients in 2016 were obtained using a metagenomic next-generation sequencing assay. Comparative genome analysis confirmed that a new EV-D68 strain belonging to subclade B3, with 3.2-4.8% divergence in nucleotide from subclade B1 strains identified during the 2014 US outbreak, was circulating in the US in 2016 and caused an outbreak in the Lower Hudson Valley, New York with 160 laboratory-confirmed cases. Our data highlight the genetic variability and capacity in causing outbreak by diverse EV-D68 strains, and the necessity of awareness and more surveillance on their active circulation worldwide.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

BACKGROUND: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. OBJECTIVE: To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. METHODS: A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. RESULTS: The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. CONCLUSIONS: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

Assessing next-generation sequencing and 4 bioinformatics tools for detection of Enterovirus D68 and other respiratory viruses in clinical samples.

We used 4 different bioinformatics algorithms to evaluate the application of a metagenomic shot-gun sequencing method in detection of Enterovirus D68 and other respiratory viruses in clinical specimens. Our data supported that next-generation sequencing, combined with improved bioinformatics tools, is practically feasible and useful for clinical diagnosis of viral infections.

Brief-exposure to preoperative bevacizumab reveals a TGF-ß signature predictive of response in HER2-negative breast cancers.

To best define biomarkers of response, and to shed insight on mechanism of action of certain clinically important agents for early breast cancer, we used a brief-exposure paradigm in the preoperative setting to study transcriptional changes in patient tumors that occur with one dose of therapy prior to combination chemotherapy. Tumor biopsies from breast cancer patients enrolled in two preoperative clinical trials were obtained at baseline and after one dose of bevacizumab (HER2-negative), trastuzumab (HER2-positive) or nab-paclitaxel, followed by treatment with combination chemo-biologic therapy. RNA-Sequencing based PAM50 subtyping at baseline of 46 HER2-negative patients revealed a strong association between the basal-like subtype and pathologic complete response (pCR) to chemotherapy plus bevacizumab (p ≤ 0.0027), but did not provide sufficient specificity to predict response. However, a single dose of bevacizumab resulted in down-regulation of a well-characterized TGF-ß activity signature in every single breast tumor that achieved pCR (p ≤ 0.004). The TGF-ß signature was confirmed to be a tumor-specific read-out of the canonical TGF-ß pathway using pSMAD2 (p ≤ 0.04), with predictive power unique to brief-exposure to bevacizumab (p ≤ 0.016), but not trastuzumab or nab-paclitaxel. Down-regulation of TGF-ß activity was associated with reduction in tumor hypoxia by transcription and protein levels, suggesting therapy-induced disruption of an autocrine-loop between tumor stroma and malignant cells. Modulation of the TGF-ß pathway upon brief-exposure to bevacizumab may provide an early functional readout of pCR to preoperative anti-angiogenic therapy in HER2-negative breast cancer, thus providing additional avenues for exploration in both preclinical and clinical settings with these agents.