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 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.

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.

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 sparse sequencing of single cells for highly multiplex copy number profiling.

Genome-wide analysis at the level of single cells has recently emerged as a powerful tool to dissect genome heterogeneity in cancer, neurobiology, and development. To be truly transformative, single-cell approaches must affordably accommodate large numbers of single cells. This is feasible in the case of copy number variation (CNV), because CNV determination requires only sparse sequence coverage. We have used a combination of bioinformatic and molecular approaches to optimize single-cell DNA amplification and library preparation for highly multiplexed sequencing, yielding a method that can produce genome-wide CNV profiles of up to a hundred individual cells on a single lane of an Illumina HiSeq instrument. We apply the method to human cancer cell lines and biopsied cancer tissue, thereby illustrating its efficiency, reproducibility, and power to reveal underlying genetic heterogeneity and clonal phylogeny. The capacity of the method to facilitate the rapid profiling of hundreds to thousands of single-cell genomes represents a key step in making single-cell profiling an easily accessible tool for studying cell lineage.

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.

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.

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.

Identification of a novel PARP14-TFE3 gene fusion from 10-year-old FFPE tissue by RNA-seq.

Xp11 (TFE3) translocation renal cell carcinoma (RCC) is officially recognized as a distinct subtype of RCC in the 2004 WHO classification. This neoplasm is characterized by several chromosomal translocations between the TFE3-involving Xp11.2 breakpoint and various fusion partners. To date, five partner genes have been identified, that is, PRCC in 1q21, PSF in 1q34, ASPL in 17q25, CLTC in 17q23, and NONO in Xq12; and three additional translocations have been reported with no partner gene being defined: t(X;3)(p11;q23), t(X;10)(p11;q23), and t(X;19)(p11;q13). Here, we report the identification of a novel TFE3 fusion partner, PARP14 in chromosome band3q21. We used RNA-seq on a 10-year-old FFPE (formalin-fixed, paraffin-embedded) tissue sample, which carried t(X;3)(p11;q23) as detected in the original cytogenetic study. The fusion transcript connected the 5'-end of the first two exons of PARP14 to the 3'-end of five exons of TFE3, which was verified by reverse transcription PCR (RT-PCR) and Sanger sequencing. Similar to other TFE3 fusions previously reported, the predicted PARP14-TFE3 product retains the nuclear localization and DNA-binding domains of TFE3. This finding expands the list of TFE3 translocation partner genes and re-emphasizes the essential oncogenic role of TFE3 fusion proteins in this tumor. Our result also clearly demonstrated the feasibility of identifying chromosomal translocation by RNA-seq in clinical FFPE, which are easily accessible and associated with valuable clinical information. © 2015 Wiley Periodicals, Inc.

Identification of a novel clone, ST736, among Enterococcus faecium clinical isolates and its association with daptomycin nonsusceptibility.

Resistance to daptomycin in enterococcal clinical isolates remains rare but is being increasingly reported in the United States and worldwide. There are limited data on the genetic relatedness and microbiological and clinical characteristics of daptomycin-nonsusceptible enterococcal clinical isolates. In this study, we assessed the population genetics of daptomycin-nonsusceptible Enterococcus faecium (DNSE) clinical isolates by multilocus sequence typing (MLST) and whole-genome sequencing analysis. Forty-two nonduplicate DNSE isolates and 43 randomly selected daptomycin-susceptible E. faecium isolates were included in the analysis. All E. faecium isolates were recovered from patients at a tertiary care medical center in suburban New York City from May 2009 through December 2013. The daptomycin MICs of the DNSE isolates ranged from 6 to >256 µg/ml. Three major clones of E. faecium (ST18, ST412, and ST736) were identified among these clinical isolates by MLST and whole-genome sequence-based analysis. A newly recognized clone, ST736, was seen in 32 of 42 (76.2%) DNSE isolates and in only 14 of 43 (32.6%) daptomycin-susceptible E. faecium isolates (P < 0.0001). This report provides evidence of the association between E. faecium clone ST736 and daptomycin nonsusceptibility. The identification and potential spread of this novel E. faecium clone and its association with daptomycin nonsusceptibility constitute a challenge for patient management and infection control at our medical center.

A microRNA panel to discriminate carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissue.

OBJECTIVE: It is a challenge to differentiate invasive carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues. In this study, microRNA profiles were evaluated in the transformation of colorectal carcinogenesis to discover new molecular markers for identifying a carcinoma in colonoscopy biopsy tissues where the presence of stromal invasion cells is not detectable by microscopic analysis. METHODS: The expression of 723 human microRNAs was measured in laser capture microdissected epithelial tumours from 133 snap-frozen surgical colorectal specimens. Three well-known classification algorithms were used to derive candidate biomarkers for discriminating carcinomas from adenomas. Quantitative reverse-transcriptase PCR was then used to validate the candidates in an independent cohort of macrodissected formalin-fixed paraffin-embedded colorectal tissue samples from 91 surgical resections. The biomarkers were applied to differentiate carcinomas from high-grade intraepithelial neoplasms in 58 colonoscopy biopsy tissue samples with stromal invasion cells undetectable by microscopy. RESULTS: One classifier of 14 microRNAs was identified with a prediction accuracy of 94.1% for discriminating carcinomas from adenomas. In formalin-fixed paraffin-embedded surgical tissue samples, a combination of miR-375, miR-424 and miR-92a yielded an accuracy of 94% (AUC=0.968) in discriminating carcinomas from adenomas. This combination has been applied to differentiate carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues with an accuracy of 89% (AUC=0.918). CONCLUSIONS: This study has found a microRNA panel that accurately discriminates carcinomas from high-grade intraepithelial neoplasms in colonoscopy biopsy tissues. This microRNA panel has considerable clinical value in the early diagnosis and optimal surgical decision-making of colorectal cancer.

Differential Deep RNA Sequencing for Diagnostic Detection of Microbial Infections in Inflammatory Cardiomyopathy.

BACKGROUND: Inflammatory heart disease can be triggered by a variety of causes, both infectious and noninfectious in nature. We hypothesized that inflammatory cardiomyopathy is potentially related to microbial infection. METHODS: In this retrospective study, we used deep RNA sequencing on formalin-fixed paraffin-embedded heart tissue specimens to detect pathogenic agents. We first investigated 4 single-sample cases to test the feasibility of this diagnostic protocol and further 3 control-sample paired cases to improve the protocol with differential metatranscriptomics next-generation sequencing (mtNGS) analysis. RESULTS: We demonstrate that differential mtNGS allows identification of various microbials as potentially pathogenic, for example, Cutibacterium acnes, Corynebacterium aurimucosum, and Pseudomonas denitrificans, which are usually commensal in healthy individuals. Differential mtNGS also allows characterization of human host response in each individual by profiling alterations of gene expression, networked pathways, and inferred immune cell compositions, information of which is beneficial for us to understand different etiologies and immunity roles in each case. Additionally, differential mtNGS allows the identification of genetic variants in patients that may contribute to their susceptibility to particular microbial infections. CONCLUSIONS: The demonstrated power of differential mtNGS in simultaneous capture of both the infectious microbial(s) and the status of human host immune response could help us better understand the pathogenesis of complex inflammatory cardiomyopathy, if conducted on a larger scale of the population. The developed differential mtNGS method could also shed light on its translation and adoption of such a laboratory test in clinic practice, allowing for a more effective diagnosis to guide therapeutic treatment of the disease.

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.

Loss of DOK2 induces carboplatin resistance in ovarian cancer via suppression of apoptosis.

OBJECTIVE: Ovarian cancers are highly heterogeneous and while chemotherapy is the preferred treatment many patients are intrinsically resistant or quickly develop resistance. Furthermore, all tumors that recur ultimately become resistant. Recent evidence suggests that epigenetic deregulation may be a key factor in the onset and maintenance of chemoresistance. We set out to identify epigenetically silenced genes that affect chemoresistance. METHODS: The epigenomes of a total of 45 ovarian samples were analyzed to identify epigenetically altered genes that segregate with platinum response, and further filtered with expression data to identify genes that were suppressed. A tissue culture carboplatin resistance screen was utilized to functionally validate this set of candidate platinum resistance genes. RESULTS: Our screen correctly identified 19 genes that when suppressed altered the chemoresistance of the cells in culture. Of the genes identified in the screen we further characterized one gene, docking protein 2 (DOK2), an adapter protein downstream of tyrosine kinase, to determine if we could elucidate the mechanism by which it increased resistance. The loss of DOK2 decreased the level of apoptosis in response to carboplatin. Furthermore, in cells with reduced DOK2, the level of anoikis was decreased. CONCLUSIONS: We have developed a screening methodology that analyzes the epigenome and informatically identifies candidate genes followed by in vitro culture screening of the candidate genes. To validate our screening methodology we further characterized one candidate gene, DOK2, and showed that loss of DOK2 induces chemotherapy resistance by decreasing the level of apoptosis in response to treatment.

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.

Effective normalization for copy number variation detection from whole genome sequencing.

BACKGROUND: Whole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations. METHODS: We apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions. RESULTS: The GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls. CONCLUSIONS: Choice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Methylation detection oligonucleotide microarray analysis: a high-resolution method for detection of CpG island methylation.

Methylation of CpG islands associated with genes can affect the expression of the proximal gene, and methylation of non-associated CpG islands correlates to genomic instability. This epigenetic modification has been shown to be important in many pathologies, from development and disease to cancer. We report the development of a novel high-resolution microarray that detects the methylation status of over 25,000 CpG islands in the human genome. Experiments were performed to demonstrate low system noise in the methodology and that the array probes have a high signal to noise ratio. Methylation measurements between different cell lines were validated demonstrating the accuracy of measurement. We then identified alterations in CpG islands, both those associated with gene promoters, as well as non-promoter-associated islands in a set of breast and ovarian tumors. We demonstrate that this methodology accurately identifies methylation profiles in cancer and in principle it can differentiate any CpG methylation alterations and can be adapted to analyze other species.

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.

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.

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.