Endogenous UMIs as quantifiable reporter elements – validation studies & applications in rAAV vectorology

In the creation of recombinant adeno-associated viral (rAAV) vectors, terminal DNA elements known ITRs (inverted terminal repeats) of the direct the intracellular synthesis and packaging of nonviral DNA. The need to clonally amplify ITR sequences in one form or another thereby underlies the existence of all rAAV clinical products and research materials worldwide. Their tendency to form strong nonduplex structures raises problems. The genetic precursors to rAAV vectors – typically prokaryotic plasmids – are known to possess heterogenous ITR sequences as a result of replicational instability, the effects of which on vector yield and efficacy are unclear and have not been systematically explored. To shed much-needed light on this decades-old problem, I utilised unique molecular identifiers (UMIs) as reporter elements for different rAAV plasmid preparations, so that massively parallel sequencing could be used to analyse their DNA and RNA derivatives through the course of production and in vivo gene transfer. The range of vector potencies observed, while not calamitous, definitively erases the notion that this problem can be further overlooked. The success of this unconventional strategy proved to be an equally notable outcome, offering unprecedented insights into population kinetics, and achieving quantitative consistency between biological replicates comparable to q/dPCR measurement replicates of single samples. This triggered concerted efforts to formally investigate the capabilities of UMIs used in this fashion. The probabilistic principles underlying the technique were formalised and empirically validated, confirming precision capabilities akin if not superior to dPCR and qPCR at equivalent levels of stringency. Experiments also revealed a pattern of measurement bias with potentially adverse implications for other areas of count analysis including differential gene expression

AAV-p40 Bioengineering Platform for Variant Selection Based on Transgene Expression

The power of AAV directed evolution for identifying novel vector variants with improved properties is well established, as evidenced by numerous publications reporting novel AAV variants. However, most capsid variants reported to date have been identified using either replication-competent selection platforms or PCR-based capsid DNA recovery methods, which can bias the selection towards efficient replication or unproductive intracellular trafficking, respectively. A central objective of this study was to validate a functional transduction (FT)-based method for rapid identification of novel AAV variants based on AAV capsid mRNA expression in target cells. We performed a comparison of the FT platform to existing replication competent strategies. Based on the selection kinetics and function of novel capsids identified in an in vivo screen in a xenograft model of human hepatocytes, we identified the mRNA-based FT selection as the most optimal AAV selection method. Lastly, to gain insight into the mRNA-based selection mechanism driven by the native AAV-p40 promoter, we studied its activity in a range of in vitro and in vivo targets. We found AAV-p40 to be a ubiquitously active promoter that can be modified for cell type-specific expression by incorporating binding sites for silencing transcription factors, allowing for cell-type-specific library selection

Association between rheumatoid arthritis disease activity, progression of functional limitation and long-term risk of orthopaedic surgery : Combined analysis of two prospective cohorts supports EULAR treat to target DAS thresholds

Objectives: To examine the association between disease activity in early rheumatoid arthritis (RA), functional limitation and long-term orthopaedic episodes. Methods: Health Assessment Questionnaire (HAQ) disability scores were collected from two longitudinal early RA inception cohorts in routine care; Early Rheumatoid Arthritis Study and Early Rheumatoid Arthritis Network from 1986 to 2012. The incidence of major and intermediate orthopaedic surgical episodes over 25 years was collected from national data sets. Disease activity was categorised by mean disease activity score (DAS28) annually between years 1 and 5; remission (RDAS≤2.6), low (LDAS>2.6-3.2), low-moderate (LMDAS≥3.2-4.19), high-moderate (HMDAS 4.2-5.1) and high (HDAS>5.1). Results: Data from 2045 patients were analysed. Patients in RDAS showed no HAQ progression over 5 years, whereas there was a significant relationship between rising DAS28 category and HAQ at 1 year, and the rate of HAQ progression between years 1 and 5. During 27 986 person-years follow-up, 392 intermediate and 591 major surgeries were observed. Compared with the RDAS category, there was a significantly increased cumulative incidence of intermediate surgery in HDAS (OR 2.59 CI 1.49 to 4.52) and HMDAS (OR 1.8 CI 1.05 to 3.11) categories, and for major surgery in HDAS (OR 2.48 CI 1.5 to 4.11), HMDAS (OR 2.16 CI 1.32 to 3.52) and LMDAS (OR 2.07 CI 1.28 to 3.33) categories. There was no significant difference in HAQ progression or orthopaedic episodes between RDAS and LDAS categories. Conclusions: There is an association between disease activity and both poor function and long-term orthopaedic episodes. This illustrates the far from benign consequences of persistent moderate disease activity, and supports European League Against Rheumatism treat to target recommendations to secure low disease activity or remission in all patients.Peer reviewedFinal Published versio

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation