Wide range of phenotypic severity in individuals with late truncations unique to the predominant CDKL5 transcript in the brain.

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD) is caused by heterozygous or hemizygous variants in CDKL5 and is characterized by refractory epilepsy, cognitive and motor impairments, and cerebral visual impairment. CDKL5 has multiple transcripts, of which the longest transcripts, NM_003159 and NM_001037343, have been used historically in clinical laboratory testing. However, the transcript NM_001323289 is the most highly expressed in brain and contains 170 nucleotides at the 3' end of its last exon that are noncoding in other transcripts. Two truncating variants in this region have been reported in association with a CDD phenotype. To clarify the significance and range of phenotypes associated with late truncating variants in this region of the predominant transcript in the brain, we report detailed information on two individuals, updated clinical information on a third individual, and a summary of published and unpublished individuals reported in ClinVar. The two new individuals (one male and one female) each had a relatively mild clinical presentation including periods of pharmaco-responsive epilepsy, independent walking and limited purposeful communication skills. A previously reported male continued to have a severe phenotype. Overall, variants in this region demonstrate a range of clinical severity consistent with reports in CDD but with the potential for milder presentation.

Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis.

Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichment-based methods to, for example, organize biological data of this sort into collections of dysregulated pathways. However, such methods arguably are still limited to primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental roles. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on (i) metastatic cancer progression, (ii) drug treatment in human lung carcinoma cells, and (iii) diagnosis of type 2 diabetes. With these analyses, we show that LPIA can successfully identify pathways whose perturbations have latent influences on the transcriptionally altered genes.

Tailoring implementation strategies for scale-up: Preparing to take the Med-South Lifestyle program to scale statewide.

Background: Tailoring implementation strategies for scale-up involves engaging stakeholders, identifying implementation determinants, and designing implementation strategies to target those determinants. The purpose of this paper is to describe the multiphase process used to engage stakeholders in tailoring strategies to scale-up the Med-South Lifestyle Program, a research-supported lifestyle behavior change intervention that translates the Mediterranean dietary pattern for the southeastern US. Methods: Guided by Barker et al. framework, we tailored scale-up strategies over four-phases. In Phase 1, we engaged stakeholders from delivery systems that implement lifestyle interventions and from support systems that provide training and other support for statewide scale-up. In Phase 2, we partnered with delivery systems (community health centers and health departments) to design and pilot test implementation strategies (2014-2019). In Phase 3, we partnered with both delivery and support systems to tailor Phase 2 strategies for scale-up (2019-2021) and are now testing those tailored strategies in a type 3 hybrid study (2021-2023). This paper reports on the Phase 3 methods used to tailor implementation strategies for scale-up. To identify determinants of scale-up, we surveyed North Carolina delivery systems (n = 114 community health centers and health departments) and elicited input from delivery and support system stakeholders. We tailored strategies to address identified determinants by adapting the form of Phase 2 strategies while retaining their functions. We pilot tested strategies in three sites and collected data on intermediate, implementation, and effectiveness outcomes. Findings: Determinants of scale-up included limited staffing, competing priorities, and safety concerns during COVID-19, among others. Tailoring yielded two levels of implementation strategies. At the level of the delivery system, strategies included implementation teams, an implementation blueprint, and cyclical small tests of change. At the level of the support system, strategies included training, educational materials, quality monitoring, and technical assistance. Findings from the pilot study provide evidence for the implementation strategies' reach, acceptability, and feasibility, with mixed findings on fidelity. Strategies were only moderately successful at building delivery system capacity to implement Med-South. Conclusions: This paper describes the multiphase approach used to plan for Med-South scale-up, including the methods used to tailor two-levels of implementation strategies by identifying and targeting multilevel determinants.

Development of an Electronic Health Record Registry to Facilitate Collection of Commission on Cancer Metrics for Patients Undergoing Surgery for Breast Cancer.

PURPOSE: Accurate and efficient data collection is a challenge for quality improvement initiatives and clinical research. We describe the development of a custom electronic health record (EHR)-based registry to automatically extract structured Commission on Cancer axillary surgery-specific metrics from a custom synoptic note template included in the operative reports for patients with breast cancer undergoing surgery. METHODS: The smart functionality of our enterprise-based EHR system was leveraged to create a custom smart phrase to capture axillary surgery-specific variables. A multidisciplinary team developed structured data elements correlating to each axillary surgery-specific variable. These data elements were then included in a note template for the operative report. Each variable could be aggregated and converted into a single flat database through the EHR's reporting workbench and serve as a live, prospective registry for all users within the EHR. RESULTS: The final axillary surgery-specific note template in a synoptic format allowed for efficient and easy entry and automatic collection of breast cancer-specific metrics. From initial adoption in February 2021-December 2021, there were 1,254 patients who underwent breast surgery with axillary surgery. The operative notes allowed for automatic capture of metrics from 60.5% (n = 759) of patients. Data capture improved from 37.6% in the initial adoption period of 6 months to 86.2% in the last 5 months. CONCLUSION: We were able to demonstrate successful implementation of provider-driven structured data entry into EHR systems that permits automatic data capture. The end result is a custom synoptic note template and a real-time, prospective registry of breast cancer-specific Commission on Cancer metrics that are robust enough to use for quality improvement initiatives and clinical research.

Challenges of creating a one-size-fits-all oncology CPOE application.

Since August 1996, the oncology clinicians at Massachusetts General Hospital (MGH) have been using an in-house developed CPOE system to order chemotherapy for their adult population. While the pediatric chemotherapy ordering remained on paper until November 2003, designing the pediatric chemotherapy functionality into the existing CPOE product proved to be very challenging. It was helpful in one sense because there was an existing framework that was familiar to everyone. It was also restrictive, in the sense that it is hard to enhance it without impacting others already using the same system.

Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach.

Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases.

Peripheral Blood Transcriptomic Signatures of Fasting Glucose and Insulin Concentrations.

Genome-wide association studies (GWAS) have successfully identified genetic loci associated with glycemic traits. However, characterizing the functional significance of these loci has proven challenging. We sought to gain insights into the regulation of fasting insulin and fasting glucose through the use of gene expression microarray data from peripheral blood samples of participants without diabetes in the Framingham Heart Study (FHS) (n = 5,056), the Rotterdam Study (RS) (n = 723), and the InCHIANTI Study (Invecchiare in Chianti) (n = 595). Using a false discovery rate q <0.05, we identified three transcripts associated with fasting glucose and 433 transcripts associated with fasting insulin levels after adjusting for age, sex, technical covariates, and complete blood cell counts. Among the findings, circulating IGF2BP2 transcript levels were positively associated with fasting insulin in both the FHS and RS. Using 1000 Genomes-imputed genotype data, we identified 47,587 cis-expression quantitative trait loci (eQTL) and 6,695 trans-eQTL associated with the 433 significant insulin-associated transcripts. Of note, we identified a trans-eQTL (rs592423), where the A allele was associated with higher IGF2BP2 levels and with fasting insulin in an independent genetic meta-analysis comprised of 50,823 individuals. We conclude that integration of genomic and transcriptomic data implicate circulating IGF2BP2 mRNA levels associated with glucose and insulin homeostasis.

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets.

BACKGROUND: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. RESULTS: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug's transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions--exposure time and concentration and (ii) Network training conditions--training compendium modifications. Two analyses of SSEM-Lasso output--gene set and single gene--were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. CONCLUSIONS: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.

Dissection of immune gene networks in primary melanoma tumors critical for antitumor surveillance of patients with stage II-III resectable disease.

Patients with resected stage II-III cutaneous melanomas remain at high risk for metastasis and death. Biomarker development has been limited by the challenge of isolating high-quality RNA for transcriptome-wide profiling from formalin-fixed and paraffin-embedded (FFPE) primary tumor specimens. Using NanoString technology, RNA from 40 stage II-III FFPE primary melanomas was analyzed and a 53-immune-gene panel predictive of non-progression (area under the curve (AUC)=0.920) was defined. The signature predicted disease-specific survival (DSS P<0.001) and recurrence-free survival (RFS P<0.001). CD2, the most differentially expressed gene in the training set, also predicted non-progression (P<0.001). Using publicly available microarray data from 46 primary human melanomas (GSE15605), a coexpression module enriched for the 53-gene panel was then identified using unbiased methods. A Bayesian network of signaling pathways based on this data identified driver genes. Finally, the proposed 53-gene panel was confirmed in an independent test population of 48 patients (AUC=0.787). The gene signature was an independent predictor of non-progression (P<0.001), RFS (P<0.001), and DSS (P=0.024) in the test population. The identified driver genes are potential therapeutic targets, and the 53-gene panel should be tested for clinical application using a larger data set annotated on the basis of prospectively gathered data.