Developing a Tool to Assess Administrative Evidence-Based Practices in Local Health Departments

There is need for assessing the practices undertaken by local health departments in order to improve the implementation of evidence-based actions. This paper describes the development and testing of a survey instrument for assessing Administrative Evidence-Based Practices (A-EBPs) in Local Health Departments. A-EBPs identified through a review of the literature were used to develop a survey composed of nine sections and tested in a sample of local health department practitioners. The resulting tool showed adequate test-retest reliability and internal consistency. Practitioners and researchers may apply this tool in practice-based and evaluation research

Going Beyond Rote Auditory Learning: Neural Patterns of Generalized Auditory Learning

The ability to generalize across specific experiences is vital for the recognition of new patterns, especially in speech perception considering acoustic–phonetic pattern variability. Indeed, behavioral research has demonstrated that listeners are able via a process of generalized learning to leverage their experiences of past words said by difficult-to-understand talker to improve their understanding for new words said by that talker. Here, we examine differences in neural responses to generalized versus rote learning in auditory cortical processing by training listeners to understand a novel synthetic talker. Using a pretest–posttest design with EEG, participants were trained using either (1) a large inventory of words where no words were repeated across the experiment (generalized learning) or (2) a small inventory of words where words were repeated (rote learning). Analysis of long-latency auditory evoked potentials at pretest and posttest revealed that rote and generalized learning both produced rapid changes in auditory processing, yet the nature of these changes differed. Generalized learning was marked by an amplitude reduction in the N1–P2 complex and by the presence of a late negativity wave in the auditory evoked potential following training; rote learning was marked only by temporally later scalp topography differences. The early N1–P2 change, found only for generalized learning, is consistent with an active processing account of speech perception, which proposes that the ability to rapidly adjust to the specific vocal characteristics of a new talker (for which rote learning is rare) relies on attentional mechanisms to selectively modify early auditory processing sensitivity

What Influences the Use of Administrative Evidence-Based Practices in Local Health Departments?

Evidence based public health (EBPH) in local health departments (LHDs) is a process that involves translating the best available scientific evidence into practice. However, EBPH and implementation of evidence based programs and policies in LHDs are not widespread. This report outlines the patterns and predictors of the use of administrative evidence based practices (A-EBPs) in a national sample of LHD directors. LHDs can improve performance, prepare for accreditation and ultimately improve community health by utilizing an administrative evidence based process

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology

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

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

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

The molecular basis of breast cancer pathological phenotypes

The histopathological evaluation of morphological features in breast tumours provides prognostic information to guide therapy. Adjunct molecular analyses provide further diagnostic, prognostic and predictive information. However, there is limited knowledge of the molecular basis of morphological phenotypes in invasive breast cancer. This study integrated genomic, transcriptomic and protein data to provide a comprehensive molecular profiling of morphological features in breast cancer. Fifteen pathologists assessed 850 invasive breast cancer cases from The Cancer Genome Atlas (TCGA). Morphological features were significantly associated with genomic alteration, DNA methylation subtype, PAM50 and microRNA subtypes, proliferation scores, gene expression and/or RPPA subtype. Marked nuclear pleomorphism, necrosis, inflammation and high mitotic count were associated with Basal-like subtype and have similar molecular basis. Omics-based signatures were constructed to predict morphological features. The association of morphology transcriptome signatures with overall survival in oestrogen receptor (ER)-positive and ER-negative breast cancer was first assessed using the METABRIC dataset; signatures that remained prognostic in the METABRIC multivariate analysis were further evaluated in five additional datasets. The transcriptomic signature of epithelial tubule formation was prognostic in ER-positive breast cancer. No signature was prognostic in ER-negative. This study provided new insights into the molecular basis of breast cancer morphological phenotypes. The integration of morphological with molecular data has potential to refine breast cancer classification, predict response to therapy, enhance our understanding of breast cancer biology and improve clinical management. This work is publicly accessible at www.dx.ai/tcga_breast

An examination of polygenic score risk prediction in individuals with first-episode psychosis

Background Polygenic risk scores (PRSs) have successfully summarized genome-wide effects of genetic variants in schizophrenia with significant predictive power. In a clinical sample of first-episode psychosis (FEP) patients, we estimated the ability of PRSs to discriminate case-control status and to predict the development of schizophrenia as opposed to other psychoses. Methods The sample (445 case and 265 control subjects) was genotyped on the Illumina HumanCore Exome BeadChip with an additional 828 control subjects of African ancestry genotyped on the Illumina Multi-Ethnic Genotyping Array. To calculate PRSs, we used the results from the latest Psychiatric Genomics Consortium schizophrenia meta-analysis. We examined the association of PRSs with case-control status and with schizophrenia versus other psychoses in European and African ancestry FEP patients and in a second sample of 248 case subjects with chronic psychosis. Results PRS had good discriminative ability of case-control status in FEP European ancestry individuals (9.4% of the variance explained, p < 10−6), but lower in individuals of African ancestry (R2 = 1.1%, p = .004). Furthermore, PRS distinguished European ancestry case subjects who went on to acquire a schizophrenia diagnosis from those who developed other psychotic disorders (R2 = 9.2%, p = .002). Conclusions PRS was a powerful predictor of case-control status in a European sample of patients with FEP, even though a large proportion did not have an established diagnosis of schizophrenia at the time of assessment. PRS was significantly different between those case subjects who developed schizophrenia from those who did not, although the discriminative accuracy may not yet be sufficient for clinical utility in FEP