Attitude as a Measure for Acceptance: Monitoring IS Implementation in a Hospital Setting

The aim of this study was to assess whether Attitude Toward Technology (ATT) is a better measure of technology acceptance than Behavioral Intention (BI) in a mandatory medical setting. A questionnaire was taken in two hospitals, one university (Setting 1) and one private (Setting 2). The technology studied was PACS (Picture Archiving and Communication System). The questionnaire was taken on several occasions: pre-implementation (T1, both Settings); three months post-implementation (T2, S2); and one year after the transition was completed (T3, S1; S2 is underway). Four models were assessed: (1a) original TAM with ATT, (1b) TAM with BI replacing ATT, (2a) UTAUT, and (2b) UTAUT with ATT replacing BI. Our preliminary results indicate that ATT is indeed a better measure for acceptance than BI. Variance explained in ATT ranged from .47 to .72, in BI from .12 to .45. BI was the best predictor of USE

Assessing hospital physicians' acceptance of clinical information systems : a review of the relevant literature

In view, of the tremendous potential benefits of clinical information systems (CIS) for the quality of patient care; it is hard to understand why not every CIS is embraced by its targeted users, the physicians. The aim of this study is to propose a framework for assessing hospital physicians' CIS-acceptance that can serve as a guidance for future research into this area. Hereto, a review of the relevant literature was performed in the ISI Web-of-Science database. Eleven studies were withheld from an initial dataset of 797 articles. Results show that just as in business settings, there are four core groups of variables that influence physicians' acceptance of a CIS: its usefulness and ease of use social norms, and factors in the working environment that facilitate use of the CIS (such as providing computers/workstations, compatibility between the new and existing system...). We also identified some additional variables as predictors of CIS-acceptance

Exome and whole genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity

The incidence of esophageal adenocarcinoma (EAC) has risen 600% over the last 30 years. With a five-year survival rate of 15%, identification of new therapeutic targets for EAC is greatly important. We analyze the mutation spectra from whole exome sequencing of 149 EAC tumors/normal pairs, 15 of which have also been subjected to whole genome sequencing. We identify a mutational signature defined by a high prevalence of A to C transversions at AA dinucleotides. Statistical analysis of exome data identified significantly mutated 26 genes. Of these genes, four (TP53, CDKN2A, SMAD4, and PIK3CA) have been previously implicated in EAC. The novel significantly mutated genes include chromatin modifying factors and candidate contributors: SPG20, TLR4, ELMO1, and DOCK2. Functional analyses of EAC-derived mutations in ELMO1 reveal increased cellular invasion. Therefore, we suggest a new hypothesis about the potential activation of the RAC1 pathway to be a contributor to EAC tumorigenesis

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