Roles of cofactors and chromatin accessibility in Hox protein target specificity.

BACKGROUND: The regulation of specific target genes by transcription factors is central to our understanding of gene network control in developmental and physiological processes yet how target specificity is achieved is still poorly understood. This is well illustrated by the Hox family of transcription factors as their limited in vitro DNA-binding specificity contrasts with their clear in vivo functional specificity. RESULTS: We generated genome-wide binding profiles for three Hox proteins, Ubx, Abd-A and Abd-B, following transient expression in Drosophila Kc167 cells, revealing clear target specificity and a striking influence of chromatin accessibility. In the absence of the TALE class homeodomain cofactors Exd and Hth, Ubx and Abd-A bind at a very similar set of target sites in accessible chromatin, whereas Abd-B binds at an additional specific set of targets. Provision of Hox cofactors Exd and Hth considerably modifies the Ubx genome-wide binding profile enabling Ubx to bind at an additional novel set of targets. Both the Abd-B specific targets and the cofactor-dependent Ubx targets are in chromatin that is relatively DNase1 inaccessible prior to the expression of Hox proteins/Hox cofactors. CONCLUSIONS: Our experiments demonstrate a strong role for chromatin accessibility in Hox protein binding and suggest that Hox protein competition with nucleosomes has a major role in Hox protein target specificity in vivo.This work was supported by University of Malaya High Impact Research Grant UM-HIR UM.C/625/HIR/MOHE/CHAN-08 from the Ministry of Higher Education Malaysia.This is the final version of the article. It was first available from BioMed Central via http://dx.doi.org/10.1186/s13072-015-0049-

PDX Finder: A portal for patient-derived tumor xenograft model discovery.

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients\u27 tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations. The distributed nature of PDX repositories and the use of different metadata standards for describing model characteristics presents a significant challenge to identifying PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for 1985 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the \u27findability\u27 of their models by participating in the PDX Finder initiative at www.pdxfinder.org

Patterns of genomic change in residual disease after neoadjuvant chemotherapy for estrogen receptor-positive and HER2-negative breast cancer

Background: Treatment of patients with residual disease after neoadjuvant chemotherapy for breast cancer is an unmet clinical need. We hypothesised that tumour subclones showing expansion in residual disease after chemotherapy would contain mutations conferring drug resistance. Methods: We studied oestrogen receptor and/or progesterone receptor-positive, HER2-negative tumours from 42 patients in the EORTC 10994/BIG 00-01 trial who failed to achieve a pathological complete response. Genes commonly mutated in breast cancer were sequenced in pre and post-treatment samples. Results: Oncogenic driver mutations were commonest in PIK3CA (38% of tumours), GATA3 (29%), CDH1 (17%), TP53 (17%) and CBFB (12%); and amplification was commonest for CCND1 (26% of tumours) and FGFR1 (26%). The variant allele fraction frequently changed after treatment, indicating that subclones had expanded and contracted, but there were changes in both directions for all of the commonly mutated genes. Conclusions: We found no evidence that expansion of clones containing recurrent oncogenic driver mutations is responsible for resistance to neoadjuvant chemotherapy. The persistence of classic oncogenic mutations in pathways for which targeted therapies are now available highlights their importance as drug targets in patients who have failed chemotherapy but provides no support for a direct role of driver oncogenes in resistance to chemotherapy. ClinicalTrials.gov: EORTC 10994/BIG 1-00 Trial registration number NCT00017095.SCOPUS: ar.jDecretOANoAutActifinfo:eu-repo/semantics/publishe

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Genomic patterns of malignant peripheral nerve sheath tumor (MPNST) evolution correlate with clinical outcome and are detectable in cell-free DNA

Malignant peripheral nerve sheath tumor (MPNST), an aggressive soft-tissue sarcoma, occurs in people with neurofibromatosis type 1 (NF1) and sporadically. Whole-genome and multiregional exome sequencing, transcriptomic, and methylation profiling of 95 tumor samples revealed the order of genomic events in tumor evolution. Following biallelic inactivation of NF1, loss of CDKN2A or TP53 with or without inactivation of polycomb repressive complex 2 (PRC2) leads to extensive somatic copy-number aberrations (SCNA). Distinct pathways of tumor evolution are associated with inactivation of PRC2 genes and H3K27 trimethylation (H3K27me3) status. Tumors with H3K27me3 loss evolve through extensive chromosomal losses followed by whole-genome doubling and chromosome 8 amplification, and show lower levels of immune cell infiltration. Retention of H3K27me3 leads to extensive genomic instability, but an immune cell-rich phenotype. Specific SCNAs detected in both tumor samples and cell-free DNA (cfDNA) act as a surrogate for H3K27me3 loss and immune infiltration, and predict prognosis

Bayesian networks elucidate complex genomic landscapes in cancer.

Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be constructed from observed data and can provide a guiding, graphical tool in exploring such relations. Here we propose BNs for elucidating the relations between driver events in large cancer genomic datasets. We present a methodology that is specifically tailored to biologists and clinicians as they are the main producers of such datasets. We achieve this by using an optimal BN learning algorithm based on well established likelihood functions and by utilising just two tuning parameters, both of which are easy to set and have intuitive readings. To enhance value to clinicians, we introduce (a) the use of heatmaps for families in each network, and (b) visualising pairwise co-occurrence statistics on the network. For binary data, an optional step of fitting logic gates can be employed. We show how our methodology enhances pairwise testing and how biologists and clinicians can use BNs for discussing the main relations among driver events in large genomic cohorts. We demonstrate the utility of our methodology by applying it to 5 cancer datasets revealing complex genomic landscapes. Our networks identify central patterns in all datasets including a central 4-way mutual exclusivity between HDR, t(4,14), t(11,14) and t(14,16) in myeloma, and a 3-way mutual exclusivity of three major players: CALR, JAK2 and MPL, in myeloproliferative neoplasms. These analyses demonstrate that our methodology can play a central role in the study of large genomic cancer datasets

Coping Skills Used by Nurses After the Death of a Pediatric Patient

abstract: As the complexity and severity of hospitalized patients increase, nurses working in an acute care setting will experience patient deaths. From novice to expert, nurses may utilize a range of coping strategies. When the patient is a pediatric patient, the coping strategies become critical. The purpose of this study is to explore the coping strategies used by novice and expert nurses when a pediatric patient dies. The second objective is to compare the coping strategies used by novice and expert nurses. The final objective is to determine if nurses feel nursing school and employee training prepared them for the death of a pediatric patient. Research has shown that nurses use many different coping strategies when faced with a patient's death (Abdullah, 2015; Kellogg, Baker, & McCune, 2014; Plante & Cry, 2011). Expert nurses who have years of experience should have more options for coping strategies than novice nurses, yet there is little evidence to support this. This qualitative descriptive study used structured in-depth interviews to explore the coping strategies of pediatric nurses when experiencing a patient's death. Using thematic analysis, transcripts of the interviews were coded such that themes emerged. Themes for novice nurses were compared to expert nurses. These themes were also placed into concepts that encompassed many similar themes. The findings help determine that there is a difference in the coping mechanisms used by novice and expert nurses, and there is a need for more education on coping strategies after the death of a pediatric patient

STABILITY OF ACTIONABLE MUTATIONS IN PRIMARY AND RECURRENT GLIOBLASTOMAS

Neurolog