In the context of pancreatic cancer, metastasis remains the most critical determinant of resectability, and hence survival. The objective of this study was to determine whether Hedgehog (Hh) ...signaling plays a role in pancreatic cancer invasion and metastasis because this is likely to have profound clinical implications. In pancreatic cancer cell lines, Hh inhibition with cyclopamine resulted in down-regulation of snail and up-regulation of E-cadherin, consistent with inhibition of epithelial-to-mesenchymal transition, and was mirrored by a striking reduction of in vitro invasive capacity (P < 0.0001). Conversely, Gli1 overexpression in immortalized human pancreatic ductal epithelial cells led to a markedly invasive phenotype (P < 0.0001) and near total down-regulation of E-cadherin. In an orthotopic xenograft model, cyclopamine profoundly inhibited metastatic spread; only one of seven cyclopamine-treated mice developed pulmonary micrometastases versus seven of seven mice with multiple macrometastases in control animals. Combination of gemcitabine and cyclopamine completely abrogated metastases while also significantly reducing the size of "primary" tumors. Gli1 levels were up-regulated in tissue samples of metastatic human pancreatic cancer samples compared with matched primary tumors. Aldehyde dehydrogenase (ALDH) overexpression is characteristic for both hematopoietic progenitors and leukemic stem cells; cyclopamine preferentially reduced "ALDH-high" cells by approximately 3-fold (P = 0.048). We confirm pharmacologic Hh pathway inhibition as a valid therapeutic strategy for pancreatic cancer and show for the first time its particular efficacy against metastatic spread. By targeting specific cellular subpopulations likely involved in tumor initiation at metastatic sites, Hh inhibitors may provide a new paradigm for therapy of disseminated malignancies, particularly when used in combination with conventional antimetabolites that reduce "bulk" tumor size.
Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers ...and false positives.
To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms.
In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016.
Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated.
Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity.
While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.
Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be used to annotate the biomedical ...literature. Statistical learning approaches have produced good results, but the size of the UMLS makes the production of training data infeasible to cover all the domain.
We present research on existing WSD approaches based on knowledge bases, which complement the studies performed on statistical learning. We compare four approaches which rely on the UMLS Metathesaurus as the source of knowledge. The first approach compares the overlap of the context of the ambiguous word to the candidate senses based on a representation built out of the definitions, synonyms and related terms. The second approach collects training data for each of the candidate senses to perform WSD based on queries built using monosemous synonyms and related terms. These queries are used to retrieve MEDLINE citations. Then, a machine learning approach is trained on this corpus. The third approach is a graph-based method which exploits the structure of the Metathesaurus network of relations to perform unsupervised WSD. This approach ranks nodes in the graph according to their relative structural importance. The last approach uses the semantic types assigned to the concepts in the Metathesaurus to perform WSD. The context of the ambiguous word and semantic types of the candidate concepts are mapped to Journal Descriptors. These mappings are compared to decide among the candidate concepts. Results are provided estimating accuracy of the different methods on the WSD test collection available from the NLM.
We have found that the last approach achieves better results compared to the other methods. The graph-based approach, using the structure of the Metathesaurus network to estimate the relevance of the Metathesaurus concepts, does not perform well compared to the first two methods. In addition, the combination of methods improves the performance over the individual approaches. On the other hand, the performance is still below statistical learning trained on manually produced data and below the maximum frequency sense baseline. Finally, we propose several directions to improve the existing methods and to improve the Metathesaurus to be more effective in WSD.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Objective Moderate‐to‐vigorous physical activity (MVPA) improves glucose levels; however, whether its timing affects daily glycemic control remains unclear. This study aims to investigate ...the impact of lifestyle MVPA timing on daily glycemic control in sedentary adults with overweight/obesity and metabolic impairments. Methods A total of 186 adults (50% women; age, 46.8 SD 6.2 years) with overweight/obesity (BMI, 32.9 SD 3.5 kg/m 2 ) and at least one metabolic impairment participated in this cross‐sectional study. MVPA and glucose patterns were simultaneously monitored over a 14‐day period using a triaxial accelerometer worn on the nondominant wrist and a continuous glucose‐monitoring device, respectively. Each day was classified as “inactive” if no MVPA was accumulated; as “morning,” “afternoon,” or “evening” if >50% of the MVPA minutes for that day were accumulated between 0600 and 1200, 1200 and 1800, or 1800 and 0000 hours, respectively; or as “mixed” if none of the defined time windows accounted for >50% of the MVPA for that day. Results Accumulating >50% of total MVPA during the evening was associated with lower 24‐h (mean difference 95% CI, −1.26 mg/dL 95% CI: −2.2 to −0.4), diurnal (−1.10 mg/dL 95% CI: −2.0 to −0.2), and nocturnal mean glucose levels (−2.16 mg/dL 95% CI: −3.5 to −0.8) compared with being inactive. This association was stronger in those participants with impaired glucose regulation. The pattern of these associations was similar in both men and women. Conclusions These findings suggest that timing of lifestyle MVPA is significant. Specifically, accumulating more MVPA during the evening appears to have a beneficial effect on glucose homeostasis in sedentary adults with overweight/obesity and metabolic impairments.
Motivation: Text-mining (TM) solutions are developing into efficient services to researchers in the biomedical research community. Such solutions have to scale with the growing number and size of ...resources (e.g. available controlled vocabularies), with the amount of literature to be processed (e.g. about 17 million documents in PubMed) and with the demands of the user community (e.g. different methods for fact extraction). These demands motivated the development of a server-based solution for literature analysis. Whatizit is a suite of modules that analyse text for contained information, e.g. any scientific publication or Medline abstracts. Special modules identify terms and then link them to the corresponding entries in bioinformatics databases such as UniProtKb/Swiss-Prot data entries and gene ontology concepts. Other modules identify a set of selected annotation types like the set produced by the EBIMed analysis pipeline for proteins. In the case of Medline abstracts, Whatizit offers access to EBI's in-house installation via PMID or term query. For large quantities of the user's own text, the server can be operated in a streaming mode (http://www.ebi.ac.uk/webservices/whatizit). Contact: rebholz@ebi.ac.uk
Recognizing the layout of unstructured digital documents is an important step when parsing the documents into structured machine-readable format for downstream applications. Deep neural networks that ...are developed for computer vision have been proven to be an effective method to analyze layout of document images. However, document layout datasets that are currently publicly available are several magnitudes smaller than established computing vision datasets. Models have to be trained by transfer learning from a base model that is pre-trained on a traditional computer vision dataset. In this paper, we develop the PubLayNet dataset for document layout analysis by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central. The size of the dataset is comparable to established computer vision datasets, containing over 360 thousand document images, where typical document layout elements are annotated. The experiments demonstrate that deep neural networks trained on PubLayNet accurately recognize the layout of scientific articles. The pre-trained models are also a more effective base mode for transfer learning on a different document domain. We release the dataset (https://github.com/ibm-aur-nlp/PubLayNet) to support development and evaluation of more advanced models for document layout analysis.
In the normal human colon, aldehyde dehydrogenase 1B1 (ALDH1B1) is expressed only at the crypt base, along with stem cells. It is also highly expressed in the human colonic adenocarcinomas. This ...pattern of expression corresponds closely to that observed for Wnt/β-catenin signaling activity. The present study examines the role of ALDH1B1 in colon tumorigenesis and signalling pathways mediating its effects. In a 3-dimensional spheroid growth model and a nude mouse xenograft tumor model, shRNA-induced suppression of ALDH1B1 expression decreased the number and size of spheroids formed in vitro and the size of xenograft tumors formed in vivo by SW 480 cells. Six binding elements for Wnt/β-catenin signalling transcription factor binding elements (T-cell factor/lymphoid enhancing factor) were identified in the human ALDH1B1 gene promoter (3 kb) but shown by dual luciferase reporter assay to not be necessary for ALDH1B1 mRNA expression in colon adenocarcinoma cell lines. We examined Wnt-reporter activity and protein/mRNA expression for Wnt, Notch and PI3K/Akt signaling pathways. Wnt/β-catenin, Notch and PI3K/Akt-signaling pathways were down-regulated in SW 480 cells in which ALDH1B1 expression had been suppressed. In summary, our data demonstrate that ALDH1B1 may promote colon cancer tumorigenesis by modulating the Wnt/β-catenin, Notch and PI3K/Akt signaling pathways. Selective targeting of ALDH1B1 may represent a novel means to prevent or treat colon cancer.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We have an incomplete understanding of the differences between cancer stem cells (CSCs) in human papillomavirus-positive (HPV-positive) and -negative (HPV-negative) head and neck squamous cell cancer ...(HNSCC). The PI3K pathway has the most frequent activating genetic events in HNSCC (especially HPV-positive driven), but the differential signaling between CSCs and non-CSCs is also unknown.
We addressed these unresolved questions using CSCs identified from 10 HNSCC patient-derived xenografts (PDXs). Sored populations were serially passaged in nude mice to evaluate tumorigenicity and tumor recapitulation. The transcription profile of HNSCC CSCs was characterized by mRNA sequencing, and the susceptibility of CSCs to therapy was investigated using an in vivo model. SOX2 transcriptional activity was used to follow the asymmetric division of PDX-derived CSCs. All statistical tests were two-sided.
CSCs were enriched by high aldehyde dehydrogenase (ALDH) activity and CD44 expression and were similar between HPV-positive and HPV-negative cases (percent tumor formation injecting ≤ 1x10(3) cells: ALDH(+)CD44(high) = 65.8%, ALDH(-)CD44(high) = 33.1%, ALDH(+)CD44(high) = 20.0%; and injecting 1x10(5) cells: ALDH(-)CD44(low) = 4.4%). CSCs were resistant to conventional therapy and had PI3K/mTOR pathway overexpression (GSEA pathway enrichment, P < .001), and PI3K inhibition in vivo decreased their tumorigenicity (40.0%-100.0% across cases). PI3K/mTOR directly regulated SOX2 protein levels, and SOX2 in turn activated ALDH1A1 (P < .001 013C and 067C) expression and ALDH activity (ALDH(+) % empty-control vs SOX2, 0.4% ± 0.4% vs 14.5% ± 9.8%, P = .03 for 013C and 1.7% ± 1.3% vs 3.6% ± 3.4%, P = .04 for 067C) in 013C and 067 cells. SOX2 enhanced sphere and tumor growth (spheres/well, 013C P < .001 and 067C P = .04) and therapy resistance. SOX2 expression prompted mesenchymal-to-epithelial transition (MET) by inducing CDH1 (013C P = .002, 067C P = .01), followed by asymmetric division and proliferation, which contributed to tumor formation.
The molecular link between PI3K activation and CSC properties found in this study provides insights into therapeutic strategies for HNSCC. Constitutive expression of SOX2 in HNSCC cells generates a CSC-like population that enables CSC studies.
Recent advances in the understanding of prostate cancer biology and its progression to bone metastasis have led to the development
of drugs directed against precise molecular alterations in the ...prostate tumor cell and host cells in the normal bone environment
such as osteoclasts and osteoblasts. Endothelins (ETs) and their receptors have emerged as a potential target in prostate
cancer bone metastasis. By activating the ET A receptor, ET-1 is pathogenically involved in facilitating several aspects of prostate cancer progression, including proliferation,
escape from apoptosis, invasion, and new bone formation, processes that are general to many malignancies. Notwithstanding,
there are a number of features specifically driven by the ET axis in prostate cancer, such as creating and perpetuating a
unique interaction between the metastatic prostate cancer cell and the bone microenvironment (osteoblast, osteoclast, and
stroma) or altering the equilibrium in pain modulation. These features have led to the preferential clinical evaluation of
atrasentan (ABT-627) as a biological therapy in prostate carcinoma, first in hormone-refractory prostate cancer. Biological
activity of atrasentan in patients with prostate cancer has been shown by the suppression of biochemical markers of prostate
cancer progression in bone, and clinical activity is evidenced by a consistent trend demonstrating a delay in time to disease
progression when compared with placebo, especially in patients with bone metastases. Further studies of atrasentan and other
selective ET-1 antagonists (ZD4054) are ongoing.
Abstract
Motivation
Structured semantic resources, for example, biological knowledge bases and ontologies, formally define biological concepts, entities and their semantic relationships, manifested ...as structured axioms and unstructured texts (e.g. textual definitions). The resources contain accurate expressions of biological reality and have been used by machine-learning models to assist intelligent applications like knowledge discovery. The current methods use both the axioms and definitions as plain texts in representation learning (RL). However, since the axioms are machine-readable while the natural language is human-understandable, difference in meaning of token and structure impedes the representations to encode desirable biological knowledge.
Results
We propose ERBK, a RL model of bio-entities. Instead of using the axioms and definitions as a textual corpus, our method uses knowledge graph embedding method and deep convolutional neural models to encode the axioms and definitions respectively. The representations could not only encode more underlying biological knowledge but also be further applied to zero-shot circumstance where existing approaches fall short. Experimental evaluations show that ERBK outperforms the existing methods for predicting protein–protein interactions and gene–disease associations. Moreover, it shows that ERBK still maintains promising performance under the zero-shot circumstance. We believe the representations and the method have certain generality and could extend to other types of bio-relation.
Availability and implementation
The source code is available at the gitlab repository https://gitlab.com/BioAI/erbk.
Supplementary information
Supplementary data are available at Bioinformatics online.
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