Effective development of targeted anticancer agents includes the definition of the optimal biological dose and biomarkers
of drug activity. Currently available preclinical models are not optimal to ...this end. We aimed at generating a model for translational
drug development using pancreatic cancer as a prototype. Resected pancreatic cancers from 14 patients were xenografted and
expanded in successive groups of nude mice to develop cohorts of tumor-bearing mice suitable for drug therapy in simulated
early clinical trials. The xenografted tumors maintain their fundamental genotypic features despite serial passages and recapitulate
the genetic heterogeneity of pancreatic cancer. The in vivo platform is useful for integrating drug screening with biomarker discovery. Passages of tumors in successive cohorts of mice
do not change their susceptibility to anticancer agents and represent a perpetual live bank, facilitating the application
of new technologies that will result in the creation of an integrated stable database of tumor-drug response data and biomarkers.
The rapidly expanding knowledge of the pathogenesis of pancreatic cancer at the molecular level is providing new targets for
disease characterization, early diagnosis, and drug discovery and ...development. Gene mutation analysis has provided insight
on the pathogenesis and progression from preinvasive lesions to invasive cancer. Gene and protein expression profiling has
advanced our understanding of pancreatic ductal adenocarcinoma identifying genes that are highly expressed in pancreatic cancers,
providing more insight into the clinicopathologic features of pancreatic cancer, and revealing novel features related to the
process of tissue invasion by these tumors. The increasing knowledge of the pathway activation profile in pancreatic cancer
is yielding new targets but also new markers to select patients and guide and predict therapy efficacy. The discovery of genetic
factors of which the presence predisposes pancreatic cancer to successful targeting, such as the association of BRCA2/Fanconi
anemia genes defects and sensitivity to mitomycin C, will eventually lead to a more individualized treatment approach. In
summary, several decades of intensive research have originated multiple factors or biomarkers that are likely to be helpful
in the diagnosis, characterization, and therapy selection of pancreatic cancer patients. A deep understanding of the relative
relevance of each biomarker will be key to efficiently diagnose this disease and direct our patients towards the drugs more
likely to be of benefit based on their particular profile. The development of new preclinical models is of paramount importance
to achieve these goals. Mol Cancer Ther 2006;5(4):787–96
Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties ...have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations.
Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance.
We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Local control and overall survival in patients with advanced head and neck squamous cell cancer (HNSCC) remains dismal. Signaling through the Hedgehog (Hh) pathway is associated with ...epithelial-to-mesenchymal transition, and activation of the Hh effector transcription factor Gli1 is a poor prognostic factor in this disease setting. Here, we report that increased GLI1 expression in the leading edge of HNSCC tumors is further increased by irradiation, where it contributes to therapeutic inhibition. Hh pathway blockade with cyclopamine suppressed GLI1 activation and enhanced tumor sensitivity to radiotherapy. Furthermore, radiotherapy-induced GLI1 expression was mediated in part by the mTOR/S6K1 pathway. Stroma exposed to radiotherapy promoted rapid tumor repopulation, and this effect was suppressed by Hh inhibition. Our results demonstrate that Gli1 that is upregulated at the tumor-stroma intersection in HNSCC is elevated by radiotherapy, where it contributes to stromal-mediated resistance, and that Hh inhibitors offer a rational strategy to reverse this process to sensitize HNSCC to radiotherapy.
The treatment of head and neck squamous cell carcinoma (HNSCC) is set to undergo rapid changes, as novel treatment targets informed by genomic profiling and novel molecularly targeted therapies ...continue to make strides. In this review we provide an overview of the latest developments regarding (1) EGFR targeting for HNSCC, (2) PI3K as a novel treatment target, and (3) newly described key genetic events in HNSCC such as NOTCH1 mutations and emerging candidate targets including ALK1 and hedgehog. The first molecular targeting strategy to demonstrate a survival advantage for patients with HNSCC has emerged in the context of EGFR biology. Cetuximab remains the only U.S. Food and Drug Administration (FDA)-approved targeted therapy available for HNSCC, but EGFR as a target has not been individualized in this disease. The PI3K-AKT pathway is downstream of EGFR and is emerging as potentially one of the most important pathways in HNSCC. PIK3CA is the most frequently mutated oncogene for HNSCC (approximately 20%) and may play a role for both HPV-negative and HPV-positive tumors. Multiple therapeutic strategies targeting PI3K are being explored, and multiple agents either alone or in combination are in development. NOTCH1 is a key tumor suppressor gene and its genetic alterations lead to abnormal pathway activation. ALK1 is a novel target involved in angiogenesis, and efficacy in patients with HNSCC was documented in an early inhibitor trial. The hedgehog pathway modulates EGFR dependence and epithelial to mesenchymal transition (EMT), a key invasion and drug-resistance mechanism in HNSCC.
Word sense disambiguation (WSD) attempts to solve lexical ambiguities by identifying the correct meaning of a word based on its context. WSD has been demonstrated to be an important step in ...knowledge-based approaches to automatic summarization. However, the correlation between the accuracy of the WSD methods and the summarization performance has never been studied.
We present three existing knowledge-based WSD approaches and a graph-based summarizer. Both the WSD approaches and the summarizer employ the Unified Medical Language System (UMLS) Metathesaurus as the knowledge source. We first evaluate WSD directly, by comparing the prediction of the WSD methods to two reference sets: the NLM WSD dataset and the MSH WSD collection. We next apply the different WSD methods as part of the summarizer, to map documents onto concepts in the UMLS Metathesaurus, and evaluate the summaries that are generated. The results obtained by the different methods in both evaluations are studied and compared.
It has been found that the use of WSD techniques has a positive impact on the results of our graph-based summarizer, and that, when both the WSD and summarization tasks are assessed over large and homogeneous evaluation collections, there exists a correlation between the overall results of the WSD and summarization tasks. Furthermore, the best WSD algorithm in the first task tends to be also the best one in the second. However, we also found that the improvement achieved by the summarizer is not directly correlated with the WSD performance. The most likely reason is that the errors in disambiguation are not equally important but depend on the relative salience of the different concepts in the document to be summarized.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Head and neck squamous cell cancer (HNSCC) bear a significant degree of functional intratumor heterogeneity, as evidenced by differential morphology, key protein expression, immune ...infiltration, as well as response to therapy. Some of these differences can be explained by the ability of HNSCC cells to move along a continuum of plasticity, with a small fraction of cells having higher potential; these can be defined as tumor initiating cells (TICs) or the more broadly used term cancer stem cells (CSCs). We have an incomplete understanding of the key characteristics defining HNSCC CSCs, including the differences between human papillomavirus positive (HPV-positive) and negative (HPV-negative) cases. The PI3K pathway has the most frequent activating genetic events in HNSCC (especially HPV-positive driven), but how CSCs exploit oncogenic signaling between is also unknown.
In this review presentation we will summarize the work studying these issues (JNCI 2016, PMID: 27634934), and we will lay out ongoing lines of investigation. In this work we investigated these unresolved issues using CSCs identified from ten HNSCC patient-derived xenografts (PDXs). Sorted populations were then serially passaged in mice to evaluate tumorigenic capacity and tumor reconstitution. HNSCC CSCs and non-CSCs were compared by mRNA-sequencing. To assess another key characteristic of CSCs we compared the susceptibility of CSCs to therapy using an in vivo model.
CSCs were defined by high aldehyde dehydrogenase (ALDH) activity and CD44 expression, and were equivalent between HPV-positive and HPV-negative cases. CSCs had PI3K/mTOR pathway over-expression, and PI3K inhibition in vivo decreased their tumorigenicity. PI3K/mTOR regulated SOX2 protein levels, and SOX2 in turn activated ALDH1A1 expression and ALDH activity in HNSCC. SOX2 expression generated a CSC-like population in early passage HNSCC cells, in a proportion similar to that found in vivo. SOX2 expression enhanced sphere and tumor growth and therapy resistance. SOX2 expression prompted mesenchymal to epithelial transition (MET) by inducing CDH1, followed by asymmetric division and proliferation, which contributed to tumor formation. In the drug studies we evidenced differential susceptibility of CSCs and non-CSCs sub-populations to both standard and investigational agents, with CSCs being resistant to conventional therapy. SOX2 expression induced drug resistance. We have generated additional pairs of patient-derived cells where the forced expression of SOX2 leads to a CSC phenotype, and are using these models to further study CSC biology and response to therapy, and to explain the intratumor heterogeneity seen in HNSCC.
The molecular link between PI3K activation and CSC properties found in our research provides insights into therapeutic strategies for HNSCC. Constitutive expression of SOX2 in HNSCC cells generates a CSC-like population that enables CSC studies. Additional investigations into how CSCs harness oncogenic signals to achieve key cancer characteristics such as immune evasion will be discussed.
Citation Format: Antonio Jimeno. Head and neck squamous cell cancer stem cells: Harnessing oncogenic signaling to enable tumorigenicity abstract. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; April 23-25, 2017; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(23_Suppl):Abstract nr IA07.
Positron emission tomography (PET) with (18)Ffluorodeoxyglucose (FDG-PET) has increasingly been used to evaluate the efficacy of anticancer agents. We investigated the role of FDG-PET as a predictive ...marker for response to mammalian target of rapamycin (mTOR) inhibition in advanced solid tumor patients and in murine xenograft models.
Thirty-four rapamycin-treated patients with assessable baseline and treatment FDG-PET and computed tomography scans were analyzed from two clinical trials. Clinical response was evaluated according to Response Evaluation Criteria in Solid Tumors, and FDG-PET response was evaluated by quantitative changes and European Organisation for Research and Treatment of Cancer (EORTC) criteria. Six murine xenograft tumor models were treated with temsirolimus. Small animal FDG-PET scans were performed at baseline and during treatment. The tumors were analyzed for the expression of pAkt and GLUT1.
Fifty percent of patients with increased FDG-PET uptake and 46% with decreased uptake had progressive disease (PD). No objective response was observed. By EORTC criteria, the sensitivity of progressive metabolic disease on FDG-PET in predicting PD was 19%. Preclinical studies demonstrated similar findings, and FDG-PET response correlated with pAkt activation and plasma membrane GLUT1 expression.
FDG-PET is not predictive of proliferative response to mTOR inhibitor therapy in both clinical and preclinical studies. Our findings suggest that mTOR inhibitors suppress the formation of mTORC2 complex, resulting in the inhibition of Akt and glycolysis independent of proliferation in a subset of tumors. Changes in FDG-PET may be a pharmacodynamic marker for Akt activation during mTOR inhibitor therapy. FDG-PET may be used to identify patients with persistent Akt activation following mTOR inhibitor therapy.
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•We describe a method to generate word-concept statistical models from a knowledge base•This method integrates knowledge base descriptions and corpora information•Word sense ...disambiguation with this method is better than state-of-the-art approaches•Ranking of citation with the model improves the performance of baseline approaches
Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation.
In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences.
The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches.
Many genes undergo aberrant methylation in human cancers, and microarray platforms enable more comprehensive profiling of aberrant DNA methylation patterns. Methods: We used methylated CpG island ...amplification (MCA) and Agilent promoter and CpG island microarrays to identify differential DNA methylation patterns in pancreatic cancer vs. normal pancreas. We examined MCA array reproducibility, compared it to methylation profiles obtained using a cocktail of methylation-sensitive restriction enzymes and examined gene expression of methylated genes Results: 1,010 of 87,922 probes on the 88K promoter array (606 genes) had a higher signal (log2>2) in the pancreatic cancer line, Panc-1 compared to the non-neoplastic pancreatic duct line, HPDE. Using this cut-off, bisulfite sequencing and/or MSP confirmed differential methylation of all 27 genes (66 probes) predicted to be methylated by the MCA array. More than ½ of the genes aberrantly hypermethylated in Panc-1 were not expressed in the pancreatic duct (HPDE) by expression array analysis. Using the 244K CpG island array, 1,968 CpG islands were differentially methylated in MiaPaca2 compared to normal pancreas. The MCA method was more likely to identify hypermethylation within CpG islands than a cocktail of methylation sensitive restriction enzymes. DNA methylation profiles using 10ng of DNA were highly correlated with those obtained 4 using 5ug of DNA (R2=0.98). Analysis of 57 pancreatic cancers and 34 normal pancreata using MSP identified MDFI, hsa-miR-9-1, ZNF415, CNTNAP2 and ELOVL4 as methylated in 96%, 89%, 86%, 82% and 68% of the cancers vs. 9%, 15%, 6%, 3% and 9% of normal pancreata, respectively. Conclusion: Promoter and CpG island array analysis finds aberrant methylation of hundreds of promoters and CpG islands in pancreatic cancer cells.
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