Search

Methods for Real-Time Quantitative RT-PCR, Assessment of medal accuracy, Gene expression, copy number and survival analysis

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1–S transition. The model identified known components of the cell-cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1–S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 of 12 predicted genes to have a role in G1–S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NFκB and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NFκB and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NFκB signaling. Cancer Res; 77(7); 1575–85. ©2017 AACR.

Supplemental Figure Legends

Legend Figure S1: Schematic of the experimental framework Legend Figure S2: Predicted phenotypic value and assessment of predictive power of the model. Legend Figure S3: Taget knock-down. Legend Figure S4: Treatment with individual siRNAs against TRIB1 leads to G1-S arrest. Legend Figure S5: RNA levels of cell cycle genes. Legend Figure S6: TRIB1 knockdown results in inhibition of NFκB-responsive promoter activity: comparison of individual siRNA duplexes. Legend Figure S7: TRIB1 resistant to siRNA (TRIB1-siR) rescues the phenotype induced by TRIB1-siRNA. Legend Figure S8: Downregulation of NFkB target genes after treatment with TRIB1 siRNA. Legend Figure S9: TRIB1 knock down leads to inhibition of NFκB pathway. Legend Figure S10: TRIB1 siRNA promotes TRAIL-induced apoptosis through activation of caspase-8 cleavage. Legend Figure S11: TRIB1 knockdown results in decrease of YY1gene expression. Legend Figure S12: Kaplan-Meier plots Legend Figure S13: Hazard Ratios for BCCS distribution. Legend Figure S14: Hazard Ratios for OS distribution.

e18747 Background: Accurate longitudinal cancer treatments are vital for establishing primary endpoints such as outcome as well as for the investigation of adverse events. However, many longitudinal therapeutic regimens are not well captured in structured electronic health records (EHRs). Thus, their recognition in unstructured data such as clinical notes is critical to gain an accurate description of the real-world patient treatment journey. Here, we demonstrate a scalable approach to extract high-quality longitudinal cancer treatments from lung cancer patients' clinical notes using a Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) based natural language processing (NLP) pipeline. Methods: The lung cancer (LC) cohort of 4,698 patients was curated from the Mount Sinai Healthcare system (2003-2020). Two domain experts developed a structured framework of entities and semantics that captured treatment and its temporality. The framework included therapy type (chemotherapy, targeted therapy, immunotherapy, etc.), status (on, off, hold, planned, etc.) and temporal reasoning entities and relations (admin_date, duration, etc.) We pre-annotated 149 FDA-approved cancer drugs and longitudinal timelines of treatment on the training corpus. A NLP pipeline was implemented with BiLSTM-CRF-based deep learning models to train and then apply the resulting models to the clinical notes of LC cohort. A postprocessor was developed to subsequently post-coordinate and refine the output. We performed both cross-evaluation and independent evaluation to assess the pipeline performance. Results: We applied the NLP pipeline to the 853,755 clinical notes, and identified 1,155 distinct entities for 194 cancer generic drugs, including 74 chemotherapy drugs, 21 immunotherapy drugs, and 99 targeted therapy drugs. We identified chemotherapy, immunotherapy, or targeted therapy data for 3,509 patients in the LC cohort from the clinical notes. Compared to only 2,395 patients with cancer treatments in structured EHR, this pipeline identified cancer treatments from notes for additional 2,303 patients who did not have any available cancer treatment data in the structured EHR. Our evaluation schema indicates that the longitudinal cancer drug recognition pipeline delivers strong performance (named entity recognization for drugs and temporal: F1 = 95%; drug-temporal relation recognition: F1 = 90%). Conclusions: We developed a high-performance BiLSTM-CRF based NLP pipeline to recognize longitudinal cancer treatments. The pipeline recovers and encodes as twice as many patients with cancer treatments compared with structured EHR. Our study indicates deep NLP with temporal reasoning could substantially accelerate the extraction of treatment profiles at scale. The pipeline is adjustable and can be applied across different cancers.

In this paper we describe some case studies of the use of virtual worlds in corporate training as well as Higher Education. In particular for Higher Education we describe how the Virtual World constructed using the platform Avaya Live Engage, is used as an immersive environment with pre-service teachers, who are undergoing a 1-year teacher training program, and how the data analytics collected in-world is being used to monitor and direct content development. We focus our studies on the initial hypothesis that 3D immersive environments are highly engaging and offer an experience that goes beyond the 'traditional' online education. We want to combine different analysis methods to be able to get empirical evidence showing the students' engagement with the 3D space in ways that can help us in the design of the learning experience accompanying the learners in their journey. In this paper we describe the research methods we use for the study, and give an overview of the information we can collect from the in-world analytics. We also propose how these analytics can be used for a predictive model with the intention of refocusing the virtual world experience to match learner needs.

Apoptosis of nucleated cells is regulated by caspases, a group of cysteine proteases, and is characterized by phosphatidylserine expression on the outer leaflet of the plasma membrane. Reports indicate that platelets contain caspases. However, the role of caspases in platelet function is not well understood. When platelets become activated, they express phosphatidylserine (PS) on the outer leaflet of the plasma membrane. In addition, platelets aggregate when activated. The aims of this study were to determine if caspase inhibition (using the pan-caspase inhibitor zVAD-fmk): (1) decreased PS expression and (2) decreased platelet aggregation following activation. Flow cytometry was used to determine PS expression and a platelet aggregometer was used to assess aggregation. We found that platelets treated with zVAD-fmk significantly decreased both A23187-induced PS exposure (total fluorescence index, TFI: A23187=791.42+/-174; zVAD+A23187=92.97+/-57, p==0.05) and ADP-induced PS exposure (TFI: ADP=669.24+/-145, zVAD+ADP=174.6+/-151, p==0.05). Further, treatment with zVAD-fmk significantly decreased ADP-induced platelet aggregation (%: untreated=80+/-1.5, zVAD treated=69+/-3.0, p==0.05). These results indicate that caspases play a role in platelet activation, suggesting a unique physiologic role for these proteases.

The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using ‘exon’ and ‘tiling’ arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.

To determine the characteristics of neuromuscular block produced by two and three times the 95% effective dose (ED95) of doxacurium in patients undergoing coronary artery surgery with hypothermic cardiopulmonary bypass.In a prospective non randomized study, ten patients received doxacurium 0.05 mg.kg-1 (Group 1) and ten others received 0.075 mg.kg-1 (Group 2) with midazolam and sufentanil. The mechanomyographic response of the adductor pollicis muscle after supramaximal train-of-four (TOF) stimulation of the ulnar nerve was recorded intraoperatively and postoperatively. Additional doxacurium (10% of the initial dose) was administered until sternal closure whenever the first twitch (T1) had recovered to 25% of control.The onset time (time to maximal T1 depression) of doxacurium was 390 +/- 148 sec in Group 1 and 370 +/- 74 sec in Group 2 (P = 0.71). The clinical duration of neuromuscular block (time to 25% T1 recovery) was 165 +/- 90 min in Group 1 and 258 +/- 86 in Group 2 (P = 0.03). On arrival to recovery room the mean T1 was 57 +/- 23% in Group 1 and 24 +/- 21% in Group 2 (P = 0.003); the mean T4/T1 ratio was 0.25 +/- 0.15 for five patients of Group 1 with four responses to TOF stimulation and 0.10 for the only patient of Group 2 with four twitches.In contrast with findings in patients without cardiac disease, this study shows comparable onset times of doxacurium with doses of two and three times ED95. The clinical duration of doxacurium is 60 to 100% longer than previously reported in noncardiac surgery.

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate non-intuitive behaviors. We applied a novel computational strategy to decipher causal relationships between signaling network components. Gene networks were constructed from global gene expression profiles to model G1-S cell cycle arrest as a consequence of inhibition of MEK, a key component of the oncogenic RAF-MEK-ERK signal transduction pathway. Simulation of causally driven models generated a ranked list of 12 testable hypotheses, which confirmed role of known cell cycle regulators (CCND1, CCNE2, and CDC25A) and identified novel drivers of G1-S transition (IER2, TRIB1, C14ORF133). Validation confirmed 11 out 12 predictions. Second model which inferred causal relationships between genes lead to discovery of TRIB1 (tribbles homolog of Drosophila) as a regulator of NFkB-dependent gene expression. TRIB1 also mediates cross-talk to the NFκB pathway by enhancing p100 and IκBα-phosphorylation and degradation thus regulating cell survival and expression of cytokines crucial for tumor-stroma interaction. In agreement with this, knock-down of TRIB1 results in induction of apoptosis and sensitizes cells to cell killing induced by the death-receptor agonist TRAIL via upregulation of TRAIL receptor, DR5. Thus TRIB1 is a central regulator of cell cycle and survival and represents a potential therapeutic target. Therefore, applying REFS (Reverse Engineering Forward Simulation) to analyze gene expression profiles maximizes our ability to extract knowledge from experimental data.

Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet microarrays. Further, expression activity over at least six conditions was more generally assessed using genomic tiling arrays consisting of probes tiled through a repeat-masked version of the genomic sequence making up chromosomes 20 and 22. The combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts. This set of high-confidence transcripts represents the first experimentally driven annotation of the human genome. In addition, the results from genomic tiling suggest that a large amount of transcription exists outside of annotated regions of the genome and serves as an example of how this activity could be measured on a genome-wide scale. These data represent one of the most comprehensive assessments of transcriptional activity in the human genome and provide an atlas of human gene expression over a unique set of gene predictions. Before the annotation of the human genome is considered complete, however, the previously unannotated transcriptional activity throughout the genome must be fully characterized.