An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

<p>Abstract</p> <p>Background</p> <p>Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.</p> <p>Method</p> <p>To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.</p> <p>Result</p> <p>We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.</p> <p>Conclusions</p> <p>We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.</p

siRNA-mediated gene silencing: a global genome view

The task of specific gene knockdown in vitro has been facilitated through the use of short interfering RNA (siRNA), which is now widely used for studying gene function, as well as for identifying and validating new drug targets. We explored the possibility of using siRNA for dissecting cellular pathways by siRNA-mediated gene silencing followed by gene expression profiling and systematic pathway analysis. We used siRNA to eliminate the Rb1 gene in human cells and determined the effects of Rb1 knockdown on the cell by using microarray-based gene expression profiling coupled with quantitative pathway analysis using the GenMapp and MappFinder software. Retinoblastoma protein is one of the key cell cycle regulators, which exerts its function through its interactions with E2F transcription factors. Rb1 knockdown affected G(1)/S and G(2)/M transitions of the cell cycle, DNA replication and repair, mitosis, and apoptosis, indicating that siRNA-mediated transient elimination of Rb1 mimics the control of cell cycle through Rb1 dissociation from E2F. Additionally, we observed significant effects on the processes of DNA damage response and epigenetic regulation of gene expression. Analysis of transcription factor binding sites was utilized to distinguish between putative direct targets and genes induced through other mechanisms. Our approach, which combines the use of siRNA-mediated gene silencing, mediated microarray screening and quantitative pathway analysis, can be used in functional genomics to elucidate the role of the target gene in intracellular pathways. The approach also holds significant promise for compound selection in drug discovery

Assessment of Drug–Drug Interaction Risk Between Intravenous Fentanyl and the Glecaprevir/Pibrentasvir Combination Regimen in Hepatitis C Patients Using Physiologically Based Pharmacokinetic Modeling and Simulations

Abstract Introduction An unsafe injection practice is one of the major contributors to new hepatitis C virus (HCV) infections; thus, people who inject drugs are a key population to prioritize to achieve HCV elimination. The introduction of highly effective and well-tolerated pangenotypic direct-acting antivirals, including glecaprevir/pibrentasvir (GLE/PIB), has revolutionized the HCV treatment landscape. Glecaprevir is a weak cytochrome P450 3A4 (CYP3A4) inhibitor, so there is the potential for drug–drug interactions (DDIs) with some opioids metabolized by CYP3A4, such as fentanyl. This study estimated the impact of GLE/PIB on the pharmacokinetics of intravenous fentanyl by building a physiologically based pharmacokinetic (PBPK) model. Methods A PBPK model was developed for intravenous fentanyl by incorporating published information on fentanyl metabolism, distribution, and elimination in healthy individuals. Three clinical DDI studies were used to verify DDIs within the fentanyl PBPK model. This model was integrated with a previously developed GLE/PIB PBPK model. After model validation, DDI simulations were conducted by coadministering GLE 300 mg + PIB 120 mg with a single dose of intravenous fentanyl (0.5 µg/kg). Results The predicted maximum plasma concentration ratio between GLE/PIB + fentanyl and fentanyl alone was 1.00, and the predicted area under the curve ratio was 1.04, suggesting an increase of only 4% in fentanyl exposure. Conclusion The administration of a therapeutic dose of GLE/PIB has very little effect on the pharmacokinetics of intravenous fentanyl. This negligible increase would not be expected to increase the risk of fentanyl overdose beyond the inherent risks related to the amount and purity of the fentanyl received during recreational use

Efficacy and pharmacokinetics of glecaprevir and pibrentasvir with concurrent use of acid-reducing agents in patients with chronic HCV infection

BACKGROUND & AIMS: Proton pump inhibitors (PPIs) are commonly prescribed to treat acid-related disorders. Some direct-acting antiviral regimens for chronic hepatitis C virus (HCV) infection have reduced efficacy in patients taking concomitant acid-reducing agents, including PPIs, due to interactions between drugs. We analyzed data from 9 multicenter, phase 2 and 3 trials to determine the efficacy and pharmacokinetics of an HCV therapeutic regimen comprising glecaprevir and pibrentasvir (glecaprevir/pibrentasvir) in patients taking concomitant acid-reducing agents. METHODS: We analyzed data from 2369 patients infected with HCV genotypes 1-6 and compensated liver disease treated with an all-oral regimen of glecaprevir/pibrentasvir for 8-16 weeks. We compared efficacy and pharmacokinetics among patients receiving at least 1 dose of an acid-reducing agent (a PPI, an H2 blocker, or antacid). High-dose PPI was defined as daily dose greater than 20 mg omeprazole dose equivalent. The objectives were to evaluate rate of sustained virologic response 12 weeks post-treatment (SVR12) and to assess steady-state glecaprevir and pibrentasvir exposures in patients on acid-reducing agents. RESULTS: Of the 401 patients (17%) who reported use of acid-reducing agents, 263 took PPIs (11%; 109 patients took a high-dose PPI and 154 patients took a low-dose PPI). Rates of SVR12 were 97.0% among patients who used acid-reducing agents and 97.5% among those not using acid-reducing agents (P = .6). An SVR12 was achieved in 96.3% taking a high-dose PPI and 97.4% taking a low-dose PPI, with no virologic failures in those receiving a high-dose PPI (P = .7). Glecaprevir, but not pibrentasvir, bioavailability was affected; its exposure decreased by 41% in patients taking a high-dose PPI. CONCLUSIONS: In an analysis of data from 9 clinical trials, we observed a high rate of SVR12 (approximately 97%) among patients treated with glecaprevir/pibrentasvir for HCV infection-even among patients taking concomitant ARA or high-dose PPI. This was despite decreased glecaprevir exposures in patients when on high-dose PPIs. ClinicalTrials.gov numbers, NCT02243280 (SURVEYOR-I), NCT02243293 (SURVEYOR-II), NCT02604017 (ENDURANCE-1), NCT02640482 (ENDURANCE-2), NCT02640157 (ENDURANCE-3), NCT02636595 (ENDURANCE-4), NCT02642432 (EXPEDITION-1), NCT02651194 (EXPEDITION-4), NCT02446717 (MAGELLAN-I)