Pixantrone: novel mode of action and clinical readouts

<p><b>Introduction</b>: Pixantrone is a first-in-class aza-anthracenedione approved as monotherapy for treatment of relapsed or refractory aggressive diffuse B-cell non-Hodgkin’s lymphoma (NHL), a patient group which is notoriously difficult to treat. It has a unique chemical structure and pharmacologic properties distinguishing it from anthracyclines and anthracenediones.</p> <p><b>Areas covered</b>: The chemical structure and mode of action of pixantrone versus doxorubicin and mitoxantrone; preclinical evidence for pixantrone’s therapeutic effect and cardiac tolerability; efficacy and safety of pixantrone in clinical trials; ongoing and completed trials of pixantrone alone or as combination therapy; and the risk of cardiotoxicity of pixantrone versus doxorubicin and mitoxantrone.</p> <p><b>Expert commentary</b>: Currently, pixantrone is the only approved therapy for multiply relapsed or refractory NHL, an area with few available effective treatment options. Pixantrone is currently being investigated as combination therapy with other drugs including several targeted therapies, with the ultimate goal of improved survival in heavily pretreated patients. In order for pixantrone to be acknowledged in the treatment of aggressive NHL, the perception of pixantrone as an anthracycline-like agent that has anthracycline-like activity and cardiotoxicity needs to be changed. Further data from ongoing clinical trials will help in confirming pixantrone as an effective and safe option.</p

PCSF: An R-package for network-based interpretation of high-throughput data

<div><p>With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.</p></div

Size of matrices to run NMF according to the Hamming distance to consider equivalent columns.

<p>The matrix produced by using the DLBCL samples of Data set 1 as rows and their DNA copy number profiles as columns was subjected to the compaction procedure, according to the similarity between columns based on Hamming distance. As higher the maximum allowed Hamming distance used to merge columns as lower the number of resulting columns. Later, matrices of those dimensions are used as input for the NMF.</p

The results of the methods for the Breast Cancer network instances generated using the phosphoproteomic data in [11].

<p>The performance of the message passing algorithm [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005694#pcbi.1005694.ref003" target="_blank">3</a>] and the proposed method are respectively displayed under MSGP [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005694#pcbi.1005694.ref003" target="_blank">3</a>] and PCSF for <i>ω</i> = {1, 2}. The OBJ column reports the quality of the solutions (objective function values) obtained by the methods, and the running times of the algorithms are displayed under t(s) in seconds. The average statistics of 10 runs provided by both algorithms are reported for each instance.</p

Association between the clusters and the molecular subtypes in the medulloblastoma data set.

<p>This table shows the distribution of the medulloblastoma patients (Data set 4) in each cluster of rank 3, with respect to the classification in the following molecular subtypes: WNT, SHH, Group 3 and Group 4.</p

Functional enrichment analysis of the final subnetwork using the EnrichR API.

<p>The node sizes and edge widths are proportional to the amount of times that node or edge appeared in the noisy runs. Nodes are colored according to cluster membership. As in the EnrichR API, the p-value is calculated using the Fisher test and adjusted for multiple hypotheses. The top 15 functional enrichment terms for each cluster are ranked according to the adjusted p-value and displayed in a tabular format when the mouse hovers over a node in that cluster. Each cluster can be visualized separately by “Select by group” icon located at the top of the figure.</p

Prognostic significance of NMF-identified clusters among R-CHOP-21 treated DLBCL.

<p>Subfigures A and B show Kaplan-Meier estimates of OS (left panel, log-rank test p-value = 0.063) and PFS (right panel, log-rank test p-value = 0.034) in R-CHOP-21 treated DLBCL patients from Data set 1.</p

Comparison of Standard-NMF and Full-NMF over the DLBCL data of Data set 1.

<p>The matrix produced by using the DLBCL samples of Data set 1 as rows and their DNA copy number profiles as columns was used to test the distinct manners of running NMF. Full-NMF stands for the procedure which runs over all data, while Standard-NMF stands for the procedure that keeps only one of each group of similar columns. The graphs show the ratio between the divergence (the objective function we minimize) of the Standard-NMF over the divergence of the Full-NMF, so higher values mean greater error of Standard-NMF. Results are shown for different factorization ranks.</p

Summary of copy number lesions that are considered.

<p>This table shows the definition of the four types of copy number (CN) aberrations that are considered in this work.</p

Frequency plots of CN aberrations of DLBCL patients of Data set 1, according to the clustering results.

<p>The DLBCL samples of Data set 1 were divided into three subgroups, according to the clustering results of rank 3. Clusters 1, 2 and 3 have 86, 54 and 26 cases, respectively. The frequency of samples with gain in copy number is given in red, while the frequency of losses is in blue (the scale into negative numbers is just for plotting purposes).</p