Identification of potential new treatment response markers and therapeutic targets using a Gaussian process-based method in lapatinib insensitive breast cancer models

<div><p>Molecularly targeted therapeutics hold promise of revolutionizing treatments of advanced malignancies. However, a large number of patients do not respond to these treatments. Here, we take a systems biology approach to understand the molecular mechanisms that prevent breast cancer (BC) cells from responding to lapatinib, a dual kinase inhibitor that targets human epidermal growth factor receptor 2 (HER2) and epidermal growth factor receptor (EGFR). To this end, we analysed temporal gene expression profiles of four BC cell lines, two of which respond and the remaining two do not respond to lapatinib. For this analysis, we developed a Gaussian process based algorithm which can accurately find differentially expressed genes by analysing time course gene expression profiles at a fraction of the computational cost of other state-of-the-art algorithms. Our analysis identified 519 potential genes which are characteristic of lapatinib non-responsiveness in the tested cell lines. Data from the Genomics of Drug Sensitivity in Cancer (GDSC) database suggested that the basal expressions 120 of the above genes correlate with the response of BC cells to HER2 and/or EGFR targeted therapies. We selected 27 genes from the larger panel of 519 genes for experimental verification and 16 of these were successfully validated. Further bioinformatics analysis identified vitamin D receptor (VDR) as a potential target of interest for lapatinib non-responsive BC cells. Experimentally, calcitriol, a commonly used reagent for VDR targeted therapy, in combination with lapatinib additively inhibited proliferation in two HER2 positive cell lines, lapatinib insensitive MDA-MB-453 and lapatinib resistant HCC 1954-L cells.</p></div

Interrogation of VDR as potential therapeutic targets for sensitizing lapatinib insensitive breast cancer cell lines to lapatinib treatment.

<p>(A-D) Association between recursion free survival of all, HER2 negative, triple negative, HER2 positive breast cancer patients with VDR expression. (E) Association between VDR expression and the expressions of 22 miRNAs in the TCGA breast cancer patient cohort. (F) ΔΔ<i>C</i>_<i>T</i> values for Vitamin D Receptor in HER2 positive lapatinib insensitive cell lines (MDA-MB-453 and JIMT-1) showing changes between untreated cells and cells expose to lapatinib for 16 hours. (G-J) Combined treatment with lapatinib and calcitriol in MDA-MB-453, JIMT-1, HCC 1954 and HCC 1956-L cells respectively (* denotes p<0.05).Plots are based on N = 3 biological replicates.</p

Histological-subtypes, HER2 and EGFR receptor expressions and mutation status of SKBR-3, BT-474, T47D and MDA-MB-468 cell lines.

<p>Histological-subtypes, HER2 and EGFR receptor expressions and mutation status of SKBR-3, BT-474, T47D and MDA-MB-468 cell lines.</p

A schematic diagram of the GP based analysis performed on the gene expression dataset of [12].

<p>A schematic diagram of the GP based analysis performed on the gene expression dataset of [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177058#pone.0177058.ref012" target="_blank">12</a>].</p

Clinico-pathological subtypes of and receptor expressions in MDA-MB-453 and JIMT1 cell lines.

<p>Clinico-pathological subtypes of and receptor expressions in MDA-MB-453 and JIMT1 cell lines.</p

Bioinformatics analysis of the selected genes.

<p>(A) Enriched gene ontology terms are summarized and visualized by REVIGO. Here, each circle represents a gene ontology term and the size of the circle represents the extent of enrichment. (B) Pathway enrichment analysis of the selected genes. Pathways are shown in X-axis and the enrichment scores are shown in Y-axis. (C) Transcriptional module found in the identified genes. (D) Transcriptional activity of VDR. (E) PPI network induced by the identified genes. (F,G) PPIs of VDR and EIF2S2.</p

Treatment timepoints available from the Hedge <i>et al</i>.<i>’s</i> dataset.

<p>Treatment timepoints available from the Hedge <i>et al</i>.<i>’s</i> dataset.</p

Real time qPCR based validation of a set of selected potential biomarkers.

<p>Each panel consists of the log fold changes (lapatinib treated vs untreated, shown in Y-axis) of the corresponding gene (shown in the header of each panel) in all four cell lines (X-axis). Plots are based on N = 3 biological replicates.</p

Structure–Activity Relationship and Mode of Action of <i>N</i>-(6-Ferrocenyl-2-naphthoyl) Dipeptide Ethyl Esters: Novel Organometallic Anticancer Compounds

In this article, we report the findings of a comprehensive structure–activity relationship study of <i>N</i>-(6-ferrocenyl-2-naphthoyl) dipeptide ethyl esters, in which novel analogues were designed, synthesized, and evaluated <i>in vitro</i> for antiproliferative effect. Two new compounds, <b>2</b> and <b>16</b>, showed potent nanomolar activity in the H1299 NSCLC cell line, with exceptional IC₅₀ values of 0.13 and 0.14 μM, respectively. These compounds were also found to have significant activity in the Sk-Mel-28 malignant melanoma cell line (IC₅₀ values of 1.10 and 1.06 μM, respectively). Studies were also conducted to elucidate the mode of action of these novel organometallic anticancer compounds. Cell cycle analysis in the H1299 cell line suggests these compounds induce apoptosis, while guanine oxidation studies confirm that <b>2</b> is capable of generating oxidative damage via a ROS-mediated mechanism

OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets

Background: Ovarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade. Methods: We have developed a user-friendly, web-based system to evaluate the association of genes/miRNAs with outcome in ovarian cancer. The OvMark algorithm combines data from multiple microarray platforms (including probesets targeting miRNAs) and correlates them with clinical parameters (e.g. tumour grade, stage) and outcomes (disease free survival (DFS), overall survival). In total, OvMark combines 14 datasets from 7 different array platforms measuring the expression of ~17,000 genes and 341 miRNAs across 2,129 ovarian cancer samples. Results: To demonstrate the utility of the system we confirmed the prognostic ability of 14 genes and 2 miRNAs known to play a role in ovarian cancer. Of these genes, CXCL12 was the most significant predictor of DFS (HR = 1.42, p-value = 2.42x10-6). Surprisingly, those genes found to have the greatest correlation with outcome have not been heavily studied in ovarian cancer, or in some cases in any cancer. For instance, the three genes with the greatest association with survival are SNAI3, VWA3A and DNAH12. Conclusions/impact: OvMark is a powerful tool for examining putative gene/miRNA prognostic biomarkers in ovarian cancer (available at http://glados.ucd.ie/OvMark/index.html). The impact of this tool will be in the preliminary assessment of putative biomarkers in ovarian cancer, particularly for research groups with limited bioinformatics facilities.</p