Gene Expression Analysis in Ovarian Cancer – Faults and Hints from DNA Microarray Study

The introduction of microarray techniques to cancer research brought great expectations for finding biomarkers that would improve patients’ treatment; however, the results of such studies are poorly reproducible and critical analyses of these methods are rare. In this study, we examined global gene expression in 97 ovarian cancer samples. Also, validation of results by quantitative RT-PCR was performed on 30 additional ovarian cancer samples. We carried out a number of systematic analyses in relation to several defined clinicopathological features. The main goal of our study was to delineate the molecular background of ovarian cancer chemoresistance and find biomarkers suitable for prediction of patients’ prognosis. We found that histological tumor type was the major source of variability in genes expression, except for serous and undifferentiated tumors that showed nearly identical profiles. Analysis of clinical endpoints [tumor response to chemotherapy, overall survival, disease-free survival (DFS)] brought results that were not confirmed by validation either on the same group or on the independent group of patients. CLASP1 was the only gene that was found to be important for DFS in the independent group, whereas in the preceding experiments it showed associations with other clinical endpoints and with BRCA1 gene mutation; thus, it may be worthy of further testing. Our results confirm that histological tumor type may be a strong confounding factor and we conclude that gene expression studies of ovarian carcinomas should be performed on histologically homogeneous groups. Among the reasons of poor reproducibility of statistical results may be the fact that despite relatively large patients’ group, in some analyses one has to compare small and unequal classes of samples. In addition, arbitrarily performed division of samples into classes compared may not always reflect their true biological diversity. And finally, we think that clinical endpoints of the tumor probably depend on subtle changes in many and, possibly, alternative molecular pathways, and such changes may be difficult to demonstrate

Potential Biomarkers of Skin Melanoma Resistance to Targeted Therapy—Present State and Perspectives

Melanoma is the most aggressive skin cancer, the number of which is increasing worldwide every year. It is completely curable in its early stage and fatal when spread to distant organs. In addition to new therapeutic strategies, biomarkers are an important element in the successful fight against this cancer. At present, biomarkers are mainly used in diagnostics. Some biological indicators also allow the estimation of the patient’s prognosis. Still, predictive markers are underrepresented in clinics. Currently, the only such indicator is the presence of the V600E mutation in the BRAF gene in cancer cells, which qualifies the patient for therapy with inhibitors of the MAPK pathway. The identification of response markers is particularly important given primary and acquired resistance to targeted therapies. Reliable predictive tests would enable the selection of patients who would have the best chance of benefiting from treatment. Here, up-to-date knowledge about the most promising genetic and non-genetic resistance-related factors is described. These are alterations in MAPK, PI3K/AKT, and RB signaling pathways, e.g., due to mutations in NRAS, RAC1, MAP2K1, MAP2K2, and NF1, but also other changes activating these pathways, such as the overexpression of HGF or EGFR. Most of them are also potential therapeutic targets and this issue is also addressed here

NGS Analysis of Liquid Biopsy (LB) and Formalin-Fixed Paraffin-Embedded (FFPE) Melanoma Samples Using Oncomine™ Pan-Cancer Cell-Free Assay

Next-generation sequencing (NGS) in liquid biopsies may contribute to the diagnosis, monitoring, and personalized therapy of cancer through the real-time detection of a tumor’s genetic profile. There are a few NGS platforms offering high-sensitivity sequencing of cell-free DNA (cfDNA) samples. The aim of this study was to evaluate the Ion AmpliSeq HD Technology for targeted sequencing of tumor and liquid biopsy samples from patients with fourth-stage melanoma. Sequencing of 30 samples (FFPE tumor and liquid biopsy) derived from 14 patients using the Oncomine™ Pan-Cancer Cell-Free Assay was performed. The analysis revealed high concordance between the qPCR and NGS results of the BRAF mutation in FFPE samples (91%), as well as between the FFPE and liquid biopsy samples (91%). The plasma-tumor concordance of the non-BRAF mutations was 28%. A total of 17 pathogenic variants in 14 genes (from 52-gene panel), including TP53, CTNNB1, CCND1, MET, MAP2K1, and GNAS, were identified, with the CTNNB1S45F variant being the most frequent. A positive correlation between the LDH level and cfDNA concentration as well as negative correlation between the LDH level and time to progression was confirmed in a 22-patient cohort. The analysis showed both the potential and limitations of liquid biopsy genetic profiling using HD technology and the Ion Torrent platform

Global Gene Expression Profiling in Three Tumor Cell Lines Subjected to Experimental Cycling and Chronic Hypoxia

<div><p>Hypoxia is one of the most important features of the tumor microenvironment, exerting an adverse effect on tumor aggressiveness and patient prognosis. Two types of hypoxia may occur within the tumor mass, chronic (prolonged) and cycling (transient, intermittent) hypoxia. Cycling hypoxia has been shown to induce aggressive tumor cell phenotype and radioresistance more significantly than chronic hypoxia, though little is known about the molecular mechanisms underlying this phenomenon. The aim of this study was to delineate the molecular response to both types of hypoxia induced experimentally in tumor cells, with a focus on cycling hypoxia. We analyzed <i>in</i><i>vitro</i> gene expression profile in three human cancer cell lines (melanoma, ovarian cancer, and prostate cancer) exposed to experimental chronic or transient hypoxia conditions. As expected, the cell-type specific variability in response to hypoxia was significant. However, the expression of 240 probe sets was altered in all 3 cell lines. We found that gene expression profiles induced by both types of hypoxia were qualitatively similar and strongly depend on the cell type. Cycling hypoxia altered the expression of fewer genes than chronic hypoxia (6,132 <i>vs.</i> 8,635 probe sets, FDR adjusted p<0.05), and with lower fold changes. However, the expression of some of these genes was significantly more affected by cycling hypoxia than by prolonged hypoxia, such as <i>IL8, PLAU,</i> and epidermal growth factor (EGF) pathway-related genes (<i>AREG, HBEGF,</i> and <i>EPHA2</i>). These transcripts were, in most cases, validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Our results indicate that experimental cycling hypoxia exerts similar, although less intense effects, on the examined cancer cell lines than its chronic counterpart. Nonetheless, we identified genes and molecular pathways that seem to be preferentially regulated by cyclic hypoxia.</p></div

Experimental design.

<p>Cells were subjected to cycling (interchanging periods of 1% and ambient oxygen), chronic hypoxia (constant 1% oxygen) or control conditions (ambient oxygen) for 72 hours. The scheme predominantly shows the time scale of cycling hypoxia, which consisted of the following periods of hypoxia (1% oxygen) and reoxygenation (21% oxygen): 1%–4 hours, 21%–4 hours, followed by two cycles of 1%–12 hours, 21%–4 hours, 1%–4 hours, 21%–4 hours, followed by 4 hours of reoxygenation.</p

Unsupervised analysis of all samples by multidimensional scaling.

<p>The scheme shows that the difference between molecular profiles of the analyzed cell lines is the main source of variance. The hypoxic stimulus is the second factor differentiating samples. The dots represent replicas of samples: control (green), chronic hypoxia-treated (red) and cycling hypoxia-treated (blue). The axes are titled: component #1 (the X-axis) and component #3 (the Y-axis).</p

Hypoxia-regulated genes strongly related to carcinogenesis.

<p>“+” and “–” signify increase and decrease in expression, respectively.</p

Cycling hypoxia-responsive genes in different cell lines (FDR adjusted p<0.05; the highest fold change between cycling vs. chronic and/or chronic vs. cycling hypoxia).

<p>The genes in <b>bold</b> have been chosen for qRT-PCR validation. The gene marked with * was marginal regarding the criteria described in the text.</p