Amp(1q) and tetraploidy are commonly acquired chromosomal abnormalities in relapsed multiple myeloma.

Long-term disease control in multiple myeloma (MM) is typically an unmet medical need, and most patients experience multiple relapses. Fluorescence in situ hybridization (FISH) is the standard technique to detect chromosomal abnormalities (CAs), which are important to estimate the prognosis of MM and the allocation of risk adapted therapies. In advanced stages, the importance of CAs needs further investigation. From 148 MM patients, two or more paired samples, at least one of which was collected at relapse, were analyzed by FISH. Using targeted next-generation sequencing, we molecularly investigated samples harboring relapse-associated CAs. Sixty-one percent of the patients showed a change in the cytogenetic profile during the disease course, including 10% who acquired high-risk cytogenetics. Amp(1q) (≥4 copies of 1q21), driven by an additional increase in copy number in patients who already had 3 copies of 1q21, was the most common acquired CA with 16% affected patients. Tetraploidy, found in 10% of the samples collected at the last time-point, was unstable over the course of the disease and was associated with TP53 lesions. Our results indicate that cytogenetic progression is common in relapsed patients. The relatively high frequency of amp(1q) suggests an active role for this CA in disease progression

Targeted high throughput sequencing in clinical cancer Settings: formaldehyde fixed-paraffin embedded (FFPE) tumor tissues, input amount and tumor heterogeneity

<p>Abstract</p> <p>Background</p> <p>Massively parallel sequencing technologies have brought an enormous increase in sequencing throughput. However, these technologies need to be further improved with regard to reproducibility and applicability to clinical samples and settings.</p> <p>Methods</p> <p>Using identification of genetic variations in prostate cancer as an example we address three crucial challenges in the field of targeted re-sequencing: Small nucleotide variation (SNV) detection in samples of formalin-fixed paraffin embedded (FFPE) tissue material, minimal amount of input sample and sampling in view of tissue heterogeneity.</p> <p>Results</p> <p>We show that FFPE tissue material can supplement for fresh frozen tissues for the detection of SNVs and that solution-based enrichment experiments can be accomplished with small amounts of DNA with only minimal effects on enrichment uniformity and data variance.</p> <p>Finally, we address the question whether the heterogeneity of a tumor is reflected by different genetic alterations, e.g. different foci of a tumor display different genomic patterns. We show that the tumor heterogeneity plays an important role for the detection of copy number variations.</p> <p>Conclusions</p> <p>The application of high throughput sequencing technologies in cancer genomics opens up a new dimension for the identification of disease mechanisms. In particular the ability to use small amounts of FFPE samples available from surgical tumor resections and histopathological examinations facilitates the collection of precious tissue materials. However, care needs to be taken in regard to the locations of the biopsies, which can have an influence on the prediction of copy number variations. Bearing these technological challenges in mind will significantly improve many large-scale sequencing studies and will - in the long term - result in a more reliable prediction of individual cancer therapies.</p

Characterization of Transcriptional Changes in ERG Rearrangement-Positive Prostate Cancer Identifies the Regulation of Metabolic Sensors Such as Neuropeptide Y

<div><p>ERG gene rearrangements are found in about one half of all prostate cancers. Functional analyses do not fully explain the selective pressure causing ERG rearrangement during the development of prostate cancer. To identify transcriptional changes in prostate cancer, including tumors with ERG gene rearrangements, we performed a meta-analysis on published gene expression data followed by validations on mRNA and protein levels as well as first functional investigations. Eight expression studies (n = 561) on human prostate tissues were included in the meta-analysis. Transcriptional changes between prostate cancer and non-cancerous prostate, as well as ERG rearrangement-positive (ERG+) and ERG rearrangement-negative (ERG−) prostate cancer, were analyzed. Detailed results can be accessed through an online database. We validated our meta-analysis using data from our own independent microarray study (n = 57). 84% and 49% (fold-change>2 and >1.5, respectively) of all transcriptional changes between ERG+ and ERG− prostate cancer determined by meta-analysis were verified in the validation study. Selected targets were confirmed by immunohistochemistry: NPY and PLA2G7 (up-regulated in ERG+ cancers), and AZGP1 and TFF3 (down-regulated in ERG+ cancers). First functional investigations for one of the most prominent ERG rearrangement-associated genes - neuropeptide Y (NPY) - revealed increased glucose uptake <em>in vitro</em> indicating the potential role of NPY in regulating cellular metabolism. In summary, we found robust population-independent transcriptional changes in prostate cancer and first signs of ERG rearrangements inducing metabolic changes in cancer cells by activating major metabolic signaling molecules like NPY. Our study indicates that metabolic changes possibly contribute to the selective pressure favoring ERG rearrangements in prostate cancer.</p> </div

Gene expression studies included in the meta-analysis.

<p>The Affymetrix microarray technology was used in all of the studies.</p>*<p>AE, Array Express; GEO, Gene Expression Omnibus; O, Oncomine; status 10/2010.</p>**<p>n, number of samples used for the meta-analysis; samples that did not fulfill the quality criteria were excluded.</p

Study protocol.

<p>Meta-analysis was performed on eight independent gene expression studies focusing human prostate tissues. Two types of comparative analyses were used. Genes showing differentially regulated ERG+ and ERG− prostate cancer tissues were validated using an independent gene expression analysis (first validation) and immunohistochemical staining (second validation; only for selected genes).</p

Immunohistochemistry for proteins coded by differentially regulated genes in ERG+ and ERG− prostate cancer.

<p>A) Consecutive slides of prostate tissues of two representative patients. B) Quantification of tissue specimens obtained from 61 different prostate cancer patients using the immunohistochemistry quantification software HistoQuest. HistoQuest does not distinguish between epithelial and stromal cells. Differences in staining intensity in epithelial cells exceed the differences shown in the box-blots. Bar, 100 µM. Statistics, Mann Whitney U-test; *,p<0.05; **p<0.01; ***p<0.001.</p

Results of the meta-analysis.

<p>Genes differentially regulated in prostate cancer and benign prostate tissue (A, C), and ERG+ and ERG− prostate cancer tissue (B, D). A–B) volcano plots. Differentially regulated genes were highlighted when at least 2-fold down- (green) or up-regulated (red), and had an adjusted p-value smaller than 0.1. C–D) Graphic diagram of the eight top-ranked functional clusters determined by functional annotation clustering, using the DAVID database. The size of the clusters correlates with the number of identified proteins associated with functional annotation (fold-change >1.5). When proteins are present in more than one cluster, the clusters are connected by lines. The thickness of the connecting lines reflects the number of proteins present in both connected clusters. Data concerning 561 tissue samples were used for the meta-analysis.</p