sj-docx-1-urj-10.1177_03915603241238128 – Supplemental material for Predictors for surgical treatment in male patients with non-neurogenic lower urinary tract symptoms (LUTS)

Supplemental material, sj-docx-1-urj-10.1177_03915603241238128 for Predictors for surgical treatment in male patients with non-neurogenic lower urinary tract symptoms (LUTS) by Florin V Hopland-Nechita, John R Andersen and Christian Beisland in Urologia Journal</p

Real-life use of diagnostic biopsies before treatment of kidney cancer: results from a Norwegian population-based study

<p><b>Objective:</b> Interest in renal mass biopsies (RMBs) has increased in recent years. However, most publications are low-volume and/or single-center studies, so their generalizability is questionable. The aim of this study was to describe population-based, real-life use of diagnostic RMBs for localized and advanced kidney cancer (KC).</p> <p><b>Materials and methods:</b> All KC patients diagnosed during 2008–2013 extracted from the database at the Cancer Registry of Norway were included. Relationships with outcome were analyzed using multivariate logistic regression and competing risks analyses.</p> <p><b>Results:</b> Of patients treated radically for localized KC, a pretreatment RMB was used in 8.4%. For similar patients treated by observation only, the rate increased from 29.3% to 60.7% during the study period. Tumor size ≤4 cm, another malignancy, multiple tumors, old age (≥ 80 years) and second study half were independent RMB predictors. Competing risks analysis showed that among radically treated patients with localized KC, those who had undergone an RMB had a higher risk of dying of other diseases. In patients with advanced KC, biopsy was used in 54.5%, and is increasing. Study limitations include a lack of data on benign tumors, comorbidity and performance status.</p> <p><b>Conclusions:</b> For localized KC, the use of RMBs in Norway is in line with current guidelines. Because real-world data on RMB use are scarce, this study is useful for benchmarking in future studies. Furthermore, the study shows that fewer patients with advanced KC are treated without histopathological verification, and biopsies seem to have an increasing role in tailoring treatment.</p

Additional file 1: Fig. S1. of Predictive value of C-reactive protein in patients treated with sunitinib for metastatic clear cell renal cell carcinoma

CONSORT 2010 Flow Diagram. (DOCX 36 kb

Additional file 4: Table S3. of Predictive value of C-reactive protein in patients treated with sunitinib for metastatic clear cell renal cell carcinoma

Adverse effects. (DOCX 20 kb

Correlation of gene expression data.

<p>The correlation of commonly differentially expressed genes is given with respect to (A) average expression and (B) log2 fold changes.</p

Pathway signature of VEGF and NOTCH mediated EMT in ccRCC.

<p>Comparison of gene expression data from the FFPE and from the RNAlater^® dataset with published results [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149743#pone.0149743.ref020" target="_blank">20</a>] and between themselves. <i>F = FFPE samples</i>, <i>R = RNAlater</i>^<i>®</i> <i>samples</i>, <i>Numbers = fold change of up-regulation (red) or down-regulation (blue)</i>.</p

Gene network.

<p>The most differentially affected network with the central role of <i>TGFB1</i> in (A) FFPE samples and B) RNAlater data sets. <i>Proteins with cancer involvement are marked with purple outline</i>. <i>Red fill indicates overrepresentation of the gene in ccRCC</i>, <i>green indicates under-representation</i>. <i>Color intensity reflects range of fold change</i>.</p

Comparison of our gene expression data with data from literature [17].

<p>Twenty genes with smallest p-values and largest absolute fold changes in a meta-analysis of five microarray studies are compared to the corresponding genes and their fold changes and p-values of the NGS datasets. The median fold changes and standard deviations for the meta-analysis are presented. All shown genes were differentially expressed in only 2 or 3 microarray datasets. Large standard deviations indicate a large spread of values in the individual microarray studies. 17 of the 20 genes were found differentially expressed in both NGS datasets, 13 of these with fold changes within the fold change range of the microarray meta-analysis. <i>ND</i>: <i>not detected</i>, <i>did not pass initial expression filter</i>.</p

Development of a candidate marker for ccRCC.

<p>(A) Expression values of <i>CA9</i> correctly classified 30 of 32 samples in our FFPE dataset. (B) Whisker plot of expression value distribution in our FFPE dataset for <i>CA9</i>. (C) Scatterplot for the expression values of <i>CA9</i> in our FFPE and in our RNAlater dataset. (D) <i>CA9</i> expression values correctly classify 139 out of 144 samples in a microarray dataset of ccRCC (GSE53757). (E) Distribution of <i>CA9</i> expression values for normal (NO) and ccRCC tumor samples (TU) in the GSE53757 dataset. (F) Stratification of the expression values of overexpressed <i>CA9</i> into all four stages of ccRCC [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149743#pone.0149743.ref014" target="_blank">14</a>].</p

Characteristic patient features at the time of surgery.

<p>eGFR was calculated with the MDRD formula. The staging was performed based on the EAU Guidelines on renal cell carcinoma: 2014 update [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149743#pone.0149743.ref043" target="_blank">43</a>].</p