Improved genome-wide localization by ChIP-chip using double-round T7 RNA polymerase-based amplification

Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is a powerful technique to detect in vivo protein–DNA interactions. Due to low yields, ChIP assays of transcription factors generally require amplification of immunoprecipitated genomic DNA. Here, we present an adapted linear amplification method that involves two rounds of T7 RNA polymerase amplification (double-T7). Using this we could successfully amplify as little as 0.4 ng of ChIP DNA to sufficient amounts for microarray analysis. In addition, we compared the double-T7 method to the ligation-mediated polymerase chain reaction (LM-PCR) method in a ChIP-chip of the yeast transcription factor Gsm1p. The double-T7 protocol showed lower noise levels and stronger binding signals compared to LM-PCR. Both LM-PCR and double-T7 identified strongly bound genomic regions, but the double-T7 method increased sensitivity and specificity to allow detection of weaker binding sites

Exploring Gene Expression Signatures for Predicting Disease Free Survival after Resection of Colorectal Cancer Liver Metastases

<div><h3>Background and Objectives</h3><p>This study was designed to identify and validate gene signatures that can predict disease free survival (DFS) in patients undergoing a radical resection for their colorectal liver metastases (CRLM).</p> <h3>Methods</h3><p>Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France) and the University Medical Center Utrecht (The Netherlands). Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort.</p> <h3>Result</h3><p>No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort.</p> <h3>Conclusions</h3><p>No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.</p> </div

Flow charts showing the study design. A:

<p>Original set up of the study: supervised model dividing patients with DFS ≤1 year versus patients with DFS >1 year. The gene signature was discovered using the training set and subsequently tested on the independent validation set. <b>B:</b> Similar to A, using a supervised model dividing patients with DFS ≤6 months versus patients with DFS >2 years. <b>C:</b> Similar to A, including only patients treated in Paul Brousse. <b>D:</b> Similar to A, including only patients treated in UMC Utrecht. <b>E:</b> Similar to A, including only patients treated in Paul Brousse treated with neoadjuvant chemotherapy.</p

Univariate Cox regression analysis for possible risk factors associated with DFS (months).

<p>DFS, disease free survival; HR, hazard ratio; CI, confidence interval; LM, lymph nodes; CEA, carcinoembryonic antigen.</p>a<p>P values were calculated with the use of log-rank test.</p

Kaplan–Meier survival analysis for gene signatures based on training sets without neoadjuvant treatment bias.

<p>Patients are divided into a high and a low risk prediction group based on the risk prediction of the different gene signatures. Gene signatures were discovered defining high risk as DFS ≤1 year and low risk as DFS >1 year unless mentioned otherwise. In all training sets the ratio of patients treated with neoadjuvant chemotherapy to untreated patients in high and low risk group was kept as equal as possible to preclude any treatment bias. The hazard ratio of the gene signature prediction is shown with the 95% confidence interval between brackets. The p value of the log-rank test is shown as well, with the p value adjusted for multiple testing between brackets. <b>A:</b> Survival curves for patients in the validation set. Gene signature was discovered using the full training set controlled for the neoadjuvant treatment bias. <b>B:</b> Survival curves for patients in the validation set. Gene signature was discovered using the full training set defining high risk as DFS ≤6 months and low risk as DFS >2 years and controlling for the neoadjuvant treatment bias. <b>C:</b> Survival curves for UMC Utrecht patients in the validation set. Gene signature was discovered using the UMC Utrecht subset of the training set controlled for the neoadjuvant treatment bias. <b>D:</b> Survival curves for Paul Brousse patients in the validation set. Gene signature was discovered using the Paul Brousse subset of the training set controlled for the neoadjuvant treatment bias.</p

Kaplan–Meier survival analysis for the discovered gene signatures.

<p>Patients are divided into a high and a low risk prediction group based on the risk prediction of the different gene signatures. Gene signatures were discovered defining high risk as DFS ≤1 year and low risk as DFS >1 year unless mentioned otherwise. The hazard ratio of the gene signature prediction is shown with the 95% confidence interval between brackets. The p value of the log-rank test is shown as well, with the p value adjusted for multiple testing between brackets. <b>A:</b> Survival curves for patients in training set. Gene signature was discovered using the same training set. <b>B:</b> Survival curves for patients in the validation set. Gene signature was discovered using the full training set. <b>C:</b> Survival curves for patients in the validation set. Gene signature was discovered using the full training set defining high risk as DFS ≤6 months and low risk as DFS >2 years. <b>D:</b> Survival curves for UMC Utrecht patients in the validation set. Gene signature was discovered using the UMC Utrecht subset of the training set. <b>E:</b> Survival curves for Paul Brousse patients in the validation set. Gene signature was discovered using the Paul Brousse subset of the training set. <b>F:</b> Like E but including only Paul Brousse patients who received neoadjuvant chemotherapy (training and validation set).</p

Kaplan–Meier survival analysis for gene signatures based on training sets stratified according to neoadjuvant treatment.

<p>Patients are divided into a high and a low risk prediction group based on the risk prediction of the different gene signatures. Gene signatures were discovered defining high risk as DFS ≤1 year and low risk as DFS >1 year unless mentioned otherwise. Both training and validation sets were separated into neoadjuvant treated and untreated patients. Results are only shown where the training sets contained enough high and low risk patients to make signature discovery possible. The hazard ratio of the gene signature prediction is shown with the 95% confidence interval between brackets. The p value of the log-rank test is shown as well, with the p value adjusted for multiple testing between brackets. <b>A:</b> Survival curves for patients in the validation set. Gene signatures were discovered using the full training set stratified by neoadjuvant treatment. <b>B:</b> Survival curves for untreated UMC Utrecht patients in the validation set. Gene signature was discovered using untreated UMC Utrecht patients in the training set. <b>C:</b> Survival curves for neoadjuvant treated Paul Brousse patients in the validation set. Gene signature was discovered using neoadjuvant treated Paul Brousse patients of the training set.</p

Patient- and tumor characteristics of high and low risk patients.^a

<p>DFS, disease free survival; LM, lymph nodes; CEA, carcinoembryonic antigen.</p>a<p>Percentages may not total 100 because of rounding.</p>b<p><i>P</i> values were calculated with the use of Mann-Whitney test for continuous variables and Fisher’s exact test for categorical variables.</p

Univariate and multivariate Cox regression analysis for risk factors associated with DFS (months) in Paul Brousse validation set.

<p>DFS, disease free survival; HR, hazard ratio; CI, confidence interval.</p>a<p>Only showing factors with p≤0.05 as well as Gene signature prediction.</p>b<p>Multivariate model includes factors with p≤0.05 in univariate analysis (Neoadjuvant chemotherapy and Stage primary tumor) as well as Gene signature prediction.</p>c<p>P values were calculated with the use of log-rank test.</p>d<p>TNM stages 1 and 2 versus 3 and 4.</p