Frequency of differentially replicated probes on each 500-kb interval of chromosomes 21 and 22

<p><b>Copyright information:</b></p><p>Taken from "Error-pooling-based statistical methods for identifying novel temporal replication profiles of human chromosomes observed by DNA tiling arrays"</p><p></p><p>Nucleic Acids Research 2007;35(9):e69-e69.</p><p>Published online 11 Apr 2007</p><p>PMCID:PMC1888820.</p><p>© 2007 The Author(s)</p> The start and end parts of chromosomes have much higher concentration of differential replication for both chromosomes, and the number of these probes is larger near the centromere and telomere of q-arms than the remaining chromosome positions. Frequencies of differentially replicated probes in coding and noncoding regions on chromosomes 21 and 22

Replication patterns for four genes: HASF2BP, COL6A2, PCNT2 and ANKRD21, with differential replication in time

<p><b>Copyright information:</b></p><p>Taken from "Error-pooling-based statistical methods for identifying novel temporal replication profiles of human chromosomes observed by DNA tiling arrays"</p><p></p><p>Nucleic Acids Research 2007;35(9):e69-e69.</p><p>Published online 11 Apr 2007</p><p>PMCID:PMC1888820.</p><p>© 2007 The Author(s)</p> These genes are called as early (0–2 h), middle (2–6 h) or late (6–8 h) replicated genes. For example, Figure 3A shows that HASF2BP has the highest peak at early replication time, where each line in this figure represents a sequence probe for this gene

Estimated LPE baseline distributions for four time periods of replication in S phase

<p><b>Copyright information:</b></p><p>Taken from "Error-pooling-based statistical methods for identifying novel temporal replication profiles of human chromosomes observed by DNA tiling arrays"</p><p></p><p>Nucleic Acids Research 2007;35(9):e69-e69.</p><p>Published online 11 Apr 2007</p><p>PMCID:PMC1888820.</p><p>© 2007 The Author(s)</p> The LPE variance estimates of the replicated tiling arrays were found to be a non-increasing function of probe intensity. Left-hand sides of the LPE estimates were thresholded due to the artificially reduced variability, which can be easily revealed in the AM plots (see Supplementary Figure 1). The LPE baseline distributions showed significantly different magnitude between time conditions

Retrospective Analysis of Survival Improvement by Molecular Biomarker-Based Personalized Chemotherapy for Recurrent Ovarian Cancer

<div><p>Aggressive tumors such as epithelial ovarian cancer (EOC) are highly heterogeneous in their therapeutic response, making it difficult to improve overall response by using drugs in unselected patients. The goal of this study was to retrospectively, but independently, examine whether biomarker-based personalized chemotherapy selection could improve survival of EOC patients. Using <i>in vitro</i> drug sensitivity and patient clinical outcome data, we have developed <i>co-expression extrapolation</i> (COXEN) biomarker models for predicting patient response to three standard chemotherapy drugs used to treat advanced EOC: paclitaxel, cyclophosphamide, and topotecan, for which sufficient patient data were available for our modeling and independent validation. Four different cohorts of 783 EOC patients were used in our study, including two cohorts of 499 patients for independent validation. The COXEN predictors for the three drugs independently showed high prediction both for patient short-term therapeutic response and long-term survival for recurrent EOC. We then examined the potential clinical benefit of the simultaneous use of the three drug predictors for a large diverse EOC cohort in a prospective manner, finding that the median overall survival was 21 months longer for recurrent EOC patients who were treated with the predicted most effective chemotherapies. Survival improvement was greater for platinum-sensitive patients if they were treated with the predicted most beneficial drugs. Following the FDA guidelines for diagnostic prediction analysis, our study has retrospectively, yet independently, showed a potential for biomarker-based personalized chemotherapy selection to significantly improve survival of patients in the heterogeneous EOC population when using standard chemotherapies.</p></div

Logistic regression analysis for the paclitaxel prediction of primary chemotherapy response.

a<p>An univariate logistic regression analysis was performed for each of the predictor and clinical variables to predict patient clinical response to paclitaxel; statistical significance was reported with overall model significance p-value.</p>b<p>A multivariate logistic regression analysis was performed with predictor and all clinical variables in the same model; the statistical significance of each variable was derived from the fitted model.</p

Clinical response rates of COXEN-matched vs. unmatched patient groups in the TCGA cohort after the primary platinum-based chemotherapy.

a<p>Almost all patients were treated with paclitaxel in the first-line chemotherapy, so the matched patients were predicted to have the highest survival benefit from the drug (of the three) and the unmatched patients were predicted to have the highest survival benefit from the other two drugs.</p

Integrated co-expression extrapolation (COXEN) gene expression model (predictor) development and validation procedures.

<p>Integrated co-expression extrapolation (COXEN) gene expression model (predictor) development and validation procedures.</p

Kaplan-Meier survival analysis of predicted responders and nonresponders among recurrent EOC patients.

<p>(A) paclitaxel predictor prediction for OS in TCGA-448, (B) cyclophosphamide predictor prediction for OS in TCGA-448, (C) topotecan predictor prediction for OS in TCGA-test.</p

Kaplan-Meier survival stratification between COXEN-matched and unmatched patients in the TCGA-448 cohort.

<p>(A) OS difference between matched and unmatched patients, (B) PFS difference between matched and unmatched patients, (C) OS difference between matched and unmatched patients among recurrent EOC patients.</p

Cox regression survival analysis for the prediction of patient survival after primary and secondary chemotherapies.

a<p>Univariate logistic regression analysis was performed for each of the predictor and clinical variables to predict patient survival after primary and secondary chemotherapies; statistical significance was reported with overall model significance p-value.</p>b<p>A multivariate Cox regression analysis was performed with the predictor and all clinical variables in the same model; the statistical significance of each variable was derived from the fitted model. Both OS and PFS were predicted after the primary platinum-based chemotherapy with paclitaxel, and OS was predicted after the secondary chemotherapy, either with cyclophosphamide or topotecan.</p