Jan Budczies

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.

PURPOSE Preclinical data suggest a contribution of the immune system to chemotherapy response. In this study, we investigated the prespecified hypothesis that the presence of a lymphocytic infiltrate in cancer tissue predicts the response to neoadjuvant chemotherapy. METHODS We investigated intratumoral and stromal lymphocytes in a total of 1,058 pretherapeutic breast cancer core biopsies from two neoadjuvant anthracycline/taxane-based studies (GeparDuo, n = 218, training cohort; and GeparTrio, n = 840, validation cohort). Molecular parameters of lymphocyte recruitment and activation were evaluated by kinetic polymerase chain reaction in 134 formalin-fixed, paraffin-embedded tumor samples. Results In a multivariate regression analysis including all known predictive clinicopathologic factors, the percentage of intratumoral lymphocytes was a significant independent parameter for pathologic complete response (pCR) in both cohorts (training cohort: P = .012; validation cohort: P = .001). Lymphocyte-predominant breast cancer responded, with pCR rates of 42% (training cohort) and 40% (validation cohort). In contrast, those tumors without any infiltrating lymphocytes had pCR rates of 3% (training cohort) and 7% (validation cohort). The expression of inflammatory marker genes and proteins was linked to the histopathologic infiltrate, and logistic regression showed a significant association of the T-cell-related markers CD3D and CXCL9 with pCR. CONCLUSION The presence of tumor-associated lymphocytes in breast cancer is a new independent predictor of response to anthracycline/taxane neoadjuvant chemotherapy and provides useful information for oncologists to identify a subgroup of patients with a high benefit from this type of chemotherapy.

Gene or protein expression data are usually represented by metric or at least ordinal variables. In order to translate a continuous variable into a clinical decision, it is necessary to determine a cutoff point and to stratify patients into two groups each requiring a different kind of treatment. Currently, there is no standard method or standard software for biomarker cutoff determination. Therefore, we developed Cutoff Finder, a bundle of optimization and visualization methods for cutoff determination that is accessible online. While one of the methods for cutoff optimization is based solely on the distribution of the marker under investigation, other methods optimize the correlation of the dichotomization with respect to an outcome or survival variable. We illustrate the functionality of Cutoff Finder by the analysis of the gene expression of estrogen receptor (ER) and progesterone receptor (PgR) in breast cancer tissues. This distribution of these important markers is analyzed and correlated with immunohistologically determined ER status and distant metastasis free survival. Cutoff Finder is expected to fill a relevant gap in the available biometric software repertoire and will enable faster optimization of new diagnostic biomarkers. The tool can be accessed at http://molpath.charite.de/cutoff.