Summary: CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint information from segmentation and by inclusion of several biological concepts that are ignored by existing algorithms. The algorithm is validated for simulated and verified real array CGH data. By incorporating more than three classes, CGHcall improves detection of single copy gains and amplifications. Moreover, it allows effective inclusion of chromosome arm information. Availability: An R-package (GUI), a manual and an example data set are available at

Background: Gain of a large segment of chromosome 20q is associated with progression of colorectal adenomas into carcinomas, implying that multiple genes on the 20q amplicon drive carcinogenesis. Candidate driver genes are expected to be expressed at mRNA and protein levels that correlate with the 20q amplicon DNA copy number status, while functionally affecting one or several cancer-related processes. Integration of CGH profiles with mRNA profiles of a series of colorectal tumors revealed thirty-two candidate genes whose DNA copy number status correlated with mRNA expression levels. Aim: To functionally analyse the effects of the candidate oncogenes on cancer-related processes by downregulation using siRNA strategies. Results: Downmodulation of TPX2 (20q11.2) and AURKA (20q13.2) mRNA expression in CRC cell lines with 20q gain affected cell viability, anchorage-independent growth, and invasion. Moreover, immunohistochemical evaluation demonstrated a significant correlation between their protein levels and 20q DNA copy number status in a series of colorectal adenomas and carcinomas. Conclusion: These data demonstrate that at least two genes located on distinct regions of chromosome 20q promote colorectal adenoma-to-carcinoma progression and indicate that TPX2, like AURKA, is a promising target for anti-cancer drug development. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3042. doi:10.1158/1538-7445.AM2011-3042

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.