Cell line identity finding by fingerprinting, an optimized resource for short tandem repeat profile authentication.

The generation of biological data on wide panels of tumor cell lines is recognized as a valid contribution to the cancer research community. However, research laboratories can benefit from this knowledge only after the identity of each individual cell line used in the experiments is verified and matched to external sources. Among the methods employed to assess cell line identity, DNA fingerprinting by profiling Short Tandem Repeat (STR) at variable loci has become the method of choice. However, the analysis of cancer cell lines is sometimes complicated by their intrinsic genetic instability, resulting in multiple allele calls per locus. In addition, comparison of data across different sources must deal with the heterogeneity of published profiles both in terms of number and type of loci used. The aim of this work is to provide the scientific community a homogeneous reference dataset for 300 widely used tumor cell lines, profiled in parallel on 16 loci. This large dataset is interfaced with an in-house developed software tool for Cell Line Identity Finding by Fingerprinting (CLIFF), featuring an original identity score calculation, which facilitates the comparison of STR profiles from different sources and enables accurate calls when multiple loci are present. CLIFF additionally allows import and query of proprietary STR profile datasets.

Discovery of drug mode of action and drug repositioning from transcriptional responses.

A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).

Transcriptional analysis of an E2F gene signature as a biomarker of activity of the cyclin-dependent kinase inhibitor PHA-793887 in tumor and skin biopsies from a phase I clinical study.

A transcriptional signature of the pan-cyclin-dependent kinase (Cdk) inhibitor PHA-793887 was evaluated as a potential pharmacodynamic and/or response biomarker in tumor and skin biopsies from patients treated in a phase I clinical study. We first analyzed the expression of a number of known E2F-dependent genes that were predicted to be modulated after Cdk2 and Cdk4 inhibition in xenograft tumor and skin samples of mice treated with the compound. This panel of 58 selected genes was then analyzed in biopsies from seven patients treated with PHA-793887 in a phase I dose escalation clinical trial in solid tumors. Quantitative real-time PCR or microarray analyses were done in paired skin and tumor biopsies obtained at baseline and at cycle 1. Analysis by quantitative real-time PCR of the signature in skin biopsies of patients treated at three different doses showed significant transcriptional downregulation with a dose-response correlation. These data show that PHA-793887 modulates genes involved in cell cycle regulation and proliferation in a clinical setting. The observed changes are consistent with its mechanism of action and correlate with target modulation in skin and with clinical benefit in tumors.

Cross platform microarray analysis for robust identification of differentially expressed genes.

BACKGROUND: Microarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles. In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes. RESULTS: In this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms. We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with approximately 80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR. CONCLUSION: In this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes.