Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Supplementary Fig. S1 from Antitumor activity of histone deacetylase inhibitors in non-small cell lung cancer cells: development of a molecular predictive model

Supplementary Fig. S2 from Antitumor activity of histone deacetylase inhibitors in non-small cell lung cancer cells: development of a molecular predictive model

In the originally published version, the DOI in Reference 7 “Wittner, R., et al.: EOSClife common provenance model. EOSC-Life deliverable D6.2 (2021)” on p.225 was missing. The DOI “10.5281/zenodo.4705074” has been added.

The National Cancer Institute’s (NCI) Cancer MoonshotSM Biobank (the biobank) is a 5-year effort to accelerate research on cancer drug resistance and sensitivity. To accomplish this, the biobank will coordinate the donation of biospecimens from over 1000 research participants with cancer who receive standard of care therapy at participating NCI Community Oncology Research Program (NCORP) sites. As a return of value to participants and providers, the Biobank will conduct clinical tumor biomarker testing at a CLIA-certified laboratory. Individual biomarker report results will be provided directly to participants and their providers via a secure portal hosted on the biobank website (www. moonshotbiobank.cancer.gov). The report will include information about DNA and/or RNA found in the tumor that may be relevant to treatment, as well as information on potential therapies and clinical trials. Statement of the problem: Biomarker testing has grown considerably over the last decade. Unfortunately, patients and providers often lack sufficient resources and expertise to navigate the overwhelming amount of information and technical terminology in these reports. Proposed solution: To facilitate understanding of biomarker testing and key elements of the biobank biomarker report, we worked alongside providers, patient advocates, and communications experts to create supporting informational content for the biobank website. Resources include a sample report, an accompanying plain language guide to help users navigate and understand the report, a ‘Frequently Asked Questions’ area to be continuously updated based on participant and provider feedback, general information about biomarker testing, and links to biomarker testing learning modules and other relevant topics eligible for free Continuing Medical/Nursing Education (CME/CNE) credits through an external resource. Prior to launch of the project, training sessions were held to give providers an overview of the resources described here and to answer their questions about the content. Participants and providers will be invited to provide feedback regarding the usefulness of these resources via surveys distributed throughout the life of the project. Feedback will be used to iterate on and improve content. Conclusion: Through the creation of resources that promote understanding of biomarker testing and results, the biobank aims to empower participants and providers in the use of these resources to identify individualized treatments that have the potential to improve patient outcomes.

The performance indicator called limit of detection for microarray platform (LODP) was defined in ISO 16578:2013. The methods to determine practical LODP were explored. In general, + 3 SD of the background is used as the signal strength of limit of detection and criteria for dividing positive and negative results. Since the negative signal had been defined differently for each microarray platform, signals obtained from Non-Probe Spots (NPS) installed on the microarrays were defined as the "background" of microarrays. LODP was determined as the lowest concentration of which the average signal exceeded Avg. + 3 SD of the background (NPS) and the signal was significantly different from those of the lower and higher adjacent concentration points measured with a diluted series of reference materials. For reliable qualitative analysis, the positive results can be defined as signals higher than those corresponding to LODP and negative results as lower signals, without determining limit of detection for all target probes. The use of LODP also enables comparisons of platform performances without checking sequence dependencies, and assists to select reliable and fitting platforms for experimental purposes.

We developed a reference material of a single DNA molecule with a specific nucleotide sequence. The double-strand linear DNA which has PCR target sequences at the both ends was prepared as a reference DNA molecule, and we named the PCR targets on each side as confirmation sequence and standard sequence. The highly diluted solution of the reference molecule was dispensed into 96 wells of a plastic PCR plate to make the average number of molecules in a well below one. Subsequently, the presence or absence of the reference molecule in each well was checked by real-time PCR targeting for the confirmation sequence. After an enzymatic treatment of the reaction mixture in the positive wells for the digestion of PCR products, the resultant solution was used as the reference material of a single DNA molecule with the standard sequence. PCR analyses revealed that the prepared samples included only one reference molecule with high probability. The single-molecule reference material developed in this study will be useful for the absolute evaluation of a detection limit of PCR-based testing methods, the quality control of PCR analyses, performance evaluations of PCR reagents and instruments, and the preparation of an accurate calibration curve for real-time PCR quantitation.

RNA external standards, although important to ensure equivalence across many microarray platforms, have yet to be fully implemented in the research community. In this article, a set of unique RNA external standards (or RNA standards) and probe pairs that were added to total RNA in the samples before amplification and labeling are described. Concentration-response curves of RNA external standards were used across multiple commercial DNA microarray platforms and/or quantitative real-time polymerase chain reaction (RT-PCR) and next-generation sequencing to identify problematic assays and potential sources of variation in the analytical process. A variety of standards can be added in a range of concentrations spanning high and low abundances, thereby enabling the evaluation of assay performance across the expected range of concentrations found in a clinical sample. Using this approach, we show that we are able to confirm the dynamic range and the limit of detection for each DNA microarray platform, RT-PCR protocol, and next-generation sequencer. In addition, the combination of a series of standards and their probes was investigated on each platform, demonstrating that multiplatform calibration and validation is possible.

Abstract To ascertain the potential for histone deacetylase (HDAC) inhibitor-based treatment in non-small cell lung cancer (NSCLC), we analyzed the antitumor effects of trichostatin A (TSA) and suberoylanilide hydroxamic acid (vorinostat) in a panel of 16 NSCLC cell lines via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. TSA and vorinostat both displayed strong antitumor activities in 50% of NSCLC cell lines, suggesting the need for the use of predictive markers to select patients receiving this treatment. There was a strong correlation between the responsiveness to TSA and vorinostat (P < 0.0001). To identify a molecular model of sensitivity to HDAC inhibitor treatment in NSCLC, we conducted a gene expression profiling study using cDNA arrays on the same set of cell lines and related the cytotoxic activity of TSA to corresponding gene expression pattern using a modified National Cancer Institute program. In addition, pathway analysis was done with Pathway Architect software. We used nine genes, which were identified by gene-drug sensitivity correlation and pathway analysis, to build a support vector machine algorithm model by which sensitive cell lines were distinguished from resistant cell lines. The prediction performance of the support vector machine model was validated by an additional nine cell lines, resulting in a prediction value of 100% with respect to determining response to TSA and vorinostat. Our results suggested that (a) HDAC inhibitors may be promising anticancer drugs to NSCLC and (b) the nine-gene classifier is useful in predicting drug sensitivity to HDAC inhibitors and may contribute to achieving individualized therapy for NSCLC patients. [Mol Cancer Ther 2008;7(7):1923–30]

Despitetheenormousgrowthofgenomicsequencedata,computationalanalysisofregulatorysequencesremainsarelativelymarginaldomain. PolIIPromoterpredictionprogramswereassessedobjectivelythat they were still far from practical [3]. Many prediction programs use only either transcriptionfactor databases or computationally evaluated oligomers. If the prediction is performed with theboth techniques, the prediction may become more precise. The combination of both transcriptionfactordatabasesandmotifextractiontechniqueswasachievedbyPromFD[1]. However,PromFDarepoorinrepresentationof“additionalmotifs”, definedasartificiallyextracted motifsfrompromotersequences. PromFDuseonlystringsfortheadditionalmotifs,likeGCGCA.Now,moresophisticatedtechnologieslikeahiddenMarkovmodel(HMM)maybehelpfulforacquiring“additionalmotifs”andpromoterprediction.Our system provides an environment that supports systematic transcription regulatory regionanalyses, utilizingtherecentachievementsoftranscriptionfactordatabases, andamotifextractiontechnologywithHMMs.

The effects of electric stimulation on the morphological differentiation of PC12 cells are described. PC12 cells were stimulated with the 'theta' (4-7 Hz electroencephalogram (EEG) rhythm) pattern-electric stimulation, which was known to elicit stable long-term potentiation (LTP) in the CA1 region of the hippocampus. The stimulation induced the neurite outgrowth of PC12 cells, as well as nerve growth factor (NGF). This result suggests that the electric signal has a differentiating potential equivalent to the receptor-ligand interaction.

Monoclonal antibodies, 3B9 and 4C9, specific to connectin (also called titin), 3000 kDa elastic filamentous protein of vertebrate skeletal muscle, crossreacted with a high molecular weight protein (500 kDa) of the nematode Caenorhabditis elegans. However, its crossreactivity was weak to that of the unc-22 gene deficient mutant. Immunofluorescence showed that the antibodies stained both bodywall and pharynx muscles in the wild type, but only pharynx muscle in the mutant. Immunoelectron microscopy revealed that the antibodies bound to the dense bodies of bodywall muscle cells of the wild type but not to those of the mutants. In the pharynx muscles the localization of the antibodies was not clear in both normal and mutant worms.

1.1. A troponin-like protein was isolated from body wall muscle of Ascaris and separated into three components, the mol. wts of which were approx. 58,000, 36,000 and 20,000 respectively.2.2. The three components were designated as troponin-T (TNT), troponin-I (TNI) and troponin-C (TNC) in order of mol. wt, since each component had properties similar to the respective components of vertebrate skeletal-muscle troponin.3.3. Ascaris troponin were localized on actin filaments with a 44 nm repeat, an approximately 4 nm longer repeat than vertebrate troponin.