Strategic Applications of Gene Expression: From Drug Discovery/Development to Bedside

Bai, Jane P. F.; Alekseyenko, Alexander V.; Statnikov, Alexander; Wang, I‐Ming; Wong, Peggy

doi:10.1208/s12248-012-9447-1

Cited by 40 publications

(29 citation statements)

References 95 publications

(92 reference statements)

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“…1 Omics Core, Research Branch, Out Patient Clinic, Sidra Medicine, PO 26999, Doha, Qatar. 2 System Biology, Research Branch, Out Patient Clinic, Sidra Medicine, Doha, Qatar. 3 Advanced Cell Therapy Core, Sidra Medicine, Doha, Qatar.…”

Section: Supplementary Informationmentioning

confidence: 99%

“…Projects aiming at biomarker discovery often employ transcriptional profiling of whole blood [2][3][4][5]. Peripheral blood is perhaps the most practical tissue to profile gene expression of the human immune system due to its accessibility, allowing large-scale and non-invasive sampling [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this study was (1) to compare different storage conditions on small volumes of blood and to assess the effect of GenTegra storage solution with regard to quality/quantity of RNA ( Fig. 1 displays the experiment rationale) and (2) to evaluate whether expression profiling is affected by the different storage conditions.…”

Section: Introductionmentioning

confidence: 99%

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Influence of storage conditions of small volumes of blood on immune transcriptomic profiles

et al. 2020

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Objective: Transcriptome analysis of human whole blood is used to discover biomarkers of diseases and to assess phenotypic traits. Here we have collected small volumes of blood in Tempus solution and tested whether different storage conditions have an impact on transcriptomic profiling. Fifty µl of blood were collected in 100µl of Tempus solutions, freezed at − 20 °C for 1 day and eventually thawed, stored and processed under five different conditions: (i) − 20 °C for 1 week; (ii) +4 °C for 1 week; (iii) room temperature for 1 week; (iv) room temperature for 1 day, − 20 °C for 1 day, room temperature until testing at day 7, (v) − 20 °C for 1 week, RNA was isolated and stored in GenTegra solution. We used 272 immune transcript specific assays to test the expression profiling using qPCR based Fluidigm BioMark HD dynamic array. Results: RNA yield ranged between 0.17 and 1.39µg. Except for one sample, RIN values were > 7. Using Principal Component Analysis, we saw that the storage conditions did not drive sample distribution. The condition that showed larger variability was the RT-FR-RT (room temperature-freezing-room temperature), suggesting that freezing-thawing cycles may have a worse effect on data reproducibility than keeping the samples at room temperature.

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Section: Supplementary Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of storage conditions of small volumes of blood on immune transcriptomic profiles

et al. 2020

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“…130 As templates for future studies, such major tool sets for data mining, often across different technology platforms, will undoubtedly be beneficial as a system to support collaborative projects in general and particularly as public resources of highly curated data to advance the field of patient safety in cancer therapy. 131 …”

Section: Personalized Radiotherapy-perspectives and Challengesmentioning

confidence: 99%

Personalized radiotherapy: concepts, biomarkers and trial design

Ree¹,

Redalen²

2015

BJR

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In the past decade, and pointing onwards to the immediate future, clinical radiotherapy has undergone considerable developments, essentially including technological advances to sculpt radiation delivery, the demonstration of the benefit of adding concomitant cytotoxic agents to radiotherapy for a range of tumour types and, intriguingly, the increasing integration of targeted therapeutics for biological optimization of radiation effects. Recent molecular and imaging insights into radiobiology will provide a unique opportunity for rational patient treatment, enabling the parallel design of next-generation trials that formally examine the therapeutic outcome of adding targeted drugs to radiation, together with the critically important assessment of radiation volume and dose-limiting treatment toxicities. In considering the use of systemic agents with presumed radiosensitizing activity, this may also include the identification of molecular, metabolic and imaging markers of treatment response and tolerability, and will need particular attention on patient eligibility. In addition to providing an overview of clinical biomarker studies relevant for personalized radiotherapy, this communication will highlight principles in addressing clinical evaluation of combined-modality-targeted therapeutics and radiation. The increasing number of translational studies that bridge large-scale omics sciences with quality-assured phenomics end points-given the imperative development of open-source data repositories to allow investigators the access to the complex data sets-will enable radiation oncology to continue to position itself with the highest level of evidence within existing clinical practice.

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“…The use of transcriptomic data for characterizing biological effects of small molecules has become increasingly popular since the advent of the Connectivity Map [15]. Several applications ranging from pathway elucidation [16], toxicity models [17,18] and toxicogenomic classifications [19] to tool discovery and drug repurposing [20][21][22][23] have been developed based on drug-induced gene expression profiling [9]. However, whereas these studies certainly have significant scientific value, they do not address the utility of gene expression profiling for decision making during the lead optimization phase of a typical drug discovery project.…”

Section: Introductionmentioning

confidence: 99%

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project

Verbist

Klambauer

Vervoort

et al. 2015

Drug Discovery Today

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The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making.

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Strategic Applications of Gene Expression: From Drug Discovery/Development to Bedside

Cited by 40 publications

References 95 publications

Influence of storage conditions of small volumes of blood on immune transcriptomic profiles

Influence of storage conditions of small volumes of blood on immune transcriptomic profiles

Personalized radiotherapy: concepts, biomarkers and trial design

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project

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