Drug<scp>T</scp>arget<scp>I</scp>nspector: An assistance tool for patient treatment stratification

Schneider, Lara; Stöckel, Daniel; Kehl, Tim; Gerasch, Andreas; Ludwig, Nicole; Leidinger, Petra; Huwer, Hanno; Kohlbacher, Oliver; Hildebrandt, Andreas; Kaufmann, Michael; Gessler, Manfred; Keller, Andreas; Meese, Eckart; Graf, Norbert; Lenhof, Hans‐Peter

doi:10.1002/ijc.29897

Cited by 9 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This gene network was obtained from Wu et al [ 36 ], and the edge weights in the network indicate the tendency of the two genes to be joined to work together in one pathway. This network not only represents direct interactions between genes, but also includes functional interactions in a broader sense and has been used in many pathological and therapeutic studies related to cancer genes [ 37 , 38 , 39 , 40 , 41 ]. The 40,230 genes included in the entire gene network are provided in the Supplementary Materials .…”

Section: Resultsmentioning

confidence: 99%

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Zhao

Zhang

et al. 2018

Molecules

View full text Add to dashboard Cite

Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings.

show abstract

Section: Resultsmentioning

confidence: 99%

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Zhao

Zhang

et al. 2018

Molecules

View full text Add to dashboard Cite

show abstract

“…Thanks to Ensembl’s Variant Effect Predictor (VeP) database, variations can be investigated using DTI integrated genome browser. In this way, genes carrying mutations and genetic variations are identified, and can be exploited as to their potentially major effect on the tumour sensitivity to certain drugs [ 36 ].…”

Section: Poc Tools For Personalized Medicinementioning

confidence: 99%

Key Enabling Technologies for Point-of-Care Diagnostics

Primiceri

Chiriaco

Notarangelo

et al. 2018

Sensors

View full text Add to dashboard Cite

A major trend in biomedical engineering is the development of reliable, self-contained point-of-care (POC) devices for diagnostics and in-field assays. The new generation of such platforms increasingly addresses the clinical and environmental needs. Moreover, they are becoming more and more integrated with everyday objects, such as smartphones, and their spread among unskilled common people, has the power to improve the quality of life, both in the developed world and in low-resource settings. The future success of these tools will depend on the integration of the relevant key enabling technologies on an industrial scale (microfluidics with microelectronics, highly sensitive detection methods and low-cost materials for easy-to-use tools). Here, recent advances and perspectives will be reviewed across the large spectrum of their applications.

show abstract

“…Such annotation methods are currently being developed, e.g. [96], but no easy-to-use tool or API has yet been established.…”

Section: Variant Annotationmentioning

confidence: 99%

Bioinformatics for precision oncology

Singer

Irmisch

Ruscheweyh

et al. 2017

Briefings in Bioinformatics

View full text Add to dashboard Cite

Molecular profiling of tumor biopsies plays an increasingly important role not only in cancer research, but also in the clinical management of cancer patients. Multi-omics approaches hold the promise of improving diagnostics, prognostics and personalized treatment. To deliver on this promise of precision oncology, appropriate bioinformatics methods for managing, integrating and analyzing large and complex data are necessary. Here, we discuss the specific requirements of bioinformatics methods and software that arise in the setting of clinical oncology, owing to a stricter regulatory environment and the need for rapid, highly reproducible and robust procedures. We describe the workflow of a molecular tumor board and the specific bioinformatics support that it requires, from the primary analysis of raw molecular profiling data to the automatic generation of a clinical report and its delivery to decision-making clinical oncologists. Such workflows have to various degrees been implemented in many clinical trials, as well as in molecular tumor boards at specialized cancer centers and university hospitals worldwide. We review these and more recent efforts to include other high-dimensional multi-omics patient profiles into the tumor board, as well as the state of clinical decision support software to translate molecular findings into treatment recommendations.

show abstract

DrugTargetInspector: An assistance tool for patient treatment stratification

Cited by 9 publications

References 46 publications

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Key Enabling Technologies for Point-of-Care Diagnostics

Bioinformatics for precision oncology

Contact Info

Product

Resources

About