Managing the genomic revolution in cancer diagnostics

2017 ◽  
Vol 471 (2) ◽  
pp. 175-194 ◽  
Author(s):  
Doreen Nguyen ◽  
Christopher D. Gocke
2017 ◽  
Vol 471 (2) ◽  
pp. 195-197 ◽  
Author(s):  
Doreen Nguyen ◽  
Christopher D. Gocke

2004 ◽  
Vol 171 (4S) ◽  
pp. 476-476 ◽  
Author(s):  
Katharina König ◽  
Jürgen Pannek ◽  
Ulrich Scheipers ◽  
Helmut Ermert ◽  
Statis Phillippou ◽  
...  

Author(s):  
Surabhi Khandige ◽  
Vikram V. Shanbhogue ◽  
Sanjiban Chakrabarty ◽  
Satyamoorthy Kapettu

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Skaidre Jankovskaja ◽  
Johan Engblom ◽  
Melinda Rezeli ◽  
György Marko-Varga ◽  
Tautgirdas Ruzgas ◽  
...  

AbstractThe tryptophan to kynurenine ratio (Trp/Kyn) has been proposed as a cancer biomarker. Non-invasive topical sampling of Trp/Kyn can therefore serve as a promising concept for skin cancer diagnostics. By performing in vitro pig skin permeability studies, we conclude that non-invasive topical sampling of Trp and Kyn is feasible. We explore the influence of different experimental conditions, which are relevant for the clinical in vivo setting, such as pH variations, sampling time, and microbial degradation of Trp and Kyn. The permeabilities of Trp and Kyn are overall similar. However, the permeated Trp/Kyn ratio is generally higher than unity due to endogenous Trp, which should be taken into account to obtain a non-biased Trp/Kyn ratio accurately reflecting systemic concentrations. Additionally, prolonged sampling time is associated with bacterial Trp and Kyn degradation and should be considered in a clinical setting. Finally, the experimental results are supported by the four permeation pathways model, predicting that the hydrophilic Trp and Kyn molecules mainly permeate through lipid defects (i.e., the porous pathway). However, the hydrophobic indole ring of Trp is suggested to result in a small but noticeable relative increase of Trp diffusion via pathways across the SC lipid lamellae, while the shunt pathway is proposed to slightly favor permeation of Kyn relative to Trp.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marleen M. Nieboer ◽  
Luan Nguyen ◽  
Jeroen de Ridder

AbstractOver the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.


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