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- Conteduca, V., et al.
(författare)
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Plasma tumour DNA as an early indicator of treatment response in metastatic castration-resistant prostate cancer
- 2020
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Ingår i: British Journal of Cancer. - : Springer Science and Business Media LLC. - 0007-0920 .- 1532-1827. ; 123, s. 982-987
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Tidskriftsartikel (refereegranskat)abstract
- Background Plasma tumour DNA (ptDNA) levels on treatment are associated with response in a variety of cancers. However, the role of ptDNA in prostate cancer monitoring remains largely unexplored. Here we characterised on-treatment ptDNA dynamics and evaluated its potential for early assessment of therapy efficacy for metastatic castration-resistant prostate cancer (mCRPC). Methods Between 2011 and 2016, 114 sequential plasma samples from 43 mCRPC abiraterone-treated patients were collected. Targeted next-generation sequencing was performed to determine ptDNA fraction. ptDNA progressive disease was defined as a rise in the fraction compared to the pre-treatment. Results A ptDNA rise in the first on-treatment sample (interquartile range (IQR) 2.6-3.7 months) was significantly associated with increased risk of early radiographic or any prostate-specific antigen (PSA) rise (odds ratio (OR) = 15.8, 95% confidence interval (CI) 3.5-60.2,p = 0.0002 and OR = 6.0, 95% CI 1.6-20.0,p = 0.01, respectively). We also identified exemplar cases that had a rise in PSA or pseudoprogression secondary to bone flare but no rise in ptDNA. In an exploratory analysis, initial ptDNA change was found to associate with the duration of response to prior androgen deprivation therapy (p < 0.0001) but not to prior taxanes (p = 0.32). Conclusions We found that ptDNA assessment for therapy monitoring in mCRPC is feasible and provides data relevant to the clinical setting. Prospective evaluation of these findings is now merited.
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| 2. |
- Gasi Tandefelt, Delila, 1982, et al.
(författare)
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Circulating cell-free DNA: Translating prostate cancer genomics into clinical care.
- 2020
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Ingår i: Molecular aspects of medicine. - : Elsevier BV. - 1872-9452 .- 0098-2997. ; 72
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Tidskriftsartikel (refereegranskat)abstract
- Only in the past decade tremendous advances have been made in understanding prostate cancer genomics and consequently in applying new treatment strategies. As options regarding treatments are increasing so are the challenges in selecting the right treatment option for each patient and not the least, understanding the optimal time-point and sequence of applying available treatments. Critically, without reliable methods that enable sequential monitoring of evolving genotypes in individual patients, we will never reach effective personalised driven treatment approaches. This review focuses on the clinical implications of prostate cancer genomics and the potential of cfDNA in facilitating treatment management.
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| 3. |
- Kleftogiannis, Dimitrios, et al.
(författare)
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Identification of single nucleotide variants using position-specific error estimation in deep sequencing data.
- 2019
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Ingår i: BMC medical genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .
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