ReviewHepatic oncology2015
Next-generation sequencing and personalized genomic medicine in hepatobiliary malignancies.
Review in Hepatic oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver cancer is a heterogeneous group of tumors characterized by significant molecular and genomic heterogeneity. The advent of powerful genomic technologies has allowed detection of recurrent somatic alterations in liver cancer, including mutations, copy number alterations as well as changes in transcriptomes and epigenomes, with the potential to translate these data into clinically relevant predictive and prognostic factors. In this review, we discuss recent advances in the application of high-throughput genomic technologies in liver cancer and the integration of such cancer genome profiling data, highlighting specific relevant subgroups and explain how this knowledge can be used in translational clinical research, 'basket trials', molecular tumor boards, targeted therapy and for personalized genomic medicine applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.