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Poos, A.

Publications and source records attributed to Poos, A..

2 recordsLinked to original sources

Temporal Genomic Dynamics Shape Clinical Trajectory in Multiple Myeloma

To comprehensively unravel the temporal relationship between initiating and driver events and its impact on clinical outcomes, we analyzed 421 whole-genome sequencing profiles from 382 patients. Using clock-like mutational signatures, we estimated a time lag of 2-4 decades between initiating events and diagnosis. In patients with hyperdiploidy, we demonstrate that trisomies of odd-numbered chromosomes can be acquired simultaneously with other chromosomal gains, such as 1q gain. We provide evidence that hyperdiploidy is acquired after canonical IGH translocation when both events are present. Finally, patients with early 1q gain had adverse outcomes similar to those with 1q amplification (>1 extra-copies), but faring worse than those with late 1q gain. This underscores that the prognostic impact of 1q gain/amp depends more on the timing of acquisition than on the number of extra copies gained. Overall, this study contributes to a better understanding of the life history of MM and may have prognostic implications.

cancer biology↗

Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach

BackgroundReactivation of the telomerase reverse transcriptase gene TERT is a central feature for the unlimited proliferation potential of the majority of cancers but the underlying regulatory processes are only partly understood.\n\nResultsWe assembled regulator binding information from different sources to construct a generic human and mouse regulatory network. Advancing our \"Mixed Integer linear Programming based Regulatory Interaction Predictor\" (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter.\n\nConclusionOur improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/network-modeling/MIPRIP. MIPRIP 2.0 identified both common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.

bioinformatics↗