Tsukahara, T. | Search bioRxiv

bioRxiv2018

CTCF looping is established during gastrulation in medaka embryos

AbstractGenome architecture plays a critical role in gene regulation, but how the structures seen in mature cells emerge during embryonic development remains poorly understood. Here, we study early development in medaka (the Japanese killifish, Oryzias latipes) at 12 time points before, during, and after gastrulation which is the most dramatic event in early embryogenesis, and characterize transcription, protein binding, and genome architecture. We find that gastrulation is most associated with drastic changes in genome architecture, including the formation of the first loops between sites bound by the insulator protein CTCF and great increase in the size of contact domains. However, the position of CTCF is fixed throughout medaka embryogenesis. Interestingly, genome-wide transcription precedes the emergence of mature domains and CTCF-CTCF loops.

genomics↗

bioRxiv2018

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. This computational modeling approach addresses three major challenges in personalized cancer therapy, which we validate across multiple species via computationally-designed personalized synergistic drug combination predictions, identification of unifying therapeutic targets to overcome intra-tumor heterogeneity, and mitigation of cancer cell resistance and rewiring mechanisms. These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

bioinformatics↗