Incorporating DNA sequence information at intronic and intergenic enhancers should therefore improve the modeling of transcriptional regulation in tumors. However, in a few cancers-notably luminal breast and prostate cancer-ChIP-seq analyses of key transcriptional regulators, estrogen receptor (ER), and androgen receptor (AR) respectively, in both cell line models 5, 6 and tumors 7, 8 have revealed the importance of enhancers distal to gene promoters in gene regulatory programs. While several successful methods have been proposed for learning patient-specific regulatory programs, most regulatory network inference approaches in cancer use expression data only 1 or at best rely on analysis of TF motifs in annotated promoter regions 2, 3, 4. Ultimately, such strategies could be used to personalize therapy and improve patient outcomes. Data-driven computational strategies may help to infer patient-specific transcriptional regulatory programs and to identify and therapeutically target the TFs that lead to cancer phenotypes. Transcription factors (TFs) are instrumental in driving these gene expression programs, and the aberrant activity of TFs-induced downstream of activated oncogenic signaling or in concert with epigenetic modifiers-often underlies the altered developmental state of cancer cells and acquisition of cancer-related cellular phenotypes.
To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis.Ĭancers arise through the accumulation of genetic and epigenetic alterations that lead to widespread gene expression changes. Many identified TFs are significantly associated with survival outcome. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory differences between and within tumor types. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. These findings outline a novel dopamine-dependent mechanism for perceptual modulation in physiological conditions and further suggest that this mechanism may confer vulnerability to hallucinations in hyper-dopaminergic states underlying psychosis.Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. This bias could be pharmacologically induced by amphetamine, strongly correlated with striatal dopamine release, and related to cortical volume of the dorsal anterior cingulate, a brain region involved in tracking environmental uncertainty.
Hallucinations correlated with a perceptual bias, reflecting disproportional gain on expectations under uncertainty. To test for such a dopamine-dependent gain-control mechanism of hallucinations, we studied unmedicated patients with schizophrenia with varying degrees of hallucination severity and healthy individuals using molecular imaging with a pharmacological manipulation of dopamine, structural imaging, and a novel task designed to measure illusory changes in the perceived duration of auditory stimuli under different levels of uncertainty. Such excessive weight of prior expectations, in turn, could stem from a gain-control process controlled by neuromodulators such as dopamine. Bayesian models explain perception as an optimal combination of prior expectations and new sensory evidence, where perceptual distortions such as illusions and hallucinations may occur if prior expectations are afforded excessive weight.
However, an underlying cognitive mechanism linking dopamine dysregulation and the experience of hallucinatory percepts remains elusive. Hallucinations, a cardinal feature of psychotic disorders such as schizophrenia, are known to depend on excessive striatal dopamine.
0 kommentar(er)
