Escalated (Dependent) Oxycodone Self-Administration Is Associated with Cognitive Impairment and Transcriptional Evidence of Neurodegeneration in Human Immunodeficiency Virus (HIV) Transgenic Rats.

Substance use disorder is associated with accelerated disease progression in people with human immunodeficiency virus (HIV; PWH). Problem opioid use, including high-dose opioid therapy, prescription drug misuse, and opioid abuse, is high and increasing in the PWH population. Oxycodone is a broadly prescribed opioid in both the general population and PWH. Here, we allowed HIV transgenic (Tg) rats and wildtype (WT) littermates to intravenously self-administer oxycodone under short-access (ShA) conditions, which led to moderate, stable, "recreational"-like levels of drug intake, or under long-access (LgA) conditions, which led to escalated (dependent) drug intake. HIV Tg rats with histories of oxycodone self-administration under LgA conditions exhibited significant impairment in memory performance in the novel object recognition (NOR) paradigm. RNA-sequencing expression profiling of the medial prefrontal cortex (mPFC) in HIV Tg rats that self-administered oxycodone under ShA conditions exhibited greater transcriptional evidence of inflammation than WT rats that self-administered oxycodone under the same conditions. HIV Tg rats that self-administered oxycodone under LgA conditions exhibited transcriptional evidence of an increase in neuronal injury and neurodegeneration compared with WT rats under the same conditions. Gene expression analysis indicated that glucocorticoid-dependent adaptations contributed to the gene expression effects of oxycodone self-administration. Overall, the present results indicate that a history of opioid intake promotes neuroinflammation and glucocorticoid dysregulation, and excessive opioid intake is associated with neurotoxicity and cognitive impairment in HIV Tg rats.

Inferring clonal composition from multiple tumor biopsies.

Knowledge about the clonal evolution of a tumor can help to interpret the function of its genetic alterations by identifying initiating events and events that contribute to the selective advantage of proliferative, metastatic, and drug-resistant subclones. Clonal evolution can be reconstructed from estimates of the relative abundance (frequency) of subclone-specific alterations in tumor biopsies, which, in turn, inform on its composition. However, estimating these frequencies is complicated by the high genetic instability that characterizes many cancers. Models for genetic instability suggest that copy number alterations (CNAs) can influence mutation-frequency estimates and thus impede efforts to reconstruct tumor phylogenies. Our analysis suggested that accurate mutation frequency estimates require accounting for CNAs-a challenging endeavour using the genetic profile of a single tumor biopsy. Instead, we propose an optimization algorithm, Chimæra, to account for the effects of CNAs using profiles of multiple biopsies per tumor. Analyses of simulated data and tumor profiles suggested that Chimæra estimates are consistently more accurate than those of previously proposed methods and resulted in improved phylogeny reconstructions and subclone characterizations. Our analyses inferred recurrent initiating mutations in hepatocellular carcinomas, resolved the clonal composition of Wilms' tumors, and characterized the acquisition of mutations in drug-resistant prostate cancers.

Effects of Co-occurring Genomic Alterations on Outcomes in Patients with KRAS-Mutant Non-Small Cell Lung Cancer.

Purpose:KRAS mutations occur in approximately 25% of patients with non-small cell lung cancer (NSCLC). Despite the uniform presence of KRAS mutations, patients with KRAS-mutant NSCLC can have a heterogeneous clinical course. As the pattern of co-occurring mutations may describe different biological subsets of patients with KRAS-mutant lung adenocarcinoma, we explored the effects of co-occurring mutations on patient outcomes and response to therapy.Experimental Design: We identified patients with advanced KRAS-mutant NSCLC and evaluated the most common co-occurring genomic alterations. Multivariate analyses were performed incorporating the most frequent co-mutations and clinical characteristics to evaluate association with overall survival as well as response to platinum-pemetrexed chemotherapy and immune checkpoint inhibitors.Results: Among 330 patients with advanced KRAS-mutant lung cancers, the most frequent co-mutations were found in TP53 (42%), STK11 (29%), and KEAP1/NFE2L2 (27%). In a multivariate analysis, there was a significantly shorter survival in patients with co-mutations in KEAP1/NFE2L2 [HR, 1.96; 95% confidence interval (CI), 1.33-2.92; P ≤ 0.001]. STK11 (HR, 1.3; P = 0.22) and TP53 (HR 1.11, P = 0.58) co-mutation statuses were not associated with survival. Co-mutation in KEAP1/NFE2L2 was also associated with shorter duration of initial chemotherapy (HR, 1.64; 95% CI, 1.04-2.59; P = 0.03) and shorter overall survival from initiation of immune therapy (HR, 3.54; 95% CI, 1.55-8.11; P = 0.003).Conclusions: Among people with KRAS-mutant advanced NSCLC, TP53, STK11, and KEAP1/NFE2L2 are the most commonly co-occurring somatic genomic alterations. Co-mutation of KRAS and KEAP1/ NFE2L2 is an independent prognostic factor, predicting shorter survival, duration of response to initial platinum-based chemotherapy, and survival from the start of immune therapy. Clin Cancer Res; 24(2); 334-40. ©2017 AACR.

Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.

We introduce a method for simultaneous prediction of microRNA-target interactions and their mediated competitive endogenous RNA (ceRNA) interactions. Using high-throughput validation assays in breast cancer cell lines, we show that our integrative approach significantly improves on microRNA-target prediction accuracy as assessed by both mRNA and protein level measurements. Our biochemical assays support nearly 500 microRNA-target interactions with evidence for regulation in breast cancer tumors. Moreover, these assays constitute the most extensive validation platform for computationally inferred networks of microRNA-target interactions in breast cancer tumors, providing a useful benchmark to ascertain future improvements.