DNA copy number aberrations (CNAs) are a characteristic feature of cancer genomes. In this work, Rebecka Jörnsten, Sven Nelander and colleagues combine network modeling and experimental methods to analyze the systems-level effects of CNAs in glioblastoma.

Allopolyploidy is an important process during plant evolution that results in the reunion of two divergent genomes into a common nucleus. Many of the immediate as well as longer-term genomic and epigenetic responses to polyploidy have become appreciated. To investigate the modifications of gene expression at the proteome level caused by allopolyploid formation, we conducted a comparative analysis of cotton seed proteomes from the allopolyploid Gossypium hirsutum (AD genome) and its model Agenome and D-genome diploid progenitors. An unexpectedly high level of divergence among the three proteomes was found, with about one-third of all protein forms being genome specific. Comparative analysis showed that there is a higher degree of proteomic similarity between the allopolyploid and its D-genome donor than its A-genome donor, reflecting a biased accumulation of seed proteins in the allopolyploid. Protein identification and genetic characterization of high-abundance proteins revealed that two classes of seed storage proteins, vicilins and legumins, compose the major component of cotton seed proteomes. Analyses further indicate differential regulation or modification of homoeologous gene products, as well as novel patterns in the polyploid proteome that may result from the interaction between homoeologous gene products. Our findings demonstrate that genomic merger and doubling have consequences that extend beyond the transcriptome into the realm of the proteome and that unequal expression of proteins from diploid parental genomes may occur in allopolyploids.

Highlights d A resource of 100 pharmacologically characterized patientderived glioblastoma lines d Integrated analyses define associations between drug response, pathways, and mutations d The response to proteasome inhibitors is linked to TP53 and CDKN2A/B aberrations

BackgroundNew molecular biomarkers for prostate cancer (PC) prognosis are urgently needed. Ratio-based models are attractive, as they require no additional normalization. Here, we train and independently validate a novel 4-miRNA prognostic ratio model for PC.Patients and methodsBy genome-wide miRNA expression profiling of PC tissue samples from 123 men who underwent radical prostatectomy (RP) (PCA123, training cohort), we identified six top candidate prognostic miRNAs and systematically tested their ability to predict postoperative biochemical recurrence (BCR). The best miRNA-based prognostic ratio model (MiCaP) was validated in two independent cohorts (PCA352 and PCA476) including >800 RP patients in total. Clinical end points were BCR and prostate cancer-specific survival (CSS). The prognostic potential of MiCaP was assessed by univariate and multivariate Cox-regression analyses and Kaplan–Meier analyses.ResultsWe identified a 4-miRNA ratio model, MiCaP (miR-23a-3p×miR-10b-5p)/(miR-133a×miR-374b-5p), that predicted time to BCR independently of routine clinicopathologic variables in the training cohort (PCA123) and was successfully validated in two independent RP cohorts. In addition, MiCaP was a significant predictor of CSS in univariate analysis [HR 3.35 (95% CI 1.34 − 8.35), P = 0.0096] and in multivariate analysis [HR 2.43 (95% CI 1.45–4.07), P = 0.0210]. As proof-of-principle, we also analyzed MiCaP in plasma samples from 111 RP patients. A high MiCaP score in plasma was significantly associated with BCR (P = 0.0036, Kaplan–Meier analysis). Limitations include low mortality rates (CSS: 5.4%).ConclusionsWe identified a novel 4-miRNA ratio model (MiCaP) with significant independent prognostic value in three RP cohorts, indicating promising potential to improve PC risk stratification.