Summary Muscle-invasive bladder cancers (MIBCs) are biologically heterogeneous and have widely variable clinical outcomes and responses to conventional chemotherapy. We discovered 3 molecular subtypes of MIBC that resembled established molecular subtypes of breast cancer. Basal MIBCs shared biomarkers with basal breast cancers and were characterized by p63 activation, squamous differentiation, and more aggressive disease at presentation. Luminal MIBCs contained features of active PPARγ and estrogen receptor (ER) transcription and were enriched with activating FGFR3 mutations and potentially FGFR inhibitor sensitivity. p53-like MIBCs were consistently resistant to neoadjuvant MVAC chemotherapy, and all chemoresistant tumors adopted a p53-like phenotype after therapy. Our observations have important implications for prognostication, the future clinical development of targeted agents, and disease management with conventional chemotherapy.

We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.

SUMMARY We describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy number analysis revealed frequent occurrence of massive DNA loss followed by whole genome doubling (WGD) which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68 CpG probe DNA methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers.

Novel organic sensitizers comprising donor, electron-conducting, and anchoring groups were engineered at molecular level and synthesized. The functionalized unsymmetrical organic sensitizers 3-{5-[N,N-bis(9,9-dimethylfluorene-2-yl)phenyl]-thiophene-2-yl}-2-cyano-acrylic acid (JK-1) and 3-{5'-[N,N-bis(9,9-dimethylfluorene-2-yl)phenyl]-2,2'-bisthiophene-5-yl}-2-cyano-acrylic acid (JK-2), upon anchoring onto TiO2 film, exhibit unprecedented incident photon to current conversion efficiency of 91%. The photovoltaic data using an electrolyte having composition of 0.6 M M-methyl-N-butyl imidiazolium iodide, 0.04 M iodine, 0.025 M LiI, 0.05 M guanidinium thiocyanate, and 0.28 M tert-butylpyridine in a 15/85 (v/v) mixture of valeronitrile and acetonitrile revealed a short circuit photocurrent density of 14.0 +/- 0.2 mA/cm2, an open circuit voltage of 753 +/- 10 mV, and a fill factor of 0.76 +/- 0.02, corresponding to an overall conversion efficiency of 8.01% under standard AM 1.5 sunlight. DFT/TDDFT calculations have been performed on the two organic sensitizers to gain insight into their structural, electronic, and optical properties. Our results show that the cyanoacrylic acid groups are essentially coplanar with respect to the thiophene units, reflecting the strong conjugation across the thiophene-cyanoacrylic groups. Molecular orbitals analysis confirmed the experimental assignment of redox potentials, while TDDFT calculations allowed assignment of the visible absorption bands.

Hyperdiploid multiple myeloma (H-MM) is the most common form of myeloma. In this gene expression profiling study, we show that H-MM is defined by a protein biosynthesis signature that is primarily driven by a gene dosage mechanism as a result of trisomic chromosomes. Within H-MM, four independently validated patient clusters overexpressing nonoverlapping sets of genes that form cognate pathways/networks that have potential biological importance in multiple myeloma were identified. One prominent cluster, cluster 1, is characterized by high expression of cancer testis antigen and proliferation-associated genes. Tumors from these patients were more proliferative than tumors in other clusters (median plasma cell labeling index, 3.8; P < 0.05). Another cluster, cluster 3, is characterized by genes involved in tumor necrosis factor/nuclear factor-KB signaling and antiapoptosis. These patients have better response to bortezomib as compared with patients within other clusters (70% versus 29%; P = 0.02).

There has been a growing interest in using next-generation sequencing (NGS) to profile extracellular small RNAs from the blood and cerebrospinal fluid (CSF) of patients with neurological diseases, CNS tumors, or traumatic brain injury for biomarker discovery. Small sample volumes and samples with low RNA abundance create challenges for downstream small RNA sequencing assays. Plasma, serum, and CSF contain low amounts of total RNA, of which small RNAs make up a fraction. The purpose of this study was to maximize RNA isolation from RNA-limited samples and apply these methods to profile the miRNA in human CSF by small RNA deep sequencing. We systematically tested RNA isolation efficiency using ten commercially available kits and compared their performance on human plasma samples. We used RiboGreen to quantify total RNA yield and custom TaqMan assays to determine the efficiency of small RNA isolation for each of the kits. We significantly increased the recovery of small RNA by repeating the aqueous extraction during the phenol-chloroform purification in the top performing kits. We subsequently used the methods with the highest small RNA yield to purify RNA from CSF and serum samples from the same individual. We then prepared small RNA sequencing libraries using Illumina's TruSeq sample preparation kit and sequenced the samples on the HiSeq 2000. Not surprisingly, we found that the miRNA expression profile of CSF is substantially different from that of serum. To our knowledge, this is the first time that the small RNA fraction from CSF has been profiled using next-generation sequencing.

Interest in circulating RNAs for monitoring and diagnosing human health has grown significantly. There are few datasets describing baseline expression levels for total cell-free circulating RNA from healthy control subjects. In this study, total extracellular RNA (exRNA) was isolated and sequenced from 183 plasma samples, 204 urine samples and 46 saliva samples from 55 male college athletes ages 18–25 years. Many participants provided more than one sample, allowing us to investigate variability in an individual’s exRNA expression levels over time. Here we provide a systematic analysis of small exRNAs present in each biofluid, as well as an analysis of exogenous RNAs. The small RNA profile of each biofluid is distinct. We find that a large number of RNA fragments in plasma (63%) and urine (54%) have sequences that are assigned to YRNA and tRNA fragments respectively. Surprisingly, while many miRNAs can be detected, there are few miRNAs that are consistently detected in all samples from a single biofluid, and profiles of miRNA are different for each biofluid. Not unexpectedly, saliva samples have high levels of exogenous sequence that can be traced to bacteria. These data significantly contribute to the current number of sequenced exRNA samples from normal healthy individuals.

A fundamental question in biology is whether the network of interactions that regulate gene expression can be modeled by existing mathematical techniques. Studies of the ability to predict a gene's state based on the states of other genes suggest that it may be possible to abstract sufficient information to build models of the system that retain steady-state behavioral characteristics of the real system. This study tests this possibility by: (i) constructing a finite state homogeneous Markov chain model using a small set of interesting genes; (ii) estimating the model parameters based on the observed experimental data; (iii) exploring the dynamics of this small genetic regulatory network by analyzing its steady-state (long-run) behavior and comparing the resulting model behavior to the observed behavior of the original system. The data used in this study are from a survey of melanoma where predictive relationships (coefficient of determination, CoD) between 587 genes from 31 samples were examined. Ten genes with strong interactive connectivity were chosen to formulate a finite state Markov chain on the basis of their role as drivers in the acquisition of an invasive phenotype in melanoma cells. Simulations with different perturbation probabilities and different iteration times were run. Following convergence of the chain to steady-state behavior, millions of samples of the results of further transitions were collected to estimate the steady-state distribution of network. In these samples, only a limited number of states possessed significant probability of occurrence. This behavior is nicely congruent with biological behavior, as cells appear to occupy only a negligible portion of the state space available to them. The model produced both some of the exact state vectors observed in the data, and also a number of state vectors that were near neighbors of the state vectors from the original data. By combining these similar states, a good representation of the observed states in the original data could be achieved. From this study, we find that, in this limited context, Markov chain simulation emulates well the dynamic behavior of a small regulatory network.