Gene expression signatures prognostic for relapse in stage I testicular germ cell tumours.

OBJECTIVES: To identify differentially expressed genes between relapsed and non-relapsed clinical stage I testicular germ cell tumours (TGCTs). MATERIALS AND METHODS: We reviewed patients with clinical stage I non-seminoma and seminoma from an institutional database (2000-2012) who were managed by active surveillance. Patients with non-relapsed non-seminoma and non-relapsed seminoma were defined as being relapse-free after 2 and 3 years of surveillance, respectively. RNA extraction and gene expression analysis was performed on archival primary tumour samples and gene-set enrichment analysis (GSEA) was conducted in order to identify differentiating biological pathways. RESULTS: A total of 57 patients (relapsed non-seminoma, n = 12; relapsed seminoma, n =15; non-relapsed non-seminoma, n = 15; non-relapsed seminoma, n = 15) were identified, with a median (range) relapse time of 5.6 (2.5-18.1) and 19.3 (4.7-65.3) months in the relapsed non-seminoma and relapsed seminoma cohorts, respectively. A total of 1 039 differentially expressed genes were identified that separated relapsed and non-relapsed groups. In patients with relapse, GSEA revealed enrichment in pathways associated with differentiation, such as skeletal development (i.e. FGFR1, BMP4, GLI2, SPARC, COL2A1), tissue (i.e. BMP4, SPARC, COL13A1) and bone remodelling (i.e. CARTPT, GLI2, MGP). A discriminative gene expression profile between relapsed and non-relapsed cases was discovered when combining non-seminoma and seminoma samples using 10- and 30-probe signatures; however, this profile was not observed in the seminoma and non-seminoma cohorts individually. CONCLUSION: A discriminating signature for relapsed disease was identified for clinical stage I TGCT that we were not able to identify by histology alone. Further validation is required to determine if this signature provides independent prognostic information to standard pathological risk factors.

Predictive approaches for drug combination discovery in cancer.

Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear. These challenges call for rational, systematic approaches for drug combination discovery. Although high-throughput screening of single-agent therapeutics has been successfully implemented, it is not feasible to test all possible drug combinations, even for a reduced subset of anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug combinations are not practical. Therefore, devising computational methods to efficiently explore the space of drug combinations and to discover efficacious combinations has attracted a lot of attention from the scientific community in the past few years. Nevertheless, in the absence of consensus regarding the computational approaches used to predict efficacious drug combinations, a plethora of methods, techniques and hypotheses have been developed to date, while the research field lacks an elaborate categorization of the existing computational methods and the available data sources. In this manuscript, we review and categorize the state-of-the-art computational approaches for drug combination prediction, and elaborate on the limitations of these methods and the existing challenges. We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches.

Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins.

MOTIVATION: Inferring structural dependencies among a protein's side chains helps us understand their coupled motions. It is known that coupled fluctuations can reveal pathways of communication used for information propagation in a molecule. Side-chain conformations are commonly represented by multivariate angular variables, but existing partial correlation methods that can be applied to this inference task are not capable of handling multivariate angular data. We propose a novel method to infer direct couplings from this type of data, and show that this method is useful for identifying functional regions and their interactions in allosteric proteins. RESULTS: We developed a novel extension of canonical correlation analysis (CCA), which we call 'kernelized partial CCA' (or simply KPCCA), and used it to infer direct couplings between side chains, while disentangling these couplings from indirect ones. Using the conformational information and fluctuations of the inactive structure alone for allosteric proteins in the Ras and other Ras-like families, our method identified allosterically important residues not only as strongly coupled ones but also in densely connected regions of the interaction graph formed by the inferred couplings. Our results were in good agreement with other empirical findings. By studying distinct members of the Ras, Rho and Rab sub-families, we show further that KPCCA was capable of inferring common allosteric characteristics in the small G protein super-family. AVAILABILITY AND IMPLEMENTATION: https://github.com/lsgh/ismb15

Sparse networks of directly coupled, polymorphic, and functional side chains in allosteric proteins.

Recent studies have highlighted the role of coupled side-chain fluctuations alone in the allosteric behavior of proteins. Moreover, examination of X-ray crystallography data has recently revealed new information about the prevalence of alternate side-chain conformations (conformational polymorphism), and attempts have been made to uncover the hidden alternate conformations from X-ray data. Hence, new computational approaches are required that consider the polymorphic nature of the side chains, and incorporate the effects of this phenomenon in the study of information transmission and functional interactions of residues in a molecule. These studies can provide a more accurate understanding of the allosteric behavior. In this article, we first present a novel approach to generate an ensemble of conformations and an efficient computational method to extract direct couplings of side chains in allosteric proteins, and provide sparse network representations of the couplings. We take the side-chain conformational polymorphism into account, and show that by studying the intrinsic dynamics of an inactive structure, we are able to construct a network of functionally crucial residues. Second, we show that the proposed method is capable of providing a magnified view of the coupled and conformationally polymorphic residues. This model reveals couplings between the alternate conformations of a coupled residue pair. To the best of our knowledge, this is the first computational method for extracting networks of side chains' alternate conformations. Such networks help in providing a detailed image of side-chain dynamics in functionally important and conformationally polymorphic sites, such as binding and/or allosteric sites.