DriveWays: a method for identifying possibly overlapping driver pathways in cancer.

The majority of the previous methods for identifying cancer driver modules output nonoverlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution. We provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes. We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWay's output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways.

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules.

MOTIVATION: Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules. RESULTS: We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting clinical outcomes in neuroblastoma with genomic data integration.

BACKGROUND: Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis. RESULTS: Our supervised model trained on the SEQC cohort can accurately predict overall survival and event-free survival profiles of patients in two independent cohorts. We also performed extensive experiments to assess the prediction accuracy of high risk patients and patients without MYCN amplification. Our results from this part suggest that clinical endpoints can be predicted accurately across multiple cohorts. To explore the data in an unsupervised manner, we used an integrative clustering strategy named multi-view kernel k-means (MVKKM) that can effectively integrate multiple high-dimensional datasets with varying weights. We observed that integrating different gene expression datasets results in a better patient stratification compared to using these datasets individually. Also, our identified subgroups provide a better Cox regression model fit compared to the existing risk group definitions. CONCLUSION: Altogether, our results indicate that integration of multiple genomic characterizations enables the discovery of subtypes that improve over existing definitions of risk groups. Effective prediction of survival times will have a direct impact on choosing the right therapies for patients. REVIEWERS: This article was reviewed by Susmita Datta, Wenzhong Xiao and Ziv Shkedy.

Power and efficiency scaling of diode pumped Cr:LiSAF lasers: 770-1110 nm tuning range and frequency doubling to 387-463 nm.

We report significant average power and efficiency scaling of diode-pumped Cr:LiSAF lasers in continuous-wave (cw), cw frequency-doubled, and mode-locked regimes. Four single-emitter broad-area laser diodes around 660 nm were used as the pump source, which provided a total pump power of 7.2 W. To minimize thermal effects, a 20 mm long Cr:LiSAF sample with a relatively low Cr-concentration (0.8%) was used as the gain medium. In cw laser experiments, 2.4 W of output power, a slope efficiency of 50%, and a tuning range covering the 770-1110 nm region were achieved. Intracavity frequency doubling with beta-barium borate (BBO) crystals generated up to 1160 mW of blue power and a record tuning range in the 387-463 nm region. When mode locked with a saturable absorber mirror, the laser produced 195 fs pulses with 580 mW of average power around 820 nm at a 100.3 MHz repetition rate. The optical-to-optical conversion efficiency of the system was 33% in cw, 16% in cw frequency-doubled, and 8% in cw mode-locked regimes.

Diode-pumped continuous-wave and femtosecond Cr:LiCAF lasers with high average power in the near infrared, visible and near ultraviolet.

We demonstrate continuous-wave (cw), cw frequency-doubled, cw mode-locked and Q-switched mode-locked operation of multimode diode-pumped Cr:LiCAF lasers with record average powers. Up to 2.54 W of cw output is obtained around 805 nm at an absorbed pump power of 5.5 W. Using intracavity frequency doubling with a BBO crystal, 0.9 W are generated around 402 nm, corresponding to an optical-to-optical conversion efficiency of 12%. With an intracavity birefringent tuning plate, the fundamental and frequency-doubled laser output is tuned continuously in a broad wavelength range from 745 nm to 885 nm and from 375 to 440 nm, respectively. A saturable Bragg reflector is used to initiate and sustain mode locking. In the cw mode-locked regime, the Cr:LiCAF laser produces 105-fs long pulses near 810 nm with an average power of 0.75 W. The repetition rate is 96.4 MHz, resulting in pulse energies of 7.7 nJ and peak powers of 65 kW. In Q-switched mode-locked operation, pulses with energies above 150 nJ are generated.