Applications of Quantum Algorithms to Partially Observable Markov Decision Processes

Abstract Due to the enormous processing gains that are theoretically achievable by using quantum algorithms instead of classical algorithms to solve rather generic classes of numerical problems, it makes sense that one should evaluate their potential applicability, appropriateness, and efficiency for solving virtually any computationally intensive task. Since many types of control and optimization problems may be couched in terms of partially observable Markov decision processes (POMDPs), and since solutions to these types of problems are invariably extremely difficult to obtain, the use of quantum algorithms to help solve POMDP problems is investigated here. Quantum algorithms are indeed found likely to provide significant efficiency improvements in several computationally intensive tasks associated with solving POMDPs, particularly in the areas of searching, optimization, and parameter optimization and estimation

The Translation Factor eIF6 Is a Notch-Dependent Regulator of Cell Migration and Invasion

A growing body of evidence indicates that protein factors controlling translation play an important role in tumorigenesis. The protein known as eIF6 is a ribosome anti-association factor that has been implicated in translational initiation and in ribosome synthesis. Over-expression of eIF6 is observed in many natural tumours, and causes developmental and differentiation defects in certain animal models. Here we show that the transcription of the gene encoding eIF6 is modulated by the receptor Notch-1, a protein involved in embryonic development and cell differentiation, as well as in many neoplasms. Inhibition of Notch-1 signalling by γ-secretase inhibitors slowed down cell-cycle progression and reduced the amount of eIF6 in lymphoblastoid and ovarian cancer cell lines. Cultured ovarian cancer cell lines engineered to stably over-expressing eIF6 did not show significant changes in proliferation rate, but displayed an enhanced motility and invasive capacity. Inhibition of Notch-1 signalling in the cells over-expressing eIF6 was effective in slowing down the cell cycle, but did not reduce cell migration and invasion. On the whole, the results suggest that eIF6 is one of the downstream effectors of Notch-1 in the pathway that controls cell motility and invasiveness

Suppression of Cellular Transformation by Poly (A) Binding Protein Interacting Protein 2 (Paip2)

Controlling translation is crucial for the homeostasis of a cell. Its deregulation can facilitate the development and progression of many diseases including cancer. Poly (A) binding protein interacting protein 2 (Paip2) inhibits efficient initiation of translation by impairing formation of the necessary closed loop of mRNA. The over production of Paip2 in the presence of a constitutively active form of hRasV12 can reduce colony formation in a semi-solid matrix and focus formation on a cell monolayer. The ability of Paip2 to bind to Pabp is required to suppress the transformed phenotype mediated by hRasV12. These observations indicate that Paip2 is able to function as a tumor suppressor

Survival prediction from clinico-genomic models - a comparative study

<p>Abstract</p> <p>Background</p> <p>Survival prediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic. Also, for the widely used Cox regression model, it is not obvious how to handle such combined models.</p> <p>Results</p> <p>We propose a way to combine classical clinical covariates with genomic data in a clinico-genomic prediction model based on the Cox regression model. The prediction model is obtained by a simultaneous use of both types of covariates, but applying dimension reduction only to the high-dimensional genomic variables. We describe how this can be done for seven well-known prediction methods: variable selection, unsupervised and supervised principal components regression and partial least squares regression, ridge regression, and the lasso. We further perform a systematic comparison of the performance of prediction models using clinical covariates only, genomic data only, or a combination of the two. The comparison is done using three survival data sets containing both clinical information and microarray gene expression data. Matlab code for the clinico-genomic prediction methods is available at <url>http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/</url>.</p> <p>Conclusions</p> <p>Based on our three data sets, the comparison shows that established clinical covariates will often lead to better predictions than what can be obtained from genomic data alone. In the cases where the genomic models are better than the clinical, ridge regression is used for dimension reduction. We also find that the clinico-genomic models tend to outperform the models based on only genomic data. Further, clinico-genomic models and the use of ridge regression gives for all three data sets better predictions than models based on the clinical covariates alone.</p

Prognostic impact of C-REL expression in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) with a germinal center B-cell (GCB) phenotype is believed to confer a better prognosis than DLBCL with an activated B-cell (ABC) phenotype. Previous studies have suggested that nuclear factor-κB (NF-κB) activation plays an important role in the ABC subtype of DLBCL, whereas c-REL amplification is associated with the GCB subtype. Using immunohistochemical techniques, we examined 68 newly diagnosed de novo DLBCL cases (median follow-up 44 months, range 1 to 142 months) for the expression of c-REL, BCL-6, CD10, and MUM1/IRF4. Forty-four (65%) cases demonstrated positive c-REL nuclear expression. In this cohort of patients, the GCB phenotype was associated with a better overall survival (OS) than the non-GCB phenotype (Kaplan–Meier survival (KMS) analysis, p = 0.016, Breslow–Gehan–Wilcoxon test). In general, c-REL nuclear expression did not correlate with GCB vs. non-GCB phenotype, International Prognostic Index score, or OS. However, cases with a GCB phenotype and negative nuclear c-REL demonstrated better OS than cases with a GCB phenotype and positive nuclear c-REL (KMS analysis, p = 0.045, Breslow–Gehan–Wilcoxon test), whereas in cases with non-GCB phenotype, the expression of c-REL did not significantly impact the prognosis. These results suggest that c-REL nuclear expression may be a prognostic factor in DLBCL and it may improve patient risk stratification in combination with GCB/non-GCB phenotyping

Reduced expression of a gene proliferation signature is associated with enhanced malignancy in colon cancer

The association between cell proliferation and the malignant potential of colon cancer is not well understood. Here, we evaluated this association using a colon-specific gene proliferation signature (GPS). The GPS was derived by combining gene expression data obtained from the analysis of a cancer cell line model and a published colon crypt profile. The GPS was overexpressed in both actively cycling cells in vitro and the proliferate compartment of colon crypts. K-means clustering was used to independantly stratify two cohorts of colon tumours into two groups with high and low GPS expression. Notably, we observed a significant association between reduced GPS expression and an increased likelihood of recurrence (P<0.05), leading to shorter disease-free survival in both cohorts. This finding was not a result of methodological bias as we verified the well-established association between breast cancer malignancy and increased proliferation, by applying our GPS to public breast cancer data. In this study, we show that reduced proliferation is a biological feature characterizing the majority of aggressive colon cancers. This contrasts with many other carcinomas such as breast cancer. Investigating the reasons underlying this unusual observation may provide important insight into the biology of colon cancer progression and putative novel therapy options