Establishment of Prognostic Models for Astrocytic and Oligodendroglial Brain Tumors with Standardized Quantification of Marker Gene Expression and Clinical Variables

Background Prognosis models established using multiple molecular markers in cancer along with clinical variables should enable prediction of natural disease progression and residual risk faced by patients. In this study, multivariate Cox proportional hazards analyses were done based on overall survival (OS) of 100 glioblastoma multiformes (GBMs, 92 events), 49 anaplastic astrocytomas (AAs, 33 events), 45 gliomas with oligodendroglial features, including anaplastic oligodendroglioma (AO, 13 events) and oligodendraglioma (O, 9 events). The modeling included two clinical variables (patient age and recurrence at the time of sample collection) and the expression variables of 13 genes selected based on their proven biological and/or prognosis functions in gliomas ( ABCG2, BMI1, MELK, MSI1, PROM1, CDK4, EGFR, MMP2, VEGFA, PAX6, PTEN, RPS9, and IGFBP2 ). Gene expression data was a log-transformed ratio of marker and reference ( ACTB ) mRNA levels quantified using absolute real-time qRT-PCR. Results Age is positively associated with overall grade (4 for GBM, 3 for AA, 2_1 for AO_O), but lacks significant prognostic value in each grade. Recurrence is an unfavorable prognostic factor for AA, but lacks significant prognostic values for GBM and AO_O. Univariate models revealed opposing prognostic effects of ABCG2, MELK, BMI1, PROM1, IGFBP2, PAX6, RPS9 , and MSI1 expressions for astrocytic (GBM and AA) and oligodendroglial tumors (AO_O). Multivariate models revealed independent prognostic values for the expressions of MSI1 (unfavorable) in GBM, CDK4 (unfavorable) and MMP2 (favorable) in AA, while IGFBP2 and MELK (unfavorable) in AO_O. With all 13 genes and 2 clinical variables, the model R 2 was 14.2% ( P = 0.358) for GBM, 45.2% ( P = 0.029) for AA, and 62.2% ( P = 0.008) for AO_O. Conclusion The study signifies the challenge in establishing a significant prognosis model for GBM. Our success in establishing prognosis models for AA and AO_O was largely based on identification of a set of genes with independent prognostic values and application of standardized gene expression quantification to allow formation of a large cohort in analysis

State transfer in dissipative and dephasing environments

By diagonalization of a generalized superoperator for solving the master equation, we investigated effects of dissipative and dephasing environments on quantum state transfer, as well as entanglement distribution and creation in spin networks. Our results revealed that under the condition of the same decoherence rate $\gamma$, the detrimental effects of the dissipative environment are more severe than that of the dephasing environment. Beside this, the critical time $t_c$ at which the transfer fidelity and the concurrence attain their maxima arrives at the asymptotic value $t_0=\pi/2\lambda$ quickly as the spin chain length $N$ increases. The transfer fidelity of an excitation at time $t_0$ is independent of $N$ when the system subjects to dissipative environment, while it decreases as $N$ increases when the system subjects to dephasing environment. The average fidelity displays three different patterns corresponding to $N=4r+1$, $N=4r-1$ and $N=2r$. For each pattern, the average fidelity at time $t_0$ is independent of $r$ when the system subjects to dissipative environment, and decreases as $r$ increases when the system subjects to dephasing environment. The maximum concurrence also decreases as $N$ increases, and when $N\rightarrow\infty$, it arrives at an asymptotic value determined by the decoherence rate $\gamma$ and the structure of the spin network.Comment: 12 pages, 6 figure

Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials

A recent joint meeting was held on January 30, 2014, with the US Food and Drug Administration (FDA), National Cancer Institute (NCI), clinical scientists, imaging experts, pharmaceutical and biotech companies, clinical trials cooperative groups, and patient advocate groups to discuss imaging endpoints for clinical trials in glioblastoma. This workshop developed a set of priorities and action items including the creation of a standardized MRI protocol for multicenter studies. The current document outlines consensus recommendations for a standardized Brain Tumor Imaging Protocol (BTIP), along with the scientific and practical justifications for these recommendations, resulting from a series of discussions between various experts involved in aspects of neuro-oncology neuroimaging for clinical trials. The minimum recommended sequences include: (i) parameter-matched precontrast and postcontrast inversion recovery-prepared, isotropic 3D T1-weighted gradient-recalled echo; (ii) axial 2D T2-weighted turbo spin-echo acquired after contrast injection and before postcontrast 3D T1-weighted images to control timing of images after contrast administration; (iii) precontrast, axial 2D T2-weighted fluid-attenuated inversion recovery; and (iv) precontrast, axial 2D, 3-directional diffusion-weighted images. Recommended ranges of sequence parameters are provided for both 1.5 T and 3 T MR system

State transfer in intrinsic decoherence spin channels

By analytically solving the master equation, we investigate quantum state transfer, creation and distribution of entanglement in the model of Milburn's intrinsic decoherence. Our results reveal that the ideal spin channels will be destroyed by the intrinsic decoherence environment, and the detrimental effects become severe as the decoherence rate $\gamma$ and the spin chain length $N$ increase. For infinite evolution time, both the state transfer fidelity and the concurrence of the created and distributed entanglement approach steady state values, which are independent of the decoherence rate $\gamma$ and decrease as the spin chain length $N$ increases. Finally, we present two modified spin chains which may serve as near perfect spin channels for long distance state transfer even in the presence of intrinsic decoherence environments $\mathcal {F}{[\rho(t)]}$.Comment: 11 pages, 11 figure

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes

The somatic genomic landscape of glioblastoma

We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer