FOXM1 coming of age: time for translation into clinical benefits?

A decade since the first evidence implicating the cell cycle transcription factor Forkhead Box M1 (FOXM1) in human tumorigenesis, a slew of subsequent studies revealed an oncogenic role of FOXM1 in the majority of human cancers including oral, nasopharynx, oropharynx, esophagus, breast, ovary, prostate, lung, liver, pancreas, kidney, colon, brain, cervix, thyroid, bladder, uterus, testis, stomach, skin, and blood. Its aberrant upregulation in almost all different cancer types suggests a fundamental role for FOXM1 in tumorigenesis. Its dose-dependent expression pattern correlated well with tumor progression starting from cancer predisposition and initiation, early premalignancy and progression, to metastatic invasion. In addition, emerging studies have demonstrated a causal link between FOXM1 and chemotherapeutic drug resistance. Despite the well-established multifaceted roles for FOXM1 in all stages of oncogenesis, its translation into clinical benefit is yet to materialize. In this contribution, I reviewed and discussed how our current knowledge on the oncogenic mechanisms of FOXM1 could be exploited for clinical use as biomarker for risk prediction, early cancer screening, molecular diagnostics/prognostics, and/or companion diagnostics for personalized cancer therapy

Comments on the Monopole-Antimonopole Pair Solutions

Recently, the monopole-antimonopole pair and monopole-antimonopole chain solutions are solved with internal space coordinate system of $\theta$-winding number $m$ greater than one. However, we notice that it is also possible to solve these solutions numerically in terms of $\theta$-winding number $m=1$ instead. When $m=1$, the exact asymptotic solutions at small and large distances are parameterized by a single integer parameter $s$. Here we once again study the monopole-antimonopole pair solution of the SU(2) Yang-Mills-Higgs theory which belongs to the topological trivial sector numerically in its new form. This solution with $\theta$-winding and $\phi$-winding number one is parameterized by $s=0$ at small $r$ and $s=1$ at large $r$.Comment: Two figures, 13 pages, to be sent for publicatio

Oral Cancer Biomarkers: Is it a Meaningless Game?

Editoria

Dyons of One Half Monopole Charge

We would like to present some exact SU(2) Yang-Mills-Higgs dyon solutions of one half monopole charge. These static dyon solutions satisfy the first order Bogomol'nyi equations and are characterized by a parameter, $m$. They are axially symmetric. The gauge potentials and the electromagnetic fields possess a string singularity along the negative z-axis and hence they possess infinite energy density along the line singularity. However the net electric charges of these dyons which varies with the parameter $m$ are finite.Comment: 16 pages, 7 figure

Particle Gibbs for Bayesian Additive Regression Trees

Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, where there is also demand for probabilistic predictions with measures of uncertainty, the Bayesian additive regression trees (BART) model, introduced by Chipman et al. (2010), is increasingly popular. As data sets have grown in size, however, the standard Metropolis-Hastings algorithms used to perform inference in BART are proving inadequate. In particular, these Markov chains make local changes to the trees and suffer from slow mixing when the data are high-dimensional or the best fitting trees are more than a few layers deep. We present a novel sampler for BART based on the Particle Gibbs (PG) algorithm (Andrieu et al., 2010) and a top-down particle filtering algorithm for Bayesian decision trees (Lakshminarayanan et al., 2013). Rather than making local changes to individual trees, the PG sampler proposes a complete tree to fit the residual. Experiments show that the PG sampler outperforms existing samplers in many settings

Mondrian Forests for Large-Scale Regression when Uncertainty Matters

Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to large-scale data sets comes at the cost of approximating the uncertainty estimates. We extend Mondrian forests, first proposed by Lakshminarayanan et al. (2014) for classification problems, to the large-scale non-parametric regression setting. Using a novel hierarchical Gaussian prior that dovetails with the Mondrian forest framework, we obtain principled uncertainty estimates, while still retaining the computational advantages of decision forests. Through a combination of illustrative examples, real-world large-scale datasets, and Bayesian optimization benchmarks, we demonstrate that Mondrian forests outperform approximate GPs on large-scale regression tasks and deliver better-calibrated uncertainty assessments than decision-forest-based methods.Comment: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 5

Identification of multidrug chemoresistant genes in head and neck squamous cell carcinoma cells

Multidrug resistance renders treatment failure in a large proportion of head and neck squamous cell carcinoma (HNSCC) patients that require multimodal therapy involving chemotherapy in conjunction with surgery and/or radiotherapy. Molecular events conferring chemoresistance remain unclear. Through transcriptome datamining, 28 genes were subjected to pharmacological and siRNA rescue functional assays on 12 strains of chemoresistant cell lines each against cisplatin, 5-fluorouracil (5FU), paclitaxel (PTX) and docetaxel (DTX). Ten multidrug chemoresistance genes (TOP2A, DNMT1, INHBA, CXCL8, NEK2, FOXO6, VIM, FOXM1B, NR3C1 and BIRC5) were identified. Of these, four genes (TOP2A, DNMT1, INHBA and NEK2) were upregulated in an HNSCC patient cohort (n = 221). Silencing NEK2 abrogated chemoresistance in all drug-resistant cell strains. INHBA and TOP2A were found to confer chemoresistance in majority of the drug-resistant cell strains whereas DNMT1 showed heterogeneous results. Pan-cancer Kaplan-Meier survival analysis on 21 human cancer types revealed significant prognostic values for INHBA and NEK2 in at least 16 cancer types. Drug library screens identified two compounds (Sirodesmin A and Carfilzomib) targeting both INHBA and NEK2 and re-sensitised cisplatin-resistant cells. We have provided the first evidence for NEK2 and INHBA in conferring chemoresistance in HNSCC cells and siRNA gene silencing of either gene abrogated multidrug chemoresistance. The two existing compounds could be repurposed to counteract cisplatin chemoresistance in HNSCC. This finding may lead to novel personalised biomarker-linked therapeutics that can prevent and/or abrogate chemoresistance in HNSCC and other tumour types with elevated NEK2 and INHBA expression. Further investigation is necessary to delineate their signalling mechanisms in tumour chemoresistance