Evolutionary Action Score of TP53 Identifies High-Risk Mutations Associated with Decreased Survival and Increased Distant Metastases in Head and Neck Cancer

TP53 is the most frequently altered gene in head and neck squamous cell carcinoma, with mutations occurring in over two-thirds of cases, but the prognostic significance of these mutations remains elusive. In the current study, we evaluated a novel computational approach termed evolutionary action (EAp53) to stratify patients with tumors harboring TP53 mutations as high or low risk, and validated this system in both in vivo and in vitro models. Patients with high-risk TP53 mutations had the poorest survival outcomes and the shortest time to the development of distant metastases. Tumor cells expressing high-risk TP53 mutations were more invasive and tumorigenic and they exhibited a higher incidence of lung metastases. We also documented an association between the presence of high-risk mutations and decreased expression of TP53 target genes, highlighting key cellular pathways that are likely to be dysregulated by this subset of p53 mutations that confer particularly aggressive tumor behavior. Overall, our work validated EAp53 as a novel computational tool that may be useful in clinical prognosis of tumors harboring p53 mutations

Driver Missense Mutation Identification Using Feature Selection and Model Fusion

Driver mutations propel oncogenesis and occur much less frequently than passenger mutations. The need for automatic and accurate identification of driver mutations has increased dramatically with the exponential growth of mutation data. Current computational solutions to identify driver mutations rely on sequence homology. Here we construct a machine learning–based framework that does not rely on sequence homology or domain knowledge to predict driver missense mutations. A windowing approach to represent the local environment of the sequence around the mutation point as a mutation sample is applied, followed by extraction of three sequence-level features from each sample. After selecting the most significant features, the support vector machine and multimodal fusion strategies are employed to give final predictions. The proposed framework achieves relatively high performance and outperforms current state-of-the-art algorithms. The ease of deploying the proposed framework and the relatively accurate performance make this solution applicable to large-scale mutation data analyses

miR-551a and miR-551b-3p target GLIPR2 and promote tumor growth in high-risk head and neck cancer by modulating autophagy

The potential role for microRNA (miRNA) in the metastatic process that occurs in head and neck squamous cell carcinoma (HNSCC) was examined. miRNA was extracted from surgically excised tumor samples from 41 HNSCC cancer patients diagnosed with distant metastasis (DM) and from 53 patients who displayed no evidence of disease (NED) for a minimum of two years a minimum of two years after treatment with post-operative radiotherapy (PORT). A comparative two-way ANOVA of miRNA expression between DM and NED specimens identified 28 differentially expressed miRNAs with a false discovery rate (FDR)  1.5. Two miRNA, miR-551a and miR-551b-3p, which share the same seed sequence, were associated with the DM group and with poor survival. Cell proliferation, migration, and invasion assays using the HN5 and UMSCC-17B HNSCC cell lines were performed after transfecting mimics or inhibitors of these miRNA uncovered an oncogenic role for miR-551a and miR-551b-3p. Furthermore, it was determined that miR-551a and miR-551b-3p directly target GLIPR2 mRNA, a negative regulator of autophagy. Overexpression of GLIPR2 reduced proliferation, migration and invasion of HNSCC cells. In addition, overexpression of miR-551a and miR-551b-3p increased radioresistance while GLIPR2 overexpression increased the radiosensitivity of HNSCC cell lines. These results propose that the miR-551a, miR-551b-3p and GLIPR2 axis plays an important role in tumor growth, invasion and metastasis, at least in part by modulating autophagy and that the proliferative and pro-survival roles of miR-551a and miR-551b-3p may represent potential therapeutic targets by inhibiting autophagy through the regulation of GLIPR2 expression in HNSCC

Wavelet Analysis in Current Cancer Genome Research: A Survey

With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences