Identification of Novel Molecular and Clinical Biomarkers of Survival in Glioblastoma Multiforme Patients: A Study Based on The Cancer Genome Atlas Data.

Glioblastoma multiforme (GBM) is the most prevalent type of brain tumour; although advancements in treatment have been made, the median survival time for GBM patients has persisted at 15 months. This study is aimed at investigating the genetic alterations and clinical features of GBM patients to find predictors of survival. GBM patients' methylation and gene expression data along with clinical information from TCGA were retrieved.

Integrated Multi-Omics Signature Predicts Survival in Head and Neck Cancer.

Head and Neck Cancer (HNC) is characterized by phenotypic, biological, and clinical heterogeneity. Despite treatment modalities, approximately half of all patients will die of the disease. Several molecular biomarkers have been investigated, but until now, without clinical translation.

A seven-gene signature to predict the prognosis of oral squamous cell carcinoma.

The prognosis of oral squamous cell carcinoma (OSCC) patients remains poor without implemented biomarkers in the clinical routine practice to help in the patient's management. With this study we aimed to identify specific prognostic biomarkers for OSCC using a whole genome technology as well as to verify the clinical utility of a head and neck cancer-specific multiplex ligation-dependent probe amplification (MLPA) panel. A genomic characterization of tumor samples from 62 OSCC patients was performed using array comparative genomic hybridization (aCGH) and a more straightforward and cost-effective molecular technology, MLPA.

Development of a genomic predictive model for cholangiocarcinoma using copy number alteration data.

Aims: Cholangiocarcinoma (CC) is a rare tumour arising from the biliary tract epithelium. The aim of this study was to perform a genomic characterisation of CC tumours and to implement a model to differentiate extrahepatic (ECC) and intrahepatic (ICC) cholangiocarcinoma.

Methods: DNA extracted from tumour samples of 23 patients with CC, namely 10 patients with ECC and 13 patients with ICC, was analysed by array comparative genomic hybridisation.

Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles.

Copy number alterations (CNAs) comprise deletions or amplifications of fragments of genomic material that are particularly common in cancer and play a major contribution in its development and progression. High resolution microarray-based genome-wide technologies have been widely used to detect CNAs, generating complex datasets that require further steps to allow for the determination of meaningful results. In this work, we propose a methodology to determine common regions of CNAs from these datasets, that in turn are used to infer the probability distribution of disease profiles in the population.

Proteomics-based Predictive Model for the Early Detection of Metastasis and Recurrence in Head and Neck Cancer.

Background/aim: Head and neck squamous cell carcinoma (HNSCC) presents high morbidity, an overall poor prognosis and survival, and a compromised quality of life of the survivors. Early tumor detection, prediction of its behavior and prognosis as well as the development of novel therapeutic strategies are urgently needed for a more successful HNSCC management.

Materials And Methods: In this study, a proteomics analysis of HNSCC tumor and non-tumor samples was performed and a model to predict the risk of recurrence and metastasis development was built.

Genomic and epigenetic signatures associated with survival rate in oral squamous cell carcinoma patients.

Although oral squamous cell carcinoma (OSCC) presents great mortality and morbidity worldwide, the mechanisms behind its clinical behavior remain unclear. Biomarkers are needed to forecast patients' survival and, among those patients undergoing curative therapy, which are more likely to develop tumor recurrence/metastasis. Demonstrating clinical relevance of these biomarkers could be crucial both for surveillance and in helping to establish adjuvant therapy strategies.

Genomic predictive model for recurrence and metastasis development in head and neck squamous cell carcinoma patients.

The head and neck squamous cell carcinoma (HNSCC) population consists mainly of high-risk for recurrence and locally advanced stage patients. Increased knowledge of the HNSCC genomic profile can improve early diagnosis and treatment outcomes. The development of models to identify consistent genomic patterns that distinguish HNSCC patients that will recur and/or develop metastasis after treatment is of utmost importance to decrease mortality and improve survival rates.