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. The most overrepresented pathways were identified independently for each omics dataset. From the genes found in at least 30% of these pathways, one gene that was identified in both sets was further examined using the Kaplan-Meier method for survival analysis. Additionally, three groups of patients who started radio and chemotherapy at different times were identified, and the influence of these variations in treatment modality on patient survival was evaluated. Four pathways that seemed to negatively impact survival and two with the opposite effect were identified. The methylation status of PRKCB was highlighted as a potential novel biomarker for patient survival. The study also found that treatment with chemotherapy prior to radiotherapy can have a significant impact on patient survival, which could lead to improvements in clinical management and therapeutic approaches for GBM patients.

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. Here, we identified an integrative nine-gene multi-omics signature correlated with HNC patients' survival independently of relapses or metastasis development. This prognosis multi-omic signature comprises genes mapped in the chromosomes 1q, 3p, 8q, 17q, 19p, and 19q and encompasses alterations at copy number, gene expression, and methylation. Copy number alterations in LMCD1-A1S and GRM7, the methylation status of CEACAM19, KRT17, and ST18, and the expression profile of RPL29, UBA7, FCGR2C, and RPSAP58 can predict the HNC patients' survival. The difference higher than two years observed in the survival of HNC patients that harbor this nine-gene multi-omics signature can represent a significant step forward to improve patients' management and guide new therapeutic targets development.

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. A support vector machine algorithm for classification was applied to the genomic data to distinguish between ICC and ECC. A survival analysis comparing both groups of patients was also performed. RESULTS: With these whole genome results, we observed several common alterations between tumour samples of the same CC anatomical type, namely gain of Xp and loss of 3p, 11q11, 14q, 16q, Yp and Yq in ICC tumours, and gain of 16p25.3 and loss of 3q26.1, 6p25.3-22.3, 12p13.31, 17p, 18q and Yp in ECC tumours. Gain of 2q37.3 was observed in the samples of both tumour subtypes, ICC and ECC. The developed genomic model comprised four chromosomal regions that seem to enable the distinction between ICC and ECC, with an accuracy of 71.43% (95% CI 43% to 100%). Survival analysis revealed that in our cohort, patients with ECC survived on average 8 months less than patients with ICC. CONCLUSIONS: This genomic characterisation and the introduction of genomic models to clinical practice could be important for patient management and for the development of targeted therapies. The power of this genomic model should be evaluated in other CC populations.

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. The identification of a genomic signature and prognosis biomarkers was carried out by applying several statistical methods. With aCGH we observed that the chromosomes most commonly altered were 3p, 3q, 5q, 6p, 7q, 8p, 8q, 11q, 15q, 17q, and 18q. The MLPA results showed that the chromosomes with a higher frequency of alterations were 3p, 3q, 8p, 8q, and 11q. We identified a genomic signature with seven genes OCLN (3p21.31), CLDN16 (3q29), SCRIB (3q29), IKBKB (3q22.3), PAK2 (8q22.3), PIK3CB (3q28), and YWHAZ (8q24.3) that together allow to differentiate the patients that developed metastases or relapses after primary tumor treatment, with an overall accuracy of 79%. Amplification of PIK3CB as a predictor of metastases or relapses development was validated using TCGA data. This amplified gene showed a reduction in more than 5 years in the median survival of the patients. The identified biomarkers might have a significant impact in the patients' management and could leverage the OSCC precision medicine.

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. This methodology was validated using simulated data and assessed using real data from Head and Neck Squamous Cell Carcinoma and Lung Adenocarcinoma, from the TCGA platform. Probability distribution profiles were produced allowing for the distinction between different phenotypic groups established within that cohort. This method may be used to distinguish between groups in the diseased population, within well-established degrees of confidence. The application of such methods may be of greater value in the clinical context both as a diagnostic or prognostic tool and, even as a useful way for helping to establish the most adequate treatment and care plans.

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. RESULTS: This predictive model presented good accuracy (>80%) and comprises as variables the tumor staging along with DHB12, HMGB3 and COBA1 proteins. Differences at the intensity levels of these proteins were correlated with the development of metastasis and recurrence as well as with patient's survival. CONCLUSION: The translation of proteomic predictive models to routine clinical practice may contribute to a more precise and individualized clinical management of the HNSCC patients, reducing recurrences and improving patients' quality of life. The capability of generalization of this proteomic model to predict the recurrence and metastases development should be evaluated and validated in other HNSCC populations.

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

Purpose: 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. We aimed to identify genomic and epigenetic biomarkers of OSCC prognosis as well as to explore a noninvasive strategy to perform its detection. Methods: OSCC tumor and non-tumor tissue samples and cells scrapped from the tumor surface were genomic and epigenetically evaluated by Methylation-Specific Multiplex Ligation-dependent Probe Amplification technique. Results: Copy number alterations in ATM, CASR, TP73, CADM1, RARB, CDH13, PAX5, RB1 genes and GATA5, PAX6, CADM1 and CHFR promoter methylation were shown to be associated with worse OSCC patients' survival. Copy number alterations in BRCA1, CDKN2A, CHFR, GATA5, PYCARD, STK11, TP53, VHL genes and GATA5, CADM1, KLLN, MSH6, PAX5, WT1 promoter methylation were shown to be associated with development of metastasis/relapses during or after OSCC patients' treatment. We also found a good agreement in the status of CDKN2A promoter methylation evaluated noninvasively or in the tumor tissue. Conclusions: Genomic and epigenetic signatures were validated in a larger and geographically separate cohort, from TCGA database, which reinforce their clinical applicability. Noninvasive methodologies for detection of these signatures require further studies before translation in to clinical practice.

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. In this study, we used array comparative genomic hybridization data from HNSCC patients to implement a robust model to predict HNSCC recurrence/metastasis. This predictive model showed a good accuracy (>80%) and was validated in an independent population from TCGA data portal. This predictive genomic model comprises chromosomal regions from 5p, 6p, 8p, 9p, 11q, 12q, 15q and 17p, where several upstream and downstream members of signaling pathways that lead to an increase in cell proliferation and invasion are mapped. The introduction of genomic predictive models in clinical practice might contribute to a more individualized clinical management of the HNSCC patients, reducing recurrences and improving patients' quality of life. The power of this genomic model to predict the recurrence and metastases development should be evaluated in other HNSCC populations.