Chromosomal breakpoints in a cohort of head and neck squamous cell carcinoma patients

Epidemiological, Clinical, and Genomic Profile in Head and Neck Cancer Patients and Their Families

Inherited cancer predisposition genes are described as risk factors in head and neck cancer (HNC) families. To explore the clinical and epidemiological data and their association with a family history of cancer, we recruited 74 patients and 164 relatives affected by cancer. The germline copy number alterations were evaluated in 18 patients using array comparative genomic hybridization. Two or more first-degree relatives with HNC, tobacco-associated tumor sites (lung, esophagus, and pancreas), or other related tumors (breast, colon, kidney, bladder, cervix, stomach carcinomas, and melanoma) were reported in 74 families. Ten index patients had no exposure to any known risk factors. Family members presented tumors of 19 topographies (30 head and neck, 26 breast, 21 colon). In first-degree relatives, siblings were frequently affected by cancer (n = 58, 13 had HNC). Breast cancer (n = 21), HNC (n = 19), and uterine carcinoma (n = 15) were commonly found in first-degree relatives and HNC in second-degree relatives (n = 11). Nineteen germline genomic imbalances were detected in 13 patients; three presented gains of WRD genes. The number of HNC patients, the degree of kinship, and the tumor types detected in each relative support the role of heredity in these families. Germline alterations may potentially contribute to cancer development.

Hotspots of Human Mutation

Mutation of the human genome results in three classes of genomic variation: single nucleotide variants; short insertions or deletions; and large structural variants (SVs). Some mutations occur during normal processes, such as meiotic recombination or B cell development, and others result from DNA replication or aberrant repair of breaks in sequence-specific contexts. Regardless of mechanism, mutations are subject to selection, and some hotspots can manifest in disease. Here, we discuss genomic regions prone to mutation, mechanisms contributing to mutation susceptibility, and the processes leading to their accumulation in normal and somatic genomes. With further, more accurate human genome sequencing, additional mutation hotspots, mechanistic details of their formation, and the relevance of hotspots to evolution and disease are likely to be discovered.

Comprehensive in silico analysis for identification of novel candidate target genes, including DHX36, OPA1, and SENP2, located on chromosome 3q in head and neck cancers

BACKGROUND

Major milestones of head and neck carcinogenesis have been associated with various genetic abnormalities; however, a clear picture of the molecular networks deregulated during the carcinogenesis of head and neck squamous cell carcinoma (HNSC) has not yet completely revealed.

METHODS

In this study, we used in silico tools and online data sets to evaluate the underlying reasons for the expressional changes of genes residing within the chromosome 3q and to help understanding their contributions to HNSC carcinogenesis.

RESULTS

We found that 13 of 20 most upregulated genes in HNSC are localized to 3q. Further analysis revealed a gene signature consisting of DHX36, OPA1, and SENP2, which showed significant correlation in HNSC samples and potentially be deregulated through similar mechanisms including DNA amplification, transcriptional, and posttranscriptional regulation.

CONCLUSIONS

Considering our findings, we suggest DHX36, OPA1, and SENP2 genes as overexpressed in HNSC tumors and that might be concurrently involved in HNSC carcinogenesis, tumor progression, and induction of angiogenic pathways.

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.