Comparing Genomic Landscapes of Oral and Cutaneous Squamous Cell Carcinoma of the Head and Neck: Quest for Novel Diagnostic Markers.

Squamous cell carcinoma is the most common head and neck malignancy arising from the oral mucosa and the skin. The histologic and immunohistochemical features of oral squamous cell carcinoma (OSCC) and head and neck cutaneous squamous cell carcinoma (HNcSCC) are similar, making it difficult to identify the primary site in cases of metastases. With the advent of immunotherapy, reliable distinction of OSCC and HNcSCC at metastatic sites has important treatment and prognostic implications. Here, we investigate and compare the genomic landscape of OSCC and HNcSCC to identify diagnostically useful biomarkers. Whole-genome sequencing data from 57 OSCC and 41 HNcSCC patients were obtained for tumor and matched normal samples. Tumor mutation burden (TMB), Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures, frequent chromosomal alterations, somatic single nucleotide, and copy number variations were analyzed. The median TMB of 3.75 in primary OSCC was significantly lower (P < .001) than that of 147.51 mutations/Mb in primary HNcSCC. The COSMIC mutation signatures were significantly different (P < .001) between OSCC and HNcSCC. OSCC showed COSMIC single-base substitution (SBS) mutation signature 1 and AID/APOBEC activity-associated signature 2 and/or 13. All except 1 HNcSCC from hair-bearing scalp showed UV damage-associated COSMIC SBS mutation signature 7. Both OSCC and HNcSCC demonstrated a predominance of tumor suppressor gene mutations, predominantly TP53. The most frequently mutated oncogenes were PIK3CA and MUC4 in OSCC and HNcSCC, respectively. The metastases of OSCC and HNcSCC demonstrated TMB and COSMIC SBS mutation signatures similar to their primary counterparts. The combination of high TMB and UV signature in a metastatic keratinizing squamous cell carcinoma suggests HNcSCC as the primary site and may also facilitate decisions regarding immunotherapy. HNcSCC and OSCC show distinct genomic profiles despite histologic and immunohistochemical similarities. Their genomic characteristics may underlie differences in behavior and guide treatment decisions in recurrent and metastatic settings.

Whole genome duplication in oral squamous cell carcinoma in patients younger than 50 years: implications for prognosis and adverse clinicopathological factors.

INTRODUCTION: Oral squamous cell carcinoma (OSCC) in the young (<50 years), without known carcinogenic risk factors, is on the rise globally. Whole genome duplication (WGD) has been shown to occur at higher rates in cancers without an identifiable carcinogenic agent. We aimed to evaluate the prevalence of WGD in a cohort of OSCC patients under the age of 50 years. METHODS: Whole genome sequencing (WGS) was performed on 28 OSCC patients from the Sydney Head and Neck Cancer Institute (SHNCI) biobank. An additional nine cases were obtained from The Cancer Genome Atlas (TCGA). RESULTS: WGD was seen in 27 of 37 (73%) cases. Non-synonymous, somatic TP53 mutations occurred in 25 of 27 (93%) cases of WGD and were predicted to precede WGD in 21 (77%). WGD was significantly associated with larger tumor size (p = 0.01) and was frequent in patients with recurrences (87%, p = 0.36). Overall survival was significantly worse in those with WGD (p = 0.05). CONCLUSIONS: Our data, based on one of the largest WGS datasets of young patients with OSCC, demonstrates a high frequency of WGD and its association with adverse pathologic characteristics and clinical outcomes. TP53 mutations also preceded WGD, as has been described in other tumors without a clear mutagenic driver.

DCARS: differential correlation across ranked samples.

MOTIVATION: Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform the outcome of interest called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the dichotomisation of samples into two distinct classes and can identify differences in gene correlation across early, mid or late stages of the outcome of interest. RESULTS: When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across several cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing several distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multi-layered and complex data. AVAILABILITY AND IMPLEMENTATION: DCARS R package and sample data are available at https://github.com/shazanfar/DCARS. Publicly available data from The Cancer Genome Atlas (TCGA) was used using the TCGABiolinks R package. Supplementary Files and DCARS R package is available at https://github.com/shazanfar/DCARS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential distribution improves gene selection stability and has competitive classification performance for patient survival.

A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription factors or epigenetic signals become deregulated, a change in expression variability (DV) of target genes is frequently observed. This suggests that assessing the importance of genes by either differential expression or variability alone potentially misses sets of important biomarkers that could lead to improved predictions and treatments. Here, we describe a new approach for assessing the importance of genes based on differential distribution (DD), which combines information from differential expression and differential variability into a unified metric. We show that feature ranking and selection stability based on DD can perform two to three times better than DE or DV alone, and that DD yields equivalent error rates to DE and DV. Finally, assessing genes via differential distribution produces a complementary set of selected genes to DE and DV, potentially opening up new categories of biomarkers.

Quantitative Performance Evaluator for Proteomics (QPEP): Web-based Application for Reproducible Evaluation of Proteomics Preprocessing Methods.

Tandem mass spectrometry is one of the most popular techniques for quantitation of proteomes. There exists a large variety of options in each stage of data preprocessing that impact the bias and variance of the summarized protein-level values. Using a newly released data set satisfying a replicated Latin squares design, a diverse set of performance metrics has been developed and implemented in a web-based application, Quantitative Performance Evaluator for Proteomics (QPEP). QPEP has the flexibility to allow users to apply their own method to preprocess this data set and share the results, allowing direct and straightforward comparison of new methodologies. Application of these new metrics to three case studies highlights that (i) the summarization of peptides to proteins is robust to the choice of peptide summary used, (ii) the differences between iTRAQ labels are stronger than the differences between experimental runs, and (iii) the commercial software ProteinPilot performs equivalently well at between-sample normalization to more complicated methods developed by academics. Importantly, finding (ii) underscores the benefits of using the principles of randomization and blocking to avoid the experimental measurements being confounded by technical factors. Data are available via ProteomeXchange with identifier PXD003608.

Nodal and BMP expression during the transition to pentamery in the sea urchin Heliocidaris erythrogramma: insights into patterning the enigmatic echinoderm body plan.

BACKGROUND: The molecular mechanisms underlying the development of the unusual echinoderm pentameral body plan and their likeness to mechanisms underlying the development of the bilateral plans of other deuterostomes are of interest in tracing body plan evolution. In this first study of the spatial expression of genes associated with Nodal and BMP2/4 signalling during the transition to pentamery in sea urchins, we investigate Heliocidaris erythrogramma, a species that provides access to the developing adult rudiment within days of fertilization. RESULTS: BMP2/4, and the putative downstream genes, Six1/2, Eya, Tbx2/3 and Msx were expressed in the earliest morphological manifestation of pentamery during development, the five hydrocoele lobes. The formation of the vestibular ectoderm, the specialized region overlying the left coelom that forms adult ectoderm, involved the expression of putative Nodal target genes Chordin, Gsc and BMP2/4 and putative BMP2/4 target genes Dlx, Msx and Tbx. The expression of Nodal, Lefty and Pitx2 in the right ectoderm, and Pitx2 in the right coelom, was as previously observed in other sea urchins. CONCLUSION: That genes associated with Nodal and BMP2/4 signalling are expressed in the hydrocoele lobes, indicates that they have a role in the developmental transition to pentamery, contributing to our understanding of how the most unusual body plan in the Bilateria may have evolved. We suggest that the Nodal and BMP2/4 signalling cascades might have been duplicated or split during the evolution to pentamery.

ClassifyR: an R package for performance assessment of classification with applications to transcriptomics.

UNLABELLED: Although a large collection of classification software packages exist in R, a new generic framework for linking custom classification functions with classification performance measures is needed. A generic classification framework has been designed and implemented as an R package in an object oriented style. Its design places emphasis on parallel processing, reproducibility and extensibility. Finally, a comprehensive set of performance measures are available to ease post-processing. Taken together, these important characteristics enable rapid and reproducible benchmarking of alternative classifiers. AVAILABILITY AND IMPLEMENTATION: ClassifyR is implemented in R and can be obtained from the Bioconductor project: http://bioconductor.org/packages/release/bioc/html/ClassifyR.html.

Copy-number-aware differential analysis of quantitative DNA sequencing data.

Developments in microarray and high-throughput sequencing (HTS) technologies have resulted in a rapid expansion of research into epigenomic changes that occur in normal development and in the progression of disease, such as cancer. Not surprisingly, copy number variation (CNV) has a direct effect on HTS read densities and can therefore bias differential detection results. We have developed a flexible approach called ABCD-DNA (affinity-based copy-number-aware differential quantitative DNA sequencing analyses) that integrates CNV and other systematic factors directly into the differential enrichment engine.

Acetylation of H2A.Z is a key epigenetic modification associated with gene deregulation and epigenetic remodeling in cancer.

Histone H2A.Z (H2A.Z) is an evolutionarily conserved H2A variant implicated in the regulation of gene expression; however, its role in transcriptional deregulation in cancer remains poorly understood. Using genome-wide studies, we investigated the role of promoter-associated H2A.Z and acetylated H2A.Z (acH2A.Z) in gene deregulation and its relationship with DNA methylation and H3K27me3 in prostate cancer. Our results reconcile the conflicting reports of positive and negative roles for histone H2A.Z and gene expression states. We find that H2A.Z is enriched in a bimodal distribution at nucleosomes, surrounding the transcription start sites (TSSs) of both active and poised gene promoters. In addition, H2A.Z spreads across the entire promoter of inactive genes in a deacetylated state. In contrast, acH2A.Z is only localized at the TSSs of active genes. Gene deregulation in cancer is also associated with a reorganization of acH2A.Z and H2A.Z nucleosome occupancy across the promoter region and TSS of genes. Notably, in cancer cells we find that a gain of acH2A.Z at the TSS occurs with an overall decrease of H2A.Z levels, in concert with oncogene activation. Furthermore, deacetylation of H2A.Z at TSSs is increased with silencing of tumor suppressor genes. We also demonstrate that acH2A.Z anti-correlates with promoter H3K27me3 and DNA methylation. We show for the first time, that acetylation of H2A.Z is a key modification associated with gene activity in normal cells and epigenetic gene deregulation in tumorigenesis.

Savant Genome Browser 2: visualization and analysis for population-scale genomics.

High-throughput sequencing (HTS) technologies are providing an unprecedented capacity for data generation, and there is a corresponding need for efficient data exploration and analysis capabilities. Although most existing tools for HTS data analysis are developed for either automated (e.g. genotyping) or visualization (e.g. genome browsing) purposes, such tools are most powerful when combined. For example, integration of visualization and computation allows users to iteratively refine their analyses by updating computational parameters within the visual framework in real-time. Here we introduce the second version of the Savant Genome Browser, a standalone program for visual and computational analysis of HTS data. Savant substantially improves upon its predecessor and existing tools by introducing innovative visualization modes and navigation interfaces for several genomic datatypes, and synergizing visual and automated analyses in a way that is powerful yet easy even for non-expert users. We also present a number of plugins that were developed by the Savant Community, which demonstrate the power of integrating visual and automated analyses using Savant. The Savant Genome Browser is freely available (open source) at www.savantbrowser.com.

Construction and optimization of multi-platform precision pathways for precision medicine.

In the enduring challenge against disease, advancements in medical technology have empowered clinicians with novel diagnostic platforms. Whilst in some cases, a single test may provide a confident diagnosis, often additional tests are required. However, to strike a balance between diagnostic accuracy and cost-effectiveness, one must rigorously construct the clinical pathways. Here, we developed a framework to build multi-platform precision pathways in an automated, unbiased way, recommending the key steps a clinician would take to reach a diagnosis. We achieve this by developing a confidence score, used to simulate a clinical scenario, where at each stage, either a confident diagnosis is made, or another test is performed. Our framework provides a range of tools to interpret, visualize and compare the pathways, improving communication and enabling their evaluation on accuracy and cost, specific to different contexts. This framework will guide the development of novel diagnostic pathways for different diseases, accelerating the implementation of precision medicine into clinical practice.

Detection and classification of peaks in 5' cap RNA sequencing data.

BACKGROUND: The large-scale sequencing of 5' cap enriched cDNA promises to reveal the diversity of transcription initiation across entire genomes. The process of transcription is noisy, and there is often no single, exact start site. This creates the need for a fast and simple method of identifying transcription start peaks based on this type of data. Due to both biological and technical noise, many of the peaks seen are not real transcription initiation events. Classification of the observed peaks is an essential filtering step in the discovery of genuine initiation locations. RESULTS: We develop a two-stage approach consisting of a fast and simple algorithm based on a sliding window with Poisson null distribution for detecting the genomic locations of peaks, followed by a linear support vector machine classifier to distinguish between peaks which represent the initiation of transcription and peaks that do not. Comparison of classification performance to the best existing method based on whole genome segmentation showed comparable precision and improved recall. Internal features, which are intrinsic to the data and require no further experiments, had high precision and recall rates. Addition of pooled external data or matched RNA sequencing data resulted in gains of recall with equivalent precision. CONCLUSIONS: The Poisson sliding window model is an effective and fast way of taking the peak neighbourhood into account, and finding statistically significant peaks over a range of transcript expression values. It is orders of magnitude faster than doing whole genome segmentation. The support vector classification scheme has better precision and recall than existing methods. Integrating additional datasets is shown to provide minor gains in recall, in comparison to using only the cap-sequencing data.

Evaluation of affinity-based genome-wide DNA methylation data: effects of CpG density, amplification bias, and copy number variation.

DNA methylation is an essential epigenetic modification that plays a key role associated with the regulation of gene expression during differentiation, but in disease states such as cancer, the DNA methylation landscape is often deregulated. There are now numerous technologies available to interrogate the DNA methylation status of CpG sites in a targeted or genome-wide fashion, but each method, due to intrinsic biases, potentially interrogates different fractions of the genome. In this study, we compare the affinity-purification of methylated DNA between two popular genome-wide techniques, methylated DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain-based capture (MBDCap), and show that each technique operates in a different domain of the CpG density landscape. We explored the effect of whole-genome amplification and illustrate that it can reduce sensitivity for detecting DNA methylation in GC-rich regions of the genome. By using MBDCap, we compare and contrast microarray- and sequencing-based readouts and highlight the impact that copy number variation (CNV) can make in differential comparisons of methylomes. These studies reveal that the analysis of DNA methylation data and genome coverage is highly dependent on the method employed, and consideration must be made in light of the GC content, the extent of DNA amplification, and the copy number.

Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine.

In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies.

Whole genome analysis reveals the genomic complexity in metastatic cutaneous squamous cell carcinoma.

Metastatic cutaneous squamous cell carcinoma (CSCC) is a highly morbid disease requiring radical surgery and adjuvant therapy, which is associated with a poor prognosis. Yet, compared to other advanced malignancies, relatively little is known of the genomic landscape of metastatic CSCC. We have previously reported the mutational signatures and mutational patterns of CCCTC-binding factor (CTCF) regions in metastatic CSCC. However, many other genomic components (indel signatures, non-coding drivers, and structural variants) of metastatic CSCC have not been reported. To this end, we performed whole genome sequencing on lymph node metastases and blood DNA from 25 CSCC patients with regional metastases of the head and neck. We designed a multifaceted computational analysis at the whole genome level to provide a more comprehensive perspective of the genomic landscape of metastatic CSCC. In the non-coding genome, 3' untranslated region (3'UTR) regions of EVC (48% of specimens), PPP1R1A (48% of specimens), and ABCA4 (20% of specimens) along with the tumor-suppressing long non-coding RNA (lncRNA) LINC01003 (64% of specimens) were significantly functionally altered (Q-value < 0.05) and represent potential non-coding biomarkers of CSCC. Recurrent copy number loss in the tumor suppressor gene PTPRD was observed. Gene amplification was much less frequent, and few genes were recurrently amplified. Single nucleotide variants driver analyses from three tools confirmed TP53 and CDKN2A as recurrently mutated genes but also identified C9 as a potential novel driver in this disease. Furthermore, indel signature analysis highlighted the dominance of ID signature 13 (ID13) followed by ID8 and ID9. ID9 has previously been shown to have no association with skin melanoma, unlike ID13 and ID8, suggesting a novel pattern of indel variation in metastatic CSCC. The enrichment analysis of various genetically altered candidates shows enrichment of "TGF-beta regulation of extracellular matrix" and "cell cycle G1 to S check points." These enriched terms are associated with genetic instability, cell proliferation, and migration as mechanisms of genomic drivers of metastatic CSCC.

Viral Integration Plays a Minor Role in the Development and Prognostication of Oral Squamous Cell Carcinoma.

Viruses are well known drivers of several human malignancies. A causative factor for oral cavity squamous cell carcinoma (OSCC) in patients with limited exposure to traditional risk factors, including tobacco use, is yet to be identified. Our study aimed to comprehensively evaluate the role of viral drivers in OSCC patients with low cumulative exposure to traditional risk factors. Patients under 50 years of age with OSCC, defined using strict anatomic criteria were selected for WGS. The WGS data was interrogated using viral detection tools (Kraken 2 and BLASTN), together examining >700,000 viruses. The findings were further verified using tissue microarrays of OSCC samples using both immunohistochemistry and RNA in situ hybridisation (ISH). 28 patients underwent WGS and comprehensive viral profiling. One 49-year-old male patient with OSCC of the hard palate demonstrated HPV35 integration. 657 cases of OSCC were then evaluated for the presence of HPV integration through immunohistochemistry for p16 and HPV RNA ISH. HPV integration was seen in 8 (1.2%) patients, all middle-aged men with predominant floor of mouth involvement. In summary, a wide-ranging interrogation of >700,000 viruses using OSCC WGS data showed HPV integration in a minority of male OSCC patients and did not carry any prognostic significance.

Discovery pipeline for epigenetically deregulated miRNAs in cancer: integration of primary miRNA transcription.

BACKGROUND: Cancer is commonly associated with widespread disruption of DNA methylation, chromatin modification and miRNA expression. In this study, we established a robust discovery pipeline to identify epigenetically deregulated miRNAs in cancer. RESULTS: Using an integrative approach that combines primary transcription, genome-wide DNA methylation and H3K9Ac marks with microRNA (miRNA) expression, we identified miRNA genes that were epigenetically modified in cancer. We find miR-205, miR-21, and miR-196b to be epigenetically repressed, and miR-615 epigenetically activated in prostate cancer cells. CONCLUSIONS: We show that detecting changes in primary miRNA transcription levels is a valuable method for detection of local epigenetic modifications that are associated with changes in mature miRNA expression.

Repitools: an R package for the analysis of enrichment-based epigenomic data.

SUMMARY: Epigenetics, the study of heritable somatic phenotypic changes not related to DNA sequence, has emerged as a critical component of the landscape of gene regulation. The epigenetic layers, such as DNA methylation, histone modifications and nuclear architecture are now being extensively studied in many cell types and disease settings. Few software tools exist to summarize and interpret these datasets. We have created a toolbox of procedures to interrogate and visualize epigenomic data (both array- and sequencing-based) and make available a software package for the cross-platform R language. AVAILABILITY: The package is freely available under LGPL from the R-Forge web site (http://repitools.r-forge.r-project.org/) CONTACT: mrobinson@wehi.edu.au.

Transcriptomic analysis of Nodal - and BMP- associated genes during development to the juvenile seastar in Parvulastra exigua (Asterinidae).

The molecular mechanisms underlying development of the pentameral body of adult echinoderms are poorly understood but are important to solve with respect to evolution of a unique body plan that contrasts with the bilateral body plan of other deuterostomes. As Nodal and BMP2/4 signalling is involved in axis formation in larvae and development of the echinoderm body plan, we used the developmental transcriptome generated for the asterinid seastar Parvulastra exigua to investigate the temporal expression patterns of Nodal and BMP2/4 genes from the embryo and across metamorphosis to the juvenile. For echinoderms, the Asteroidea represents the basal-type body architecture with a distinct (separated) ray structure. Parvulastra exigua has lecithotrophic development forming the juvenile soon after gastrulation providing ready access to the developing adult stage. We identified 39 genes associated with the Nodal and BMP2/4 network in the P. exigua developmental transcriptome. Clustering analysis of these genes resulted in 6 clusters with similar temporal expression patterns across development. A co-expression analysis revealed genes that have similar expression profiles as Nodal and BMP2/4. These results indicated genes that may have a regulatory relationship in patterning morphogenesis of the juvenile seastar. Developmental RNA-seq analyses of Parvulastra exigua show changes in Nodal and BMP2/4 signalling genes across the metamorphic transition. We provide the foundation for detailed analyses of this cascade in the evolution of the unusual pentameral echinoderm body and its deuterostome affinities.

Genome-wide analysis in Drosophila reveals diet-by-gene interactions and uncovers diet-responsive genes.

Genetic and environmental factors play a major role in metabolic health. However, they do not act in isolation, as a change in an environmental factor such as diet may exert different effects based on an individual's genotype. Here, we sought to understand how such gene-diet interactions influenced nutrient storage and utilization, a major determinant of metabolic disease. We subjected 178 inbred strains from the Drosophila genetic reference panel (DGRP) to diets varying in sugar, fat, and protein. We assessed starvation resistance, a holistic phenotype of nutrient storage and utilization that can be robustly measured. Diet influenced the starvation resistance of most strains, but the effect varied markedly between strains such that some displayed better survival on a high carbohydrate diet (HCD) compared to a high-fat diet while others had opposing responses, illustrating a considerable gene × diet interaction. This demonstrates that genetics plays a major role in diet responses. Furthermore, heritability analysis revealed that the greatest genetic variability arose from diets either high in sugar or high in protein. To uncover the genetic variants that contribute to the heterogeneity in starvation resistance, we mapped 566 diet-responsive SNPs in 293 genes, 174 of which have human orthologs. Using whole-body knockdown, we identified two genes that were required for glucose tolerance, storage, and utilization. Strikingly, flies in which the expression of one of these genes, CG4607 a putative homolog of a mammalian glucose transporter, was reduced at the whole-body level, displayed lethality on a HCD. This study provides evidence that there is a strong interplay between diet and genetics in governing survival in response to starvation, a surrogate measure of nutrient storage efficiency and obesity. It is likely that a similar principle applies to higher organisms thus supporting the case for nutrigenomics as an important health strategy.