Genomewide gene expression profiles of HPV-positive and HPV-negative oropharyngeal cancer: potential implications for treatment choices.

OBJECTIVE: To study the difference in gene expression between human papillomavirus (HPV)-positive and HPV-negative oral cavity and oropharyngeal squamous cell carcinoma (OSCC). DESIGN: We used Affymetrix U133 plus 2.0 arrays to examine gene expression profiles of OSCC and normal oral tissue. The HPV DNA was detected using polymerase chain reaction followed by the Roche LINEAR ARRAY HPV Genotyping Test, and the differentially expressed genes were analyzed to examine their potential biological roles using the Ingenuity Pathway Analysis Software, version 5.0. SETTING: Three medical centers affiliated with the University of Washington. PATIENTS: A total of 119 patients with primary OSCC and 35 patients without cancer, all of whom were treated at the setting institutions, provided tissues samples for the study. RESULTS: Human papillomavirus DNA was found in 41 of 119 tumors (34.5%) and 2 of 35 normal tissue samples (5.7%); 39 of the 43 HPV specimens were HPV-16. A higher prevalence of HPV DNA was found in oropharyngeal cancer (23 of 31) than in oral cavity cancer (18 of 88). We found no significant difference in gene expression between HPV-positive and HPV-negative oral cavity cancer but found 446 probe sets (347 known genes) differentially expressed in HPV-positive oropharyngeal cancer than in HPV-negative oropharyngeal cancer. The most prominent functions of these genes are DNA replication, DNA repair, and cell cycling. Some genes differentially expressed between HPV-positive and HPV-negative oropharyngeal cancer (eg, TYMS, STMN1, CCND1, and RBBP4) are involved in chemotherapy or radiation sensitivity. CONCLUSION: These results suggest that differences in the biology of HPV-positive and HPV-negative oropharyngeal cancer may have implications for the management of patients with these different tumors

Integrative analysis of DNA copy number and gene expression in metastatic oral squamous cell carcinoma identifies genes associated with poor survival

<p>Abstract</p> <p>Background</p> <p>Lymphotropism in oral squamous cell carcinoma (OSCC) is one of the most important prognostic factors of 5-year survival. In an effort to identify genes that may be responsible for the initiation of OSCC lymphotropism, we examined DNA copy number gains and losses and corresponding gene expression changes from tumor cells in metastatic lymph nodes of patients with OSCC.</p> <p>Results</p> <p>We performed integrative analysis of DNA copy number alterations (CNA) and corresponding mRNA expression from OSCC cells isolated from metastatic lymph nodes of 20 patients using Affymetrix 250 K Nsp I SNP and U133 Plus 2.0 arrays, respectively. Overall, genome CNA accounted for expression changes in 31% of the transcripts studied. Genome region 11q13.2-11q13.3 shows the highest correlation between DNA CNA and expression. With a false discovery rate < 1%, 530 transcripts (461 genes) demonstrated a correlation between CNA and expression. Among these, we found two subsets that were significantly associated with OSCC (n = 122) when compared to controls, and with survival (n = 27), as tested using an independent dataset with genome-wide expression profiles for 148 primary OSCC and 45 normal oral mucosa. We fit Cox models to calculate a principal component analysis-derived risk-score for these two gene sets ('122-' or '27-transcript PC'). The models combining the 122- or 27-transcript PC with stage outperformed the model using stage alone in terms of the Area Under the Curve (AUC = 0.82 or 0.86 vs. 0.72, with <it>p </it>= 0.044 or 0.011, respectively).</p> <p>Conclusions</p> <p>Genes exhibiting CNA-correlated expression may have biological impact on carcinogenesis and cancer progression in OSCC. Determination of copy number-associated transcripts associated with clinical outcomes in tumor cells with an aggressive phenotype (i.e., cells metastasized to the lymph nodes) can help prioritize candidate transcripts from high-throughput data for further studies.</p

Novel Prognostic and Therapeutic Targets for Oral Squamous Cell Carcinoma

In oral squamous cell carcinoma (OSCC), metastasis to lymph nodes is associated with a 50% reduction in 5-year survival. To identify a metastatic gene set based on DNA copy number abnormalities (CNAs) of differentially expressed genes, we compared DNA and RNA of OSCC cells laser-microdissected from non-metastatic primary tumors (n = 17) with those from lymph node metastases (n = 20), using Affymetrix 250K Nsp single-nucleotide polymorphism (SNP) arrays and U133 Plus 2.0 arrays, respectively. With a false discovery rate (FDR)<5%, 1988 transcripts were found to be differentially expressed between primary and metastatic OSCC. Of these, 114 were found to have a significant correlation between DNA copy number and gene expression (FDR<0.01). Among these 114 correlated transcripts, the corresponding genomic regions of each of 95 transcripts had CNAs differences between primary and metastatic OSCC (FDR<0.01). Using an independent dataset of 133 patients, multivariable analysis showed that the OSCC-specific and overall mortality hazards ratio (HR) for patients carrying the 95-transcript signature were 4.75 (95% CI: 2.03-11.11) and 3.45 (95% CI: 1.84-6.50), respectively. To determine the degree by which these genes impact cell survival, we compared the growth of five OSCC cell lines before and after knockdown of over-amplified transcripts via a high-throughput siRNA-mediated screen. The expression-knockdown of 18 of the 26 genes tested showed a growth suppression ≥ 30% in at least one cell line (P<0.01). In particular, cell lines derived from late-stage OSCC were more sensitive to the knockdown of G3BP1 than cell lines derived from early-stage OSCC, and the growth suppression was likely caused by increase in apoptosis. Further investigation is warranted to examine the biological role of these genes in OSCC progression and their therapeutic potentials

Common \u3cem\u3eTDP1\u3c/em\u3e Polymorphisms in Relation to Survival Among Small Cell Lung Cancer Patients: A Multicenter Study from the International Lung Cancer Consortium

Background—DNA topoisomerase inhibitors are commonly used for treating small-cell lung cancer (SCLC). Tyrosyl-DNA phosphodiesterase (TDP1) repairs DNA damage caused by this class of drugs and may therefore influence treatment outcome. In this study, we investigated whether common TDP1 single-nucleotide polymorphisms (SNP) are associated with overall survival among SCLC patients. Methods—Two TDP1 SNPs (rs942190 and rs2401863) were analyzed in 890 patients from 10 studies in the International Lung Cancer Consortium (ILCCO). The Kaplan–Meier method and Cox regression analyses were used to evaluate genotype associations with overall mortality at 36 months postdiagnosis, adjusting for age, sex, race, and tumor stage. Results—Patients homozygous for the minor allele (GG) of rs942190 had poorer survival compared with those carrying AA alleles, with a HR of 1.36 [95% confidence interval (CI): 1.08–1.72, P = 0.01), but no association with survival was observed for patients carrying the AG genotype (HR = 1.04, 95% CI, 0.84–1.29, P = 0.72). For rs2401863, patients homozygous for the minor allele (CC) tended to have better survival than patients carrying AA alleles (HR = 0.79; 95% CI, 0.61–1.02, P = 0.07). Results from the Genotype Tissue Expression (GTEx) Project, the Encyclopedia of DNA Elements (ENCODE), and the ePOSSUM web application support the potential function of rs942190. Conclusions—We found the rs942190 GG genotype to be associated with relatively poor survival among SCLC patients. Further investigation is needed to confirm the result and to determine whether this genotype may be a predictive marker for treatment efficacy of DNA topoisomerase inhibitors

Prediction of survival of HPV16-negative, p16-negative oral cavity cancer patients using a 13-gene signature: A multicenter study using FFPE samples

Objectives: To WA the performance of an oral cancer prognostic 13-gene signature for the prediction of survival of patients diagnosed with HPV-negative and p16-negative oral cavity cancer. Materials and Methods: Diagnostic formalin-fixed paraffin-embedded oral cavity cancer tumor samples were obtained from the Fred Hutchinson Cancer Research Center/University of Washington, University of Calgary, University of Michigan, University of Utah, and seven ARCAGE study centers coordinated by the International Agency of Research on Cancer. RNA from 638 Human Papillomavirus (HPV)-negative and p16-negative samples was analyzed for the 13 genes using a NanoString assay. Ridge-penalized Cox regressions were applied to samples randomly split into discovery and validation sets to build models and evaluate the performance of the 13-gene signature in predicting 2-year oral cavity cancer-specific survival overall and separately for patients with early and late stage disease. Results: Among AJCC stage I/II patients, including the 13-gene signature in the model resulted in substantial improvement in the prediction of 2-year oral cavity cancer-specific survival. For models containing age and sex with and without the 13-gene signature score, the areas under the Receiver Operating Characteristic Curve (AUC) and partial AUC were 0.700 vs. 0.537 (p < 0.001), and 0.046 vs. 0.018 (p < 0.001), respectively. Improvement in predicting prognosis for AJCC stage III/IV disease also was observed, but to a lesser extent. Conclusions: If confirmed using tumor samples from a larger number of early stage oral cavity cancer patients, the 13-gene signature may inform personalized treatment of early stage HPV-negative and p16-negative oral cavity cancer patients

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment