Genomic and Transcriptomic Alterations Associated with STAT3 Activation in Head and Neck Cancer.

BackgroundHyperactivation of STAT3 via constitutive phosphorylation of tyrosine 705 (Y705) is common in most human cancers, including head and neck squamous carcinoma (HNSCC). STAT3 is rarely mutated in cancer and the (epi)genetic alterations that lead to STAT3 activation are incompletely understood. Here we used an unbiased approach to identify genomic and epigenomic changes associated with pSTAT3(Y705) expression using data generated by The Cancer Genome Atlas (TCGA).Methods and findingsMutation, mRNA expression, promoter methylation, and copy number alteration data were extracted from TCGA and examined in the context of pSTAT3(Y705) protein expression. mRNA expression levels of 1279 genes were found to be associated with pSTAT3(705) expression. Association of pSTAT3(Y705) expression with caspase-8 mRNA expression was validated by immunoblot analysis in HNSCC cells. Mutation, promoter hypermethylation, and copy number alteration of any gene were not significantly associated with increased pSTAT3(Y705) protein expression.ConclusionsThese cumulative results suggest that unbiased approaches may be useful in identifying the molecular underpinnings of oncogenic signaling, including STAT3 activation, in HNSCC. Larger datasets will likely be necessary to elucidate signaling consequences of infrequent alterations

Frequent mutation of receptor protein tyrosine phosphatases provides a mechanism for STAT3 hyperactivation in head and neck cancer

The underpinnings of STAT3 hyperphosphorylation resulting in enhanced signaling and cancer progression are incompletely understood. Loss-of-function mutations of enzymes that dephosphorylate STAT3, such as receptor protein tyrosine phosphatases, which are encoded by the PTPR gene family, represent a plausible mechanism of STAT3 hyperactivation. We analyzed whole exome sequencing (n = 374) and reverse-phase protein array data (n = 212) from head and neck squamous cell carcinomas (HNSCCs). PTPR mutations are most common and are associated with significantly increased phospho-STAT3 expression in HNSCC tumors. Expression of receptor-like protein tyrosine phosphatase T (PTPRT) mutant proteins induces STAT3 phosphorylation and cell survival, consistent with a “driver” phenotype. Computational modeling reveals functional consequences of PTPRT mutations on phospho-tyrosine–substrate interactions. A high mutation rate (30%) of PTPRs was found in HNSCC and 14 other solid tumors, suggesting that PTPR alterations, in particular PTPRT mutations, may define a subset of patients where STAT3 pathway inhibitors hold particular promise as effective therapeutic agents.Fil: Lui, Vivian Wai Yan. University of Pittsburgh; Estados UnidosFil: Peyser, Noah D.. University of Pittsburgh; Estados UnidosFil: Ng, Patrick Kwok-Shing. University Of Texas Md Anderson Cancer Center;Fil: Hritz, Jozef. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados Unidos. Masaryk University; República ChecaFil: Zeng, Yan. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Lu, Yiling. University Of Texas Md Anderson Cancer Center;Fil: Li, Hua. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Wang, Lin. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Gilbert, Breean R.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: General, Ignacio. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Bahar, Ivet. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Ju, Zhenlin. University Of Texas Md Anderson Cancer Center;Fil: Wang, Zhenghe. Case Western Reserve University; Estados UnidosFil: Pendleton, Kelsey P.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Xiao, Xiao. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Du, Yu. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Vries, John K.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Hammerman, Peter S.. Harvard Medical School; Estados UnidosFil: Garraway, Levi A.. Harvard Medical School; Estados UnidosFil: Mills, Gordon B.. University Of Texas Md Anderson Cancer Center;Fil: Johnson, Daniel E.. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Grandis, Jennifer R.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados Unido

PTPRT/D MUTATION OR PROMOTER METHYLATION: IMPLICATIONS FOR STAT3 INHIBITION

Head and neck squamous cell carcinoma (HNSCC) is a morbid and frequently fatal malignancy arising from the squamous epithelium of the upper aerodigestive tract. Survival rates have remained low and stagnant in recent decades even as our understanding of this disease has led to new treatment approaches, most notably the approval in 2006 of cetuximab, a monoclonal antibody targeting the epidermal growth factor receptor. The paucity of broadly effective targeted therapies for HNSCC patients illustrates the need for new targets for pharmacologic inhibition and biomarkers for predicting exquisite response to such agents. STAT3 is a potent oncogene that is hyperactivated by constitutive tyrosine phosphorylation in nearly all HNSCCs, where STAT3 represents a rational target for inhibition. As it is increasingly clear that most targeted therapies are unlikely to be broadly effective in unselected groups of patients, we have sought to identify genetic/epigenetic alterations of phosphatases that normally downregulate STAT3 in order to assess the potential utility of these alterations as predictive biomarkers for STAT3-targeted therapeutics. Our findings reveal that somatic mutation or promoter hypermethylation of PTPRT or PTPRD leads to loss of function of these phosphatases in HNSCC, concomitant with increased activation of STAT3 in preclinical models and tumor specimens. Importantly, these events are also associated with increased sensitivity to inhibitors of the STAT3 pathway in preclinical models. Together, these studies indicate that genetic or epigenetic alterations leading to loss of function of phosphatases that target STAT3 may ultimately serve as biomarkers for the selection of patients who will be most likely to respond to STAT3 inhibitors that are currently in preclinical and clinical development

DISSECTING PATHWAYS WITH THE YEAST KNOCKOUT COLLECTION

The yeast knockout collections provide opportunities to perform massively parallel phenotyping of deletion mutants for almost every yeast open reading frame. I used the knockout collection to screen for synthetic lethal partners, defined as alleles that cause lethality when combined but are nonlethal alone, with CTF4 and CTF18 and present the results in Chapter 2. I developed procedures for interpreting microarrays designed to compare changes in oligonucleotide TAGs specific to each knockout strain and present those methods in Chapters 3 and 4. These TAG microarrays allow thousands of experiments to screen for synthetic lethality among pairs of null alleles to be accomplished relatively quickly. In Chapter 4, I present 1410 novel predicted synthetic lethal interactions based on 707 currently completed screens. Interpretation of synthetic lethality is presented with a computational approach in Chapter 5, termed the congruence score. High congruence scores associate genes into common pathways, and I use the method to predict that YLL049W is a component of the dynein-dynactin nuclear orientation pathway. In Chapter 6, I propose a generalization of the congruence score to any phenotype, such as growth rate in the presence of various compounds, or even nonquantitative phenotypes such as cell morphology. This procedure connects genes based on similarity of multiple phenotypes using an application of information theory to produce a shared information score. Using gene ontology similarity, I show that high scores are associated with similarly annotated genes

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast

BACKGROUND: In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. RESULTS: We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). CONCLUSION: Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed)

Improved microarray methods for profiling the yeast knockout strain collection

A remarkable feature of the Yeast Knockout strain collection is the presence of two unique 20mer TAG sequences in almost every strain. In principle, the relative abundances of strains in a complex mixture can be profiled swiftly and quantitatively by amplifying these sequences and hybridizing them to microarrays, but TAG microarrays have not been widely used. Here, we introduce a TAG microarray design with sophisticated controls and describe a robust method for hybridizing high concentrations of dye-labeled TAGs in single-stranded form. We also highlight the importance of avoiding PCR contamination and provide procedures for detection and eradication. Validation experiments using these methods yielded false positive (FP) and false negative (FN) rates for individual TAG detection of 3–6% and 15–18%, respectively. Analysis demonstrated that cross-hybridization was the chief source of FPs, while TAG amplification defects were the main cause of FNs. The materials, protocols, data and associated software described here comprise a suite of experimental resources that should facilitate the use of TAG microarrays for a wide variety of genetic screens

Improved statistical analysis of budding yeast TAG microarrays revealed by defined spike-in pools

Saccharomyces cerevisiae knockout collection TAG microarrays are an emergent platform for rapid, genome-wide functional characterization of yeast genes. TAG arrays report abundance of unique oligonucleotide ‘TAG’ sequences incorporated into each deletion mutation of the yeast knockout collection, allowing measurement of relative strain representation across experimental conditions for all knockout mutants simultaneously. One application of TAG arrays is to perform genome-wide synthetic lethality screens, known as synthetic lethality analyzed by microarray (SLAM). We designed a fully defined spike-in pool to resemble typical SLAM experiments and performed TAG microarray hybridizations. We describe a method for analyzing two-color array data to efficiently measure the differential knockout strain representation across two experimental conditions, and use the spike-in pool to show that the sensitivity and specificity of this method exceed typical current approaches

Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loc

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan