Examination of Epigenetic and other Molecular Factors Associated with mda-9/Syntenin Dysregulation in Cancer Through Integrated Analyses of Public Genomic Datasets.

mda-9/Syntenin (melanoma differentiation-associated gene 9) is a PDZ domain containing, cancer invasion-related protein. In this study, we employed multiple integrated bioinformatic approaches to identify the probable epigenetic factors, molecular pathways, and functionalities associated with mda-9 dysregulation during cancer progression. Analyses of publicly available genomic data (e.g., expression, copy number, methylation) from TCGA, GEO, ENCODE, and Human Protein Atlas projects led to the following observations: (a) mda-9 expression correlates with both copy number and methylation level of an intronic CpG site (cg1719774) located downstream of the CpG island, (b) cg1719774 methylation is a likely prognostic marker in glioma, (c) among 22 cancer types, melanoma exhibits the highest mda-9 level, and lowest level of methylation at cg1719774, (d) cg1719774 hypomethylation is also associated with histone modifications (at the mda-9 locus) indicative of more active transcription, (e) using Gene Set Enrichment Analysis (GSEA), and the Virtual Gene Overexpression or Repression (VIGOR) analytical scheme, we were able to predict mda-9's association with extracellular matrix organization (e.g., MMPs, collagen, integrins), IGFBP2 and NF-κB signaling pathways, phospholipid metabolism, cytokines (e.g., interleukins), CTLA-4, and components of complement cascade pathways. Indeed, previous publications have shown that many of the aforementioned genes and pathways are associated with mda-9's functionality.

Neurotrophin signaling via TrkB and TrkC receptors promotes the growth of brain tumor-initiating cells.

Neurotrophins and their receptors are frequently expressed in malignant gliomas, yet their functions are largely unknown. Previously, we have shown that p75 neurotrophin receptor is required for glioma invasion and proliferation. However, the role of Trk receptors has not been examined. In this study, we investigated the importance of TrkB and TrkC in survival of brain tumor-initiating cells (BTICs). Here, we show that human malignant glioma tissues and also tumor-initiating cells isolated from fresh human malignant gliomas express the neurotrophin receptors TrkB and TrkC, not TrkA, and they also express neurotrophins NGF, BDNF, and neurotrophin 3 (NT3). Specific activation of TrkB and TrkC receptors by ligands BDNF and NT3 enhances tumor-initiating cell viability through activation of ERK and Akt pathways. Conversely, TrkB and TrkC knockdown or pharmacologic inhibition of Trk signaling decreases neurotrophin-dependent ERK activation and BTIC growth. Further, pharmacological inhibition of both ERK and Akt pathways blocked BDNF, and NT3 stimulated BTIC survival. Importantly, attenuation of BTIC growth by EGFR inhibitors could be overcome by activation of neurotrophin signaling, and neurotrophin signaling is sufficient for long term BTIC growth as spheres in the absence of EGF and FGF. Our results highlight a novel role for neurotrophin signaling in brain tumor and suggest that Trks could be a target for combinatorial treatment of malignant glioma.

p75 neurotrophin receptor cleavage by α- and γ-secretases is required for neurotrophin-mediated proliferation of brain tumor-initiating cells.

Malignant gliomas are highly invasive, proliferative, and resistant to treatment. Previously, we have shown that p75 neurotrophin receptor (p75NTR) is a novel mediator of invasion of human glioma cells. However, the role of p75NTR in glioma proliferation is unknown. Here we used brain tumor-initiating cells (BTICs) and show that BTICs express neurotrophin receptors (p75NTR, TrkA, TrkB, and TrkC) and their ligands (NGF, brain-derived neurotrophic factor, and neurotrophin 3) and secrete NGF. Down-regulation of p75NTR significantly decreased proliferation of BTICs. Conversely, exogenouous NGF stimulated BTIC proliferation through α- and γ-secretase-mediated p75NTR cleavage and release of its intracellular domain (ICD). In contrast, overexpression of the p75NTR ICD induced proliferation. Interestingly, inhibition of Trk signaling blocked NGF-stimulated BTIC proliferation and p75NTR cleavage, indicating a role of Trk in p75NTR signaling. Further, blocking p75NTR cleavage attenuated Akt activation in BTICs, suggesting role of Akt in p75NTR-mediated proliferation. We also found that p75NTR, α-secretases, and the four subunits of the γ-secretase enzyme were elevated in glioblastoma multiformes patients. Importantly, the ICD of p75NTR was commonly found in malignant glioma patient specimens, suggesting that the receptor is activated and cleaved in patient tumors. These results suggest that p75NTR proteolysis is required for BTIC proliferation and is a novel potential clinical target.

JAK1 truncating mutations in gynecologic cancer define new role of cancer-associated protein tyrosine kinase aberrations.

Cancer-associated protein tyrosine kinase (PTK) mutations usually are gain-of-function (GOF) mutations that drive tumor growth and metastasis. We have found 50 JAK1 truncating mutations in 36 of 635 gynecologic tumors in the Total Cancer Care® (TCC®) tumor bank. Among cancer cell lines containing JAK1 truncating mutations in the Cancer Cell Line Encyclopedia databank, 68% are gynecologic cancer cells. Within JAK1 the K142, P430, and K860 frame-shift mutations were identified as hot spot mutation sites. Sanger sequencing of cancer cell lines, primary tumors, and matched normal tissues confirmed the JAK1 mutations and showed that these mutations are somatic. JAK1 mediates interferon (IFN)-γ-regulated tumor immune surveillance. Functional assays show that JAK1 deficient cancer cells are defective in IFN-γ-induced LMP2 and TAP1 expression, loss of which inhibits presentation of tumor antigens. These findings identify recurrent JAK1 truncating mutations that could contribute to tumor immune evasion in gynecologic cancers, especially in endometrial cancer.

Development and application of a novel metric to assess effectiveness of biomedical data.

OBJECTIVE: Design a metric to assess the comparative effectiveness of biomedical data elements within a study that incorporates their statistical relatedness to a given outcome variable as well as a measurement of the quality of their underlying data. MATERIALS AND METHODS: The cohort consisted of 874 patients with adenocarcinoma of the lung, each with 47 clinical data elements. The p value for each element was calculated using the Cox proportional hazard univariable regression model with overall survival as the endpoint. An attribute or A-score was calculated by quantification of an element's four quality attributes; Completeness, Comprehensiveness, Consistency and Overall-cost. An effectiveness or E-score was obtained by calculating the conditional probabilities of the p-value and A-score within the given data set with their product equaling the effectiveness score (E-score). RESULTS: The E-score metric provided information about the utility of an element beyond an outcome-related p value ranking. E-scores for elements age-at-diagnosis, gender and tobacco-use showed utility above what their respective p values alone would indicate due to their relative ease of acquisition, that is, higher A-scores. Conversely, elements surgery-site, histologic-type and pathological-TNM stage were down-ranked in comparison to their p values based on lower A-scores caused by significantly higher acquisition costs. CONCLUSIONS: A novel metric termed E-score was developed which incorporates standard statistics with data quality metrics and was tested on elements from a large lung cohort. Results show that an element's underlying data quality is an important consideration in addition to p value correlation to outcome when determining the element's clinical or research utility in a study.

TNFRSF10B polymorphisms and haplotypes associated with increased risk of death in non-small cell lung cancer.

Presently, there are few validated biomarkers that can predict survival or treatment response for non-small cell lung cancer (NSCLC) and most are based on tumor markers. Biomarkers based on germ line DNA variations represent a valuable complementary strategy, which could have translational implications by subclassifying patients to tailored, patient-specific treatment. We analyzed single nucleotide polymorphisms (SNPs) in 53 inflammation-related genes among 651 NSCLC patients. Multivariable Cox proportional hazard models, adjusted for lung cancer prognostic factors, were used to assess the association of genotypes and haplotypes with overall survival. Four of the top 15 SNPs associated with survival were located in the TNF-receptor superfamily member 10b (TNFRSF10B) gene. The T-allele of the top ranked SNP (rs11785599) was associated with a 41% increased risk of death (95% confidence interval [CI] = 1.16-1.70) and the other three TNFRSF10B SNPs (rs1047275, rs4460370 and rs883429) exhibited a 35% (95% CI = 1.11-1.65), 29% (95% CI = 1.06-1.57) and 24% (95% CI = 0.99-1.54) increased risk of death, respectively. Haplotype analyses revealed that the most common risk haplotype (TCTT) was associated with a 78% (95% CI = 1.25-2.54) increased risk of death compared with the low-risk haplotype (CGCC). When the data were stratified by treatment, the risk haplotypes exhibited statistically significantly increased risk of death among patients who had surgery only and no statistically significant effects among patients who had surgery and adjuvant chemotherapy. These data suggest that possessing one or more risk alleles in TNFRSF10B is associated with an increased risk of death. Validated germ line biomarkers may have potential important clinical implications by optimizing patient-specific treatment.

Iterative rank-order normalization of gene expression microarray data.

BACKGROUND: Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population. RESULTS: We developed an approach, termed IRON, which uses the best-performing techniques from each of several popular processing methods while retaining the ability to incrementally renormalize data without altering previously normalized expression. This combination of approaches results in a method that performs comparably to existing approaches on artificial benchmark datasets (i.e. spike-in) and demonstrates promising improvements in segregating true signals within biologically complex experiments. CONCLUSIONS: By combining approaches from existing normalization techniques, the IRON method offers several advantages. First, IRON normalization occurs pair-wise, thereby avoiding the need for all chips to be normalized together, which can be important for large data analyses. Secondly, the technique does not require similarity in signal distribution across chips for normalization, which can be important for maintaining biologically relevant differences in a heterogeneous background. Lastly, IRON introduces fewer post-processing artifacts, particularly in data whose behavior violates common assumptions. Thus, the IRON method provides a practical solution to common needs of expression analysis. A software implementation of IRON is available at [http://gene.moffitt.org/libaffy/].

Comparative effectiveness research in cancer genomics and precision medicine: current landscape and future prospects.

A major promise of genomic research is information that can transform health care and public health through earlier diagnosis, more effective prevention and treatment of disease, and avoidance of drug side effects. Although there is interest in the early adoption of emerging genomic applications in cancer prevention and treatment, there are substantial evidence gaps that are further compounded by the difficulties of designing adequately powered studies to generate this evidence, thus limiting the uptake of these tools into clinical practice. Comparative effectiveness research (CER) is intended to generate evidence on the "real-world" effectiveness compared with existing standards of care so informed decisions can be made to improve health care. Capitalizing on funding opportunities from the American Recovery and Reinvestment Act of 2009, the National Cancer Institute funded seven research teams to conduct CER in genomic and precision medicine and sponsored a workshop on CER on May 30, 2012, in Bethesda, Maryland. This report highlights research findings from those research teams, challenges to conducting CER, the barriers to implementation in clinical practice, and research priorities and opportunities in CER in genomic and precision medicine. Workshop participants strongly emphasized the need for conducting CER for promising molecularly targeted therapies, developing and supporting an integrated clinical network for open-access resources, supporting bioinformatics and computer science research, providing training and education programs in CER, and conducting research in economic and decision modeling.

12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy?

We have interrogated a 12-chemokine gene expression signature (GES) on genomic arrays of 14,492 distinct solid tumors and show broad distribution across different histologies. We hypothesized that this 12-chemokine GES might accurately predict a unique intratumoral immune reaction in stage IV (non-locoregional) melanoma metastases. The 12-chemokine GES predicted the presence of unique, lymph node-like structures, containing CD20⁺ B cell follicles with prominent areas of CD3⁺ T cells (both CD4⁺ and CD8⁺ subsets). CD86⁺, but not FoxP3⁺, cells were present within these unique structures as well. The direct correlation between the 12-chemokine GES score and the presence of unique, lymph nodal structures was also associated with better overall survival of the subset of melanoma patients. The use of this novel 12-chemokine GES may reveal basic information on in situ mechanisms of the anti-tumor immune response, potentially leading to improvements in the identification and selection of melanoma patients most suitable for immunotherapy.

Informatics methods to enable sharing of quantitative imaging research data.

INTRODUCTION: The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community. METHODS: We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. RESULTS: There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. CONCLUSIONS: As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers.

Implementing personalized medicine in a cancer center.

In 2006, the Moffitt Cancer Center partnered with patients, community clinicians, industry, academia, and 17 hospitals in the United States to begin a personalized cancer care initiative called Total Cancer Care. Total Cancer Care was designed to collect tumor specimens and clinical data throughout a patient's lifetime, with the goal of finding "the right treatment, for the right patient, at the right time." Because Total Cancer Care is a partnership with the patient and involves collection of clinical data and tumor specimens for research purposes, a formal protocol and patient consent process was developed, and an information technology platform was constructed to provide a robust "warehouse" for clinical and molecular profiling data. To date, more than 76,000 cancer patients from Moffitt and consortium medical centers have been enrolled in the protocol. The Total Cancer Care initiative has developed many of the capabilities and resources that are building the foundation of personalized medicine.

The role of KRAS rs61764370 in invasive epithelial ovarian cancer: implications for clinical testing.

PURPOSE: An assay for the single-nucleotide polymorphism (SNP), rs61764370, has recently been commercially marketed as a clinical test to aid ovarian cancer risk evaluation in women with family histories of the disease. rs67164370 is in a 3'-UTR miRNA binding site of the KRAS oncogene and is a candidate for epithelial ovarian cancer (EOC) susceptibility. However, only one published article, analyzing fewer than 1,000 subjects in total, has examined this association. EXPERIMENTAL DESIGN: Risk association was evaluated in 8,669 cases of invasive EOC and 10,012 controls from 19 studies participating in the Ovarian Cancer Association Consortium, and in 683 cases and 2,044 controls carrying BRCA1 mutations from studies in the Consortium of Investigators of Modifiers of BRCA1/2. Prognosis association was also examined in a subset of five studies with progression-free survival (PFS) data and 18 studies with all-cause mortality data. RESULTS: No evidence of association was observed between genotype and risk of unselected EOC (OR = 1.02, 95% CI: 0.95-1.10), serous EOC (OR = 1.08, 95% CI: 0.98-1.18), familial EOC (OR = 1.09, 95% CI: 0.78-1.54), or among women carrying deleterious mutations in BRCA1 (OR = 1.09, 95% CI: 0.88-1.36). There was little evidence for association with survival time among unselected cases (HR = 1.10, 95% CI: 0.99-1.22), among serous cases (HR = 1.12, 95% CI = 0.99-1.28), or with PFS in 540 cases treated with carboplatin and paclitaxel (HR = 1.18, 95% CI: 0.93-1.52). CONCLUSIONS: These data exclude the possibility of an association between rs61764370 and a clinically significant risk of ovarian cancer or of familial ovarian cancer. Use of this SNP for ovarian cancer clinical risk prediction, therefore, seems unwarranted.

The 2010 Health Care Reform Act: a potential opportunity to advance cancer research by taking cancer personally.

The Patient Protection and Affordable Care Act of 2010 will have a profound influence on health care in the United States, including how we conduct cancer research and cancer care delivery. For this reason, oncologists and researchers must be intimately involved in the implementation and interpretation of this important legislation. A major goal of the Act is to improve access to affordable, quality health care. An important element in achieving this goal will be to learn from patients' experiences and build the foundation for evidence-based personalized medicine. This will require a partnership among researchers, clinicians, policy makers and regulators, and patients to design an integrated information network system that will be the basis for providing the right treatment for the right patient in the right place at the right time. In this review, we will discuss the salient points of the Act that specifically affect cancer research and care, as well as highlight opportunities for oncologists and researchers to play a primary role in developing a health care system that includes personalized medicine approaches that will in turn enhance the likelihood of achieving the goals and objectives of the health care reform act.

Proteomic contributions to personalized cancer care.

Cancer impacts each patient and family differently. Our current understanding of the disease is primarily limited to clinical hallmarks of cancer, but many specific molecular mechanisms remain elusive. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies that improve patient prognosis are not widely available for most cancers. Individualized care plans, also described as personalized medicine, still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics holds great promise in contributing to the prevention and cure of cancer because it provides unique tools for discovery of biomarkers and therapeutic targets. As such, proteomics can help translate basic science discoveries into the clinical practice of personalized medicine. Here we describe how biological mass spectrometry and proteome analysis interact with other major patient care and research initiatives and present vignettes illustrating efforts in discovery of diagnostic biomarkers for ovarian cancer, development of treatment strategies in lung cancer, and monitoring prognosis and relapse in multiple myeloma patients.

Tissue-specific RMA models to incrementally normalize Affymetrix GeneChip data.

Gene expression classifiers have been used to predict diagnosis of disease, patient prognosis and patient response to therapy. Although there have been remarkable successes in this area, a particular goal of personalized medicine is the ability predict a response from a single gene expression microarray. One aspect of this problem is the normalization of microarrays. Affymetrix GeneChip microarrays are typically processed using model-based algorithms that require all of the data in order to adequately estimate the model. We experiment with the RMA normalization procedure in an incremental fashion, adding new chips to an existing normalization model. Focusing on tissue-specific normalization models, we generate datasets that have very small differences from a batch normalization. Through several large datasets of patient samples, we provide evidence that RMA models of normalization converge to a common model in homogenous samples. These results offer the promise of maintaining large data warehouses of patient microarray samples without the requirement of constant renormalization.

Integrated analysis of independent gene expression microarray datasets improves the predictability of breast cancer outcome.

BACKGROUND: Gene expression profiles based on microarray data have been suggested by many studies as potential molecular prognostic indexes of breast cancer. However, due to the confounding effect of clinical background, independent studies often obtained inconsistent results. The current study investigated the possibility to improve the quality and generality of expression profiles by integrated analysis of multiple datasets. Profiles of recurrence outcome were derived from two independent datasets and validated by a third dataset. RESULTS: The clinical background of patients significantly influenced the content and performance of expression profiles when the training samples were unbalanced. The integrated profiling of two independent datasets lead to higher classification accuracy (71.11% vs. 70.59%) and larger ROC curve area (0.789 vs. 0.767) of the testing samples. Cell cycle, especially M phase mitosis, was significantly overrepresented by the 60-gene profile obtained from integrated analysis (p < 0.0001). This profiles significantly differentiated poor and good prognosis in a third patient cohort (p = 0.003). Simulation procedures demonstrated that the change of profile specificity had more instant influence on the performance of expression profiles than the change of profile sensitivity. CONCLUSION: The current study confirmed that the gene expression profile generated by integrated analysis of multiple datasets achieved better prediction of breast cancer recurrence. However, the content and performance of profiles was confounded by clinical background of training patients. In future studies, prognostic profile applicable to the general population should be derived from more diversified and balanced patient cohorts in larger scale.

A recurrent mutation in the BMP type I receptor ACVR1 causes inherited and sporadic fibrodysplasia ossificans progressiva.

Fibrodysplasia ossificans progressiva (FOP) is a rare autosomal dominant disorder of skeletal malformations and progressive extraskeletal ossification. We mapped FOP to chromosome 2q23-24 by linkage analysis and identified an identical heterozygous mutation (617G --> A; R206H) in the glycine-serine (GS) activation domain of ACVR1, a BMP type I receptor, in all affected individuals examined. Protein modeling predicts destabilization of the GS domain, consistent with constitutive activation of ACVR1 as the underlying cause of the ectopic chondrogenesis, osteogenesis and joint fusions seen in FOP.