Jeffrey C. Miecznikowski

Quantifying nuclear p65 as a parameter for NF-κB activation: Correlation between ImageStream cytometry, microscopy, and Western blot.

The nuclear factor kappa B (NF‐κB) pathway, which regulates many cellular processes including proliferation, apoptosis, and survival, has emerged as an important therapeutic target in cancer. Activation of the NF‐κB transcription factor is associated with nuclear translocation of the p65 component of the complex. Conventional methods employed to determine nuclear translocation of NF‐κB either lack statistical robustness (microscopy) or the ability to discern heterogeneity within the sampled populations (Western blotting and Gel Shift assays). The ImageStream platform combines the high image content information of microscopy with the high throughput and multiparameter analysis of flow cytometry which overcomes the aforementioned limitations of conventional assays. It is demonstrated that ImageStream assessment of receptor‐mediated (TNFα) and drug (Daunorubicin, DNR)‐induced NF‐κB translocation in leukemic cell lines correlates well with microscopy analysis and Western blot analysis. It is further demonstrated that ImageStream cytometry enables quantitative assessment of p65 translocation in immunophenotypically defined subpopulations; and that this assessment is highly reproducible. It is also demonstrated that, quantitatively, the DNR‐induced nuclear translocation of NF‐κB correlates well with a biological response (apoptosis). We conclude that the ImageStream has the potential to be a powerful tool to evaluate NF‐κB /p65 activity as a determinant of response to therapies designed to target aberrant NF‐κB signaling activities.

Tissue specific DNA methylation of CpG islands in normal human adult somatic tissues distinguishes neural from non-neural tissues

Although most CpG islands are generally thought to remain unmethylated in all adult somatic tissues, recent genome-wide approaches have found that some CpG islands have distinct methylation patterns in various tissues, with most differences being seen between germ cells and somatic tissues. Few studies have addressed this among human somatic tissues and fewer still have studied the same sets of tissues from multiple individuals. In the current study, we used Restriction Landmark Genomic Scanning to study tissue specific methylation patterns in a set of twelve human tissues collected from multiple individuals. We identified 34 differentially methylated CpG islands among these tissues, many of which showed consistent patterns in multiple individuals. Of particular interest were striking differences in CpG island methylation, not only among brain regions, but also between white and grey matter of the same region. These findings were confirmed for selected loci by quantitative bisulfite sequencing. Cluster analysis of the RLGS data indicated that several tissues clustered together, but the strongest clustering was in brain. Tissues from different brain regions clustered together, and, as a group, brain tissues were distinct from either mesoderm or endoderm derived tissues which demonstrated limited clustering. These data demonstrate consistent tissue specific methylation for certain CpG islands, with clear differences between white and grey matter of the brain. Furthermore, there was an overall pattern of tissue specifically methylated CpG islands that distinguished neural tissues from non-neural.

Challenges in array comparative genomic hybridization for the analysis of cancer samples

Purpose: To address some of the challenges facing the incorporation of array comparative genomic hybridization technology as a clinical tool, including archived tumor tissue, tumor heterogeneity, DNA quality and quantity, and array comparative genomic hybridization platform selection and performance.Methods: Experiments were designed to assess the impact of DNA source (e.g., archival material), quantity, and amplification on array comparative genomic hybridization results. Two microdissection methods were used to isolate tumor cells to minimize heterogeneity. These data and other data sets were used in a further performance comparison of two commonly used array comparative genomic hybridization platforms: bacterial artificial chromosome (Roswell Park Cancer Institute) and oligonucleotide (Agilent Technologies, Santa Clara, CA).Results: Array comparative genomic hybridization data from as few as 100 formalin-fixed, paraffin-embedded cells isolated by laser capture microdissection and amplified were remarkably similar to array comparative genomic hybridization copy number alterations detected in the bulk (unamplified) population. Manual microdissection from frozen sections provided a rapid and inexpensive means to isolate tumor from adjacent DNA for amplification and array comparative genomic hybridization. Whole genome amplification introduced no appreciable allele bias on array comparative genomic hybridization. The array comparative genomic hybridization results provided by the bacterial artificial chromosome and Agilent platforms were concordant in general, but bacterial artificial chromosome array comparative genomic hybridization showed far fewer outliers and overall less technical noise, which could adversely affect the statistical interpretation of the data.Conclusions: This study demonstrates that copy number alterations can be robustly and reproducibly detected by array comparative genomic hybridization in DNA isolated from challenging tumor types and sources, including archival materials, low DNA yield, and heterogeneous tissues. Furthermore, bacterial artificial chromosome array comparative genomic hybridization offers the advantage over the Agilent oligonucleotide platform of presenting fewer outliers, which could affect data interpretation.

Evaluation of the Zurich Guidelines and exercise testing for return to play in adolescents following concussion.

Objective:To evaluate return to play (RTP) and return to classroom outcomes when the Zurich guidelines are combined with a standardized exercise treadmill test [Buffalo Concussion Treadmill Test (BCTT)] and computerized neuropsychological (cNP) testing in adolescent athletes after concussion. Design:Retrospective chart review and follow-up. Setting:University Sports Medicine Concussion Clinic. Participants:One hundred seventeen athletes (75% male) with sport concussion ages 13 to 19 years and telephone follow-up of 91 (77.8%) athletes and their parents. Interventions:Concussed athletes who were asymptomatic at rest completed Automated Neuropsychological Assessment Metrics or Immediate Post-concussion Assessment and Cognitive Test cNP testing followed by the BCTT on the same day. Athletes then followed the Zurich consensus guidelines for RTP. Main Outcome Measures:The primary outcome measure was the degree of success in RTP, that is, RTP with or without return of concussive symptoms. Secondary outcome measure was return to school with or without symptoms. Results:All athletes returned to sport without exacerbation of symptoms. Telephone follow-up revealed that 38.5% experienced new issues upon return to the classroom. Forty-eight percent of athletes had 1 or more cNP subtests below average (<ninth percentile) when asymptomatic. Ultimately, performance on cNP was not predictive of return to school issues. Conclusions:The BCTT in combination with the Zurich consensus guidelines seems to be safe and successful for RTP. There is evidence to suggest that cNP testing performed in athletes who do not have a preinjury baseline test was not related to RTP or problems upon return to school.

PP2A-B56α controls oncogene-induced senescence in normal and tumor human melanocytic cells

Oncoprotein C-MYC is overexpressed in human metastatic melanomas and melanoma-derived cells where it is required for the suppression of oncogene-induced senescence (OIS). The genetic events that maintain high levels of C-MYC in melanoma cells and their role in OIS are unknown. Here we report that C-MYC in cells from several randomly chosen melanoma lines was upregulated at the protein level, and largely because of the increased protein stability. Of all known regulators of C-MYC stability, levels of B56α subunit of the PP2A tumor suppressor complex were substantially suppressed in all human melanoma cells compared with normal melanocytes. Accordingly, immunohistochemical analysis revealed that the lowest and the highest amounts of PP2A-B56α were predominantly detected in metastatic melanoma tissues and in primary melanomas from patients with good clinical outcome, respectively. Importantly, PP2A-B56α overexpression suppressed C-MYC in melanoma cells and induced OIS, whereas depletion of PP2A-B56α in normal human melanocytes upregulated C-MYC protein levels and suppressed BRAFV600E- and, less efficiently, NRASQ61R-induced senescence. Our data reveal a mechanism of C-MYC overexpression in melanoma cells and identify a functional role for PP2A-B56α in OIS of melanocytic cells.

Comparative survival analysis of breast cancer microarray studies identifies important prognostic genetic pathways

BackgroundAn estimated 12% of females in the United States will develop breast cancer in their lifetime. Although, there are advances in treatment options including surgery and chemotherapy, breast cancer is still the second most lethal cancer in women. Thus, there is a clear need for better methods to predict prognosis for each breast cancer patient. With the advent of large genetic databases and the reduction in cost for the experiments, researchers are faced with choosing from a large pool of potential prognostic markers from numerous breast cancer gene expression profile studies.MethodsFive microarray datasets related to breast cancer were examined using gene set analysis and the cancers were categorized into different subtypes using a scoring system based on genetic pathway activity.ResultsWe have observed that significant genes in the individual studies show little reproducibility across the datasets. From our comparative analysis, using gene pathways with clinical variables is more reliable across studies and shows promise in assessing a patients prognosis.ConclusionsThis study concludes that, in light of clinical variables, there are significant gene pathways in common across the datasets. Specifically, several pathways can further significantly stratify patients for survival. These candidate pathways should help to develop a panel of significant biomarkers for the prognosis of breast cancer patients in a clinical setting.

FUSIM: a software tool for simulating fusion transcripts

BackgroundGene fusions are the result of chromosomal aberrations and encode chimeric RNA (fusion transcripts) that play an important role in cancer genesis. Recent advances in high throughput transcriptome sequencing have given rise to computational methods for new fusion discovery. The ability to simulate fusion transcripts is essential for testing and improving those tools.ResultsTo facilitate this need, we developed FUSIM (FUsion SIMulator), a software tool for simulating fusion transcripts. The simulation of events known to create fusion genes and their resulting chimeric proteins is supported, including inter-chromosome translocation, trans-splicing, complex chromosomal rearrangements, and transcriptional read through events.ConclusionsFUSIM provides the ability to assemble a dataset of fusion transcripts useful for testing and benchmarking applications in fusion gene discovery.

A GOG 210 aCGH study of gain at 1q23 in endometrioid endometrial cancer in the context of racial disparity and outcome

The goal of this study was to identify recurrent regions of genomic gain or loss in endometrial cancer of the endometrioid type in the context of racial disparities in mortality for this disease. Array comparative genomic hybridization (aCGH) analysis was performed on 80 frozen primary tumors from the Gynecologic Oncology Group (GOG)‐210 bank using the RPCI 19K BAC arrays. The 80 patients included 20 African American (AA) Stage I, 20 White (W) Stage I, 20 African American (AA) Stage IIIC/IV, and 20 White (W) Stage IIIC/IV. A separate subset of 220 endometrial cancers with outcome data was used for validation. A 1.6‐Mbp region of gain at 1q23 was identified by aCGH in all AA patients and high grade W patients, but not W low grade patients. In the validation arm of 220 patients copy number gain at this region was validated using FISH and locus specific BACs. The number of AA patients in the validation arm was too small to confirm the aCGH association with racial disparity. Kaplan‐Meier curves for survival showed a significant difference for gain at 1q23 versus no gain (log rank P = 0.0014). When subdivided into various groups of risk by stage and grade the survival curves showed a decreased survival for high grade and/or stage tumors, but not for low grade and/or stage endometrioid tumors. Univariate analyses for gain at 1q23 showed a significant association (P = 0.009) with survival. Multivariate analysis for gain at 1q23 did not show a significant association with survival (P = 0.14).

High stathmin expression is a marker for poor clinical outcome in endometrial cancer: An NRG oncology group/gynecologic oncology group study

OBJECTIVE Gynecologic Oncology Group (GOG) 177 demonstrated that addition of paclitaxel to a backbone of adriamycin/cisplatin improves overall survival (OS) and progression-free survival (PFS) for patients with advanced or recurrent endometrial cancer. Using patient specimens from GOG-177, our objective was to identify potential mechanisms underlying the improved clinical response to taxanes. Stathmin (STMN1) is a recognized poor prognostic marker in endometrial cancer that functions as a microtubule depolymerizing protein, allowing cells to transit rapidly through mitosis. Therefore, we hypothesized that one possible mechanism underlying the beneficial effects of paclitaxel could be to counter the impact of stathmin. METHODS We analyzed the expression of stathmin by immunohistochemistry (IHC) in 69 specimens from patients enrolled on GOG-177. We also determined the correlation between stathmin mRNA expression and clinical outcomes in The Cancer Genome Atlas (TCGA) dataset for endometrial cancer. RESULTS We first established that stathmin expression was significantly associated with shorter PFS and OS for all analyzed cases in both GOG-177 and TCGA. However, subgroup analysis from GOG-177 revealed that high stathmin correlated with poor PFS and OS particularly in patients who received adriamycin/cisplatin only. In contrast, there was no statistically significant association between stathmin expression and OS or PFS in patients treated with paclitaxel/adriamycin/cisplatin. CONCLUSIONS Our findings demonstrate that high stathmin expression is a poor prognostic marker in endometrial cancer. Paclitaxel may help to negate the impact of stathmin overexpression when treating high risk endometrial cancer cases.

A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data

Numerous gel-based softwares exist to detect protein changes potentially associated with disease. The data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. A particularly important topic is how the various softwares handle missing data. To date, no one has extensively studied the impact that interpolating missing data has on subsequent analysis of protein spots. This work highlights the existing algorithms for handling missing data in two-dimensional gel analysis and performs a thorough comparison of the various algorithms and statistical tests on simulated and real datasets. For imputation methods, the best results in terms of root mean squared error are obtained using the least squares method of imputation along with the expectation maximization (EM) algorithm approach to estimate missing values with an array covariance structure. The bootstrapped versions of the statistical tests offer the most liberal option for determining protein spot significance while the generalized family wise error rate (gFWER) should be considered for controlling the multiple testing error. In summary, we advocate for a three-step statistical analysis of two-dimensional gel electrophoresis (2-DE) data with a data imputation step, choice of statistical test, and lastly an error control method in light of multiple testing. When determining the choice of statistical test, it is worth considering whether the protein spots will be subjected to mass spectrometry. If this is the case a more liberal test such as the percentile-based bootstrap t can be employed. For error control in electrophoresis experiments, we advocate that gFWER be controlled for multiple testing rather than the false discovery rate.BackgroundNumerous gel-based softwares exist to detect protein changes potentially associated with disease. The data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. A particularly important topic is how the various softwares handle missing data. To date, no one has extensively studied the impact that interpolating missing data has on subsequent analysis of protein spots.ResultsThis work highlights the existing algorithms for handling missing data in two-dimensional gel analysis and performs a thorough comparison of the various algorithms and statistical tests on simulated and real datasets. For imputation methods, the best results in terms of root mean squared error are obtained using the least squares method of imputation along with the expectation maximization (EM) algorithm approach to estimate missing values with an array covariance structure. The bootstrapped versions of the statistical tests offer the most liberal option for determining protein spot significance while the generalized family wise error rate (gFWER) should be considered for controlling the multiple testing error.ConclusionsIn summary, we advocate for a three-step statistical analysis of two-dimensional gel electrophoresis (2-DE) data with a data imputation step, choice of statistical test, and lastly an error control method in light of multiple testing. When determining the choice of statistical test, it is worth considering whether the protein spots will be subjected to mass spectrometry. If this is the case a more liberal test such as the percentile-based bootstrap t can be employed. For error control in electrophoresis experiments, we advocate that gFWER be controlled for multiple testing rather than the false discovery rate.