P14.21 Tehila Kaisman-Elbaz MD/PhD

Abstract BACKGROUND Glioblastoma is known for its dismal prognosis though its dependency on patients’ readily available RBCs parameters defining the patient’s anemic status such as hemoglobin level and Red blood cells distribution Width (RDW) is not fully established. Several works demonstrated a connection between low hemoglobin level or high RDW values to overall glioblastoma patient’s survival, but in other works, a clear connection was not found. This study addresses this unclarity. MATERIAL AND METHODS In this work, 170 glioblastoma patients, diagnosed and treated in Soroka University Medical Center (SUMC) in the last 12 years were retrospectively inspected for their survival dependency on pre-operative RBCs parameters using multivariate analysis followed by false discovery rate procedure due to the multiple hypothesis testing. A survival stratification tree and Kaplan-Meier survival curves that indicate the patient’s prognosis according to these parameters were prepared. RESULTS Beside KPS>70 and tumor resection supplemented by oncological treatment, age<70 (HR=0.4, 95% CI 0.24–0.65), low hemoglobin level (HR=1.79, 95% CI 1.06–2.99) and RDW<14% (HR=0.57, 95% CI 0.37–0.88) were found to be prognostic to patients’ overall survival in multivariate analysis, accounting for false discovery rate of less than 5%. CONCLUSION A survival stratification highlighted a non-anemic subgroup of nearly 30% of the cohort’s patients whose median overall survival was 21.1 months (95% CI 16.2–27.2) - higher than the average Stupp protocol overall median survival of about 15 months. A discussion on the beneficial or detrimental effect of RBCs parameters on glioblastoma prognosis and its possible causes is given.

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Thresholding of cryo-EM density maps by false discovery rate control

10.1101/374546 ◽

2018 ◽

Author(s):

Maximilian Beckers ◽

Arjen J. Jakobi ◽

Carsten Sachse

Keyword(s):

False Discovery Rate ◽

Background Noise ◽

Multiple Hypothesis Testing ◽

Molecular Structures ◽

Atomic Resolution ◽

Detection Rates ◽

False Discovery ◽

Density Maps ◽

Density Values ◽

Subtomogram Averaging

AbstractCryo-EM now commonly generates close-to-atomic resolution as well as intermediate resolution maps from macromolecules observed in isolation and in situ. Interpreting these maps remains a challenging task due to poor signal in the highest resolution shells and the necessity to select a threshold for density analysis. In order to facilitate this process, we developed a statistical framework for the generation of confidence maps by multiple hypothesis testing and false discovery rate (FDR) control. In this way, 3D confidence maps contain separated signal from background noise in the form of local detection rates of EM density values. We demonstrate that confidence maps and FDR-based thresholding can be used for the interpretation of near-atomic resolution single-particle structures as well as lower resolution maps determined by subtomogram averaging. Confidence maps represent a conservative way of interpreting molecular structures due to minimized noise. At the same time they provide a detection error with respect to background noise, which is associated with the density and particularly beneficial for the interpretation of weaker cryo-EM densities in cases of conformational flexibility and lower occupancy of bound molecules and ions to the structure.

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Prognostic Factors for Myelodysplastic Syndrome in the Veteran Population: Single Institution Experience.

Blood ◽

10.1182/blood.v110.11.4622.4622 ◽

2007 ◽

Vol 110 (11) ◽

pp. 4622-4622

Author(s):

Michael Axelson ◽

Shirisha Reddy ◽

Crystal Lumby ◽

Sue Sivess-Franks ◽

Jonathan Dowell ◽

...

Keyword(s):

Overall Survival ◽

Multivariate Analysis ◽

Prognostic Factors ◽

Blood Transfusion ◽

Cox Regression ◽

Medical Center ◽

Univariate Analysis ◽

Single Institution ◽

Number Of Patients

Abstract Background: Myelodyplastic syndrome (MDS) is the disease of the elderly and increasingly common in the veteran population. Here we report a single institution experience with MDS at the Dallas VA Medical Center. Patients and Method: From a period of 1998–2007, eighty three pts were identified out of which 54 pts had bone marrow (BM) biopsy proven diagnosis of MDS. Overall survival (OS) analysis with dependent variables (Age at diagnosis, IPSS Score, WHO morphologic diagnosis, number of blood and platelet transfusions required, Hb level, ANC, cytogenetics, blast percentage, BM cellularity at diagnosis) were conducted by selection method “foreward” and only these significant variables were used in the Cox regression for multivariate analysis. Methods of Kaplan and Meier were used to generate OS curves. Results: The median age of diagnosis was 74 yrs with a median follow up time of 12.5 months. The WHO morphologic subtype was RA/RARS (n=13), Del5q (n=1), RCMD/RCMDRS (n=34), RAEB1 (n=3), RAEB2 (n=1), missing (n=2). The distribution of IPSS score was 0 (n=25); 0.5 (n=15); 1.0 (n=8), 1.5 (n=4), missing (n=2). Five pts had treatment related MDS and 3 pts transformed to AML. One patient had concurrent MGUS and one patient developed multiple myeloma. At diagnosis, 23 pts had a hemoglobin (Hb) value of less than 10g/dl. Only 4 pts had ANC less than 500; sixteen pts had ANC 500–1800 and 34 pts had normal counts. A majority of pts had normal cytogenetics (n=37), 5 pts had good risk, 5 pts had intermediate risk and 7 pts had poor risk cytogenetics. Six pts had hypocellular (<30%) BM at diagnosis whereas 16 pts had a hypercellular marrow (> 50%). Only 4 pts had more than 5% blast in the BM. Twenty nine pts eventually became blood transfusion dependent and 12 pts needed platelet transfusion at some point. Thirty six pts were treated with erythropoietin (with or without neupogen) and 13 pts received some type of disease modifying therapy (5-azacytidine/lenalidomide/ATG/clinical trial). The mean survival time was 106 months. Median survival was not reached at the time of analysis. In the univariate analysis, IPSS score (p=0.003), No. of blood transfusions (p=0.028), cytogenetics (p=0.0001) and blast percentage (p=0.0015), were statistically significant. BM cellularity (p=0.06) and Hb level (p=0.09) showed a trend towards significance. On multivariate analysis, Hb greater than 10 (HR 0.08; p=0.011), abnormal cytogenetics (HR 4.2; p=0.001), BM Blast > 5% (p=0.026) and BM cellularity < 30% (HR 4.6; p=0.033) emerged as the significant predictors of overall survival. IPSS score or Blood transfusion requirement did not pan out to be significant. Conclusion: MDS in the veteran population may be different from general population and may have unique predictors of survival. A larger number of patients and longer duration of follow up is required to further evaluate these prognostic factors.

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Using mixture models to detect differentially expressed genes

Australian Journal of Experimental Agriculture ◽

10.1071/ea05051 ◽

2005 ◽

Vol 45 (8) ◽

pp. 859 ◽

Cited By ~ 13

Author(s):

G. J. McLachlan ◽

R. W. Bean ◽

L. Ben-Tovim Jones ◽

J. X. Zhu

Keyword(s):

False Discovery Rate ◽

Mixture Models ◽

False Negative ◽

False Negative Rate ◽

Multiple Hypothesis Testing ◽

Differentially Expressed ◽

False Discovery ◽

Mixture Model Approach ◽

Number Of Classes ◽

Selection Of

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local false discovery rate is provided for each gene, and it can be implemented so that the implied global false discovery rate is bounded as with the Benjamini-Hochberg methodology based on tail areas. The latter procedure is too conservative, unless it is modified according to the prior probability that a gene is not differentially expressed. An attractive feature of the mixture model approach is that it provides a framework for the estimation of this probability and its subsequent use in forming a decision rule. The rule can also be formed to take the false negative rate into account.

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A direct approach to estimating false discovery rates conditional on covariates

10.1101/035675 ◽

2015 ◽

Cited By ~ 3

Author(s):

Simina M. Boca ◽

Jeffrey T. Leek

Keyword(s):

False Discovery Rate ◽

Meta Analysis ◽

Error Rates ◽

Multiple Hypothesis Testing ◽

False Discovery Rates ◽

False Discovery ◽

A Genome ◽

The Individual ◽

False Discoveries ◽

Discovery Rates

AbstractModern scientific studies from many diverse areas of research abound with multiple hypothesis testing concerns. The false discovery rate is one of the most commonly used error rates for measuring and controlling rates of false discoveries when performing multiple tests. Adaptive false discovery rates rely on an estimate of the proportion of null hypotheses among all the hypotheses being tested. This proportion is typically estimated once for each collection of hypotheses. Here we propose a regression framework to estimate the proportion of null hypotheses conditional on observed covariates. This may then be used as a multiplication factor with the Benjamini-Hochberg adjusted p-values, leading to a plug-in false discovery rate estimator. Our case study concerns a genome-wise association meta-analysis which considers associations with body mass index. In our framework, we are able to use the sample sizes for the individual genomic loci and the minor allele frequencies as covariates. We further evaluate our approach via a number of simulation scenarios.

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The Prognostic Value of TP53 Mutations in Chronic Lymphocytic Leukemia (CLL) Is Independent of del17p13: Implications for Overall Survival and Chemorefractoriness

Blood ◽

10.1182/blood.v112.11.3137.3137 ◽

2008 ◽

Vol 112 (11) ◽

pp. 3137-3137

Author(s):

Davide Rossi ◽

Michaela Cerri ◽

Clara Deambrogi ◽

Elisa Sozzi ◽

Stefania Cresta ◽

...

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Multivariate Analysis ◽

Tp53 Mutation ◽

Independent Predictor ◽

Lymphocytic Leukemia ◽

Tp53 Mutations ◽

Rank Analysis ◽

Dismal Prognosis ◽

Short Time

Abstract CLL harboring del17p13 displays a dismal prognosis and a high risk of chemorefractoriness. We tested whether TP53 inactivation through mutation harbors the same prognostic role as del17p13 on 308 consecutive CLL. TP53 mutation status was assessed at diagnosis by direct sequencing of TP53 exons 2–10. Thirty-two TP53 mutations were observed in 31/308 (10.0%) CLL. Among 26 missense mutations, the median residual transactivation activity compared to germline TP53 was 8.9% (25th–75th percentile: 0.6–13.0%), and was ≤20% in 22/26 (84.6%) mutations. All microdeletions and microinsertions (4/32; 12.5%) led to frameshift. One non-sense mutation introduced a stop codon at position 213, and one mutation affected the 3′ splicing site of exon 8. Despite the statistical association between TP53 mutations and del17p13 in the whole cohort, the overlap between the two genetic lesions was restricted to a fraction of patients. By combining results of FISH and TP53 mutation analysis, 44/297 (14.8%) CLL carried TP53 inactivation: 18/44 (40.9%) showed TP53 mutation paired to del17p13; 10/44 (22.7%) showed TP53 mutations in the absence of del17p13; and 16/44 (36.3%) showed del17p13 in the absence of TP53 mutations. Univariate log-rank analysis identified both TP53 mutations and del17p13 as risk factors for short overall survival (OS). Median OS for patients with TP53 mutations was 79.6 months (5-year OS 66.2%), whereas median OS for patients without TP53 mutations was not reached and the 5-year OS was 85.2% (p<.001). Multivariate analysis selected TP53 mutations (HR 3.20; p=.002), along with age >65 years (HR 4.98; p<.001) and advanced Binet stage (HR 3.01; p<.001), as independent predictors of OS after adjustment for del17p13 and other potentially confounding biological and clinical covariates. Univariate log-rank analysis identified both TP53 mutations and del17p13 as risk factors of short time to chemorefractoriness. Median time to chemorefractoriness for patients with TP53 mutations was 6.3 months versus 72.7 months for patients without TP53 mutations (p<.001). Multivariate analysis selected TP53 mutations (HR 3.97; p<.001) as independent predictor of short time to chemorefractoriness after adjustment for del17p13 and other potentially confounding covariates. OS and time to chemorefractoriness did not differ between CLL harboring TP53 mutations in the absence of del17p13 and CLL harboring del17p13. Patients harboring TP53 mutations only and patients harboring both TP53 mutation and del17p13 were characterized by short time to chemorefractoriness compared to patients without any TP53 alteration or to patients harboring del17p13 only (p<.05). CLL that at diagnosis harbored del17p13 without TP53 mutations showed a significantly longer time to chemorefractoriness compared to CLL who at diagnosis carried both del17p13 and TP53 mutations (median: 55.2 months vs 5.5 months, respectively; p=.003). Notably, at diagnosis, the percentage of deleted nuclei was significantly lower in CLL harboring del17p13 only (median: 45.0%, 25th–75th percentiles: 7.3–67.4%) compared to CLL with both del17p13 and TP53 mutations (median: 66.5%, 25th–75th percentiles: 52.1–77.9%, respectively) (p=.020). The value of TP53 mutations in predicting chemorefractoriness was also assessed in patients who had been exposed to fludarabine (n=88). Multivariate analysis selected TP53 mutations as the sole independent predictor of short time to fludarabine refractoriness (HR 6.72; p<.001). Sequential samples were available for 14 CLL who had no TP53 abnormalities at diagnosis and who later developed chemorefractoriness. At the time of chemorefractoriness, TP53 abnormalities were acquired in 5/14 (35.7%) cases. The implications of this study are threefold: mutation in the absence of del17p13 is the sole mechanism of TP53 inactivation in 20% CLL harboring TP53 disruption; TP53 mutations carry the same prognostic relevance as del17p13 in terms of CLL progression, survival and risk of chemorefractoriness; because of the practical implications for choice of therapy, screening for TP53 mutations, in addition to del17p13 assessment, should be included in the initial prognostic assessment of CLL.

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Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00001 ◽

2019 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Yonghui Wu ◽

Jeremy L. Warner ◽

Liwei Wang ◽

Min Jiang ◽

Jun Xu ◽

...

Keyword(s):

False Discovery Rate ◽

Mayo Clinic ◽

Drug Exposure ◽

Cancer Survival ◽

Medical Center ◽

Drug Repurposing ◽

Biomedical Literature ◽

Patients With Cancer ◽

False Discovery ◽

Electronic Health

PURPOSEDrug development is becoming increasingly expensive and time consuming. Drug repurposing is one potential solution to accelerate drug discovery. However, limited research exists on the use of electronic health record (EHR) data for drug repurposing, and most published studies have been conducted in a hypothesis-driven manner that requires a predefined hypothesis about drugs and new indications. Whether EHRs can be used to detect drug repurposing signals is not clear. We want to demonstrate the feasibility of mining large, longitudinal EHRs for drug repurposing by detecting candidate noncancer drugs that can potentially be used for the treatment of cancer.PATIENTS AND METHODSBy linking cancer registry data to EHRs, we identified 43,310 patients with cancer treated at Vanderbilt University Medical Center (VUMC) and 98,366 treated at the Mayo Clinic. We assessed the effect of 146 noncancer drugs on cancer survival using VUMC EHR data and sought to replicate significant associations (false discovery rate < .1) using the identical approach with Mayo Clinic EHR data. To evaluate replicated signals further, we reviewed the biomedical literature and clinical trials on cancers for corroborating evidence.RESULTSWe identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, β-blockers, nonsteroidal anti-inflammatory drugs, and α-1 blockers) associated with improved overall cancer survival (false discovery rate < .1) from VUMC; nine of the 22 drug associations were replicated at the Mayo Clinic. Literature and cancer clinical trial evaluations also showed very strong evidence to support the repurposing signals from EHRs.CONCLUSIONMining of EHRs for drug exposure–mediated survival signals is feasible and identifies potential candidates for antineoplastic repurposing. This study sets up a new model of mining EHRs for drug repurposing signals.

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Estimating the Arm-Wise False Discovery Rate in Array Comparative Genomic Hybridization Experiments

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1236 ◽

2007 ◽

Vol 6 (1) ◽

Cited By ~ 3

Author(s):

Daniel P Gaile ◽

Elizabeth D Schifano ◽

Jeffrey C Miecznikowski ◽

James J Java ◽

Jeffrey M Conroy ◽

...

Keyword(s):

False Discovery Rate ◽

Comparative Genomic Hybridization ◽

Array Comparative Genomic Hybridization ◽

Multiple Hypothesis Testing ◽

Comparative Genomic ◽

Test Statistic ◽

Genomic Hybridization ◽

Rejection Region ◽

Spatially Correlated ◽

False Discovery

Array Comparative Genomic Hybridization (aCGH) is an array-based technology which provides simultaneous spot assays of relative genetic abundance (RGA) levels at multiple sites across the genome. These spot assays are spatially correlated with respect to genomic location and, as a result, the univariate tests conducted using data generated from these spot assays are also spatially correlated. In the context of multiple hypothesis testing, this spatial correlation complicates the question of how best to define a `discovery' and consequently, how best to estimate the false discovery rate (FDR) corresponding to a given rejection region.One can quantify the number of discoveries as the total number of spots for which the spot-based univariate test statistic falls within a given rejection region. Under this spot-based method, separate but correlated discoveries are identified. We show via a simulation study that the method of Benjamini and Hochberg (1995) can provide a reasonable estimate of the spot-wise FDR, but these results require that the simulated spot assays are categorized as true or false discoveries in a particular way. However, laboratory researchers may actually be interested in estimating a `regional' FDR, rather than a `local' spot-wise FDR. We describe an example of such circumstances, and present a method for estimating the (chromosome) arm-wise False Discovery Rate. In this framework, one can quantify the number of discoveries as the total number of chromosome arms for which at least one spot-based test statistic falls into a given rejection region. Defining the discoveries in this way, both the biological and testing objectives coincide. We provide results from a series of simulations which involved the analysis of preferentially re-sampled spot assay values from a real aCGH dataset.

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Thresholding of cryo-EM density maps by false discovery rate control

IUCrJ ◽

10.1107/s2052252518014434 ◽

2019 ◽

Vol 6 (1) ◽

pp. 18-33 ◽

Cited By ~ 17

Author(s):

Maximilian Beckers ◽

Arjen J. Jakobi ◽

Carsten Sachse

Keyword(s):

False Discovery Rate ◽

Background Noise ◽

Three Dimensional ◽

Multiple Hypothesis Testing ◽

Molecular Structures ◽

Atomic Resolution ◽

Detection Rates ◽

False Discovery ◽

Density Values ◽

Subtomogram Averaging

Cryo-EM now commonly generates close-to-atomic resolution as well as intermediate resolution maps from macromolecules observed in isolation and in situ. Interpreting these maps remains a challenging task owing to poor signal in the highest resolution shells and the necessity to select a threshold for density analysis. In order to facilitate this process, a statistical framework for the generation of confidence maps by multiple hypothesis testing and false discovery rate (FDR) control has been developed. In this way, three-dimensional confidence maps contain signal separated from background noise in the form of local detection rates of EM density values. It is demonstrated that confidence maps and FDR-based thresholding can be used for the interpretation of near-atomic resolution single-particle structures as well as lower resolution maps determined by subtomogram averaging. Confidence maps represent a conservative way of interpreting molecular structures owing to minimized noise. At the same time they provide a detection error with respect to background noise, which is associated with the density and is particularly beneficial for the interpretation of weaker cryo-EM densities in cases of conformational flexibility and lower occupancy of bound molecules and ions in the structure.

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A comparison of random-field-theory and false-discovery-rate inference results in the analysis of registered one-dimensional biomechanical datasets

PeerJ ◽

10.7717/peerj.8189 ◽

2019 ◽

Vol 7 ◽

pp. e8189 ◽

Cited By ~ 1

Author(s):

Hanaa Naouma ◽

Todd C. Pataky

Keyword(s):

Field Theory ◽

Random Field ◽

False Discovery Rate ◽

Multiple Hypothesis Testing ◽

Small Sample ◽

Alternative Methods ◽

Sample Sizes ◽

Random Field Theory ◽

False Discovery ◽

True Signal

Background The inflation of falsely rejected hypotheses associated with multiple hypothesis testing is seen as a threat to the knowledge base in the scientific literature. One of the most recently developed statistical constructs to deal with this problem is the false discovery rate (FDR), which aims to control the proportion of the falsely rejected null hypotheses among those that are rejected. FDR has been applied to a variety of problems, especially for the analysis of 3-D brain images in the field of Neuroimaging, where the predominant form of statistical inference involves the more conventional control of false positives, through Gaussian random field theory (RFT). In this study we considered FDR and RFT as alternative methods for handling multiple testing in the analysis of 1-D continuum data. The field of biomechanics has recently adopted RFT, but to our knowledge FDR has not previously been used to analyze 1-D biomechanical data, nor has there been a consideration of how FDR vs. RFT can affect biomechanical interpretations. Methods We reanalyzed a variety of publicly available experimental datasets to understand the characteristics which contribute to the convergence and divergence of RFT and FDR results. We also ran a variety of numerical simulations involving smooth, random Gaussian 1-D data, with and without true signal, to provide complementary explanations for the experimental results. Results Our results suggest that RFT and FDR thresholds (the critical test statistic value used to judge statistical significance) were qualitatively identical for many experimental datasets, but were highly dissimilar for others, involving non-trivial changes in data interpretation. Simulation results clarified that RFT and FDR thresholds converge as the true signal weakens and diverge when the signal is broad in terms of the proportion of the continuum size it occupies. Results also showed that, while sample size affected the relation between RFT and FDR results for small sample sizes (<15), this relation was stable for larger sample sizes, wherein only the nature of the true signal was important. Discussion RFT and FDR thresholds are both computationally efficient because both are parametric, but only FDR has the ability to adapt to the signal features of particular datasets, wherein the threshold lowers with signal strength for a gain in sensitivity. Additional advantages and limitations of these two techniques as discussed further. This article is accompanied by freely available software for implementing FDR analyses involving 1-D data and scripts to replicate our results.

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A Non-Parametric Maximum for Reasonable Number of Rejected Hypotheses: Objective Optima for False Discovery Rate and Significance Threshold in Exploratory Research with Application to Ordinal Survey Analysis

10.20944/preprints201703.0191.v1 ◽

2017 ◽

Author(s):

Amir hassan Ghaseminejad tafreshi

Keyword(s):

False Discovery Rate ◽

Multiple Hypothesis Testing ◽

Exploratory Research ◽

Survey Analysis ◽

Significance Threshold ◽

False Discovery ◽

Multiple Hypothesis ◽

Reasonable Number ◽

Level Of Significance ◽

Non Parametric

This paper identifies a criterion for choosing the largest set of rejected hypotheses in high-dimensional data analysis where Multiple Hypothesis testing is used in exploratory research to identify significant associations among many variables. The method neither requires predetermined thresholds for level of significance, nor uses presumed thresholds for false discovery rate. The upper limit for number of rejected hypotheses is determined by finding maximum difference between expected true hypotheses and false hypotheses among all possible sets of rejected hypotheses. Methods of choosing a reasonable number of rejected hypotheses and application to non-parametric analysis of ordinal survey data are presented.

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