scholarly journals Proteomic Landscape of Acute Leukemia: A Comparison between ALL and AML in Adults

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1461-1461
Author(s):  
Fieke W Hoff ◽  
Yihua Qiu ◽  
Wendy Hu ◽  
Amina A Qutub ◽  
Eveline S. de Bont ◽  
...  
Keyword(s):  
Protein Expression ◽  
Finite Number ◽  
Clinical Utility ◽  
Expression Patterns ◽  
Optimal Number ◽  
The Other ◽  
Differential Proteomics ◽  
Data Set ◽  
Functional Relationships ◽  
Protein Clusters

Background: Acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) are both heterogeneous diseases. The underlying changes that results in leukemia are due to developmental, genetic, or environmental effects, and are mostly mediated by changes in protein expression or modification. We hypothesize that there is a finite number of patterns of protein expression and protein pathway utilization, whose perturbations result in the hallmarks of cancer. In this study we performed differential proteomics of ALL and AML, with the goal to understand the underlying (disease-specific) cellular changes of AML and ALL, as well as to identify protein utilizations that are shared between AML and ALL. Method: Reverse phase protein arrays (RPPA) was generated for 230 strictly validated antibodies using samples from 130 ALL and 241 AML patient samples, and 10 CD34+ samples from healthy controls. Expression levels were normalized relative to the normal CD34+ cells. Due to some inherent considerations of the traditional hierarchical clustering (HC) (e.g. HC weighs all proteins equally in all situations, HC is agnostic to all known functional relationships between proteins, HC requires that all data be considered and placed into a group), expression data was analyzed using the MetaGalaxyanalysis. This approach starts with the allocation of proteins into 31 protein functional groups (PFG). Progeny clustering was applied to identify an optimal number of protein clusters within each PFG. Block clustering identified protein clusters that recurrently co-occurred (protein constellation (CON)), and for each subgroup of patients that expressed similar combinations of protein constellations (patient signature (SIG)). Proteins that were differentially expressed were identified using the student's T-test or ANOVA, and a Bonferroni adjusted P-value (0.05/ 230 = 0.00021739). Results: The MetaGalaxy approach identified a substantial amount of structure across the data set (Figure 1), with an optimal number of 12 CON (horizontally) and 13 SIG (vertically). The majority of SIG were correlated with either ALL (SIG 1, 3, 4, 5) or AML (SIG7, 8, 9, 10, 11, 12) (annotation bar Figure 1), although SIG2, 6, and 13 contained a mixture of both (P< 0.001). Similarly, CON1, 2, 3 were mostly associated with ALL, CON6, 8, 9 and 11 with AML, while CON4, 5, 7 and 10 were observed in both diseases. To understand more about the protein signaling utilizations deregulated proteins were identified for each CON and SIG. For example, CON1 was associated with PFG Apoptosis Occurring (e.g. CASP9-cl330, PARP1), autophagy (e.g. PRKAA1_2, PRKAA1_2-pTyr172), and apoptosis BH3 (e.g. BCL2, BAD-pSer112). In ALL, signature membership of CON5 was associated with a superior overall survival and complete remission duration (P= 0.016; P= 0.035). CON4 was associated with a high rate of early deaths (P = 0.041), but not with a higher frequency of relapses (P = 0.520). In AML, signatures were predictive for OS and CR, with SIG7, 10, 12 as being favorable vs SIG2, 6, 9, 11, 13 as being unfavorable. CON4 was predictive of late relapses (≥ 2 yr.). Interestingly, CON5 was associated with a trend toward inferior CR duration in AML (P= 0.093), whereas this CON5 was favorably prognostic in ALL. Conclusion: In this study we confirmed our original hypothesis that there is a finite number of SIG in ALL and AML. Although, ALL and AML are both hematological diseases that share many molecular events, SIG and CON membership were significantly correlated with ALL and AML, confirming that protein expression patterns for the majority of cases of ALL ≠ AML. However, given that some CON were associated with both disease, this indicates that common features between both also exist. Proteins or pathways with similar utilization in both diseases may allow for information on clinical utility from one disease to be transitive to the other, while those with differential utilization are likely to be uninformative with respect to clinical utility in the other disease. Figure 1. MetaGalaxy analysis for AML and ALL. Each row represents one protein clusters (n = 123), each column represents one patient (n = 371). Blue indicates membership for that particular protein cluster. Annotation bar shows strong correlation with disease (yellow = B-ALL, pink = T ALL, blue = AML). Figure Disclosures No relevant conflicts of interest to declare.

scholarly journals Proteomics in Pediatric Acute Myeloid and T-Cell Lymphoblastic Leukemia: Shared Individual Protein Expression Patterns Co-Cluster into Overall Distinct Combinations

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 35-36
Author(s):  
Fieke W Hoff ◽  
Anneke D. van Dijk ◽  
Yihua Qiu ◽  
Eveline S. de Bont ◽  
Steven M. Kornblau ◽  
...  
Keyword(s):  
Protein Expression ◽  
Clinical Utility ◽  
Lymphoblastic Leukemia ◽  
Expression Patterns ◽  
The Other ◽  
Differentially Expressed ◽  
Protein Clusters ◽  
Cd34 Cells ◽  
Pediatric Aml ◽  
Acute Myeloid

INTRODUCTION: Pediatric acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) are heterogeneous diseases mediated by changes in protein expression. As most chemotherapeutic agents target proteins, and because overall survival of pediatric AML is far inferior to both pre-B and T-ALL, we aimed to compare the proteomic landscape of pediatric T-ALL and AML, with the goal of determining common AML-T-ALL pathways that are potentially targetable with novel agents. METHODS: Reverse phase protein arrays (RPPA) analysis was used to measure protein expression in 858 acute leukemia samples (358 T-ALL and 500 AML, 723 pediatric (< 18 yrs.), 135 adults (≥18 yrs.)) and 61 normal CD34+ samples using 270 validated antibodies. Expression levels were normalized against CD34+ cells. Proteins were allocated into 30 functionally related subgroups (Protein Functional Group (PFG)). A progeny clustering algorithm was applied to each PFG to search for strong correlations between proteins and to identify an optimal number of Protein Clusters (PC). Block clustering identified PC that recurrently co-occurred together (Protein Constellation (CON)) and patients that expressed similar combination of CON were defined as Protein Signature (SIG). Proteins that were differentially expressed were identified using the Student's t-test or ANOVA, with a Bonferroni adjusted p-value (0.05/ 270 = 0.00019)). RESULTS: Of the 270 analyzed proteins, 131 proteins (49%) were differentially expressed between T-ALL and AML; 60 were higher in T-ALL, 71 in AML. Similar to our previous analysis in adult AML and ALL, cell cycle regulators (CDKN1A, CDKN1B) and 2 of the 5 histone marks (H3K36Me3 & H3K4Me3) were higher expressed in T-ALL compared to AML. Heat shock proteins (HSP90AA1_B1, HSPA1A_L, HSPB1 and HSPB1-pSer82) were higher in AML as well as translation proteins EIF2S1, EIF4E and EIF4EBP1 and ribosomal proteins RPS6-pSer235_236 and RPS6KB1, while expression of the translation inhibitory proteins EIF2S1-pSer51 and EIF2AK2-pThr451 was lower in AML compared to T-ALL. Next, cluster analysis in the context of 30 PFG resulted in 133 PC. The majority (n=102) of PC were expressed in both diseases, 30 PC (22.6%) were AML-specific, and only one PC was specific to T-ALL (characterized by high CDKN1A, CDKN1B and CCND1, but low WEE1, CCNB1 and RB1-pSer). Co-clustering of the 133 PC identified 14 CON that formed 17 SIG. Three CON (5, 9, 10) were associated with AML, 2 with T-ALL (2, 13) and 8 CON were observed in both diseases. In contrast, 15 of SIG were associated with either T-ALL or AML, and two SIG (9, 10) included a mixture of both diseases (P < 0.001, annotation bar Figure 1 "Disease")). SIG were associated with gender (P < 0.001), but not with CNS-status and ethnicity (Hispanic vs. non-Hispanic). No age-specific (kids vs. adults) signatures were observed. For each SIG and CON, proteins significantly higher or lower expressed compared to the normal CD34+ cells were identified. CONCLUSIONS: This study provides support for our previous hypothesis that pediatric T-ALL and AML can be characterized by recurrent protein expression patterns. While most PC and CON were found in both diseases, SIG (i.e. combinations of protein expression patterns) were specific to either T-ALL or AML. We found similar results when comparing B-ALL to AML in adults. Shared CON indicate that there are common protein expression patterns between pediatric T-ALL and AML. Proteins or pathways with similar utilization (e.g. CON3, 5) in both diseases may allow for information on clinical utility from one disease to be applicable to the other. Those with differential utilization are likely to be uninformative with respect to clinical utility in the other disease. Figure. "MetaGalaxy" analysis for pediatric AML and T-ALL. Each row represents one protein clusters (n = 133), each column represents one patient (n = 858). Blue indicates membership for that particular protein cluster. Annotation bar shows strong correlation with disease (yellow = T-ALL, blue = AML). No associations were seen for age (blue = adult, pink = pediatric) or Ethnicity (blue = Hispanic, yellow = non-Hispanic). Figure 1 Disclosures No relevant conflicts of interest to declare.

scholarly journals Identification of differential proteomics in Epstein-Barr virus-associated gastric cancer and related functional analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zeyang Wang ◽  
Zhi Lv ◽  
Qian Xu ◽  
Liping Sun ◽  
Yuan Yuan
Keyword(s):  
Gastric Cancer ◽  
Functional Analysis ◽  
Protein Expression ◽  
Epstein Barr Virus ◽  
Protein Profile ◽  
Expression Patterns ◽  
Clinicopathological Parameters ◽  
Differential Proteomics ◽  
Barr Virus ◽  
Epstein Barr

Abstract Background Epstein-Barr virus-associated gastric cancer (EBVaGC) is the most common EBV-related malignancy. A comprehensive research for the protein expression patterns in EBVaGC established by high-throughput assay remains lacking. In the present study, the protein profile in EBVaGC tissue was explored and related functional analysis was performed. Methods Epstein-Barr virus-encoded RNA (EBER) in situ hybridization (ISH) was applied to EBV detection in GC cases. Data-independent acquisition (DIA) mass spectrometry (MS) was performed for proteomics assay of EBVaGC. Functional analysis of identified proteins was conducted with bioinformatics methods. Immunohistochemistry (IHC) staining was employed to detect protein expression in tissue. Results The proteomics study for EBVaGC was conducted with 7 pairs of GC cases. A total of 137 differentially expressed proteins in EBV-positive GC group were identified compared with EBV-negative GC group. A PPI network was constructed for all of them, and several proteins with relatively high interaction degrees could be the hub genes in EBVaGC. Gene enrichment analysis showed they might be involved in the biological pathways related to energy and biochemical metabolism. Combined with GEO datasets, a highly associated protein (GBP5) with EBVaGC was screened out and validated with IHC staining. Further analyses demonstrated that GBP5 protein might be associated with clinicopathological parameters and EBV infection in GC. Conclusions The newly identified proteins with significant differences and potential central roles could be applied as diagnostic markers of EBVaGC. Our study would provide research clues for EBVaGC pathogenesis as well as novel targets for the molecular-targeted therapy of EBVaGC.

scholarly journals Proteomic Landscape of De Novo Pediatric Acute Myeloid Leukemia

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 294-294
Author(s):  
Fieke W Hoff ◽  
Yihua Qiu ◽  
Wendy Hu ◽  
Amina A Qutub ◽  
Alan S Gamis ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Protein Expression ◽  
Board Of Directors ◽  
Myeloid Leukemia ◽  
Expression Patterns ◽  
Advisory Committees ◽  
Pediatric Acute Myeloid Leukemia ◽  
Protein Clusters ◽  
Protein Signatures ◽  
Acute Myeloid

Abstract Background: Many genetic drivers that are implicated in disease pathology and risk stratification have been identified for pediatric acute myeloid leukemia (pedi-AML). However, only a minority have been exploited for therapeutic interventions and most of the identified genetic events currently lack targeted therapy to address the mutations. The combined consequences of genetic and epigenetic events culminate in a net effect manifested at the protein level and most of the chemotherapies target proteins. Yet little is known about the proteomic landscape of pedi-AML. Methods: Reverse Phase Protein Array (RPPA) was performed with 291 strictly validated antibodies to determine the protein expression levels of bulk leukemic cells from 505 pedi-AML patient samples that were collected prior to therapy. All patients participated on the COG AAML1031 Phase 3 clinical trial, that compared standard therapy (ADE) to ADE plus bortezomib (ADE+B). Proteins were allocated into 31 protein functional groups (PFG) (e.g. cell cycle, apoptosis) to analyze proteins in relation to related proteins. Progeny clustering was performed to identify patients with correlated protein expression patterns within each PFG (protein cluster). Block clustering searched for protein clusters that recurrently co-occurred (protein constellation), and for subgroups of patients that expressed similar combinations of protein constellations (protein signatures). Protein signatures were correlated with known cytogenetics and mutational state. Results: For each PFG, cluster analysis identified an optimal number of protein clusters, resulting in a total of 120. From this we constructed 11 protein constellations (PRCON) and 10 protein signatures (SIG) (Fig. 1A). A training set (n=334) and test set (n=171) showed high reproducibility (Pearson's X2; p < 0.001). SIG were prognostic for event-free survival (EFS) (p = 0.029), with a favorable EFS for SIG 1, 2, 3, 5, 7 & 9, and an unfavorable EFS for SIG 4, 6, 8, 10. Notably, patients that formed SIG 3 had a significant better EFS after receiving ADE+B than patients that received ADE (p = 0.039) (Fig. 1B). This SIG was highly enriched for CEBPA mutated cases; 43% vs. 9% overall (p < 0.001). SIG were associated with cytogenetic aberrations (p < 0.001) and mutational state, as well as with the traditional risk groups (p < 0.001). For example, t(8;21) was overrepresented in SIG 9 (39% vs. 16% overall) and MLL-rearrangement in SIG 6 (61% vs. 19% overall). Multivariate analysis was performed using variables definable at time of diagnosis and known prognostic factors. This resulted in a final model including unfavorable protein SIG together with low risk cytogenetics and NPM1 mutation state as independent prognostic factors, suggesting that proteins add to known prognostic factors. Proteomics could also identify aberrantly expressed proteins within each SIG compared to normal CD34+ cells. This recognized 31 proteins as universally down regulated (e.g. CDKN1A, PPP2R2A) and 13 as universally up regulated (e.g. PIK3CA, NCL). Many other proteins were different between SIG, and thus potentially targetable in particular patient groups: high KIT (SIG 3, 4 & 5), high BCL2 (SIG 3, 4, 5, 6, 9 & 10) and high BRD4 (SIG 6). SIG 1 & 2 both had a very characteristic expression pattern and were most distinct from the others. IGFR1, IGFR1.pY1135, AKT3, SMAD5.pS463-7 and RICTOR.pT1135 were all high, whereas they were low in most other SIG. Conversely, STAT3 was normal in SIG 1&2, but changed in the other SIG. SIG 6, 7 & 8 also had relatively similar expression patterns, but had different outcomes. Compared to SIG 6 & 8, SIG 7 had higher GAB2 and MCL1 and lower RAD50, ERCC1. Conclusions: Pedi-AML is characterized by a finite number (n=10) of recurrent protein signatures. SIG were only partially correlated with cytogenetics and mutation state, indicating that protein expression could add to genetics in the process of risk stratification. We identified SIG that did well vs. SIG that did not, independent of known risk factors, and identified a group of patients that could potentially benefit from ADE+B. Recognition of differentially expressed proteins suggest potential targets for combinational treatment (Hoff et al. ASH HSF1 abstract). Figure 1. A. Identification of 11 PRCON (horizontally) and 10 SIG (vertically). B. SIG were prognostic for EFS. C. SIG 3 significantly benefitted from ADE+B (green) compared to ADE alone (blue). Disclosures Kolb: Roche- Genentech: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees.

The Determinants of Capital Adequacy Ratio: The Case of the Vietnamese Banking System in the Period 2011-2015

2017 ◽  
Vol 33 (2) ◽  
Author(s):  
Ngoc Anh Nguyen
Keyword(s):  
Banking System ◽  
Precious Metals ◽  
Capital Adequacy ◽  
The Other ◽  
Total Asset ◽  
Data Set ◽  
Hand Size ◽  
Net Interest Margin ◽  
Capital Adequacy Ratio ◽  
Positive Effect

The analysis of a data set of observation for Vietnamese banks in period from 2011 - 2015 shows how Capital Adequacy Ratio (CAR) is influenced by selected factors: asset of the bank SIZE, loans in total asset LOA, leverage LEV, net interest margin NIM, loans lost reserve LLR, Cash and Precious Metals in total asset LIQ. Results indicate based on data that NIM, LIQ have significant effect on CAR. On the other hand, SIZE and LEV do not appear to have significant effect on CAR. Variables NIM, LIQ have positive effect on CAR, while variables LLR and LOA are negatively related with CAR.

scholarly journals Measurement of the D → K−π+π+π− and D → K−π+π0 coherence factors and average strong-phase differences in quantum-correlated $$ D\overline{D} $$ decays

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
M. Ablikim ◽  
◽  
M. N. Achasov ◽  
P. Adlarson ◽  
S. Ahmed ◽  
...  
Keyword(s):  
The Other ◽  
Unitarity Triangle ◽  
Quantum Superposition ◽  
Final State ◽  
Data Set ◽  
Strong Phase ◽  
Signal Decay ◽  
Belle Ii ◽  
Coherence Factors ◽  
Phase Differences

Abstract The decays D → K−π+π+π− and D → K−π+π0 are studied in a sample of quantum-correlated $$ D\overline{D} $$ D D ¯ pairs produced through the process e+e− → ψ(3770) → $$ D\overline{D} $$ D D ¯ , exploiting a data set collected by the BESIII experiment that corresponds to an integrated luminosity of 2.93 fb−1. Here D indicates a quantum superposition of a D0 and a $$ {\overline{D}}^0 $$ D ¯ 0 meson. By reconstructing one neutral charm meson in a signal decay, and the other in the same or a different final state, observables are measured that contain information on the coherence factors and average strong-phase differences of each of the signal modes. These parameters are critical inputs in the measurement of the angle γ of the Unitarity Triangle in B− → DK− decays at the LHCb and Belle II experiments. The coherence factors are determined to be RK3π = $$ {0.52}_{-0.10}^{+0.12} $$ 0.52 − 0.10 + 0.12 and $$ {R}_{K{\pi \pi}^0} $$ R K ππ 0 = 0.78 ± 0.04, with values for the average strong-phase differences that are $$ {\delta}_D^{K3\pi }=\left({167}_{-19}^{+31}\right){}^{\circ} $$ δ D K 3 π = 167 − 19 + 31 ° and $$ {\delta}_D^{K{\pi \pi}^0}=\left({196}_{-15}^{+14}\right){}^{\circ} $$ δ D K ππ 0 = 196 − 15 + 14 ° , where the uncertainties include both statistical and systematic contributions. The analysis is re-performed in four bins of the phase-space of the D → K−π+π+π− to yield results that will allow for a more sensitive measurement of γ with this mode, to which the BESIII inputs will contribute an uncertainty of around 6°.

scholarly journals An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3311
Author(s):  
Riccardo Ballarini ◽  
Marco Ghislieri ◽  
Marco Knaflitz ◽  
Valentina Agostini
Keyword(s):  
Motor Task ◽  
Optimal Number ◽  
The Other ◽  
Simulated Dataset ◽  
Muscle Synergies ◽  
Mean Square ◽  
Real Dataset ◽  
Traditional Approaches ◽  
Selection Of ◽  
Semg Signals

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds.

scholarly journals Evaluating Presence/Absence of Target Microbes in Microbiological Tests

2000 ◽  
Vol 83 (6) ◽  
pp. 1429-1434
Author(s):  
Robert J Blodgett ◽  
Anthony D Hitchins
Keyword(s):  
Performance Studies ◽  
Collaborative Study ◽  
False Negative ◽  
The Other ◽  
Test Results ◽  
Logistic Curve ◽  
Microbiological Method ◽  
Test Portion ◽  
Method Performance ◽  
Data Set

Abstract A typical qualitative microbiological method performance (collaborative) study gathers a data set of responses about a test for the presence or absence of a target microbe. We developed 2 models that estimate false-positive and false-negative rates. One model assumes a constant probability that the tests will indicate the target microbe is present for any positive concentration in the test portion. The other model assumes that this probability follows a logistic curve. Test results from several method performance studies illustrate these estimates.

On the mN, M Relation for Eastern North American Earthquakes

1987 ◽  
Vol 58 (4) ◽  
pp. 119-124 ◽  
Author(s):  
Gail M. Atkinson ◽  
David M. Boore
Keyword(s):  
North America ◽  
Stochastic Model ◽  
Ground Motion ◽  
Seismic Moment ◽  
The Other ◽  
Moment Magnitude ◽  
Scaling Relations ◽  
Eastern North America ◽  
Data Set ◽  
Meaningful Data

Abstract A stochastic model of ground motion has been used as a basis for comparison of data and theoretically-predicted relations between mN (commonly denoted by mbLg) and moment magnitude for eastern North America (ENA) earthquakes. mN magnitudes are recomputed for several historical ENA earthquakes, to ensure consistency of definition and provide a meaningful data set. We show that by itself the magnitude relation cannot be used as a discriminant between two specific spectral scaling relations, one with constant stress and the other with stress increasing with seismic moment, that have been proposed for ENA earthquakes.

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