MDS and AML Are Closely Related Diseases: Gene Expression Patterns Reveal Clear Similarities with Respect to Karyotypes and Are Less Related to the Bone Marrow Blast Percentages.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 471-471
Author(s):  
Torsten Haferlach ◽  
Wolfgang Kern ◽  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Sylvia Merk ◽  
...  

Abstract MDS and AML are discriminated by percentages of blasts in the bone marrow (BM) according to the FAB as well as to the WHO classification. However, thresholds are arbitrary and demonstrate only a limited reproducibility in interlaboratory testings. Thus, other parameters have been assessed to discriminate these entities with respect to diagnosis and prognosis. In particular, in the majority of cases common karyotype aberrations have been observed between MDS and AML which have a higher prognostic impact than blast percentages. We applied gene expression profiling (U133A+B, Affymetrix) in 70 MDS and 238 AML cases. In accordance with the WHO classification we excluded cases with balanced translocations (i.e. t(8;21), t(15;17), inv(16), or 11q23) which are classified as AML irrespective of BM blast percentage. First we aimed at identifying genes of which the expression correlated to blast count (Spearman correlation). Out of the top 50 genes this analysis revealed only the FLT3 gene which showed a higher expression in cases with high blast count, while 12 genes with a higher expression in cases with lower blast counts were identified (ANXA3, ARG1, CAMP, CD24, CEACAM1, CEACAM6, CEACAM8, CRISP3, KIAA0922, LCN2, MMP9, STOM). Most of the latter genes are expressed in mature granulocytes and are involved in differentiation and apoptosis. In a second step we performed class prediction using support vector machines (SVM) to separate MDS and AML according to blast percentages as defined in the WHO classification (<5%: RA and 5q- syndrome; 5–9%: RAEB-1; 10–19%: RAEB-2; >19% AML). Using 10-fold cross validation and support vector machines the overall prediction accuracy was only 80%. In detail, 230/238 AML cases were correctly assigned to the AML group while 8 cases were classified as MDS RAEB-2. However, none of the RA, 5q- syndrome and RAEB-1 cases were correctly assigned to their groups, respectively, but were either classified as AML or RAEB-2. Furthermore, only 16 of 38 RAEB-2 cases were correctly predicted, while the 20 remaining cases were assigned to the AML group. Thus, no clear gene expression patterns were identified which correlated with AML and MDS subtypes according to WHO classification. Taking the common genetic background observed in MDS and AML into account, both entities were categorized in a third step according to cytogenetics and classified based on their gene expression profiles. In order to assess the impact of the common genetic background, the largest cytogenetically defined subgroups were compared to each other, i.e. AML and MDS with normal karyotype and with complex aberrant karyotype. Intriguingly, while correct classification of AML or MDS was found in 91%, classification into the correct cytogenetic groups was achieved in 95%. Consequently, all cases were devided into the two groups, complex aberrant karyotype (n=60) and other or no aberrations (n=248) irrespective of AML or MDS. A classification into these groups also yielded an accuracy of 93%. Our data suggests that gene expression profiling reveales the biology of MDS or AML to highly correlate with cytogenetics and less with the percentages of BM blasts. These results strengthen the need for a revision of the current MDS and AML classification centering now genetic abnormalities, which may also be used for clinical decisions.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2897-2897
Author(s):  
Torsten Haferlach ◽  
Helmut Loeffler ◽  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Wolfgang Hiddemann ◽  
...  

Abstract Balanced chromosomal rearrangements leading to fusion genes on the molecular level define distinct biological subsets in AML. The four balanced rearrangements (t(15;17), t(8;21), inv(16), and 11q23/MLL) show a close correlation to cytomorphology and gene expression patterns. We here focused on seven AML with t(8;16)(p11;p13). This translocation is rare (7/3515 cases in own cohort). It is more frequently found in therapy-related AML than in de novo AML (3/258 t-AML, and 4/3287 de novo, p=0.0003). Cytomorphologically, AML with t(8;16) is characterized by striking features: In all 7 cases the positivity for myeloperoxidase on bone marrow smears was >70% and intriguingly, in parallel >80% of blast cells stained strongly positive for non-specific esterase (NSE) in all cases. Thus, these cases can not be classified according to FAB categories. These data suggest that AML-t(8;16) arise from a very early stem cell with both myeloid and monoblastic potential. Furthermore, we detected erythrophagocytosis in 6/7 cases that was described as specific feature in AML with t(8;16). Four pts. had chromosomal aberrations in addition to t(8;16), 3 of these were t-AML all showing aberrations of 7q. Survival was poor with 0, 1, 1, 2, 20 and 18+ (after alloBMT) mo., one lost to follow-up, respectively. We then analyzed gene expression patterns in 4 cases (Affymetrix U133A+B). First we compared t(8;16) AML with 46 AML FAB M1, 41 M4, 9 M5a, and 16 M5b, all with normal karyotype. Hierachical clustering and principal component analyses (PCA) revealed that t(8;16) AML were intercalating with FAB M4 and M5b and did not cluster near to M1. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid. Next we compared the t(8;16) AML with the 4 other balanced subtypes according to the WHO classification (t(15;17): 43; t(8;21): 40; inv(16): 49; 11q23/MLL-rearrangements: 50). Using support vector machines the overall accuracy for correct subgroup assignment was 97.3% (10-fold CV), and 96.8% (2/3 training and 1/3 test set, 100 runs). In PCA and hierarchical cluster analysis the t(8;16) were grouped in the vicinity of the 11q23 cases. However, in a pairwise comparison these two subgroups could be discriminated with an accuracy of 94.4% (10-fold CV). Genes with a specific expression in AML-t(8;16) were further investigated in pathway analyses (Ingenuity). 15 of the top 100 genes associated with AML-t(8;16) were involved in the CMYC-pathway with up regulation of BCOR, COXB5, CDK10, FLI1, HNRPA2B1, NSEP1, PDIP38, RAD50, SUPT5H, TLR2 and USP33, and down regulation of ERG, GATA2, NCOR2 and RPS20. CEBP beta, known to play a role in myelomonocytic differentiation, was also up-regulated in t(8;16)-AML. Ten additional genes out of the 100 top differentially expressed genes were also involved in this pathway with up-regulation of DDB2, HIST1H3D, NSAP1, PTPNS1, RAN, USP4, TRIM8, ZNF278 and down regulation of KIT and MBD2. In conclusion, AML with t(8;16) is a specific subtype of AML with unique characteristics in morphology and gene expression patterns. It is more frequently found in t-AML, outcome is inferior in comparison to other AML with balanced translocations. Due to its unique features, it is a candidate for inclusion into the WHO classification as a specific entity.


Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1487-1487
Author(s):  
Lars Bullinger ◽  
Thomas Hielscher ◽  
Ursula Botzenhardt ◽  
Sabrina Heinrich ◽  
Richard Schlenk ◽  
...  

Abstract Cytogenetically normal acute myeloid leukemia (CN-AML) comprises a biologically and clinically heterogeneous group of AML. In the past years, molecular markers like FLT3, CEBPA and NPM1 gene mutations have been identified in CN-AML, and the presence of such mutations carries important prognostic information. Furthermore, DNA microarray-based gene expression profiling (GEP) has been shown to capture the molecular heterogeneity of cancers, and has been applied to build classifiers and clinical outcome predictors in AML. While prior studies have defined gene expression patterns associated with NPM1, CEBPA, and FLT3, we assessed the clinical relevance of gene signatures. We profiled a large set of clinically well annotated CN-AML specimens (n=296 entered on two multicenter trials for patients <60 years (AMLSG HD98A and AMLSG 07-04). The 142 cases from the AMLSG HD98A trial were analyzed using a 40k cDNA microarray platform and the 154 cases from trial AMLSG 07-04 using Affymetrix microarrays (Human Genome U133 Plus 2.0 Arrays). In this data set we applied supervised analyses (LASSO penalized logistic regression) to define gene expression patterns characterizing FLT3 internal tandem duplication (ITD), CEPBA and NPM1 mutations as well as outcome signatures. We were able to define distinct signatures associated with NPM1, CEBPA, and FLT3 consisting of 39, 27, and 47 genes, respectively. The NPM1 signature revealed a high prediction accuracy of >95% in leave-one-out cross validated classification. Prediction of FLT3-ITD or CEBPA mutation performed less well with accuracies of 80% and 73%, respectively. However, for both CEBPA and FLT3-ITD the predicted mutation class labels performed slightly better than the marker itself with regard to the prognostic impact on overall survival (CEPBA: p=0.006 vs. p=0.007, FLT3-ITD p=9.57e-06 vs. p=5.11e-05; logrank test). In addition, using LASSO we also could define a signature associated with event free survival (EFS) in the cases from the AMLSG 07-04 trial. Adjusted for age, NPM1, and FLT3-ITD mutational status this signature was significantly associated with EFS (p=0.005; Wald test), and validation in our independent cDNA data set also provided significant prognostic information (p=0.02; Wald test). Thus, GEP-based classification of CN-AML might help to identify alternative genetic changes that either phenocopy or block the effects of common molecular aberrations. Furthermore, gene expression patterns of yet unknown aberrations are reflected in prognostic signatures. Therefore, signature genes also provide a starting point to dissect “mutations” pathways, and our findings underscore the potential clinical utility of a gene expression based measure in CN-AML.


Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 143-143
Author(s):  
Torsten Haferlach ◽  
Alexander Kohlmann ◽  
Susanne Schnittger ◽  
Martin Dugas ◽  
Sylvia Merk ◽  
...  

Abstract So far, comprehensive diagnosis of leukemia requires a combination of cytomorphology, immunophenotyping, and genetic methods. We aimed at developing a new diagnostic tool based solely on gene expression profiling to accurately predict all clinically relevant subtypes of leukemia in adults and to distinguish these from normal bone marrow. Therefore, we analyzed samples from 1337 untreated patients at diagnosis and healthy donors using oligonucleotide microarrays. The first series of 937 cases was hybridized to HG-U133A+B microarrays (Affymetrix). The following 13 subgroups were included: 620 AML (42 t(15;17); 38 t(8;21); 49 inv(16); 47 t(11q23); 75 complex aberrant karyotype; 193 normal karyotype; 176 other cytogenetic abn.); 152 ALL (26 Pro-B-ALL/t(11q23); 12 ALL-t(8;14); 32 T-ALL; 82 c-ALL/Pre-B-ALL); 75 CML, 45 CLL, and 45 bone marrows from healthy volunteers or non-leukemia pts. (nBM). For each disease entity the top 100 differentially expressed genes were calculated in a one-versus-all (OVA) approach. Class prediction was performed using support vector machines (SVM). Prediction accuracy was estimated by 10-fold cross validation (CV) and assessed for robustness in a resampling approach. 891 of the 937 samples (95.1%) were correctly classified (10-fold CV). A resampling approach with 2/3 training and 1/3 test cohort (100 runs of SVM) confirmed this high accuracy (median, 93.8%). In particular, a median of 100% sensitivity and specificity was achieved for AML with t(15;17), t(8;21), and inv(16), as well as for Pro-B-ALL/t(11q23), and CLL. The median specificity was at least 99.7% in all subgroups except for AML normal/other (median specificity, 93.7%). In a second step T-ALL cases were separated into cortical and immature ones (accuracy, 84.4%) and c-ALL/Pre-B-ALL into cases with and without t(9;22) (accuracy, 82.9%). The second prospective series comprized 400 unselected cases which were hybridized to the new generation HG-U133 Plus 2.0 microarrays (Affymetrix). To validate the diagnostic accuracy of our approach these cases were processed blinded in parallel to routine diagnostic work-up and classified based on the gene expression signatures discovered in the first series described above. Applying a first classification step as described above the 13 different diagnoses were predicted with an accuracy of 94.5%. Failures were mostly due to misclassification into biologically related subgroups, e.g. AML with del(5q) aberrations classified as AML with complex aberrant karyotype. In the second step (separation of the two T-ALL subtypes, and c-ALL/Pre-B-ALL with or without t(9;22)) accuracies of 100% and 70.6% respectively were achieved. In conclusion, we were able to identify within a routine diagnostic workflow distinct expression profiles for all clinically and prognostically relevant adult leukemia subtypes and their discrimination from nBM based only on gene expression data. Accuracy, sensitivity, and specificity were higher than achieved with each of the gold standard techniques alone used today. Thus, gene expression patterns analyzed by microarrays qualify as a diagnostic tool in a routine setting for leukemia diagnosis and classification and may guide relevant therapeutic decisions.


2017 ◽  
Author(s):  
Ling Chen ◽  
Alexandra E. Fish ◽  
John A. Capra

AbstractIn mammals, genomic regions with enhancer activity turnover rapidly; in contrast, gene expression patterns and transcription factor binding preferences are largely conserved. Based on this conservation, we hypothesized that enhancers active in different mammals would exhibit conserved sequence patterns in spite of their different genomic locations. We tested this hypothesis by quantifying the conservation of sequence patterns underlying histone-mark defined enhancers across six diverse mammals in two machine learning frameworks. We first trained support vector machine (SVM) classifiers based on the frequency spectrum of short DNA sequence patterns. These classifiers accurately identified many adult liver, developing limb, and developing brain enhancers in each species. Then, we applied these classifiers across species and found that classifiers trained in one species and tested in another performed nearly as well as classifiers trained and tested on the same species. This indicates that the short sequence patterns predictive of enhancers are largely conserved. We also observed similar cross-species conservation when applying the models to human and mouse enhancers validated in transgenic assays. The sequence patterns most predictive of enhancers in each species matched the binding motifs for a common set of TFs enriched for expression in relevant tissues, supporting the biological relevance of the learned features. To test the conservation of more complex sequences patterns, we trained convolutional neural networks (CNNs) on enhancer sequences in each species. The CNNs demonstrated better performance overall, but worse cross-species generalization than SVMs, suggesting the importance of combinatorial interactions between motifs, but less conservation of these more complex sequence patterns. Thus, despite the rapid change of active enhancer locations between mammals, cross-species enhancer prediction is often possible. Furthermore, short sequence patterns encoding enhancer activity have been maintained across more than 180 million years of mammalian evolution, with evolutionary change in more complex sequence patterns.Author summaryAlterations in gene expression levels are a driving force of both speciation and complex disease; therefore, it is of great importance to understand the mechanisms underlying the evolution and function gene regulatory DNA sequences. Recent studies have revealed that while gene expression patterns and transcription factor binding preferences are broadly conserved across diverse animals, there is extensive turnover in distal gene regulatory regions, called enhancers, between closely related species. We investigate this seeming incongruence by analyzing genome-wide enhancer datasets from six diverse mammalian species. We trained two machine-learning classifiers—a k-mer spectrum support vector machine (SVM) and convolutional neural network (CNN)—to distinguish enhancers from the genomic background. The k-mer spectrum SVM models the occurrences of short sequence patterns while the CNN models both the short sequences patterns and their combinatorial patterns. Both the SVM and CNN enhancer prediction models trained in one species are able to predict enhancers in the same cellular context in other species. However, CNNs performed better at predicting enhancers in each species, but they generalize less well across species than the SVMs. This argues that the short sequence properties encoding regulatory activity are remarkably conserved across more than 180 million years of mammalian evolution with more evolutionary turnover in the more complex combinations of the conserved short sequence motifs.


Pneumologie ◽  
2018 ◽  
Vol 72 (S 01) ◽  
pp. S8-S9
Author(s):  
M Bauer ◽  
H Kirsten ◽  
E Grunow ◽  
P Ahnert ◽  
M Kiehntopf ◽  
...  

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