Abstract
Diagnosis and classification of acute lymphoblastic leukemias (ALL) and their distinction from biphenotypic acute leukemias (BAL) and acute myeloid leukemias with minimal differentiation (AML M0) is largely based on immunophenotyping. The EGIL classification, adopted by the WHO classification, defines 4 different subtypes of both B-precursor and T-precursor ALL as well as detailed criteria for BAL. Specific cytogenetic features useful for classificationare found in some cases only. We analyzed gene expression profiles in 173 such patients (Pro-B-ALL n=25, c-ALL/Pre-B-ALL n=65 (with t(9;22) n=35, without t(9;22) n=30), mature B-ALL n=13, Pro-T-ALL n=6, Pre-T-ALL n=13, cortical T-ALL n=20, BAL (myeloid and T-lineage) n=17, AML M0 n=14). All cases were assessed by cytomorphology, immunophenotyping, cytogenetics, and molecular genetics. All cases with Pro-B-ALL had t(4;11)/MLL-AF4, all cases with mature B-ALL had t(8;14). Samples were hybridized to both U133A and U133B microarrays (Affymetrix). Top 300 differentially expressed genes were identified for each group in comparison to all other groups and individual other groups and used for classification by various Support Vector Machines (SVM) with 10-fold cross validation (CV). Prediction accuracy for discriminating T- from B-precursor ALL was 100%. Accordingly, principal component analysis (PCA) yielded a complete separation of both groups. PCA of B-precursor ALL cases showed distinct clusters for Pro-B-ALL, c-ALL/Pre-B-ALL, and mature B-ALL, however, c-ALL/Pre-B-ALL with t(9;22) were not completely discriminated from those without. Accordingly, classifying B-precursor ALL with SVM resulted in a 87.4% accuracy. Pre-T-ALL cases clustered distinct from cortical T-ALL with hte exception of two cases. The other Pre-T-ALLs clustered together with Pro-T-ALL. Analyzing T-precusor ALL with SVM and 10-fold CV resulted in an accuracy of only 56.4%. Including BAL and AML M0 into these analyses revealed significant overlaps between samples from these entities and T-ALL cases in PCA; prediction accuracy using SVM and 10-fold CV was 79.8%. This accuracy was confirmed applying 100 runs of SVM with 2/3 of samples being randomly selected as training set and 1/3 as test set which resulted in a median accuracy of 77.2% (range, 67.5% to 85.1%). A 100% prediction accuracy was achieved in Pro-B-ALL and mature B-ALL. Misclassifications were: c-ALL/Pre-B-ALL with t(9;22) as c-ALL/Pre-B-ALL without t(9;22) (6/35) and vice versa (6/30). Of the 13 Pre-T-ALL cases 4 were classified as BAL and 3 as cortical T-ALL. Of the 6 Pro-T-ALL cases 2 were classified as AML M0, 3 as BAL, and 1 as Pre-T-ALL. Of the 17 BAL cases 2 were classified as AML M0, 1 as c-ALL/Pre-B-ALL, 2 as Pre-T-ALL, and 1 as Pro-T-ALL. These analyses confirm that gene expression profiles allow the identification of Pro-B-ALL with t(4;11) and mature B-ALL with t(8;14) but do not unequivocally identify the presence of t(9;22) in c-ALL/Pre-B-ALL. Cortical T-ALL are characterized by a specific gene expression profile which is, however, shared by few cases currently diagnosed as Pre-T-ALL. Thus, diagnostic criteria (surface expression of CD1a only) should be optimized. The same applies to diagnostic criteria for more immature T-ALL, BAL, and AML M0. Loss of 5q is frequently observed in all of these latter entities and may be a future diagnostic marker superseding flow cytometry.
New Short Term Prediction Method for Chemical Carcinogenicity by Hepatic Transcript Profiling following 28-Day Toxicity Tests in Rats
