Andrea Zangrando

Integration of genomic and gene expression data of childhood ALL without known aberrations identifies subgroups with specific genetic hallmarks.

Pediatric acute lymphoblastic leukemia (ALL) comprises genetically distinct subtypes. However, 25% of cases still lack defined genetic hallmarks. To identify genomic aberrancies in childhood ALL patients nonclassifiable by conventional methods, we performed a single nucleotide polymorphisms (SNP) array‐based genomic analysis of leukemic cells from 29 cases. The vast majority of cases analyzed (19/24, 79%) showed genomic abnormalities; at least one of them affected either genes involved in cell cycle regulation or in B‐cell development. The most relevant abnormalities were CDKN2A/9p21 deletions (7/24, 29%), ETV6 (TEL)/12p13 deletions (3/24, 12%), and intrachromosomal amplifications of chromosome 21 (iAMP21) (3/24, 12%). To identify variation in expression of genes directly or indirectly affected by recurrent genomic alterations, we integrated genomic and gene expression data generated by microarray analyses of the same samples. SMAD1 emerged as a down‐regulated gene in CDKN2A homozygous deleted cases compared with nondeleted. The JAG1 gene, encoding the Jagged 1 ligand of the Notch receptor, was among a list of differentially expressed (up‐regulated) genes in ETV6‐deleted cases. Our findings demonstrate that integration of genomic analysis and gene expression profiling can identify genetic lesions undetected by routine methods and potential novel pathways involved in B‐progenitor ALL pathogenesis.

Gene Expression–Based Classification As an Independent Predictor of Clinical Outcome in Juvenile Myelomonocytic Leukemia

PURPOSE Juvenile myelomonocytic leukemia (JMML) is a rare early childhood myelodysplastic/myeloproliferative disorder characterized by an aggressive clinical course. Age and hemoglobin F percentage at diagnosis have been reported to predict both survival and outcome after hematopoietic stem cell transplantation (HSCT). However, no genetic markers with prognostic relevance have been identified so far. We applied gene expression-based classification to JMML samples in order to identify prognostic categories related to clinical outcome. PATIENTS AND METHODS Samples of 44 patients with JMML were available for microarray gene expression analysis. A diagnostic classification (DC) model developed for leukemia and myelodysplastic syndrome classification was used to classify the specimens and identify prognostically relevant categories. Statistical analysis was performed to determine the prognostic value of the classification and the genes identifying prognostic categories were further analyzed through R software. RESULTS The samples could be divided into two major groups: 20 specimens were classified as acute myeloid leukemia (AML) -like and 20 samples as nonAML-like. Four patients could not be assigned to a unique class. The 10-year probability of survival after diagnosis of AML-like and nonAML-like patients was significantly different (7% v 74%; P = .0005). Similarly, the 10-year event-free survival after HSCT was 6% for AML-like and 63% for nonAML-like patients (P = .0010). CONCLUSION Gene expression-based classification identifies two groups of patients with JMML with distinct prognosis outperforming all known clinical parameters in terms of prognostic relevance. Gene expression-based classification could thus be prospectively used to guide clinical/therapeutic decisions.

MLL rearrangements in pediatric acute lymphoblastic and myeloblastic leukemias: MLL specific and lineage specific signatures

BackgroundThe presence of MLL rearrangements in acute leukemia results in a complex number of biological modifications that still remain largely unexplained. Armstrong et al. proposed MLL rearrangement positive ALL as a distinct subgroup, separated from acute lymphoblastic (ALL) and myeloblastic leukemia (AML), with a specific gene expression profile. Here we show that MLL, from both ALL and AML origin, share a signature identified by a small set of genes suggesting a common genetic disregulation that could be at the basis of mixed lineage leukemia in both phenotypes.MethodsUsing Affymetrix® HG-U133 Plus 2.0 platform, gene expression data from 140 (training set) + 78 (test set) ALL and AML patients with (24+13) and without (116+65) MLL rearrangements have been investigated performing class comparison (SAM) and class prediction (PAM) analyses.ResultsWe identified a MLL translocation-specific (379 probes) signature and a phenotype-specific (622 probes) signature which have been tested using unsupervised methods. A final subset of 14 genes grants the characterization of acute leukemia patients with and without MLL rearrangements.ConclusionOur study demonstrated that a small subset of genes identifies MLL-specific rearrangements and clearly separates acute leukemia samples according to lineage origin. The subset included well-known genes and newly discovered markers that identified ALL and AML subgroups, with and without MLL rearrangements.

MLL partner genes drive distinct gene expression profiles and genomic alterations in pediatric acute myeloid leukemia: an AIEOP study

MLL partner genes drive distinct gene expression profiles and genomic alterations in pediatric acute myeloid leukemia: an AIEOP study

A functional biological network centered on XRCC3: A new possible marker of chemoradiotherapy resistance in rectal cancer patients

Preoperative chemoradiotherapy is widely used to improve local control of disease, sphincter preservation and to improve survival in patients with locally advanced rectal cancer. Patients enrolled in the present study underwent preoperative chemoradiotherapy, followed by surgical excision. Response to chemoradiotherapy was evaluated according to Mandards Tumor Regression Grade (TRG). TRG 3, 4 and 5 were considered as partial or no response while TRG 1 and 2 as complete response. From pretherapeutic biopsies of 84 locally advanced rectal carcinomas available for the analysis, only 42 of them showed 70% cancer cellularity at least. By determining gene expression profiles, responders and non-responders showed significantly different expression levels for 19 genes (P < 0.001). We fitted a logistic model selected with a stepwise procedure optimizing the Akaike Information Criterion (AIC) and then validated by means of leave one out cross validation (LOOCV, accuracy = 95%). Four genes were retained in the achieved model: ZNF160, XRCC3, HFM1 and ASXL2. Real time PCR confirmed that XRCC3 is overexpressed in responders group and HFM1 and ASXL2 showed a positive trend. In vitro test on colon cancer resistant/susceptible to chemoradioterapy cells, finally prove that XRCC3 deregulation is extensively involved in the chemoresistance mechanisms. Protein-protein interactions (PPI) analysis involving the predictive classifier revealed a network of 45 interacting nodes (proteins) with TRAF6 gene playing a keystone role in the network. The present study confirmed the possibility that gene expression profiling combined with integrative computational biology is useful to predict complete responses to preoperative chemoradiotherapy in patients with advanced rectal cancer.

Validation of NG2 antigen in identifying BP-ALL patients with MLL rearrangements using qualitative and quantitative flow cytometry: a prospective study

Validation of NG2 antigen in identifying BP-ALL patients with MLL rearrangements using qualitative and quantitative flow cytometry: a prospective study

New data on robustness of gene expression signatures in leukemia: comparison of three distinct total RNA preparation procedures

BackgroundMicroarray gene expression (MAGE) signatures allow insights into the transcriptional processes of leukemias and may evolve as a molecular diagnostic test. Introduction of MAGE into clinical practice of leukemia diagnosis will require comprehensive assessment of variation due to the methodologies. Here we systematically assessed the impact of three different total RNA isolation procedures on variation in expression data: method A: lysis of mononuclear cells, followed by lysate homogenization and RNA extraction; method B: organic solvent based RNA isolation, and method C: organic solvent based RNA isolation followed by purification.ResultsWe analyzed 27 pediatric acute leukemias representing nine distinct subtypes and show that method A yields better RNA quality, was associated with more differentially expressed genes between leukemia subtypes, demonstrated the lowest degree of variation between experiments, was more reproducible, and was characterized with a higher precision in technical replicates. Unsupervised and supervised analyses grouped leukemias according to lineage and clinical features in all three methods, thus underlining the robustness of MAGE to identify leukemia specific signatures.ConclusionThe signatures in the different subtypes of leukemias, regardless of the different extraction methods used, account for the biggest source of variation in the data. Lysis of mononuclear cells, followed by lysate homogenization and RNA extraction represents the optimum method for robust gene expression data and is thus recommended for obtaining robust classification results in microarray studies in acute leukemias.

Immunophenotype signature as a tool to define prognostic subgroups in childhood acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous disease group morphologically classified, based on the French–American–British (FAB) classification, into eight main subgroups defined as subtypes M0–M7. Besides morphologic differences, genetic abnormalities have been recognized; cytogenetics and molecular analyses are currently used to identify subgroups of AML with different clinical prognosis. However, in spite of available prognostic factors, accurate prediction of risk for treatment failure or relapse is not completely satisfactory. In order to improve risk assignment and develop new therapeutic strategies, gene expression profiling and proteomic analysis seem to offer important improvements in leukemia classification.

Identification of immunophenotypic signatures by clustering analysis in pediatric patients with Philadelphia chromosome-positive acute lymphoblastic leukemia

Detection of Philadelphia chromosome t(9;22) (Ph) in children with precursor-B-ALL (pB-ALL) is an adverse prognostic factor, thus leading to a high-risk protocol for treatment. RT-PCR is the gold-standard for the detection of this abnormality. Specific gene and protein expression signatures have recently been identified for genetic subclasses in childhood and adult ALL using gene profiling and flow cytometric analyses, respectively. Our aim is the characterization of Ph+ pB-ALL for a fast and cheap screening approach in routine immunophenotyping applied at diagnosis. Forty-one children with Ph+ and 99 Ph- newly diagnosed pB-ALL (AIEOP cohort) were analyzed. The expression level of 16 marker proteins was monitored by five color flow cytometry (FC) and quantified in terms of Geometric Mean Fluorescence (GMF). Computational analyses were applied to the patient cohort: we identified a Cluster A, including the majority of Ph+ patients (35/41), associated with upregulation of CD52, TdT, CD45, CD34, HLA-DR, CD33, and downregulation of CD38, CD24, CD58, CD22, CD19; a Cluster B+C gathers most of the Ph- patients (86/99) showing the opposite tendency for listed markers. The immunophenotypic method identifies Ph+ cases with a comprehensive accuracy of 86% providing a rapid and effectual screening method for the identification of Ph+ pB-ALL.