CD56, HLA-DR, and CD45 recognize a subtype of childhood AML harboring CBFA2T3-GLIS2 fusion transcript.

The presence of CBFA2T3-GLIS2 fusion gene has been identified in childhood Acute Myeloid Leukemia (AML). In view of the genomic studies indicating a distinct gene expression profile, we evaluated the role of immunophenotyping in characterizing a rare subtype of AML-CBFA2T3-GLIS2 rearranged. Immunophenotypic data were obtained by studying a cohort of 20 pediatric CBFA2T3-GLIS2-AML and 77 AML patients not carrying the fusion transcript. Enrolled cases were included in the Associazione Italiana di Ematologia Oncologia Pediatrica (AIEOP) AML trials and immunophenotypes were compared using different statistical approaches. By multiple computational procedures, we identified two main core antigens responsible for the identification of the CBFA2T3-GLIS2-AML. CD56 showed the highest performance in single marker evaluation (AUC = 0.89) and granted the most accurate prediction when used in combination with HLA-DR (AUC = 0.97) displaying a 93% sensitivity and 99% specificity. We also observed a weak-to-negative CD45 expression, being exceptional in AML. We here provide evidence that the combination of HLA-DR negativity and intense bright CD56 expression detects a rare and aggressive pediatric AML genetic lesion improving the diagnosis performance.

Relative expansion of CD19-negative very-early normal B-cell precursors in children with acute lymphoblastic leukaemia after CD19 targeting by blinatumomab and CAR-T cell therapy: implications for flow cytometric detection of minimal residual disease.

CD19-directed treatment in B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) frequently leads to the downmodulation of targeted antigens. As multicolour flow cytometry (MFC) application for minimal/measurable residual disease (MRD) assessment in BCP-ALL is based on B-cell compartment study, CD19 loss could hamper MFC-MRD monitoring after blinatumomab or chimeric antigen receptor T-cell (CAR-T) therapy. The use of other antigens (CD22, CD10, CD79a, etc.) as B-lineage gating markers allows the identification of CD19-negative leukaemia, but it could also lead to misidentification of normal very-early CD19-negative BCPs as tumour blasts. In the current study, we summarized the results of the investigation of CD19-negative normal BCPs in 106 children with BCP-ALL who underwent CD19 targeting (blinatumomab, n = 64; CAR-T, n = 25; or both, n = 17). It was found that normal CD19-negative BCPs could be found in bone marrow after CD19-directed treatment more frequently than in healthy donors and children with BCP-ALL during chemotherapy or after stem cell transplantation. Analysis of the antigen expression profile revealed that normal CD19-negative BCPs could be mixed up with residual leukaemic blasts, even in bioinformatic analyses of MFC data. The results of our study should help to investigate MFC-MRD more accurately in patients who have undergone CD19-targeted therapy, even in cases with normal CD19-negative BCP expansion.

An integrative approach for the identification of prognostic and predictive biomarkers in rectal cancer.

INTRODUCTION: Colorectal cancer is the third most common cancer in the world, a small fraction of which is represented by locally advanced rectal cancer (LARC). If not medically contraindicated, preoperative chemoradiotherapy, represent the standard of care for LARC patients. Unfortunately, patients shows a wide range of response rates in which approximately 20% has a complete pathological response, whereas in 20 to 40% the response is poor or absent. RESULTS: The following specific gene signature, able to discriminate responders' patients from non-responders, were founded: AKR1C3, CXCL11, CXCL10, IDO1, CXCL9, MMP12 and HLA-DRA. These genes are mainly involved in immune system pathways and interact with drugs traditionally used in the adjuvant treatment of rectal cancer. DISCUSSION: The present study suggests that new ideas for therapy could be found not only limited to studying genes differentially expressed between the two groups of patients but deepening the mechanisms, associated to response, in which they are involved. METHODS: Gene expression studies performed by: Agostini et al., Rimkus et al. and Kim et al. have been merged through a meta-analysis of the raw data. Gene expression data-sets have been processed using A-MADMAN. Common differentially expressed gene (DEG) were identified through SAM analysis. To further characterize the identified DEG we deeply investigated its biological role using an integrative computational biology approach.

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 Mandard's 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.

Early relapse in ALL is identified by time to leukemia in NOD/SCID mice and is characterized by a gene signature involving survival pathways.

We investigated the engraftment properties and impact on patient outcome of 50 pediatric acute lymphoblastic leukemia (ALL) samples transplanted into NOD/SCID mice. Time to leukemia (TTL) was determined for each patient sample engrafted as weeks from transplant to overt leukemia. Short TTL was strongly associated with high risk for early relapse, identifying an independent prognostic factor. This high-risk phenotype is reflected by a gene signature that upon validation in an independent patient cohort (n = 197) identified a high-risk cluster of patients with early relapse. Furthermore, the signature points to independent pathways, including mTOR, involved in cell growth and apoptosis. The pathways identified can directly be targeted, thereby offering additional treatment approaches for these high-risk patients.

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.

BACKGROUND: The 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. METHODS: Using Affymetrix(R) 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. RESULTS: We 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. CONCLUSION: Our 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.

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.

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

BACKGROUND: Microarray 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. RESULTS: We 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. CONCLUSION: The 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.

Two consecutive immunophenotypic switches in a child with MLL-rearranged acute lymphoblastic leukemia.

An 18-month-old girl was diagnosed with pre-pre-B ALL/t(4;11) leukemia, which during the treatment and after matched bone marrow transplantation (BMT), underwent two consecutive switches from lymphoid to myeloid lineage and vice versa. The high expression of HOXA9 and FLT3 genes remaining genotypically stable in a leukemia throughout phenotypic switches, suggests that this leukemia may have originated as a common B/myeloid progenitors.