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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.

Current treatments have reduced to less than 10% the incidence of central nervous system (CNS) relapses of pediatric acute lymphoblastic leukemia (ALL). Nevertheless, CNS relapses still account for...

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.

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.

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. © 2008 Wiley-Liss, Inc.

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 D 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.

SummaryWe 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.

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

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.