Survival Following Relapse in Children with Acute Myeloid Leukemia: A Report from AML-BFM and COG.

Post-relapse therapy remains critical for survival in children with acute myeloid leukemia (AML). We evaluated survival, response and prognostic variables following relapse in independent cooperative group studies conducted by COG and the population-based AML-BFM study group. BFM included 197 patients who relapsed after closure of the last I-BFM relapse trial until 2017, while COG included 852 patients who relapsed on the last Phase 3 trials (AAML0531, AAML1031). Overall survival at 5 years (OS) was 42 ± 4% (BFM) and 35 ± 2% (COG). Initial high-risk features (BFM 32 ± 6%, COG 26 ± 4%) and short time to relapse (BFM 29 ± 4%, COG 25 ± 2%) predicted diminished survival. In the BFM dataset, there was no difference in OS for patients who had a complete remission with full hematopoietic recovery (CR) following post-relapse re-induction compared to those with partial neutrophil and platelet recovery (CRp and CRi) only (52 ± 7% vs. 63 ± 10%, p = 0.39). Among 90 patients alive at last follow-up, 87 had received a post-relapse hematopoietic stem cell transplant (HSCT). OS for patients with post-relapse HSCT was 54 ± 4%. In conclusion, initial high-risk features and early relapse remain prognostic. Response assessment with full hematopoietic recovery following initial relapse therapy does not predict survival. These data indicate the need for post-relapse risk stratification in future studies of relapse therapies.

Distinct gene expression profiles determine molecular treatment response in childhood acute lymphoblastic leukemia.

Treatment resistance, as indicated by the presence of high levels of minimal residual disease (MRD) after induction therapy and induction consolidation, is associated with a poor prognosis in childhood acute lymphoblastic leukemia (ALL). We hypothesized that treatment resistance is an intrinsic feature of ALL cells reflected in the gene expression pattern and that resistance to chemotherapy can be predicted before treatment. To test these hypotheses, gene expression signatures of ALL samples with high MRD load were compared with those of samples without measurable MRD during treatment. We identified 54 genes that clearly distinguished resistant from sensitive ALL samples. Genes with low expression in resistant samples were predominantly associated with cell-cycle progression and apoptosis, suggesting that impaired cell proliferation and apoptosis are involved in treatment resistance. Prediction analysis using randomly selected samples as a training set and the remaining samples as a test set revealed an accuracy of 84%. We conclude that resistance to chemotherapy seems at least in part to be an intrinsic feature of ALL cells. Because treatment response could be predicted with high accuracy, gene expression profiling could become a clinically relevant tool for treatment stratification in the early course of childhood ALL.

Mass spectrometry for the detection of differentially expressed proteins: a comparison of surface-enhanced laser desorption/ionization and capillary electrophoresis/mass spectrometry.

The discovery of biomarkers is currently attracting much interest as it harbors great potential for the diagnosis and monitoring of human diseases. Here we have used two advanced mass spectroscopy based technologies, surface enhanced laser desorption ionization (SELDI-MS) and capillary electrophoresis/mass spectrometry (CE/MS), to obtain proteomic patterns of urine samples from patients suffering from membranous glomerulonephritis (MGN) and healthy volunteers. The results indicate that CE/MS analysis is able to display a rich and complex pattern of polypeptides with high resolution and high mass accuracy. In order to analyze these patterns, the MosaiqueVisu software was developed for peak identification, deconvolution and the display of refined maps in a three-dimensional format. The polypeptide profiles obtained with SELDI-MS from the same samples are much sparser and show lower resolution and mass accuracy. The SELDI-MS profiles are further heavily dependent on analyte concentration. SELDI-MS analysis identified three differentially expressed polypeptides, which are potential biomarkers that can distinguish healthy donors from patients with MGN. In contrast, approximately 200 potential biomarkers could be identified by CE/MS. Thus, while SELDI-MS is easy to use and requires very little sample, CE/MS generates much richer data sets that enable an in-depth analysis.