Molecular Characterization of the Response to Conventional Chemotherapeutics in Pro-B-ALL Cell Lines in Terms of Tumor Relapse.

Little is known about optimally applying chemotherapeutic agents in a specific temporal sequence to rapidly reduce the tumor load and to improve therapeutic efficacy. The clinical optimization of drug efficacy while reducing side effects is still restricted due to an incomplete understanding of the mode of action and related tumor relapse mechanisms on the molecular level. The molecular characterization of transcriptomic drug signatures can help to identify the affected pathways, downstream regulated genes and regulatory interactions related to tumor relapse in response to drug application. We tried to outline the dynamic regulatory reprogramming leading to tumor relapse in relapsed MLL-rearranged pro-B-cell acute lymphoblastic leukemia (B-ALL) cells in response to two first-line treatments: dexamethasone (Dexa) and cytarabine (AraC). We performed an integrative molecular analysis of whole transcriptome profiles of each treatment, specifically considering public knowledge of miRNA regulation via a network-based approach to unravel key driver genes and miRNAs that may control the relapse mechanisms accompanying each treatment. Our results gave hints to the crucial regulatory roles of genes leading to Dexa-resistance and related miRNAs linked to chemosensitivity. These genes and miRNAs should be further investigated in preclinical models to obtain more hints about relapse processes.

Genetic Mutations in a Patient with Chronic Myeloid Leukemia Showing Blast Crisis 10 Years After Presentation.

Since the introduction of tyrosine kinase inhibitors (TKI), the prospects for patients with chronic myeloid leukemia (CML) have improved significantly. Herein we present the case of a patient with CML who experienced blast crisis and development of acute myeloid leukemia (AML) 10 years after presentation. The CML was characterized by the gene fusion of breakpoint cluster region BCR and tyrosine-protein kinase ABL1. During treatment different therapeutic protocols including imatinib, nilotinib, dasatinib and ponatinib were applied due to development of resistance or non-response. Fluorescence in situ hybridization (FISH) and next-generation sequencing (NGS) were used to describe cytogenetic and molecular aberrations elucidating the development into AML: A loss of chromosome 7, as well as an arising frequency of variants in the gene met proto-oncogene MET (p.T110I) and tyrosine-protein phosphatase non-receptor type 11 PTPN11 (p.Q510L) was observed. This report describes the comprehensive characterization of a clinical case showing multiple therapeutic resistances correlated with genetic aberrations.

FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.