The chemosensitivity to therapy of childhood early B acute lymphoblastic leukemia could be determined by the combined expression of CD34, SPI-B and BCR genes

Expression and Clinical Significance of Spi-B in B-cell Acute Lymphoblastic Leukemia

Spi-B, a member of the E26 transformation-specific (ETS) family of transcription factors, plays an important role in B cell differentiation. Spi-B also functions in development of diffuse large B-cell lymphoma; thus, we hypothesized that it may participate in leukemogenesis of B-cell acute lymphoblastic leukemia (B-ALL). To test this hypothesis, we first generated an anti-Spi-B monoclonal antibody that recognized Spi-B on formalin-fixed, paraffin-embedded tissue sections. This antibody, designated S28-5, selectively stained B cell nuclei at the pre-plasma cell stage (including centrocytes and centroblasts in germinal centers) and nuclei of plasmacytoid dendritic cells, but not fully differentiated plasma cells, T cells, macrophages, or follicular dendritic cells. Employing S28-5, we then performed immunohistochemical staining of bone marrow aspiration biopsy specimens obtained from B-ALL patients (n=62). Cases that showed stronger nuclear S28-5 signals than T-cell ALL were scored positive. In 26 (42%) of 62 specimens, leukemic cells showed nuclear Spi-B expression, and positivity was associated with patient age at diagnosis, and serum uric acid and creatinine levels. Moreover, Spi-B-positive patients demonstrated significantly shorter overall survival than did Spi-B-negative patients. These results suggest that Spi-B expression may serve as a prognostic indicator of B-ALL.

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.

Detection of Differentially Methylated Regions Using Bayes Factor for Ordinal Group Responses

Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation, because they play an important role in regulating gene expression without changes in the DNA sequence. There have been significant advances in developing statistical methods to detect differentially methylated regions (DMRs) associated with binary disease status. Most of these methods are being developed for detecting differential methylation rates between cases and controls. We consider multiple severity levels of disease, and develop a Bayesian statistical method to detect the region with increasing (or decreasing) methylation rates as the disease severity increases. Patients are classified into more than two groups, based on the disease severity (e.g., stages of cancer), and DMRs are detected by using moving windows along the genome. Within each window, the Bayes factor is calculated to test the hypothesis of monotonic increase in methylation rates corresponding to severity of the disease versus no difference. A mixed-effect model is used to incorporate the correlation of methylation rates of nearby CpG sites in the region. Results from extensive simulation indicate that our proposed method is statistically valid and reasonably powerful. We demonstrate our approach on a bisulfite sequencing dataset from a chronic lymphocytic leukemia (CLL) study.

A myelopoiesis gene signature during remission in anti-neutrophil cytoplasm antibody-associated vasculitis does not predict relapses but seems to reflect ongoing prednisolone therapy

A myelopoiesis gene signature in circulating leucocytes, exemplified by increased myeloperoxidase (MPO) and proteinase 3 (PR3) mRNA levels, has been reported in patients with active anti-neutrophil cytoplasm antibody-associated vasculitis (AAV), and to a lesser extent during remission. We hypothesized that this signature could predict disease relapse. mRNA levels of PR3, MPO, selected myelopoiesis transcription factors [CCAAT/enhancer binding protein α (CEBP-α), CCAAT/enhancer binding protein β (CEBP-β), SPI1/PU.1-related transcription factor (SPIB), spleen focus forming virus proviral integration oncogene, PU.1 homologue (SPI1)] and microRNAs (miRNAs) from patient and control peripheral blood mononuclear cells (PBMC) and polymorphonuclear cells (PMN) were analysed and associated with clinical data. Patients in stable remission had higher mRNA levels for PR3 (PBMC, PMN) and MPO (PBMC). PR3 and SPIB mRNA correlated positively in controls but negatively in patient PBMC. Statistically significant correlations existed between PR3 mRNA and several miRNAs in controls, but not in patients. PR3/MPO mRNA levels were not associated with previous or future relapses, but correlated with steroid treatment. Prednisolone doses were negatively linked to SPIB and miR-155-5p, miR-339-5p (PBMC) and to miR-221, miR-361 and miR-505 (PMN). PR3 mRNA in PBMC correlated with time since last flare, blood leucocyte count and estimated glomerular filtration rate. Our results show that elevated leucocyte PR3 mRNA levels in AAV patients in remission do not predict relapse. The origin seems multi-factorial, but to an important extent explainable by prednisolone action. Gene signatures in patients with AAV undergoing steroid treatment should therefore be interpreted accordingly.

Genomic approach towards personalized anticancer drug therapy

Stratification of patients for multidrug response is a promising strategy for cancer treatment. Genome-based prediction models have great potential for this purpose because the extent of drug sensitivity may be attributed to the heterogeneity of the underlying genetic characteristics of cancer. However, microarray data is difficult to analyze and is not reproducible. Several machine-learning algorithms have therefore been developed in a repeatable manner. Random forests algorithm, which uses an ensemble approach based on classification and regression trees, appears to be superior for predicting multidrug sensitivity. This is because ensemble methods are more effective when there are much more predictors than samples. Here, we review recent advances in the development of classification algorithms using microarray technology for prediction of anticancer sensitivity, discuss the availability of ensemble methods for prediction models, and present data regarding the identification of potential responders to FOLFOX therapy using random forests algorithm.

Platelets alter gene expression profile in human brain endothelial cells in an in vitro model of cerebral malaria

Platelet adhesion to the brain microvasculature has been associated with cerebral malaria (CM) in humans, suggesting that platelets play a role in the pathogenesis of this syndrome. In vitro co-cultures have shown that platelets can act as a bridge between Plasmodium falciparum-infected red blood cells (pRBC) and human brain microvascular endothelial cells (HBEC) and potentiate HBEC apoptosis. Using cDNA microarray technology, we analyzed transcriptional changes of HBEC in response to platelets in the presence or the absence of tumor necrosis factor (TNF) and pRBC, which have been reported to alter gene expression in endothelial cells. Using a rigorous statistical approach with multiple test corrections, we showed a significant effect of platelets on gene expression in HBEC. We also detected a strong effect of TNF, whereas there was no transcriptional change induced specifically by pRBC. Nevertheless, a global ANOVA and a two-way ANOVA suggested that pRBC acted in interaction with platelets and TNF to alter gene expression in HBEC. The expression of selected genes was validated by RT-qPCR. The analysis of gene functional annotation indicated that platelets induce the expression of genes involved in inflammation and apoptosis, such as genes involved in chemokine-, TREM1-, cytokine-, IL10-, TGFβ-, death-receptor-, and apoptosis-signaling. Overall, our results support the hypothesis that platelets play a pathogenic role in CM.

Analysis of select members of the E26 (ETS) transcription factors family in colorectal cancer

The E-twenty-six (ETS) family of transcription factors is known to act as positive or negative regulators of the expression of genes that are involved in diverse biological processes, including those that control cellular proliferation, differentiation, hematopoiesis, apoptosis, metastasis, tissue remodeling, and angiogenesis. Identification of target gene promoters of normal and oncogenic transcription factors provides new insights into the regulation of genes that are involved in the control of normal cell growth and differentiation. The aim of the present investigation was to analyze the differential expression of 11 ETS (ELF-3, ESE3, ETS1, ETV3, ETV4, ETV6, NERF, PDEF, PU1, Spi-B, and Spi-C) as potential markers for prognostic of colorectal cancer. A series of paired tissue biopsies consisting of a tumor and a non-affected control sample were harvested from 28 individuals suffering from diagnosed colorectal lesions. Total RNA was isolated from the samples, and after reverse transcription, differential expression of the select ETS was carried out through real-time polymerase chain reaction. Tumor staging as determined by histopathology was carried out to correlate the degree of tumor invasiveness with the expression of the ETS genes. The results demonstrated a different quantitative profile of expression in tumors and normal tissues. ETV4 was significantly upregulated with further increase in the event of lymph node involvement. PDEF and Spi-B presented downregulation, which was more significant when lymph node involvement was present. These findings were supported by immunohistochemistry of tumoral tissues. The results suggest that select ETS may serve as potential markers of colorectal cancer invasiveness and metastasis.

Olfactory stem cells, a new cellular model for studying molecular mechanisms underlying familial dysautonomia

Background

Familial dysautonomia (FD) is a hereditary neuropathy caused by mutations in the IKBKAP gene, the most common of which results in variable tissue-specific mRNA splicing with skipping of exon 20. Defective splicing is especially severe in nervous tissue, leading to incomplete development and progressive degeneration of sensory and autonomic neurons. The specificity of neuron loss in FD is poorly understood due to the lack of an appropriate model system. To better understand and modelize the molecular mechanisms of IKBKAP mRNA splicing, we collected human olfactory ecto-mesenchymal stem cells (hOE-MSC) from FD patients. hOE-MSCs have a pluripotent ability to differentiate into various cell lineages, including neurons and glial cells.

Methodology/Principal Findings

We confirmed IKBKAP mRNA alternative splicing in FD hOE-MSCs and identified 2 novel spliced isoforms also present in control cells. We observed a significant lower expression of both IKBKAP transcript and IKAP/hELP1 protein in FD cells resulting from the degradation of the transcript isoform skipping exon 20. We localized IKAP/hELP1 in different cell compartments, including the nucleus, which supports multiple roles for that protein. We also investigated cellular pathways altered in FD, at the genome-wide level, and confirmed that cell migration and cytoskeleton reorganization were among the processes altered in FD. Indeed, FD hOE-MSCs exhibit impaired migration compared to control cells. Moreover, we showed that kinetin improved exon 20 inclusion and restores a normal level of IKAP/hELP1 in FD hOE-MSCs. Furthermore, we were able to modify the IKBKAP splicing ratio in FD hOE-MSCs, increasing or reducing the WT (exon 20 inclusion):MU (exon 20 skipping) ratio respectively, either by producing free-floating spheres, or by inducing cells into neural differentiation.

Conclusions/Significance

hOE-MSCs isolated from FD patients represent a new approach for modeling FD to better understand genetic expression and possible therapeutic approaches. This model could also be applied to other neurological genetic diseases.

CD10 AND CD34 expression in childhood acute lymphoblastic leukemia in Morocco: clinical relevance and outcome

CD10 and CD34 expression in 86 Moroccan children with acute lymphoblastic leukemias (ALL) and the relevance to prognosis, diagnosis, and outcome during a 5-year follow-up were examined. At diagnosis, 57% of patients had CD10(+) blasts, while 35% had CD34(+) blasts. The CD10(+) blast frequency was much higher (80%) in B-ALL than in T-ALL (20%). The frequency of CD34(+) blasts was higher in B-ALL (48%) compared to T-ALL (16%). The 5-year survival curves showed that children with CD10(+) B-ALL had a significantly longer survival rate than those with CD10(-), as observed for T-ALL. The survival rate of B-ALL expressing CD34 was higher than that of CD34(-). Thus, CD34 and CD10 expression may have prognostic value and is associated with a better clinical outcome.

Utilization of genomic signatures to direct use of primary chemotherapy

The success of treatment of cancer patients depends on matching the most effective therapeutic regimen with the characteristics of the individual patient, balancing benefit against risk of adverse events. The primary challenge in achieving this goal is the heterogeneity of the disease, recognizing that breast, lung, colon and other cancers are not single diseases but rather an array of disorders with distinct molecular mechanisms. Genomic analyses, and in particular gene expression profiling, has been shown to have the capacity to dissect this heterogeneity and afford opportunities to match therapies with the characteristics of the individual patient's tumor. Here we review the success in developing gene expression signatures that have the capability of predicting response to various commonly used and newly developing cancer therapeutics. We further discuss the challenges and the opportunities in utilizing these tools in present-day clinical practice.

A pig multi-tissue normalised cDNA library: large-scale sequencing, cluster analysis and 9K micro-array resource generation

Background

Domestic animal breeding and product quality improvement require the control of reproduction, nutrition, health and welfare in these animals. It is thus necessary to improve our knowledge of the major physiological functions and their interactions. This would be greatly enhanced by the availability of expressed gene sequences in the databases and by cDNA arrays allowing the transcriptome analysis of any function.

The objective within the AGENAE French program was to initiate a high-throughput cDNA sequencing program of a 38-tissue normalised library and generate a diverse microarray for transcriptome analysis in pig species.

Results

We constructed a multi-tissue cDNA library, which was normalised and subtracted to reduce the redundancy of the clones. Expressed Sequence Tags were produced and 24449 high-quality sequences were released in EMBL database. The assembly of all the public ESTs (available through SIGENAE website) resulted in 40786 contigs and 54653 singletons. At least one Agenae sequence is present in 11969 contigs (12.5%) and in 9291 of the deeper-than-one-contigs (22.8%). Sequence analysis showed that both normalisation and subtraction processes were successful and that the initial tissue complexity was maintained in the final libraries. A 9K nylon cDNA microarray was produced and is available through CRB-GADIE. It will allow high sensitivity transcriptome analyses in pigs.

Conclusion

In the present work, a pig multi-tissue cDNA library was constructed and a 9K cDNA microarray designed. It contributes to the Expressed Sequence Tags pig data, and offers a valuable tool for transcriptome analysis.

Molecular mechanisms involved in chemoresistance in paediatric acute lymphoblastic leukaemia

Acute lymphoblastic leukaemia (ALL) is the most common paediatric cancer. Despite cure rates approaching 80%, resistance to treatment and disease relapse remain a significant clinical problem. Identification of the genes and biological pathways responsible for chemoresistance is therefore crucial for the design of novel therapeutic approaches aiming to improve patient survival. Mutations in the membrane transporter P-glycoprotein genes, genetic variations in drug-metabolising enzymes and defects in apoptotic pathways are mechanisms of chemoresistance common to a wide spectrum of cancers and also play a role in paediatric ALL. In addition, several recent microarray studies have identified transcriptional profiles specifically associated with chemoresistance and pointed to a number of potentially novel therapeutic targets. These microarray studies have shown that genes discriminating between clinically responsive and resistant leukaemias tend to be involved in cellular processes such as regulation of cell cycle, proliferation, and DNA repair. Here we review the outcomes of these microarray studies and also present our own investigations into apoptotic resistance to DNA double strand breaks (DSBs) in paediatric ALL. We present stratification of paediatric ALL by the profile of DNA damage response following ionising radiation (IR) in vitro. This approach allows classification of ALL tumours at presentation into IR-apoptotic sensitive and IR-apoptotic resistant. Furthermore, apoptotic resistant leukaemias exhibit abnormal response of NFkB pathway following irradiation and inhibition of this pathway can sensitise leukaemic cells to IR-induced DSBs.

Pharmacogenomics in cancer treatment defining genetic bases for inter-individual differences in responses to chemotherapy

PURPOSE OF REVIEW

Pharmacogenomics is evolving rapidly due to the expansion of genomics and proteomics, the emerging technologies, knowledge of the molecular basis of neoplasms and of drug pathways. This article will give an update on the genetic basis of variable therapeutic responses to anticancer agents in children.

RECENT FINDINGS

The majority of recent findings concern the pharmacogenetics of key components of acute lymphoblastic leukemia treatment, 6-mercaptopurine and methotrexate. This is not surprising given that leukemia is the most common cancer affecting children, accounting for 25-35% of childhood malignancies worldwide with acute lymphoblastic leukemia comprising 80% of leukemia cases. In certain patients treatment fails due to drug resistance, rendering acute lymphoblastic leukemia the leading cause of cancer-related death in children. Most of the studies use a candidate gene approach adding a new body of evidence to existing knowledge. Recent findings relating to other childhood tumors and the potential to optimize treatment of these malignancies are briefly discussed.

SUMMARY

Interindividual differences in drug responses are an important cause of resistance to treatment and adverse drug reactions. Pharmacogenetics tends to identify the genetic basis of these suboptimal responses allowing traditional treatment to be complemented by genotype-based drug dose adjustment.