Purging in autologous hematopoietic stem cell transplantation using adenosine triphosphate (ATP) and 4-hydroperoxycyclophosphamide (4-HC)

A Purinergic Trail for Metastases

Nucleotides and nucleosides have emerged as important modulators of tumor biology. Recently acquired evidence shows that, when these molecules are released by cancer cells or surrounding tissues, they act as potent prometastatic factors, favoring tumor cell migration and tissue colonization. Therefore, nucleotides and nucleosides should be considered as a new class of prometastatic factors. In this review, we focus on the prometastatic roles of nucleotides and discuss future applications of purinergic signaling modulation in view of antimetastatic therapies.

Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia

Background

Current outcome predictors based on "molecular profiling" rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate "mechanism-anchored profiling". We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.

Results

This proof-of-concept study, which focuses on epigenetic mechanisms, was conducted in a well-studied set of 132 acute lymphoblastic leukemia (ALL) microarrays annotated with nine distinct phenotypes and three measures of response to therapy. We used established parametric and non parametric statistics to derive the PGnet tripartite network that consisted of 10 phenotypes and 33 significant clusters of GEMs comprising 535 distinct genes. The significance of PGnet was estimated from empirical p-values, and a robust subnetwork derived from ALL outcome data was produced by repeated random sampling. The evaluation of derived robust network to predict outcome (relapse of ALL) was significant (p = 3%), using one hundred three-fold cross-validations and the shrunken centroids classifier.

Conclusion

To our knowledge, this is the first method predicting co-expression networks of genes associated with epigenetic mechanisms and to demonstrate its inherent capability to predict therapeutic outcome. This PGnet approach can be applied to any regulatory mechanisms including transcriptional or microRNA regulation in order to derive predictive molecular profiles that are mechanistically anchored. The implementation of PGnet in R is freely available at .

Autologous stem cell transplantation and purging in adult acute lymphoblastic leukaemia

The prognosis for adult acute lymphoblastic leukaemia (ALL) is poor. Only 20-30% of patients will be cured with conventional chemotherapy. Haematopoietic progenitor transplantation is thus an attractive option in these patients. Even if allogeneic transplantation allows a better control of the disease, autologous transplantation remains an important alternative for patients lacking a suitable donor or when allogeneic transplants imply excessive risk. Relapse is the main drawback of autologous transplants, but many strategies are being explored to overcome this problem. We focus here on transplant modality, the source of haematopoietic progenitors, and the best timing to apply the procedure. Also reviewed are the current situation and future strategies for improving results in this setting, such as ex vivo purging; immunotherapy and maintenance chemotherapy.