Mixed lineage leukemia (MLL) is a distinctive type of leukemia “ distinguished from the more prevalent acute lymphoblastic leukemia (ALL) by the presence of a break and rearrangement of chromosome number 11. The design of effective therapies to combat MLL leukemia depends upon the understanding of the unique genetic signature that underlies this disease.

This chromosomal translocation that characterizes MLL activates the histone methyltranferase enzyme called MLL, inducing it to turn-on downstream gene targets that transform blood progenitor cells into leukemia stem cells (LSCs).

While some of the downstream targets of MLL are known (Hox genes, for example), the genetic changes that are sufficient to drive MLL leukomogeneis have remained elusive. Dr. Cleary and colleagues focused their work on another group of proteins that are mis-expressed in MLL leukemias: the TALE (three-amino-acid loop extension) class of proteins.

The researchers found that one gene in particular “ called Meis1 “ is required for leukemia stem cell maintenance. In fact, the researchers showed that Meis1 regulates many important biological properties of the disease including differentiation arrest, cycling activity, in vivo progression and self-renewal of LSCs.

Dr. Cleary is confident that The critical role of Meis1 and other TALE class proteins in MLL leukemia stem cells provides a promising avenue for future studies to design more selective therapies for this poor prognosis subtype of leukemia.

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Using a mathematical approach for the prediction of metastasis (involving both machine learning and dimensionality reduction), the researchers calculated the average behavior of subnetworks of proteins and used this information to uncover subnetworks that predict metastasis better than individual gene markers.

The team uncovered 149 discriminative subnetworks consisting of 618 genes from the patients from the van de Vijver et al. data set and 243 discriminative subnetworks with 906 genes from the Wang et al. data set.

Each subnetwork is suggestive of a distinct functional pathway or complex, yielding many known and novel pathway hypotheses in organisms for which sufficient protein interaction data have been measured, the authors write in their Molecular Systems Biology paper.

For example, the researchers show that a well-known breast cancer susceptibility gene, P53, plays a central role in several protein subnetworks; it interconnects many expression-responsive genes (genes that show up as potential markers in expression-only analyses). Interestingly, P53 itself does not show up as significant in conventional expression clustering or classification methods.

A key feature of our approach is the ability to identify crucial genes that fly under the radar of conventional gene expression analyses, said Ideker.

The phenotypic changes most indicative of breast cancer metastasis need not be regulated at the level of expression, the authors write.

The researchers also show that their subnetwork markers are significantly more reproducible between data sets than individual marker genes selected without network information.

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