Mitochondrial autophagosomes as a mechanism of drug resistance in breast carcinoma.

We have previously described the process by which mitochondria donate their membranes for the formation of autophagosomes, and in this study we show that the same process could be involved in drug sequestration and exocytosis resulting in multidrug-resistant cancerous cells. We examine the implications of mitochondrial vesicle formation of mitoautophagosomes (MAPS) in response to the cytotoxic drug MKT-077, which targets mortalin, in a drug-resistant breast carcinoma cell line overexpressing P-glycoprotein (P-gp). The breast cancer cell line MCF-7Adr is derived from MCF-7, but differs from its ancestral line in tolerance of MKT-077-induced mitochondrial toxicity. Our ultrastructural observations suggest that autophagy in the MCF-7Adr cells entails regional sequestration of MKT077 in multilamellar LC3-labeled MAPS, which then separate from their mitochondria, and fuse with or engulf each other. MAPS appeared to be migrating through the cytoplasm and fusing with the plasma membrane, thus carrying out exocytotic secretion. This mechanism, which seems ineffective in the ancestral cell line, provides a resistance mechanism for MKT-077 by enhancing the efflux process of the cells. After 8 hr of MKT-077 exposure, a fraction of the resistant cells appeared viable and contained larger number of smaller sized mitochondria. Mitoautophagosomes, therefore, provide a potentially novel model for multidrug resistance in cancerous cells and may contribute to the P-gp efflux process.

A Systems Biology Interpretation of Array Comparative Genomic Hybridization (aCGH) Data through Phylogenetics.

Array Comparative Genomic Hybridization (aCGH) is a rapid screening technique to detect gene deletions and duplications, providing an overview of chromosomal aberrations throughout the entire genome of a tumor, without the need for cell culturing. However, the heterogeneity of aCGH data obfuscates existing methods of data analysis. Analysis of aCGH data from a systems biology perspective or in the context of total aberrations is largely absent in the published literature. We present here a novel alternative to the functional analysis of aCGH data using the phylogenetic paradigm that is well-suited to high dimensional datasets of heterogeneous nature, but has not been widely adapted to aCGH data. Maximum parsimony phylogenetic analysis sorts out genetic data through the simplest presentation of the data on a cladogram, a graphical evolutionary tree, thus providing a powerful and efficient method for aCGH data analysis. For example, the cladogram models the multiphasic changes in the cancer genome and identifies shared early mutations in the disease progression, providing a simple yet powerful means of aCGH data interpretation. As such, applying maximum parsimony phylogenetic analysis to aCGH results allows for the differentiation between drivers and passenger genes aberrations in cancer specimens. In addition to offering a novel methodology to analyze aCGH results, we present here a crucial software suite that we wrote to carry out the analysis. In a broader context, we wish to underscore that phylogenetic analysis of aCGH data is a non-parametric method that circumvents the pitfalls and frustrations of standard analytical techniques that rely on parametric statistics. Organizing the data in a cladogram as explained in this research article provides insights into the disease common aberrations, as well as the disease subtypes and their shared aberrations (the synapomorphies) of each subtype. Hence, we report the method and make the software suite publicly and freely available at http://software.phylomcs.com so that researchers can test alternative and innovative approaches to the analysis of aCGH data.