Proteomic identification of predictive biomarkers of resistance to neoadjuvant chemotherapy in luminal breast cancer: a possible role for 14-3-3 theta/tau and tBID?

Introduction

Chemotherapy resistance is a major obstacle in effective neoadjuvant treatment for estrogen receptor-positive breast cancer. The ability to predict tumour response would allow chemotherapy administration to be directed towards only those patients who would benefit, thus maximising treatment efficiency. We aimed to identify putative protein biomarkers associated with chemotherapy resistance, using fresh tumour samples with antibody microarray analysis and then to perform pilot clinical validation experiments.

Materials and methods

Chemotherapy resistant and chemotherapy sensitive tumour samples were collected from breast cancer patients who had received anthracycline based neoadjuvant therapy consisting of epirubicin with cyclophosphamide followed by docetaxel. A total of 5 comparative proteomics experiments were performed using invasive ductal carcinomas which demonstrated estrogen receptor positivity (luminal subtype). Protein expression was compared between chemotherapy resistant and chemotherapy sensitive tumour samples using the Panorama XPRESS Profiler725 antibody microarray containing 725 antibodies from a wide variety of cell signalling and apoptosis pathways. A pilot series of archival samples was used for clinical validation of putative predictive biomarkers.

Results

AbMA analysis revealed 38 differentially expressed proteins which demonstrated at least 1.8 fold difference in expression in chemotherapy resistant tumours and 7 of these proteins (Zyxin, 14-3-3 theta/tau, tBID, Pinin, Bcl-xL, RIP and MyD88) were found in at least 2 experiments. Clinical validation in a pilot series of archival samples revealed 14-3-3 theta/tau and tBID to be significantly associated with chemotherapy resistance.

Conclusions

For the first time, antibody microarrays have been used to identify proteins associated with chemotherapy resistance using fresh breast cancer tissue. We propose a potential role for 14-3-3 theta/tau and tBID as predictive biomarkers of neoadjuvant chemotherapy resistance in breast cancer. Further validation in a larger sample series is now required.     

Kidney proteome responses in the teleost fish Paralichthys olivaceus indicate a putative immune response against Streptococcus parauberis

The proteomic response to bacterial infection in a teleost fish (Paralichthys olivaceus) infected with Streptococcus parauberis was analyzed using label-free protein quantitation coupled with LC-MS(E) tandem mass spectrometry. A total of 82 proteins from whole kidney, a major lymphoid organ in this fish, were found to be differentially expressed between healthy and diseased fish analyzed 6, 24, 72 and 120 h post-infection. Among the differentially expressed proteins, those involved in mediating immune responses (e.g., heat shock proteins, cathepsins, goose-type lysozyme and complement components) were most significantly up-regulated by infection. In addition, cell division cycle 48 (CDC48) and calreticulin, which are associated with cellular recovery and glycoprotein synthesis, were up-regulated in the universal protein group, whereas the other proteins in that group were down-regulated. There was continuous activation of expression of immune-associated proteins during infection, but there was also loss of expression of proteins not involved in immune function. We expect that our findings regarding immune response at the protein level would offer new insight into the systemic response to bacterial infection of a major immune organ in teleost fish.     

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,^5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods^{3,4,9,10,13-15,17-19,22,23,25}. In this application, we demonstrate a program which follows Rabotyagov et al.''s approach and integrates a modern and commonly used SWAT water quality model⁷ with a multiobjective evolutionary algorithm SPEA2²⁶, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.     

Characterization of a variety of neutralizing anti-heparin-binding epidermal growth factor-like growth factor monoclonal antibodies by different immunization methods

Heparin-binding epidermal growth factor-like growth factor (HB-EGF) is a member of the epidermal growth factor family. The accumulated evidence on the tumor-progressing roles of HB-EGF has suggested that HB-EGF-targeted cancer therapy is expected to be promising. However, the generation of neutralizing anti-HB-EGF monoclonal antibodies (mAbs) has proved difficult. To overcome this difficulty, we performed a hybridoma approach using mice from different genetic backgrounds, as well as different types of HB-EGF immunogens. To increase the number of hybridoma clones to screen, we used an electrofusion system to generate hybridomas and a fluorometric microvolume assay technology to screen anti-HB-EGF mAbs. We succeeded in obtaining neutralizing anti-HB-EGF mAbs, primarily from BALB/c and CD1 mice, and these were classified into 7 epitope bins based on their competitive binding to the soluble form of HB-EGF (sHB-EGF). The mAbs showed several epitope bin-dependent characteristics, including neutralizing and binding activity to human sHB-EGF, cross-reactivity to mouse/rat sHB-EGF and binding activity to the precursor form of HB-EGF. The neutralizing activity was also validated in colony formation assays. Interestingly, we found that the populations of mAb bins and the production rates of the neutralizing mAbs were strikingly different by mouse strain and by immunogen type. We succeeded in generating a variety of neutralizing anti-HB-EGF mAbs, including potent sHB-EGF neutralizers that may have potential as therapeutic agents for treating HB-EGF-dependent cancers. Our results also suggest that immunization approaches using different mouse strains and immunogen types affect the biological activity of individual neutralizing antibodies.     

Glycan Profiling of Plant Cell Wall Polymers using Microarrays

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g. wood, paper, textile and biofuel industries)^1,2. However, understanding the biosynthesis and function of these components remains challenging.Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall³. Furthermore, their composition is also modified during development¹, or in response to environmental cues⁴.In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis⁵. However, relatively few of the biosynthetic genes have been functionally characterized ^4,5. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types⁶. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained⁷. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.Monoclonal antibodies (mAbs)^8,9 have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously^9,10,11.Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 & 2)^10,11 that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems¹² and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.     

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability^{1, 2}. The worker bees spend the first half of their life inside the dark hive, where they are nursing the larvae or building the regular hexagonal combs for food (e.g. pollen or nectar) and brood³. The antennae are extraordinary multisensory feelers and play a pivotal role in various tactile mediated tasks⁴, including hive building⁵ and pattern recognition⁶. Later in life, each single bee leaves the hive to forage for food. Then a bee has to learn to discriminate profitable food sources, memorize their location, and communicate it to its nest mates⁷. Bees use different floral signals like colors or odors^{7, 8}, but also tactile cues from the petal surface⁹ to form multisensory memories of the food source. Under laboratory conditions, bees can be trained in an appetitive learning paradigm to discriminate tactile object features, such as edges or grooves with their antennae^{10, 11, 12, 13}. This learning paradigm is closely related to the classical olfactory conditioning of the proboscis extension response (PER) in harnessed bees¹⁴. The advantage of the tactile learning paradigm in the laboratory is the possibility of combining behavioral experiments on learning with various physiological measurements, including the analysis of the antennal movement pattern.     

Recurrent Herpetic Stromal Keratitis in Mice,a Model for Studying Human HSK

Herpetic eye disease, termed herpetic stromal keratitis (HSK), is a potentially blinding infection of the cornea that results in over 300,000 clinical visits each year for treatment. Between 1 and 2 percent of those patients with clinical disease will experience loss of vision of the infected cornea. The vast majority of these cases are the result of reactivation of a latent infection by herpes simplex type I virus and not due to acute disease. Interestingly, the acute infection is the model most often used to study this disease. However, it was felt that a recurrent model of HSK would be more reflective of what occurs during clinical disease. The recurrent animal models for HSK have employed both rabbits and mice. The advantage of rabbits is that they experience reactivation from latency absent any known stimulus. That said, it is difficult to explore the role that many immunological factors play in recurrent HSK because the rabbit model does not have the immunological and genetic resources that the mouse has. We chose to use the mouse model for recurrent HSK because it has the advantage of there being many resources available and also we know when reactivation will occur because reactivation is induced by exposure to UV-B light. Thus far, this model has allowed those laboratories using it to define several immunological factors that are important to this disease. It has also allowed us to test both therapeutic and vaccine efficacy.