Purpose: Personalized medicine strategies using genomic profiling are particularly pertinent for pancreas cancer. The Individualized Molecular Pancreatic Cancer Therapy (IMPaCT) trial was initially designed to exploit results from genome sequencing of pancreatic cancer under the auspices of the International Cancer Genome Consortium (ICGC) in Australia. Sequencing revealed small subsets of patients with aberrations in their tumor genome that could be targeted with currently available therapies. Experimental Design: The pilot stage of the IMPaCT trial assessed the feasibility of acquiring suitable tumor specimens for molecular analysis and returning high-quality actionable genomic data within a clinically acceptable timeframe. We screened for three molecular targets: HER2 amplification; KRAS wild-type; and mutations in DNA damage repair pathways (BRCA1, BRCA2, PALB2, ATM). Results: Tumor biopsy and archived tumor samples were collected from 93 patients and 76 were screened. To date 22 candidate cases have been identified: 14 KRAS wild-type, 5 cases of HER2 amplification, 2 mutations in BRCA2, and 1 ATM mutation. Median time from consent to the return of validated results was 21.5 days. An inability to obtain a biopsy or insufficient tumor content in the available specimen were common reasons for patient exclusion from molecular analysis while deteriorating performance status prohibited a number of patients from proceeding in the study. Conclusions: Documenting the feasibility of acquiring and screening biospecimens for actionable molecular targets in real time will aid other groups embarking on similar trials. Key elements include the need to better prescreen patients, screen more patients, and offer more attractive clinical trial options. Clin Cancer Res; 21(9); 2029–37. ©2015 AACR.

Among preterm infants, delayed cord clamping did not result in a lower incidence of the combined outcome of death or major morbidity at 36 weeks of gestation than immediate cord clamping. (Funded by the Australian National Health and Medical Research Council [NHMRC] and the NHMRC Clinical Trials Centre; APTS Australian and New Zealand Clinical Trials Registry number, ACTRN12610000633088 .).

Abundant and hydrophilic nonmembrane proteins with isoelectric points below pH 8 are the predominant proteins identified in most proteomics projects. In yeast, however, low-abundance proteins make up 80% of the predicted proteome, approximately 50% have pl's above pH 8 and 30% of the yeast ORFs are predicted to encode membrane proteins with at least 1 trans-membrane span. By applying highly solubilizing reagents and isoelectric fractionation to a membrane fraction of yeast we have a purified and identified 780 protein isoforms, representing 323 gene products, including 28% low abundance proteins and 49% membrane or membrane associated proteins. More importantly, considering the frequency and importance of co- and post-translational modifications, the separation of protein isoforms is essential and two-dimensional electrophoresis remains the only technique which offers sufficient resolution to address this at a proteomic level.

Quantitative proteomic studies, based on two-dimensional gel electrophoresis, are commonly used to find proteins that are differentially expressed between samples or groups of samples. These proteins are of interest as potential diagnostic or prognostic biomarkers, or as proteins associated with a trait. The complexity of proteomic data poses many challenges, so while experiments may reveal proteins that are differentially expressed, these are often not significant when subjected to rigorous statistical analysis. However, this can be addressed through appropriate experimental design. A good experimental design considers the impact of different sources of variation, both analytical and biological, on the statistical importance of the results. The design should address the number of samples that must be analyzed and the number of replicate gels per sample, in the context of a particular minimum difference that one is seeking to achieve. In this study, we explore the ways to improve the quality of protein expression data from 2-DE gels, and describe an approach for defining the number of samples required and the number of gels per sample. It has been developed for the simplest of situations, two groups of samples with variation at two levels: between samples and between gels. This approach will also be useful as a guide for more complex designs involving more than two groups of samples. We describe some Internet-accessible tools that can assist in the design of proteomic studies.