Recently, researchers have discovered the direct impact of the tumor mechanical environment on the growth, drug uptake and prognosis of tumors. While estimating the mechanical parameters (solid stress, fluid pressure, stiffness) can aid in the treatment planning and monitoring, most of these parameters cannot be quantified noninvasively. Shear wave elastography (SWE) has shown promise as a means of noninvasively measuring the stiffness of soft tissue. However, stiffness is still not a recognized imaging biomarker. While SWE has been shown to be capable of measuring tumor stiffness in humans, much important research is done in small animal preclinical models, where tumors are often too small for the resolution of traditional SWE tools. Single-track location SWE (STL-SWE) has previously been shown to overcome the fundamental resolution limit of SWE imposed by ultrasound speckle, which may make it suitable for preclinical imaging. Using STL-SWE, in this work, we demonstrate, for the first time, that the stiffness changes occurring inside metastatic murine pancreatic tumors can be monitored over long time scales (up to 9 weeks). To prevent the respiration motion from degrading the STL-SWE estimates, we developed a real-time software-based respiration gating scheme that we implemented on a Verasonics ultrasound imaging system. By imaging the liver of three healthy mice and performing correlation analysis, we confirmed that the respiration-gated STL-SWE data was free from motion corruption. By performing coregistered power-doppler imaging, we found that the local variability in liver shear wave speed (SWS) measurements increased from 5.4% to 9.9% due to blood flow. We performed a longitudinal study using a murine model of pancreatic cancer liver metastasis to assess the temporal changes (over nine weeks) in SWS in two groups: a controlled group receiving no treatment (n=8), and an experimental group (n=6) treated with Gemcitabine, a chemotherapy agent. We independently evaluated tumor burden using bioluminescence imaging (BLI). The initial and endpoint SWS measurements were statistically different (p2/sr. Longitudinal measurements on the liver of four healthy mice revealed a maximum coefficient of variation of 11.4%. The results of this investigation demonstrate that with appropriate gating, researchers can use STL-SWE for small animal imaging and perform longitudinal studies using preclinical cancer models.

MOTIVATION: Protein phosphorylation, driven by specific recognition of substrates by kinases and phosphatases, plays central roles in a variety of important cellular processes such as signaling and enzyme activation. Mass spectrometry enables the determination of phosphorylated peptides (and thereby proteins) in scenarios ranging from targeted in vitro studies to in vivo cell lysates under particular conditions. The characterization of commonalities among identified phosphopeptides provides insights into the specificities of the kinases involved in a study. Several algorithms have been developed to uncover linear motifs representing position-specific amino acid patterns in sets of phosphopeptides. To more fully capture the available information, reduce sensitivity to both parameter choices and natural experimental variation, and develop more precise characterizations of kinase specificities, it is necessary to determine all statistically significant motifs represented in a dataset. RESULTS: We have developed MMFPh (Maximal Motif Finder for Phosphoproteomics datasets), which extends the approach of the popular phosphorylation motif software Motif-X (Schwartz and Gygi, 2005) to identify all statistically significant motifs and return the maximal ones (those not subsumed by motifs with more fixed amino acids). In tests with both synthetic and experimental data, we show that MMFPh finds important motifs missed by the greedy approach of Motif-X, while also finding more motifs that are more characteristic of the dataset relative to the background proteome. Thus MMFPh is in some sense both more sensitive and more specific in characterizing the involved kinases. We also show that MMFPh compares favorably to other recent methods for finding phosphorylation motifs. Furthermore, MMFPh is less dependent on parameter choices. We support this powerful new approach with a web interface so that it may become a useful tool for studies of kinase specificity and phosphorylation site prediction. AVAILABILITY: A web server is at www.cs.dartmouth.edu/~cbk/.

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