A rubber cleat is instrumented with two triaxial accelerometers to measure the multi-directional response of the cleat due to the forces within the tire footprint of a ground vehicle. The cleat data is used to detect faults in the front and rear suspension in addition to the wheel tire despite variability in the data. This offboard diagnostic technique is proposed to enable condition-based maintenance.

BACKGROUND:The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma. METHODS:We sought to derive a decision algorithm for classification of prostate cancer from SELDI-TOF MS spectral data from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the previous study described in the companion paper. RESULTS:The resulting new classifier failed to separate patients with prostate cancer from biopsy-negative controls; nor did it separate patients with prostate cancer with Gleason scores <7 from those with Gleason scores > or =7. CONCLUSIONS:In this, the 2nd stage of our planned validation process, the SELDI-TOF MS-based protein expression profiling approach did not perform well enough to advance to the 3rd (prospective study) stage. We conclude that the results from our previous studies-in which differentiation between prostate cancer and noncancer was demonstrated-are not generalizable. Earlier study samples likely had biases in sample selection that upon removal, as in the present study, resulted in inability of the technique to discriminate cancer from noncancer cases.

BACKGROUND:Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates.RESULTS:Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably.CONCLUSIONS:Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.

We have previously reported that Fms-like tyrosine kinase-3 ligand (flt3-L) induced tumor stabilization and regression of palpable ectopic prostate tumors (TRAMP-C1). Although some mice remained "tumor free" for several months following termination of therapy, tumors invariably reappeared and grew progressively in all animals. The lack of a curative response suggests that TRAMP-C1 tumors may inhibit the development of a flt3-L-induced anti-tumor immune response. Consistent with this view, we demonstrate herein that TRAMP-C1 tumors isolated from flt3-L treated animals contained a marked dendritic cell (DC) infiltrate that was temporally correlated with tumor regression. However, tumor-associated DCs, especially in a flt3-L setting, progressively lost MHC class II antigen expression during tumor growth. Treatment with the DC maturation factor trimeric CD40 ligand (CD40-L) either alone or in combination with fl3-L neither prevented loss of DC class II antigens nor disease relapse. Because loss of class II antigens would prevent CD4+ helper T (Th) cell development, we treated tumor-bearing mice with agonistic anti-4-1BB antibody (Ab), which can promote cytotoxic T lymphocyte (CTL) development independent of Th cell function. However, anti-4-1BB Ab alone did not alter TRAMP-C1 growth kinetics, and, when used in combination, was no more effective than flt3-L alone. The inability of the 4-1BB co-stimulatory signal to promote tumor regression may have been related to two additional features of TRAMP-C1 tumors. First, tumor-associated T cells, but not splenic T cells from tumor-bearing animals, were profoundly deficient in expression of CD3-epsilon (CD3epsilon) and T cell receptor-beta chain (TCRbeta). Second, CTLs required 24 h to efficiently kill TRAMP-C1 target cells even after up-regulation of MHC class I antigens by interferon-gamma. This rate of tumor cell destruction by CTLs may not be sufficient to prevent tumor progression. Taken together, these data reveal several important immunosuppressive characteristics of the prostate tumor microenvironment (TME) that immunotherapeutic interventions must first overcome to achieve longterm cures. These data also highlight the importance of utilizing treatment versus vaccination models in the evaluation of immunotherapeutic modalities.

Mammography remains the diagnostic test of choice for breast cancer, but 20% of cancers still go undetected. Many serum biomarkers have been reported for breast cancer but none have proven to represent effective diagnostic strategies. ProteinChip mass spectrometry is an innovative technology that searches the proteome for differentially expressed proteins, allowing for the creation of a panel or profile of biomarkers. The objective of this study was to construct unique cancer-associated serum profiles that, combined with a classification algorithm, would enhance the detection of breast cancer Pretreatment serum samples from 134 female patients (45 with cancer, 42 with benign disease, 47 normal) were procured prospectively following institutional review board-approved protocols. Proteins were denatured, applied onto ProteinChip affinity surfaces, and subjected to surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry. The SELDI output was analyzed using Biomarker Pattern Software to develop a classification tree based on group-specific protein profiles. The cross-validation analysis of cancer versus normal revealed sensitivity and specificity rates of 80% and 79%, and for cancer versus benign disease, 78% and 83%, respectively. When 2 different chip surfaces were combined the sensitivity and specificity increased to 90% and 93%, respectively. The sensitivity and specificity of this technique are comparable to those of mammography and, if confirmed in a larger study, this technique could provide the means toward development of a simple blood test to aid in the early detection of breast cancer. The combination of SELDI ProteinChip mass spectrometry and a classification- and regression-tree algorithm has the potential to use serum protein expression profiles for detection and diagnosis of breast cancer.