Protein biomarkers on tissue as imaged via MALDI mass spectrometry: A systematic approach to study the limits of detection.

MALDI mass spectrometry imaging (MSI) is emerging as a tool for protein and peptide imaging across tissue sections. Despite extensive study, there does not yet exist a baseline study evaluating the potential capabilities for this technique to detect diverse proteins in tissue sections. In this study, we developed a systematic approach for characterizing MALDI-MSI workflows in terms of limits of detection, coefficients of variation, spatial resolution, and the identification of endogenous tissue proteins. Our goal was to quantify these figures of merit for a number of different proteins and peptides, in order to gain more insight in the feasibility of protein biomarker discovery efforts using this technique. Control proteins and peptides were deposited in serial dilutions on thinly sectioned mouse xenograft tissue. Using our experimental setup, coefficients of variation were <30% on tissue sections and spatial resolution was 200 µm (or greater). Limits of detection for proteins and peptides on tissue were in the micromolar to millimolar range. Protein identification was only possible for proteins present in high abundance in the tissue. These results provide a baseline for the application of MALDI-MSI towards the discovery of new candidate biomarkers and a new benchmarking strategy that can be used for comparing diverse MALDI-MSI workflows.

Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments.

Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures.We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.

Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments.

Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.

Development and evaluation of a prediction model for underestimated invasive breast cancer in women with ductal carcinoma in situ at stereotactic large core needle biopsy.

BACKGROUND: We aimed to develop a multivariable model for prediction of underestimated invasiveness in women with ductal carcinoma in situ at stereotactic large core needle biopsy, that can be used to select patients for sentinel node biopsy at primary surgery. METHODS: From the literature, we selected potential preoperative predictors of underestimated invasive breast cancer. Data of patients with nonpalpable breast lesions who were diagnosed with ductal carcinoma in situ at stereotactic large core needle biopsy, drawn from the prospective COBRA (Core Biopsy after RAdiological localization) and COBRA2000 cohort studies, were used to fit the multivariable model and assess its overall performance, discrimination, and calibration. RESULTS: 348 women with large core needle biopsy-proven ductal carcinoma in situ were available for analysis. In 100 (28.7%) patients invasive carcinoma was found at subsequent surgery. Nine predictors were included in the model. In the multivariable analysis, the predictors with the strongest association were lesion size (OR 1.12 per cm, 95% CI 0.98-1.28), number of cores retrieved at biopsy (OR per core 0.87, 95% CI 0.75-1.01), presence of lobular cancerization (OR 5.29, 95% CI 1.25-26.77), and microinvasion (OR 3.75, 95% CI 1.42-9.87). The overall performance of the multivariable model was poor with an explained variation of 9% (Nagelkerke's R(2)), mediocre discrimination with area under the receiver operating characteristic curve of 0.66 (95% confidence interval 0.58-0.73), and fairly good calibration. CONCLUSION: The evaluation of our multivariable prediction model in a large, clinically representative study population proves that routine clinical and pathological variables are not suitable to select patients with large core needle biopsy-proven ductal carcinoma in situ for sentinel node biopsy during primary surgery.

Optical imaging with her2-targeted affibody molecules can monitor hsp90 treatment response in a breast cancer xenograft mouse model.

PURPOSE: To determine whether optical imaging can be used for in vivo therapy response monitoring as an alternative to radionuclide techniques. For this, we evaluated the known Her2 response to 17-dimethylaminoethylamino-17-demethoxygeldanamycin hydrochloride (17-DMAG) treatment, an Hsp90 inhibitor. EXPERIMENTAL DESIGN: After in vitro 17-DMAG treatment response evaluation of MCF7 parental cells and 2 HER2-transfected clones (clone A medium, B high Her2 expression), we established human breast cancer xenografts in nude mice (only parental and clone B) for in vivo evaluation. Mice received 120 mg/kg of 17-DMAG in 4 doses at 12-hour intervals intraperitonially (n = 14) or PBS as carrier control (n = 9). Optical images were obtained both pretreatment (day 0) and posttreatment (day 3, 6, and 9), always 5 hours postinjection of 500 pmol of anti-Her2 Affibody-AlexaFluor680 via tail vein (with preinjection background subtraction). Days 3 and 9 in vivo optical imaging signal was further correlated with ex vivo Her2 levels by Western blot after sacrifice. RESULTS: Her2 expression decreased with 17-DMAG dose in vitro. In vivo optical imaging signal was reduced by 22.5% in clone B (P = 0.003) and by 9% in MCF7 parental tumors (P = 0.23) 3 days after 17-DMAG treatment; optical imaging signal recovered in both tumor types at days 6 to 9. In the carrier group, no signal reduction was observed. Pearson correlation of in vivo optical imaging signal with ex vivo Her2 levels ranged from 0.73 to 0.89. CONCLUSIONS: Optical imaging with an affibody can be used to noninvasively monitor changes in Her2 expression in vivo as a response to treatment with an Hsp90 inhibitor, with results similar to response measurements in positron emission tomography imaging studies.

Optical mammography combined with fluorescence imaging: lesion detection using scatterplots.

Using scatterplots of 2 or 3 parameters, diffuse optical tomography and fluorescence imaging are combined to improve detectability of breast lesions. Small or low contrast phantom-lesions that were missed in the optical and fluorescence images were detected in the scatterplots. In patient measurements, all tumors were visible and easily differentiated from artifacts and areolas in the scatterplots. The different rate of intake and wash out of the fluorescent contrast agent in the healthy versus malignant tissues was also observed in the scatterplot: this information can be used to discriminate malignant lesion from normal structures.

Molecular imaging using light-absorbing imaging agents and a clinical optical breast imaging system--a phantom study.

PURPOSE: The aim of the study was to determine the feasibility of using a clinical optical breast scanner with molecular imaging strategies based on modulating light transmission. PROCEDURES: Different concentrations of single-walled carbon nanotubes (SWNT; 0.8-20.0 nM) and black hole quencher-3 (BHQ-3; 2.0-32.0 µM) were studied in specifically designed phantoms (200-1,570 mm(3)) with a clinical optical breast scanner using four wavelengths. Each phantom was placed in the scanner tank filled with optical matching medium. Background scans were compared to absorption scans, and reproducibility was assessed. RESULTS: All SWNT phantoms were detected at four wavelengths, with best results at 684 nm. Higher concentrations (≥8.0 µM) were needed for BHQ-3 detection, with the largest contrast at 684 nm. The optical absorption signal was dependent on phantom size and concentration. Reproducibility was excellent (intraclass correlation 0.93-0.98). CONCLUSION: Nanomolar concentrations of SWNT and micromolar concentrations of BHQ-3 in phantoms were reproducibly detected, showing the potential of light absorbers, with appropriate targeting ligands, as molecular imaging agents for clinical optical breast imaging.

Freehand MRI-guided preoperative needle localization of breast lesions after MRI-guided vacuum-assisted core needle biopsy without marker placement.

PURPOSE: To evaluate the feasibility of magnetic resonance imaging (MRI)-guided preoperative needle localization (PNL) of breast lesions previously sampled by MRI-guided vacuum-assisted core needle biopsy (VACNB) without marker placement. MATERIALS AND METHODS: We reviewed 15 women with 16 breast lesions undergoing MRI-guided VACNB without marker placement who subsequently underwent MRI-guided PNL, both on an open 0.5T magnet using freehand techniques. Mammograms and specimen radiographs were rated for lesion visibility; MRI images were rated for lesion visibility and hematoma formation. Imaging findings were correlated with pathology. RESULTS: The average prebiopsy lesion size was 16 mm (range 4-50 mm) with 13/16 lesions located in mammographically dense breasts. Eight hematomas formed during VACNB (average size 13 mm, range 8-19 mm). PNL was performed for VACNB pathologies of cancer (5), high-risk lesions (5), or benign but discordant findings (6) at 2-78 days following VACNB. PNL targeted the lesion (2), hematoma (4), or surrounding breast architecture (10). Wire placement was successful in all 16 lesions. Final pathology showed six cancers, five high-risk lesions, and five benign findings. CONCLUSION: MRI-guided PNL is successful in removing lesions that have previously undergone VACNB without marker placement by targeting the residual lesion, hematoma, or surrounding breast architecture, even in mammographically dense breasts.

Computer-aided detection (CAD) for breast MRI: evaluation of efficacy at 3.0 T.

OBJECTIVE: The purpose of the study was to evaluate the accuracy of 3.0-T breast MRI interpretation using manual and fully automated kinetic analyses. MATERIAL AND METHODS: Manual MRI interpretation was done on an Advantage Workstation. Retrospectively, all examinations were processed with a computer-aided detection (CAD) system. CAD data sets were interpreted by two experienced breast radiologists and two residents. For each lesion automated analysis of enhancement kinetics was evaluated at 50% and 100% thresholds. Forty-nine malignant and 22 benign lesions were evaluated. RESULTS: Using threshold enhancement alone, the sensitivity and specificity of CAD were 97.9% and 86.4%, respectively, for the 50% threshold, and 97.9% and 90%, respectively, for the 100% threshold. Manual interpretation by two breast radiologists showed a sensitivity of 84.6% and a specificity of 68.8%. For the same two radiologists the mean sensitivity and specificity for CAD-based interpretation was 90.4% (not significant) and 81.3% (significant at p < 0.05), respectively. With one-way ANOVA no significant differences were found between the two breast radiologists and the two residents together, or between any two readers separately. CONCLUSION: CAD-based analysis improved the specificity compared with manual analysis of enhancement. Automated analysis at 50% and 100% thresholds showed a high sensitivity and specificity for readers with varying levels of experience.

Diffuse optical tomography of the breast: preliminary findings of a new prototype and comparison with magnetic resonance imaging.

This paper presents an evaluation of a prototype diffuse optical tomography (DOT) system. Seventeen women with 18 breast lesions (10 invasive carcinomas, 2 fibroadenomas, and 6 benign cysts; diameters 13-54 mm) were evaluated with DOT and magnetic resonance imaging (MRI). A substantial fraction of the original 36 recruited patients could not be examined using this prototype due to technical problems. A region of interest (ROI) was drawn at the lesion position as derived from MRI and at the mirror image site in the contralateral healthy breast. ROIs were assessed quantitatively and qualitatively by two observers independently in two separate readings. Intra- and interobserver agreements were calculated using kappa statistics (k) and intraclass correlation coefficients (ICCs). Discriminatory values for presence of malignancy were determined by receiver operating characteristic (ROC) analyses. Intraobserver agreements were excellent (k 0.88 and 0.88; ICC 0.978 and 0.987), interobserver agreements were good to excellent (k 0.77-0.95; ICC 0.96-0.98). Discriminatory values for presence of malignancy were 0.92-0.93 and 0.97-0.99 for quantitative and qualitative ROC analysis, respectively. This DOT system has the potential to discriminate malignant from benign breast tissue in a reproducible qualitative and quantitative manner. Important technical improvements are required before this technique is ready for clinical application.