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A subset of patients with neuroblastoma are at extremely high risk for treatment failure, though they are not identifiable at diagnosis and therefore have the highest mortality with conventional treatment approaches. Despite tremendous understanding of clinical and biological features that correlate with prognosis, neuroblastoma at ultra-high risk for treatment failure remains a diagnostic challenge. As a first step towards improving prognostic risk stratification within the high-risk group of patients, we determined the feasibility of using computerized image analysis and proteomic profiling on single slides from diagnostic tissue specimens. After expert pathologist review of tumor sections to ensure quality and representative material input, we evaluated multiple regions of single slides as well as multiple sections from different patients' tumors using computational histologic analysis and semiquantitative proteomic profiling. We found that both approaches determined that intertumor heterogeneity was greater than intratumor heterogeneity. Unbiased clustering of samples was greatest within a tumor, suggesting a single section can be representative of the tumor as a whole. There is expected heterogeneity between tumor samples from different individuals with a high degree of similarity among specimens derived from the same patient. Both techniques are novel to supplement pathologist review of neuroblastoma for refined risk stratification, particularly since we demonstrate these results using only a single slide derived from what is usually a scarce tissue resource. Due to limitations of traditional approaches for upfront stratification, integration of new modalities with data derived from one section of tumor hold promise as tools to improve outcomes.

In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the "curse of dimensionality" problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman's rank correlation which, unlike Pearson's correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6-8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p < 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone.

Background: Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however, computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities and scales. To the best of our knowledge relatively few attempts have been made to quantitatively fuse such data to construct classifiers and none have attempted to quantitatively combine histology (imaging) and proteomic (non-imaging) measurements for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data (and hence data corresponding to different scales and dimensionalities), called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. Canonical Correlation Analysis (CCA) and Regularized CCA (RCCA) are statistical techniques that extract correlations between two modes of data to construct a homogeneous, uniform representation of heterogeneous data channels. In this paper, we present a novel modification to CCA and RCCA, Supervised Regularized Canonical Correlation Analysis (SRCCA), that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at the risk of 5 year biochemical recurrence following radical prostatectomy., Results: A cohort of 19 grade, stage matched prostate cancer patients, all of whom had radical prostatectomy, including 10 of whom had biochemical recurrence within 5 years of surgery and 9 of whom did not, were considered in this study. The aim was to construct a lower fused dimensional metaspace comprising both the histological and proteomic measurements obtained from the site of the dominant nodule on the surgical specimen. In conjunction with SRCCA, a random forest classifier was able to identify prostate cancer patients, who developed biochemical recurrence within 5 years, with a maximum classification accuracy of 93%., Conclusions: The classifier performance in the SRCCA space was found to be statistically significantly higher compared to the fused data representations obtained, not only from CCA and RCCA, but also two other statistical techniques called Principal Component Analysis and Partial Least Squares Regression. These results suggest that SRCCA is a computationally efficient and a highly accurate scheme for representing multimodal (histologic and proteomic) data in a metaspace and that it could be used to construct fused biomarkers for predicting disease recurrence and prognosis.

This unit describes how to extract tryptic peptides from formalin-fixed, paraffin-embedded (FFPE) tissues for analysis using nano-reverse-phase liquid chromatography/tandem mass spectrometry (nRPLC-MS/MS). The tissues are deparaffinized in the first protocol. Following deparaffinization, the cells are harvested via one of two methods: needle dissection or laser capture microdissection (LCM). Needle dissection is performed using hydrated, unstained tissue, whereas LCM is performed with dehydrated, hematoxylin-stained tissue. Heat is applied to the collected cells to reverse the cross-links that have formed during the formalin fixation process. Finally, the cells are digested using filter-aided sample preparation, in which buffers are exchanged throughout the process. An alternate protocol using commercially available Liquid Tissue is also described. These samples are then ready for mass spectrometric analysis., (© 2011 by John Wiley & Sons, Inc.)

Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data, called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. In this paper, we present a novel Supervised Regularized Canonical Correlation Analysis (SRCCA) algorithm that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at risk for biochemical recurrence following radical prostatectomy. For a cohort of 19 prostate cancer patients, SRCCA was able to yield a lower fused dimensional metaspace comprising both the histological and proteomic attributes. In conjunction with SRCCA, a random forest classifier was able to identify patients at risk for biochemical failure with a maximum accuracy of 93%. The classifier performance in the SRCCA space was statistically significantly higher compared to the fused data representations obtained either with Canonical Correlation Analysis (CCA) or Regularized CCA.

Objective: Examination of the amniotic fluid (AF) proteome has been previously attempted to identify useful biomarkers in predicting the outcome of preterm labor (PTL). Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) labeling allows direct ratiometric comparison of relative abundance of identified protein species among multiplexed samples. The purpose of this study was to apply, for the first time, the combination of iTRAQ and tandem mass spectrometry to identify proteins differentially regulated in AF samples of women with spontaneous PTL and intact membranes with and without intra-amniotic infection/inflammation (IAI)., Methods: A cross-sectional study was designed and included AF samples from patients with spontaneous PTL and intact membranes in the following groups: (1) patients without IAI who delivered at term (n = 26); (2) patients who delivered preterm without IAI (n = 25); and (3) patients with IAI (n = 24). Proteomic profiling of AF samples was performed using a workflow involving tryptic digestion, iTRAQ labeling and multiplexing, strong cation exchange fractionation, and liquid chromatography tandem mass spectrometry. Twenty-five separate 4-plex samples were prepared and analyzed., Results: Collectively, 123,011 MS(2) spectra were analyzed, and over 25,000 peptides were analyzed by database search (X!Tandem and Mascot), resulting in the identification of 309 unique high-confidence proteins. Analysis of differentially present iTRAQ reporter peaks revealed many proteins that have been previously reported to be associated with preterm delivery with IAI. Importantly, many novel proteins were found to be up-regulated in the AF of patients with PTL and IAI including leukocyte elastase precursor, Thymosin-like 3, and 14-3-3 protein isoforms. Moreover, we observed differential expression of proteins in AF of patients who delivered preterm in the absence of IAI in comparison with those with PTL who delivered at term including Mimecan precursor, latent-transforming growth factor beta-binding protein isoform 1L precursor, and Resistin. These findings have been confirmed for Resistin in an independent cohort of samples using ELISA. Gene ontology enrichment analysis was employed to reveal families of proteins participating in distinct biological processes. We identified enrichment for host defense, anti-apoptosis, metabolism/catabolism and cell and protein mobility, localization and targeting., Conclusions: (1) Proteomics with iTRAQ labeling is a profiling tool capable of revealing differential expression of proteins in AF; (2) We discovered 82 proteins differentially expressed in three clinical subgroups of premature labor, 67 which were heretofore unknown. Of particular importance is the identification of proteins differentially expressed in AF from women who delivered preterm in the absence of IAI. This is the first report of the positive identification of biomarkers in this subgroup of patients.

"Multi-stage" search strategies have become widely accepted for peptide identification and are implemented in a number of available software packages. We describe limitations of these strategies for validation and decoy-based statistical analyses and demonstrate these limitations using a set of control sample spectra. We propose a solution that corrects the statistical deficiencies and describe its implementation using the open-source software X!Tandem.