Peptide extraction from formalin-fixed paraffin-embedded tissue

XPO1 inhibition with selinexor synergizes with proteasome inhibition in neuroblastoma by targeting nuclear export of IkB

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XPO1 is overabundant in high-risk neuroblastoma and correlates with poor survival.

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Neuroblastoma cells are sensitive to XPO1 inhibition with selinexor.

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Use of selinexor results in nuclear retention of IkB, diminishing NF-kB activity.

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Selinexor and bortezomib act synergistically through promotion of apoptosis.

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Synergy is mediated in part, through IkB regulation of NF-kB activity.

Across many cancer types in adults, upregulation of the nuclear-to-cytoplasmic transport protein Exportin-1 (XPO1) correlates with poor outcome and responsiveness to selinexor, an FDA-approved XPO1 inhibitor. Similar data are emerging in childhood cancers, for which selinexor is being evaluated in early phase clinical studies. Using proteomic profiling of primary tumor material from patients with high-risk neuroblastoma, as well as gene expression profiling from independent cohorts, we have demonstrated that XPO1 overexpression correlates with poor patient prognosis. Neuroblastoma cell lines are also sensitive to selinexor in the low nanomolar range. Based on these findings and knowledge that bortezomib, a proteasome inhibitor, blocks degradation of XPO1 cargo proteins, we hypothesized that combination treatment with selinexor and bortezomib would synergistically inhibit neuroblastoma cellular proliferation. We observed that selinexor promoted nuclear retention of IkB and that bortezomib augmented the ability of selinexor to induce cell-cycle arrest and cell death by apoptosis. This synergy was abrogated through siRNA knockdown of IkB. The synergistic effect of combining selinexor and bortezomib in vitro provides rationale for further investigation of this combination treatment for patients with high-risk neuroblastoma.

Identification of disease-promoting stromal components by comparative proteomic and transcriptomic profiling of canine mammary tumors using laser-capture microdissected FFPE tissue

Cancer-associated stroma (CAS) profoundly influences progression of tumors including mammary carcinoma (mCA). Canine simple mCA represent relevant models of human mCA, notably also with respect to CAS. While transcriptomic changes in CAS of mCA are well described, it remains unclear to what extent these translate to the protein level. Therefore, we sought to gain insight into the proteomic changes in CAS and compare them with transcriptomic changes in the same tissue. To this end, we analyzed CAS and matched normal stroma using laser-capture microdissection (LCM) and LC-MS/MS in a cohort of 14 formalin-fixed paraffin embedded (FFPE) canine mCAs that we had previously characterized using LCM-RNAseq. Our results reveal clear differences in protein abundance between CAS and normal stroma, which are characterized by changes in the extracellular matrix, the cytoskeleton, and cytokines such as TNF. The proteomics- and RNAseq-based analyses of LCM-FFPE show a substantial degree of correlation, especially for the most deregulated targets and a comparable activation of pathways. Finally, we validate transcriptomic upregulation of LTBP2, IGFBP2, COL6A5, POSTN, FN1, COL4A1, COL12A1, PLOD2, COL4A2, and IGFBP7 in CAS on the protein level and demonstrate their adverse prognostic value for human breast cancer. Given the relevance of canine mCA as a model for the human disease, our analysis substantiates these targets as disease-promoting stromal components with implications for breast cancer in both species.

Advancing Clinicopathologic Diagnosis of High-risk Neuroblastoma Using Computerized Image Analysis and Proteomic Profiling

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.

Biomarker discovery: success as a function of risk mitigation

Protein biomarker discovery is a fascinating enterprise; however, success in terms of products for in vitro diagnostic use is sparse. New developments in mass spectrometry-based quantitative proteomics as discovery technology have opened up new avenues for this endeavor. In addition to choice of technology, sample properties, study design and validation strategy are potent pillars required for project success. The challenge for successful biomarker discovery can be described by a series of risks that need to be mitigated. This article intends to describe the major risks along with possible solutions.

Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer

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.

Complete solubilization of formalin-fixed, paraffin-embedded tissue may improve proteomic studies

Tissue-based proteomic approaches (tissue proteomics) are essential for discovering and evaluating biomarkers for personalized medicine. In any proteomics study, the most critical issue is sample extraction and preparation. This problem is especially difficult when recovering proteins from formalin-fixed, paraffin-embedded (FFPE) tissue sections. However, improving and standardizing protein extraction from FFPE tissue is a critical need because of the millions of archival FFPE tissues available in tissue banks worldwide. Recent progress in the application of heat-induced antigen retrieval principles for protein extraction from FFPE tissue has resulted in a number of published FFPE tissue proteomics studies. However, there is currently no consensus on the optimal protocol for protein extraction from FFPE tissue or accepted standards for quantitative evaluation of the extracts. Standardization is critical to ensure the accurate evaluation of FFPE protein extracts by proteomic methods such as reverse phase protein arrays, which is now in clinical use. In our view, complete solubilization of FFPE tissue samples is the best way to achieve the goal of standardizing the recovery of proteins from FFPE tissues. However, further studies are recommended to develop standardized protein extraction methods to ensure quantitative and qualitative reproducibility in the recovery of proteins from FFPE tissues.

Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery

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.