Quantitative assessment of tissue biomarkers and construction of a model to predict outcome in breast cancer using multiple imputation

7-UP: Generating in silico CODEX from a small set of immunofluorescence markers

Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. However, high-plex data can be slower and more costly to collect, limiting its applications, especially in clinical settings. We propose a machine learning framework, 7-UP, that can computationally generate in silico 40-plex CODEX at single-cell resolution from a standard 7-plex mIF panel by leveraging cellular morphology. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. Furthermore, 7-UP's imputations generalize well across samples from different clinical sites and cancer types. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available.

Novel role of COX6c in the regulation of oxidative phosphorylation and diseases

Cytochrome c oxidase subunit VIc (COX6c) is one of the most important subunits of the terminal enzyme of the respiratory chain in mitochondria. Numerous studies have demonstrated that COX6c plays a critical role in the regulation of oxidative phosphorylation (OXPHOS) and energy production. The release of COX6c from the mitochondria may be a hallmark of the intrinsic apoptosis pathway. Moreover, The changes in COX6c expression are widespread in a variety of diseases and can be chosen as a potential biomarker for diagnosis and treatment. In light of its exclusive effects, we present the elaborate roles that COX6c plays in various diseases. In this review, we first introduced basic knowledge regarding COX6c and its functions in the OXPHOS and apoptosis pathways. Subsequently, we described the regulation of COX6c expression and activity in both positive and negative ways. Furthermore, we summarized the elaborate roles that COX6c plays in various diseases, including cardiovascular disease, kidney disease, brain injury, skeletal muscle injury, and tumors. This review highlights recent advances and provides a comprehensive summary of COX6c in the regulation of OXPHOS in multiple diseases and may be helpful for drug design and the prediction, diagnosis, treatment, and prognosis of diseases.

Differential expression and clinical significance of COX6C in human diseases

Mitochondria, independent double-membrane organelles, are intracellular power plants that feed most eukaryotic cells with the ATP produced via the oxidative phosphorylation (OXPHOS). Consistently, cytochrome c oxidase (COX) catalyzes the electron transfer chain's final step. Electrons are transferred from reduced cytochrome c to molecular oxygen and play an indispensable role in oxidative phosphorylation of cells. Cytochrome c oxidase subunit 6c (COX6C) is encoded by the nuclear genome in the ribosome after translation and is transported to mitochondria via different pathways, and eventually forms the COX complex. In recent years, many studies have shown the abnormal level of COX6C in familial hypercholesterolemia, chronic kidney disease, diabetes, breast cancer, prostate cancer, uterine leiomyoma, follicular thyroid cancer, melanoma tissues, and other conditions. Its underlying mechanism may be related to the cellular oxidative phosphorylation pathway in tissue injury disease. Here reviews the varied function of COX6C in non-tumor and tumor diseases.

Multiple imputation and analysis for high-dimensional incomplete proteomics data

Multivariable analysis of proteomics data using standard statistical models is hindered by the presence of incomplete data. We faced this issue in a nested case–control study of 135 incident cases of myocardial infarction and 135 pair‐matched controls from the Framingham Heart Study Offspring cohort. Plasma protein markers (K = 861) were measured on the case–control pairs (N = 135), and the majority of proteins had missing expression values for a subset of samples. In the setting of many more variables than observations (K ≫ N), we explored and documented the feasibility of multiple imputation approaches along with subsequent analysis of the imputed data sets. Initially, we selected proteins with complete expression data (K = 261) and randomly masked some values as the basis of simulation to tune the imputation and analysis process. We randomly shuffled proteins into several bins, performed multiple imputation within each bin, and followed up with stepwise selection using conditional logistic regression within each bin. This process was repeated hundreds of times. We determined the optimal method of multiple imputation, number of proteins per bin, and number of random shuffles using several performance statistics. We then applied this method to 544 proteins with incomplete expression data (≤40% missing values), from which we identified a panel of seven proteins that were jointly associated with myocardial infarction. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

NRAGE promotes cell proliferation by stabilizing PCNA in a ubiquitin-proteasome pathway in esophageal carcinomas

Neurotrophin receptor-interacting melanoma antigen-encoding gene homolog (NRAGE) is generally recognized as a tumor suppressor as it induces cell apoptosis and suppresses cell metastasis. However, it has recently been reported that NRAGE is overexpressed in lung cancer, melanoma and colon cancer, implicating a complicated role of NRAGE as we have expected. In the study, we aim to elucidate the functional roles and molecular mechanisms of NRAGE in esophageal carcinoma. We found that both NRAGE mRNA and protein were significantly overexpressed in esophageal tumor tissues. Consistently, both in vivo and in vitro analyses demonstrated that knockdown of NRAGE apparently inhibited cell growth, and cell cycle analysis further demonstrated that NRAGE knockdown cells were mainly arrested in G2M cell phase, accompanied with an apparent reduction of S phase. In the process of exploring molecular mechanisms, we found that either knockdown in vitro or knockout in vivo of NRAGE reduced proliferating cell nuclear antigen (PCNA) protein, expression of which could completely rescue the inhibited proliferation in NRAGE defective cells. Furthermore, NRAGE physically interacted with PCNA in esophageal cancer cells through DNA polymerase III subunit, and knockdown of NRAGE facilitated PCNA K48-linked polyubiquitination, leading PCNA to the proteasome-dependent degradation and a ubiquitin-specific protease USP10 was identified to be a key regulator in the process of K48 polyubiquitination in NRAGE-deleted cells. In conclusion, our study highlights a unique role of NRAGE and implies that NRAGE is likely to be an attractive oncotarget in developing novel genetic anticancer therapeutic strategies for esophageal squamous cell carcinomas.

Construction and analysis of multiparameter prognostic models for melanoma outcome

The outcome of Stage II melanoma is uncertain. Despite that 10-year melanoma-specific survival can approach 50 % following curative-intent wide local excision and negative sentinel lymph node biopsy, the adverse risk-benefit ratio of interferon-based adjuvant regimens precludes their use in most patients. The discovery and translation of protein-based prognostic biomarkers into the clinic offers the promise for residual risk stratification of Stage II melanoma patients beyond conventional clinicopathologic criteria to identify an additional subset of patients who, based upon tumor molecular profiles, might also derive benefit from adjuvant regimens. Despite incorporation of Ki-67 assays into clinical practice, systematic review of REMARK-compliant, immunostain-based prognostic biomarker assays in melanoma suggests that residual risk of recurrence might be best explained by a composite score derived from a small panel of proteins representing independent features of melanoma biology. Reflecting this trend, to date, five such multiparameter melanoma prognostic models have been published. Here, we review these five models and provide detailed protocols for discovering and validating multiparameter models including: appropriate cohort recruitment strategies, comprehensive laboratory protocols supporting fully quantitative chromogenic or fluorescent immunostaining platforms, statistical approaches to create composite prognostic indices recommended steps for model validation in independent cohorts.

Digital imaging in pathology: whole-slide imaging and beyond

Digital imaging in pathology has undergone an exponential period of growth and expansion catalyzed by changes in imaging hardware and gains in computational processing. Today, digitization of entire glass slides at near the optical resolution limits of light can occur in 60 s. Whole slides can be imaged in fluorescence or by use of multispectral imaging systems. Computational algorithms have been developed for cytometric analysis of cells and proteins in subcellular locations by use of multiplexed antibody staining protocols. Digital imaging is unlocking the potential to integrate primary image features into high-dimensional genomic assays by moving microscopic analysis into the digital age. This review highlights the emerging field of digital pathology and explores the methods and analytic approaches being developed for the application and use of these methods in clinical care and research settings.

hSAGEing: an improved SAGE-based software for identification of human tissue-specific or common tumor markers and suppressors

BACKGROUND

SAGE (serial analysis of gene expression) is a powerful method of analyzing gene expression for the entire transcriptome. There are currently many well-developed SAGE tools. However, the cross-comparison of different tissues is seldom addressed, thus limiting the identification of common- and tissue-specific tumor markers.

METHODOLOGY/PRINCIPAL FINDINGS

To improve the SAGE mining methods, we propose a novel function for cross-tissue comparison of SAGE data by combining the mathematical set theory and logic with a unique "multi-pool method" that analyzes multiple pools of pair-wise case controls individually. When all the settings are in "inclusion", the common SAGE tag sequences are mined. When one tissue type is in "inclusion" and the other types of tissues are not in "inclusion", the selected tissue-specific SAGE tag sequences are generated. They are displayed in tags-per-million (TPM) and fold values, as well as visually displayed in four kinds of scales in a color gradient pattern. In the fold visualization display, the top scores of the SAGE tag sequences are provided, along with cluster plots. A user-defined matrix file is designed for cross-tissue comparison by selecting libraries from publically available databases or user-defined libraries.

CONCLUSIONS/SIGNIFICANCE

The hSAGEing tool provides a combination of friendly cross-tissue analysis and an interface for comparing SAGE libraries for the first time. Some up- or down-regulated genes with tissue-specific or common tumor markers and suppressors are identified computationally. The tool is useful and convenient for in silico cancer transcriptomic studies and is freely available at http://bio.kuas.edu.tw/hSAGEing.

Translational bioinformatics and systems biology approaches for personalized medicine

Systems biology and pharmacogenomics are emerging and promising fields that will provide a thorough understanding of diseases and enable personalized therapy. However, one of the most significant obstacles in the practice of personalized medicine is the translation of scientific discoveries into better therapeutic outcomes. Translational bioinformatics is a powerful method to bridge the gap between systems biology research and clinical practice. This goal can be achieved through providing integrative methods to enable predictive models for therapeutic responses. As a media between bench and bedside, translational bioinformatics has the mission to meet challenges in the development of personalized medicine. On the biomedical side, translational bioinformatics would enable the identification of biomarkers based on systemic analyses. It can improve the understanding of the correlations between genotypes and phenotypes. It would enable novel insights of interactions and interrelationships among different parts in a whole system. On the informatics side, methods based on data integration, data mining, and knowledge representation can provide decision support for both researchers and clinicians. Data integration is not only for better data access, but also for knowledge discovery. Decision support based on translational bioinformatics means better information and workflow management, efficient literature and resource retrieval, and communication improvement. These approaches are crucial for understanding diseases and applying personalized therapeutics at systems levels.