Proteomics Discovery of Disease Biomarkers

Recent technological developments in proteomics have shown promising initiatives in identifying novel biomarkers of various diseases. Such technologies are capable of investigating multiple samples and generating large amount of data end-points. Examples of two promising proteomics technologies are mass spectrometry, including an instrument based on surface enhanced laser desorption/ionization, and protein microarrays. Proteomics data must, however, undergo analytical processing using bioinformatics. Due to limitations in proteomics tools including shortcomings in bioinformatics analysis, predictive bioinformatics can be utilized as an alternative strategy prior to performing elaborate, high-throughput proteomics procedures. This review describes mass spectrometry, protein microarrays, and bioinformatics and their roles in biomarker discovery, and highlights the significance of integration between proteomics and bioinformatics

Editorial: Recent advancements in neoadjuvant chemotherapy for specific breast cancer subtypes

Most locally advanced breast cancer patients undergo pre-surgery treatment known as neoadjuvant chemotherapy (NAC). The purpose of NAC is to reduce the tumor’s size and improve surgical outcomes, cosmetic results, and chances of conservative breast surgery, control tumor progression and observe tumor sensitivity (or resistance) to the chosen treatment regimen (1–4). Several studies have suggested better survival outcomes in patients achieving complete pathological remission than in patients with residual or progressive disease at the time of definitive surgery (5, 6). However, the mechanisms of primary resistance and strategies to overcome those are a matter of intense research. Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and is responsible for most of the annual mortality rate of breast cancer (7, 8). This Research Topic focused on studies that tackle the most recent advances in treating breast cancer using NAC. Pegylated liposomal doxorubicin (PLD) is used safely to treat breast cancer patients (9). In addition, it has a superior benefit over free doxorubicin since it is distributed in smaller volumes with extended circulation time (10). A recent clinical trial demonstrated that pegylated liposomal doxorubicin (PLD) is safe for TNBC with a particular focus on elderly patients and those with risks of developing cardiotoxicity (Gil-Gil et al.

Using Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence

Class prediction using “omics” data is playing an increasing role in toxicogenomics, diagnosis/prognosis, and risk assessment. These data are usually noisy and represented by relatively few samples and a very large number of predictor variables (e.g., genes of DNA microarray data or m/z peaks of mass spectrometry data). These characteristics manifest the importance of assessing potential random correlation and overfitting of noise for a classification model based on omics data. We present a novel classification method, decision forest (DF), for class prediction using omics data. DF combines the results of multiple heterogeneous but comparable decision tree (DT) models to produce a consensus prediction. The method is less prone to overfitting of noise and chance correlation. A DF model was developed to predict presence of prostate cancer using a proteomic data set generated from surface-enhanced laser deposition/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The degree of chance correlation and prediction confidence of the model was rigorously assessed by extensive cross-validation and randomization testing. Comparison of model prediction with imposed random correlation demonstrated biologic relevance of the model and the reduction of overfitting in DF. Furthermore, two confidence levels (high and low confidences) were assigned to each prediction, where most misclassifications were associated with the low-confidence region. For the high-confidence prediction, the model achieved 99.2% sensitivity and 98.2% specificity. The model also identified a list of significant peaks that could be useful for biomarker identification. DF should be equally applicable to other omics data such as gene expression data or metabolomic data. The DF algorithm is available upon request

A biological approach to computational models of proteomic networks.

Computational modeling is useful as a means to assemble and test what we know about proteins and networks. Models can help address key questions about the measurement, definition and function of proteomic networks. Here, we place these biological questions at the forefront in reviewing the computational strategies that are available to analyze proteomic networks. Recent examples illustrate how models can extract more information from proteomic data, test possible interactions between network proteins and link networks to cellular behavior. No single model can achieve all these goals, however, which is why it is critical to prioritize biological questions before specifying a particular modeling approach. Introduction Our current understanding of the proteins, interactions and pathways that comprise signaling networks is detailed, yet it remains incomplete. Recent experimental techniques for unraveling intricate signaling networks have become increasingly quantitative and multiplex. New approaches are now needed to compile the existing quantitative biological knowledge and to maximize the information extracted from large-scale signaling and proteomic datasets. Computational models formalize a complex biological or experimental process mathematically, which can be useful for assembling and analyzing quantitative data. Modeling is thus critical for fields such as proteomics, genomics and systems biology. As a discipline, biology thrives on clarity through consensus (take, for instance, the central dogma). To model biological networks, however, we and others have argued against a consensus 'one size fits all' philosophy, favoring instead a spectrum of computational techniques Anchoring model sophistication with experimental data Proteomics research is clearly directed at uncovering more biological detail within networks -new proteins, new interactions, new complexes How does increasing the level of model detail decrease believability? With model detail come parameters. In a model, these parameters might define a signaling protein's starting concentration, rate of turnover or diffusivity through the cytoplasm. Model parameters are frequently unknown and must therefore be estimated from data, which reduces believability. Importantly, the number of required parameters multiplies as more biological detail is added Current Opinion in Chemical Biology Network-measurement models Modeling has played an increasingly important role in the measurement of proteomes and networks. Measurement models are a useful way to condense different methodological considerations into a single quantitative description of an experiment In the field of 'global' proteomics, no other experimental method has had as significant an impact as mass spectrometry (MS) Network definition 1a, 1b, 2b, 3c Current Opinion in Chemical Biology Two distinct perspectives on computational models of proteomic networks. sence of proteins in a complex starting mixture [11], which used prior MS measurements as training data to fit an initial frequency distribution of peptides whose assignments were correct and incorrect. The initial distributions were used as prior information in a model that calculates the probability of correct peptide assignment given an MS spectrum. Running the model through all of the spectra in an MS experiment generates a new distribution of (probably) correct and (probably) incorrect peptides, which can update the prior information for the next iteration through the model. In this way, the model learns the most likely peptide assignments from the spectra itself, with initialization provided by a high-quality training dataset. The resulting peptide probabilities can then be fed into downstream models that calculate protein assignments from a set of likely peptides Measurement models are also useful for analyzing data quality itself. Often, quality is synonymous with information [14 ] selected for high-quality proteomic data by calculating the intersection of large-scale phenotypic, transcriptional and interaction datasets in Caenorhabditis elegans. Using the overlap among the measured networks, the Gunsalus et al. model was shown to be enriched in proteins sharing common biological functions. Gaudet et al. [15 ] used the predictive ability of a model to quantify network information content directly from a proteomic measurement set. A key conclusion from this work was the importance of measurement combinations. Different types of assays (kinase activity assays, quantitative western blotting, etc.) used over a range of time points were critical to accurately predicting the response of cells treated with multiple experimental stimuli. As quantitative MS-based experiments evolve Network-definition models An important goal for computational models is to define mathematically the proteins and pathways that constitute a signaling network. Modeling strategies for addressing the question of network definition can be subdivided into two categories: reconstruction models, which build networks from previously reported mechanisms; and inference models, which deduce network structure from large-scale datasets Model class Current Opinion in Chemical Biology What can be learned from these complex models founded on highly parameterized systems of differential equations Many biological networks lack the in-depth mechanistic understanding needed for a plausible network reconstruction. With these networks, inferential modeling approaches can be used to suggest connections between molecules Network-function models Proteomic networks are important because they ultimately control cellular functions. Diverse extracellular stimuli converge upon a common intracellular network, which can mediate an array of cellular responses [38 ] used decision-tree modeling (Box 1) to characterize cell migration based on the phosphorylation levels of five key intracellular proteins. The resulting 'branches' of the decision tree identified the sequence of conditional molecular statements that best predicted low, medium or high cellular speed -for instance, IF extracellular-regulated kinase phosphorylation is low AND IF myosin light-chain phosphorylation is high THEN migration speed is high. It would be interesting to use this approach in larger networks while constraining decision-tree branchpoints based on the approximate positions of molecules in the network (first membrane transducers, then initiators, then effectors, etc.). Prediction of cellular functions can also be achieved more quantitatively by training models on measurements of the upstream signaling network. We have used partial least squares modeling to predict 12 measured apoptotic responses from 19 time-dependent signaling profiles [39 ]. This particular modeling approach calculates the most informative combinations of signals that together predict cellular functions. The combinations of stress, prodeath and prosurvival signals identified by the model were consistent with known mechanisms but could not have been predicted by inspection. Focusing on intracellular signals with recognized but complex roles in cell death thus allowed the model to identify new mechanisms of apoptosis control within the currently understood network. Very recently, we have found that this approach to modeling network function could effectively capture cytokine-induced apoptotic responses that differ between diverse cell types (K Miller-Jensen, KA Janes, DA Lauffenburger, unpublished data). This suggests that different cell types might share a common network that converts signals into cellular responses. If true, then reconstruction models Conclusions The measurement, definition and function of proteomic networks must be addressed in such a way that models complement experiment. These networks remain too unconstrained to study by mathematics alone yet have become too complex to understand completely by intuition. We believe that insights here will come about by approaching models through biological questions. In line with this view, our review has focused on less detailed models with strong foundations in data Update Recent work has provided new examples of how existing reconstruction models can be further explored and refined to aid biological discovery. Cheong et al. [53 ] used an NF-kB signaling mode

A pilot study evaluating concordance between blood-based and patient-matched tumor molecular testing within pancreatic cancer patients participating in the Know Your Tumor (KYT) initiative

Recent improvements in next-generation sequencing (NGS) technology have enabled detection of biomarkers in cell-free DNA in blood and may ultimately replace invasive tissue biopsies. However, a better understanding of the performance of blood-based NGS assays is needed prior to routine clinical use. As part of an IRBapproved molecular profiling registry trial of pancreatic ductal adenocarcinoma (PDA) patients, we facilitated blood-based NGS testing of 34 patients from multiple community-based and high-volume academic oncology practices. 23 of these patients also underwent traditional tumor tissue-based NGS testing. cfDNA was not detected in 9/34 (26%) patients. Overall concordance between blood and tumor tissue NGS assays was low, with only 25% sensitivity of blood-based NGS for tumor tissue NGS. Mutations in KRAS, the major PDA oncogene, were only detected in 10/34 (29%) blood samples, compared to 20/23 (87%) tumor tissue biopsies. The presence of mutations in circulating DNA was associated with reduced overall survival (54% in mutation-positive versus 90% in mutation-negative). Our results suggest that in the setting of previously treated, advanced PDA, liquid biopsies are not yet an adequate substitute for tissue biopsies. Further refinement in defining the optimal patient population and timing of blood sampling may improve the value of a blood-based test. © Pishvaian et al