Stephen E. Stein

Bio: Stephen E. Stein is an academic researcher from Science Applications International Corporation. The author has contributed to research in topics: Proteome & Cancer. The author has an hindex of 3, co-authored 3 publications receiving 664 citations.

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer

Abstract: To provide a detailed analysis of the molecular com-ponents and underlying mechanisms associatedwith ovarian cancer, we performed a comprehensivemass-spectrometry-based proteomic characteriza-tion of 174 ovarian tumors previously analyzed byThe Cancer Genome Atlas (TCGA), of which 169were high-grade serous carcinomas (HGSCs). Inte-grating our proteomic measurements with thegenomic data yielded a number of insights into dis-ease, such as how different copy-number alterna-tionsinfluencetheproteome,theproteinsassociatedwith chromosomal instability, the sets of signalingpathways that diverse genome rearrangementsconverge on, and the ones most associated withshort overall survival. Specific protein acetylationsassociated with homologous recombination defi-ciency suggest a potential means for stratifying pa-tients for therapy. In addition to providing a valuableresource,thesefindingsprovideaviewofhowtheso-maticgenomedrivesthecancerproteomeandasso-ciations between protein and post-translationalmodification levels and clinical outcomes in HGSC.

Combined Blood/Tissue Analysis for Cancer Biomarker Discovery: Application to Renal Cell Carcinoma

Abstract: Mass spectrometry (MS) methods allowing for the identification of tumor proteins in blood may enable cancer biomarker. Urgent needs in this domain include assays for: cancer diagnosis, therapy selection, prognosis, and monitoring.1 Both cancer biology and clinical oncology are undergoing rapid transformations, specifically, from an organ-centric to molecular pathways focused disciplines. Therefore, methods allowing for an improved molecular characterization of a patient’s actual malignant process/tumor may facilitate the development of advanced assays for personalized cancer diagnosis and management.2 Molecular profiling of a patient’s tumor may provide better insights into the cancer-induced derangements relevant to the malignancy under study, with the eventual and ultimate hope of benefits to patient outcome.2 MS-based proteomics may play an important role in characterizations of proteins within clinical samples. Therefore, innovative approaches focused on method development for proteomic profiling of clinically relevant specimens are critically needed.3 Despite advances in cancer biomarker research, the translation of proteomic methods and findings to applicable clinical assays has been disappointing.4 Principal factors that hinder mass spectrometry (MS)-based biomarker research using clinical samples include: (i) significant heterogeneity of solid tumors,5 (ii) formidable variability of protein expression in the human population proper, serving as a potential source of analytical/statistical bias,4 (iii) significant mismatches between the dynamic range of MS instrumentation and the protein content of clinical specimens,3 and (iv) the majority of proteomics-derived “potential” cancer biomarkers were not germane to the tumor in question.4 Many of these putative cancer biomarkers fall into the categories of acute-phase reactants and likely lack specificity to the pathologic process under study.4 Identifying relevant differences within the blood proteome from healthy and cancer patients is difficult due to the common lack of specificity of the findings. This may be influenced by a plethora of physiological and analytical factors. Additionally, numerous differences can be detected when comparing such cohorts. The major obstacle is proving which differences are dependent on the presence of the cancer and which result from physiological bias or analytical variability. While blood-based biomarkers would revolutionize cancer management, the commonly followed strategy of only analyzing serum or plasma from patients makes it very difficult to trace the origin of proteomic differences back to a tumor. In this study, we present the results from a combined tumor/plasma proteome analysis of samples acquired from a single patient. This strategy aims to recognize tumor proteins within the blood and may possess a higher probability of surviving the rigors of verification and validation necessary for generating useful clinical biomarker candidates. The objective of this investigation was to develop a proteomic method capable of reliably profiling the proteome of a solid tumor, and determine whether any of the identified proteins in the tumor proper are detectable in the blood of a patient newly diagnosed with a non-metastatic cancer.

LinkedOmics: analyzing multi-omics data within and across 32 cancer types.

Abstract: The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org.

Integrative omics for health and disease.

Abstract: Advances in omics technologies - such as genomics, transcriptomics, proteomics and metabolomics - have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

Multi-omics Data Integration, Interpretation, and Its Application.

Abstract: To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

Microbiome analyses of blood and tissues suggest cancer diagnostic approach

Abstract: Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions1–10, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia–IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration. Microbial nucleic acids are detected in samples of tissues and blood from more than 10,000 patients with cancer, and machine learning is used to show that these can be used to discriminate between and among different types of cancer, suggesting a new microbiome-based diagnostic approach.