Rank discriminants for predicting phenotypes from RNA expression

Statistical methods for analyzing large-scale biomolecular data are commonplace in computational biology. A notable example is phenotype prediction from gene expression data, for instance, detecting human cancers, differentiating subtypes and predicting clinical outcomes. Still, clinical applications remain scarce. One reason is that the complexity of the decision rules that emerge from standard statistical learning impedes biological understanding, in particular, any mechanistic interpretation. Here we explore decision rules for binary classification utilizing only the ordering of expression among several genes; the basic building blocks are then two-gene expression comparisons. The simplest example, just one comparison, is the TSP classifier, which has appeared in a variety of cancer-related discovery studies. Decision rules based on multiple comparisons can better accommodate class heterogeneity, and thereby increase accuracy, and might provide a link with biological mechanism. We consider a general framework ("rank-in-context") for designing discriminant functions, including a data-driven selection of the number and identity of the genes in the support ("context"). We then specialize to two examples: voting among several pairs and comparing the median expression in two groups of genes. Comprehensive experiments assess accuracy relative to other, more complex, methods, and reinforce earlier observations that simple classifiers are competitive.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS738 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Inter-individual variation in DNA repair capacity: A need for multi-pathway functional assays to promote translational DNA repair research

Why does a constant barrage of DNA damage lead to disease in some individuals, while others remain healthy? This article surveys current work addressing the implications of inter-individual variation in DNA repair capacity for human health, and discusses the status of DNA repair assays as potential clinical tools for personalized prevention or treatment of disease. In particular, we highlight research showing that there are significant inter-individual variations in DNA repair capacity (DRC), and that measuring these differences provides important biological insight regarding disease susceptibility and cancer treatment efficacy. We emphasize work showing that it is important to measure repair capacity in multiple pathways, and that functional assays are required to fill a gap left by genome wide association studies, global gene expression and proteomics. Finally, we discuss research that will be needed to overcome barriers that currently limit the use of DNA repair assays in the clinic

Gene functional similarity search tool (GFSST)

BACKGROUND: With the completion of the genome sequences of human, mouse, and other species and the advent of high throughput functional genomic research technologies such as biomicroarray chips, more and more genes and their products have been discovered and their functions have begun to be understood. Increasing amounts of data about genes, gene products and their functions have been stored in databases. To facilitate selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. In recent years, the development of Gene Ontology (GO) has established structured, controlled vocabularies describing gene functions, which makes it possible to develop novel tools to search genes by functional similarity. RESULTS: By using a statistical model to measure the functional similarity of genes based on the Gene Ontology directed acyclic graph, we developed a novel Gene Functional Similarity Search Tool (GFSST) to identify genes with related functions from annotated proteome databases. This search engine lets users design their search targets by gene functions. CONCLUSION: An implementation of GFSST which works on the UniProt (Universal Protein Resource) for the human and mouse proteomes is available at GFSST Web Server. GFSST provides functions not only for similar gene retrieval but also for gene search by one or more GO terms. This represents a powerful new approach for selecting similar genes and gene products from proteome databases according to their functions

Personalized Medicine in Oncology

Nowhere is the explosion in comprehensive genomic testing more evident than in oncology. Multiple consensus guidelines now recommend molecular testing as the standard of care for most metastatic tumors. To aid in the advancement of this rapidly changing field, we intend this Special Issue of JPM to focus on technical developments in the genomic profiling of cancer, detail promising somatic alterations that either are, or have a high likelihood of being, relevant in the near future, and to address issues related to the pricing and value of these tests.The last few years have seen the cost of molecular testing decrease by orders of magnitude. In 2018, we saw the first “site-agnostic” drug approvals in cancer (for microsatellite unstable cancer (PD-1 inhibitors) and NTRK-fusions (TRK inhibitors)). Research on targetable mutations, determination of genetic “signatures” that can use multiple individual genes/pathways, development of targeted therapy, and insight into the value of new technology remains at the cutting edge of research in this field. We are soliciting papers that present new technologies to assess predictive biomarkers in cancer, original research (pre-clinical or clinical) that demonstrates promise for particular targeted therapies in cancer, and articles that explore the clinical and financial impacts of this paradigmatic shift in cancer diagnostics and treatment

Mechanisms and Novel Therapeutic Approaches for Gynecologic Cancer

This book—entitled “Mechanisms and Novel Therapeutic Approaches for Gynecologic Cancer”—was edited as a Special Issue of Biomedicines, focusing on basic research such as genomics, epigenomics, and proteomics, as well as clinical research in the field of gynecologic oncology. The number of patients with gynecological cancer has been increasing worldwide due to its high lethality and lack of early detection tools and effective therapeutic interventions. In this regard, basic research on its pathophysiology and novel molecular targeting intervention is required to improve the prognosis of gynecologic cancer. This book contains 13 papers, including 8 original research papers and 5 reviews focusing on the basic research of gynecologic oncology. The reader can learn about state-of-the-art research and obtain extensive knowledge of the current advances in the field of gynecologic oncology. It is my hope that this book contributes towards the progress of gynecologic oncology

Detection of prognostic biomarkers and application in clustering patients with oral squamous cell carcinoma, according to the risk of relapse

Most head and neck cancers derive from the mucosal epithelium of the oral cavity, pharynx and larynx and are known collectively as head and neck squamous cell carcinoma (HNSCC), accounting for over 600,000 new cases diagnosed per year and of these, more than 300.000 new cases annually are reported to take origin from the surface of the oral mucosa. Current evidence supports that these subsites exhibit distinctive molecular and clinical behaviors, leading to an "anatomical bias" both for research and clinical decision-making. Oral squamous cell carcinoma (OSCC), in particular involving oral tongue (OTSCC) is the most common malignancy of the head and neck region, characterized by a high rate of local and regional recurrences, which strongly decreases patients’ survival rates. The American Joint Committee on Cancer (AJCC) staging system is the standard tool used to classify oncological patients and predict their clinical outcomes. Despite advancements in patients’ prognostic stratification, the 8th edition of AJCC fails to identify patients characterized by early relapse and poor prognosis. Currently, no prognostic biomarkers have been validated to stratify these patients and their risk of recurrence and death. This scenario calls for the investigation of biomarkers from basic research combined with bioinformatics to clinical and routine diagnostic application in a translational pathway. This project aimed to investigate prognostic biomarkers in HNSCC, OSCC and OTSCC, 4 by different approaches, such as reviews and meta-analysis, histopathology, and bioinformatics. This is to highlight possible histopathologic and genetic biomarkers to be integrated in future staging systems in a precision medicine environment. Different histopathologic features were tested, such as tumour budding, eosinophils infiltration, lymph-vascular invasion, perineural invasion, lymphocytes infiltration, and tumour-stroma ratio. This investigation led to the development of promising and easy to be assessed histopathologic biomarkers, such as immune-phenotype, thresholds, and improved staging systems. Furtherly, a new prognostic classification system was developed based on TP53 gene mutations. In conclusion, the heterogeneous background of HNSCC, including OSCC and, OTSCC emerged, and new prognostic biomarkers were proposed to be furtherly evaluated in other cohorts for routine translational application in the aim of precision medicine

Clinical application of genomics- and phosphoproteomics-based selection of targeted therapy in patients with advanced solid tumors

Precision oncology has come a long way since the introduction of the first targeted drug (trastuzumab) in 1999. Broad molecular testing of tumor tissue has vastly expanded our knowledge of the biology of cancer, leading to a steep increase in the number of approved targeted drugs and an expansion of the labeled indications of these drugs. Off-label use of these new classes of targeted drugs is nowadays better documented and often performed in clinical trials to maximize the learning potential of these experimental treatments for the medical community. As long as no “cure for cancer” exists, there will be room for improvement of our knowledge and approach to treating patients with cancer. General improvements in the logistics, availability of targeted drugs and access to diagnostics and expertise will likely have the greatest impact on direct benefit for patients. In the future, standardized processing and conservation of tumor tissue/biopsies should be possible in all healthcare facilities, and collaborations and sharing of knowledge and resources with the academic institutes will be viable to delivering precision oncology to all patients. If these conditions are met, more patients may potentially benefit from the knowledge and new treatment options resulting from the precision oncology trials. Also, medical oncologists may learn more about molecular testing and interpreting test results from participation in MTBs. To maximize the impact of precision oncology, international collaborations are of utmost importance and research groups throughout the world are encouraged to share best practices and creative solutions to overcome the hurdles that still hamper new initiatives in the field today. Future clinical research may focus on prospective therapy selection using molecular information from other –omics fields, such as phosphoproteomics, especially in patients where no clear monogenetic driver mutations is identified and a comprehensive pathway analysis may give more direction for potential therapeutic strategies. More knowledge on the best method of prioritizing targets for treatments will be essential, as well as clinical trials investigating new combinations of targeted agents. With an increasing understanding of cancer biology and improved strategies for treatment selection, precision oncology will be accessible for patients with advanced cancer and more patients will benefit from the knowledge that we gain today and tomorrow. In the future, treatments based on histology alone may be considered old-fashioned, and multi-omics diagnostics may result in a comprehensible report that can be easily interpreted, and will directly guide treatment decisions for individual patients

Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance

Esophageal adenocarcinoma (EAC) has a poor outcome, and targeted therapy trials have thus far been disappointing owing to a lack of robust stratification methods. Whole-genome sequencing (WGS) analysis of 129 cases demonstrated that this is a heterogeneous cancer dominated by copy number alterations with frequent large-scale rearrangements. Co-amplification of receptor tyrosine kinases (RTKs) and/or downstream mitogenic activation is almost ubiquitous; thus tailored combination RTK inhibitor (RTKi) therapy might be required, as we demonstrate in vitro. However, mutational signatures showed three distinct molecular subtypes with potential therapeutic relevance, which we verified in an independent cohort (n = 87): (i) enrichment for BRCA signature with prevalent defects in the homologous recombination pathway; (ii) dominant T>G mutational pattern associated with a high mutational load and neoantigen burden; and (iii) C>A/T mutational pattern with evidence of an aging imprint. These subtypes could be ascertained using a clinically applicable sequencing strategy (low coverage) as a basis for therapy selection