Tumor antigens as proteogenomic biomarkers in invasive ductal carcinomas

Current Pharmacological Strategies for Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a lethal, X-linked neuromuscular disorder caused by the absence of dystrophin protein, which is essential for muscle fiber integrity. Loss of dystrophin protein leads to recurrent myofiber damage, chronic inflammation, progressive fibrosis, and dysfunction of muscle stem cells. There is still no cure for DMD so far and the standard of care is principally limited to symptom relief through glucocorticoids treatments. Current therapeutic strategies could be divided into two lines. Dystrophin-targeted therapeutic strategies that aim at restoring the expression and/or function of dystrophin, including gene-based, cell-based and protein replacement therapies. The other line of therapeutic strategies aims to improve muscle function and quality by targeting the downstream pathological changes, including inflammation, fibrosis, and muscle atrophy. This review introduces the important developments in these two lines of strategies, especially those that have entered the clinical phase and/or have great potential for clinical translation. The rationale and efficacy of each agent in pre-clinical or clinical studies are presented. Furthermore, a meta-analysis of gene profiling in DMD patients has been performed to understand the molecular mechanisms of DMD.

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties

Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunotherapeutic drugs. In this work, we evaluated fifteen machine learning algorithms for the classification of tumor antigens. For this purpose, we build robust datasets, carefully selected from the TANTIGEN and IEDB databases. The feature computation of all antigens in this study was performed by developing a script written in Python 3.8, which allowed the calculation of 544 physicochemical and biochemical properties extracted from the AAindex database. All classifiers were subjected to the training, 10-fold cross-validation, and testing on an independent dataset. The results of this study showed that the quadratic discriminant classifier presented the best performance measures over the independent dataset, accuracy = 0.7384, AUC = 0.817, recall = 0.676, precision = 0.7857, F1 = 0.713, kappa = 0.4764, and Matthews correlation coefficient = 0.4834, outperforming common machine learning classifiers used in the bioinformatics area. We believe that our prediction model could be of great importance in the field of cancer immunotherapy for the search of potential tumor antigens. Taking all aspects mentioned before, we developed an immunoinformatic tool called TAP 1.0 with a friendly interface for tumor antigens prediction, available at https://tapredictor.herokuapp.com/.

Racial Differences in Immunological Landscape Modifiers Contributing to Disparity in Prostate Cancer

Prostate cancer affects African Americans disproportionately by exhibiting greater incidence, rapid disease progression, and higher mortality when compared to their Caucasian counterparts. Additionally, standard treatment interventions do not achieve similar outcome in African Americans compared to Caucasian Americans, indicating differences in host factors contributing to racial disparity. African Americans have allelic variants and hyper-expression of genes that often lead to an immunosuppressive tumor microenvironment, possibly contributing to more aggressive tumors and poorer disease and therapeutic outcomes than Caucasians. In this review, we have discussed race-specific differences in external factors impacting internal milieu, which modify immunological topography as well as contribute to disparity in prostate cancer.

TANTIGEN: a comprehensive database of tumor T cell antigens

Tumor T cell antigens are both diagnostically and therapeutically valuable molecules. A large number of new peptides are examined as potential tumor epitopes each year, yet there is no infrastructure for storing and accessing the results of these experiments. We have retroactively cataloged more than 1000 tumor peptides from 368 different proteins, and implemented a web-accessible infrastructure for storing and accessing these experimental results. All peptides in TANTIGEN are labeled as one of the four categories: (1) peptides measured in vitro to bind the HLA, but not reported to elicit either in vivo or in vitro T cell response, (2) peptides found to bind the HLA and to elicit an in vitro T cell response, (3) peptides shown to elicit in vivo tumor rejection, and (4) peptides processed and naturally presented as defined by physical detection. In addition to T cell response, we also annotate peptides that are naturally processed HLA binders, e.g., peptides eluted from HLA in mass spectrometry studies. TANTIGEN provides a rich data resource for tumor-associated epitope and neoepitope discovery studies and is freely available at http://cvc.dfci.harvard.edu/tantigen/ or http://projects.met-hilab.org/tadb (mirror).

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation

Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic strategies are known to halt disease progression or reverse muscle weakness or atrophy. Many genes may be incorrectly regulated in affected muscle tissue, but the mechanisms responsible for the progressive muscle weakness remain largely unknown. Although machine learning (ML) has made significant inroads in biomedical disciplines such as cancer research, no reports have yet addressed FSHD analysis using ML techniques. This study explores a specific FSHD data set from a ML perspective. We report results showing a very promising small group of genes that clearly separates FSHD samples from healthy samples. In addition to numerical prediction figures, we show data visualizations and biological evidence illustrating the potential usefulness of these results.

InCoB2014: mining biological data from genomics for transforming industry and health

The 13th International Conference on Bioinformatics (InCoB2014) was held for the first time in Australia, at Sydney, July 31-2 August, 2014. InCoB is the annual scientific gathering of the Asia-Pacific Bioinformatics Network (APBioNet), hosted since 2002 in the Asia-Pacific region. Of 106 full papers submitted to the BMC track of InCoB2014, 50 (47.2%) were accepted in BMC Bioinformatics, BMC Genomics and BMC Systems Biology supplements, with three papers in a new BMC Medical Genomics supplement. While the majority of presenters and authors were from Asia and Australia, the increasing number of US and European conference attendees augurs well for the international flavour of InCoB. Next year's InCoB will be held jointly with the Genome Informatics Workshop (GIW), September 9-11, 2015 in Tokyo, Japan, with a view to integrate bioinformatics communities in the region.