Cell surface proteins are excellent targets for diagnostic and therapeutic interventions. By using bioinformatics tools, we generated a catalog of 3,702 transmembrane proteins located at the surface of human cells (human cell surfaceome). We explored the genetic diversity of the human cell surfaceome at different levels, including the distribution of polymorphisms, conservation among eukaryotic species, and patterns of gene expression. By integrating expression information from a variety of sources, we were able to identify surfaceome genes with a restricted expression in normal tissues and/or differential expression in tumors, important characteristics for putative tumor targets. A high-throughput and efficient quantitative real-time PCR approach was used to validate 593 surfaceome genes selected on the basis of their expression pattern in normal and tumor samples. A number of candidates were identified as potential diagnostic and therapeutic targets for colorectal tumors and glioblastoma. Several candidate genes were also identified as coding for cell surface cancer/testis antigens. The human cell surfaceome will serve as a reference for further studies aimed at characterizing tumor targets at the surface of human cells.colorectal tumors ͉ CT antigens ͉ glioblastoma ͉ transmembrane ͉ tumor cell surface antigens W ith the availability of the human genome sequence, an important goal of current biological research is a more specific and accurate annotation of human genes. One critical property is the subcellular localization of gene products, because this affects their use as potential diagnostic and therapeutic targets. In this respect, the identification of cell surface proteins is of particular interest (1-3) because these proteins represent ideal therapeutic targets. Indeed, cell surface proteins have proved to be relevant to many areas of medicine, and a number of monoclonal antibodies against them are approved for therapeutic applications by the Food and Drug Administration, particularly in cancer therapy. Furthermore, cell surface proteins are also excellent targets for diagnostic assays, especially in biological fluids. On the other hand, there are several issues that make cell surface proteins difficult to manipulate biochemically. First, their hydrophobic transmembrane (TM) domain makes them insoluble. Second, several posttranslational modifications are not executed in commonly used expression systems. Finally, interactions involving cell surface proteins usually have an extremely short half-life (on the order of milliseconds), which has an effect on the development of purification protocols. Despite these limitations, decades of intensive research of cell surface proteins have generated a significant information base. Ideally, this information should be analyzed in a genome-wide context.We generated here a catalog of more than 3,700 genes believed to encode proteins located at the surface of human cells. For the sake of simplicity, we will call this catalog the ''human cell surfaceome.'' An integrated ...

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Cancer/testis (CT) genes are excellent candidates for cancer immunotherapies because of their restrict expression in normal tissues and the capacity to elicit an immune response when expressed in tumor cells. In this study, we provide a genome-wide screen for CT genes with the identification of 745 putative CT genes. Comparison with a set of known CT genes shows that 201 new CT genes were identified. Integration of gene expression and clinical data led us to identify dozens of CT genes associated with either good or poor prognosis. For the CT genes related to good prognosis, we show that there is a direct relationship between CT gene expression and a signal for CD8+ cells infiltration for some tumor types, especially melanoma.

Natural Abs (NAbs) are Igs present in the serum and body fluids of healthy vertebrate animals, without any previous intentional immunization. NAbs often exhibit autoreactivity but also play an essential role in immunity, being a first line of defense against infectious microorganisms. We have previously analyzed the natural serum IgM Ab repertoire of normal mice, characterizing their reactivity with hundreds/thousands of self Ags; a significant similarity among different individuals was observed, and it was found that many reactivities of NAbs stably kept during adulthood were established early in life, implicating that period as a critical time window in the physiology of NAb repertoire selection. In the work reported here, experiments were conducted to address the role of normal lymphocyte ontogeny to the formation and stability of adult NAb repertoire. The massive destruction of the lymphoid system was promoted in adult mice with gamma-irradiation, and regeneration of hemopoietic tissues was granted by bone marrow or fetal liver inoculum. NAb repertoire regeneration was followed for 60 days after gamma-irradiation in bone marrow or fetal liver chimeric animals. The analysis of serum IgM reactivity with hundreds/thousands of self Ags showed that the NAb repertoire regenerated most of its original format after massive destruction of lymphoid compartments, characterizing autoreactive repertoire selection as a robust biological process. The data also show that regeneration of the NAb repertoire occurred similarly in fetal liver and bone marrow chimeras, although the latter animals poorly reconstituted their CD5+ B1 cell compartment, suggesting that B1 cells are not essential for natural Ab regeneration.

Background: A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. However, there is a significant discordance about the identification of mutational impact and pathogenicity among them. Therefore, manual verification is necessary for confirming the real effect of a mutation in its casuistic. Methods: In this work, we use variables categorization and selection for building a decision tree model, and later we measure and compare its accuracy with four known mutation predictors and seventeen supervised machinelearning (ML) algorithms. Results: The results showed that the proposed tree reached the highest precision among all tested variables: 91% for True Neutrals, 8% for False Neutrals, 9% for False Pathogenic, and 92% for True Pathogenic. Conclusions: The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an accuracy close to the best ones achieved from supervised ML algorithms. Besides, the decision tree algorithm is easier to apply in clinical practice by non-IT experts. From the cancer research community perspective, this approach can be successfully applied as an alternative for the determination of potential pathogenicity of VOUS.