PATRI, a Genomics Data Integration Tool for Biomarker Discovery.

The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.

T-helper cell-response to MHC class II-binding peptides of the renal cell carcinoma-associated antigen RAGE-1.

Recently, epitope prediction software for HLA-DR binding sequences has become available. In view of the importance of T helper (Th) cell activation in immunotherapy of cancer and evidences supporting immunogenicity of renal cell carcinoma (RCC), we have tested 4 peptides of RAGE-1 binding promiscuously to HLA-DR molecules for induction of an immune response. The peptides predicted by the TEPITOPE program using a stringent threshold were derived from the open reading frame 2 and 5 of RAGE-1. Induction of response was evaluated by culturing peripheral blood mononuclear cells (PBMC) in the presence of peptide-loaded dendritic cells (DC) to determine proliferative activity and cytokine expression. Two out of 5 donors did not respond to any of the 4 peptides, 2 donors responded to one peptide and one donor responded to two other peptides. Notably, as revealed by blocking studies and T cell subtype definition, peptides bound to MHC class II molecules and peptide pulsed DC exclusively activated CD4+ T cells, which were of the Th1 subtype. With respect to clinical application it is important that (un)responsiveness of individual donors' PBMC was a very consistent feature. Though we have not tested explicitly whether these peptides correspond to naturally processed peptides, the possibility to define those patients whose Th might respond to in silico predicted peptides of RAGE-1, by an in vitro assay, could well be a helpful step towards setting up a RAGE-1 based immunotherapeutic protocol.

In vitro and in vivo induction of a Th cell response toward peptides of the melanoma-associated glycoprotein 100 protein selected by the TEPITOPE program.

The melanoma-associated Ag glycoprotein 100 was analyzed by the T cell epitope prediction software TEPITOPE. Seven HLA-DR promiscuous peptides predicted with a stringent threshold were used to load dendritic cells (DC), and induction of a proliferative response was monitored. PBMC of all nine donors including two patients with malignant melanoma responded to at least one of the peptides. The proliferative response was defined as a Th response by the selective expansion of CD4(+) cells, up-regulation of CD25 and CD40L, and IL-2 and IFN-gamma expression. Peptide-loaded DC also initiated a T helper response in vivo (i.e., tumor growth in the SCID mouse was significantly retarded by the transfer of PBMC together with peptide-loaded DC). Because the use of the TEPITOPE program allows for a prediction of T cell epitopes; because the predicted peptides can be rapidly confirmed by inducing a Th response in the individual patient; and because application of peptide-loaded DC suffices for the in vivo activation of helper cells, vaccination with MHC class II-binding peptides of tumor-associated Ags becomes a feasible and likely powerful tool in the immunotherapy of cancer.

Epitope scanning using virtual matrix-based algorithms.

Protein sequence and expression databases (transcriptomes) contain the information required to identify epitopes capable of generating protective immune responses in humans. A key event in the initiation of an immune response against disease is the presentation of antigenic peptide epitopes to T cells by human leukocyte antigen (HLA) molecules. Computational filtering tools that allow the prediction of HLA/epitope interaction can be applied to sequence databases to select for candidate epitopes, thus minimising the subsequent amount of laboratory work. Here, the basic principles of epitope prediction and a summary of the available prediction approaches are presented, with a particular emphasis on the use of algorithms based on virtual HLA-II quantitative matrices, capable of predicting promiscuous HLA-II ligands.

Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices.

Most pockets in the human leukocyte antigen-group DR (HLA-DR) groove are shaped by clusters of polymorphic residues and, thus, have distinct chemical and size characteristics in different HLA-DR alleles. Each HLA-DR pocket can be characterized by "pocket profiles," a quantitative representation of the interaction of all natural amino acid residues with a given pocket. In this report we demonstrate that pocket profiles are nearly independent of the remaining HLA-DR cleft. A small database of profiles was sufficient to generate a large number of HLA-DR matrices, representing the majority of human HLA-DR peptide-binding specificity. These virtual matrices were incorporated in software (TEPITOPE) capable of predicting promiscuous HLA class II ligands. This software, in combination with DNA microarray technology, has provided a new tool for the generation of comprehensive databases of candidate promiscuous T-cell epitopes in human disease tissues. First, DNA microarrays are used to reveal genes that are specifically expressed or upregulated in disease tissues. Second, the prediction software enables the scanning of these genes for promiscuous HLA-DR binding sites. In an example, we demonstrate that starting from nearly 20,000 genes, a database of candidate colon cancer-specific and promiscuous T-cell epitopes could be fully populated within a matter of days. Our approach has implications for the development of epitope-based vaccines.

Identification of destabilizing residues in HLA class II-selected bacteriophage display libraries edited by HLA-DM.

HLA-DM (DM) functions as a peptide editor by catalyzing the release of class II-associated invariant chain peptides (CLIP) and other unstable peptides, thus supporting the formation of stable class II-peptide complexes for presentation. To investigate the general features that determine the DM susceptibility of HLA-DR1/peptide complexes, we generated a large DM-sensitive peptide repertoire from an M13 bacteriophage display library using a novel double selection protocol: we selected bacteriophage capable of binding to DR1 molecules and, subsequently, we enriched DR1-bound bacteriophage susceptible to elution by purified DM molecules. Sequence and mutational analyses of the DR1/DM double-selected peptides revealed that the amino acids Gly and Pro play a destabilizing role in the dissociation kinetics of DR1 ligands. This observation was confirmed also in natural peptide sequences such as CLIP 89-101, HA 307-319 and bovine collagen II (CII) 261-273. Our results demonstrate that DM susceptibility does not only depend on the number and nature of anchor residues, or the peptide length. Instead, less obvious sequence characteristics play a major role in the DM editing process and ultimately in the composition of peptide repertoires presented to T cells.

Different modes of peptide interaction enable HLA-DQ and HLA-DR molecules to bind diverse peptide repertoires.

The role of HLA-DQ molecules in Ag presentation has, thus far, remained elusive. Here we report that two DQ allotypes, DQ7 (DQA1*0501/B1*0301) and DQ9 (DQA1*0201/B1*0303), are capable of binding peptide repertoires in complementarity with DR molecules. The results reflect fundamental differences in the binding modes of these two HLA class II isotypes, in that DQ7 and DQ9 but not DR molecules appear to have the capacity to bind peptide structures without type 1-like anchor residues. Consistent with this is our observation that none of the amino acid side chains of the class II-associated invariant chain peptides (CLIP) are required for association with DQ7 and DQ9, even though many of them are essential for CLIP-DR interaction. Together, these data reveal a functional complementarity of HLA-DR and -DQ molecules in Ag presentation.

Antigenic peptides and autoimmunity.

MHC class II alleles play a major role in determining resistance or susceptibility to autoimmune disease. Considerable effort is being expended to establish the role polymorphisms play in influencing the binding of antigens, including autoantigens, in the peptide-binding groove. Single amino acid substitutions in the MHC cleft, for instance at DR beta 71 and DQ beta 57, influence peptide binding. Although candidate autoantigenic peptides have been identified which bind to disease-associated MHC molecules, several critical questions remain to be answered before the role of these peptides in the autoimmune disease process can be established.