The Cancer Exome Generated by Alternative mRNA Splicing Dilutes Predicted HLA Class I Epitope Density

Several studies have shown that cancers actively regulate alternative splicing. Altered splicing mechanisms in cancer lead to cancer-specific transcripts different from the pool of transcripts occurring only in healthy tissue. At the same time, altered presentation of HLA class I epitopes is frequently observed in various types of cancer. Down-regulation of genes related to HLA class I antigen processing has been observed in several cancer types, leading to fewer HLA class I antigens on the cell surface. Here, we use a peptidome wide analysis of predicted alternative splice forms, based on a publicly available database, to show that peptides over-represented in cancer splice variants comprise significantly fewer predicted HLA class I epitopes compared to peptides from normal transcripts. Peptides over-represented in cancer transcripts are in the case of the three most common HLA class I supertype representatives consistently found to contain fewer predicted epitopes compared to normal tissue. We observed a significant difference in amino acid composition between protein sequences associated with normal versus cancer tissue, as transcripts found in cancer are enriched with hydrophilic amino acids. This variation contributes to the observed significant lower likelihood of cancer-specific peptides to be predicted epitopes compared to peptides found in normal tissue

Patterns of Disease Progression and Outcome of Patients With Testicular Seminoma Who Relapse After Adjuvant or Curative Radiation Therapy.

PURPOSE Radiation therapy is a possible treatment strategy for patients with testicular seminoma after orchiectomy in clinical stage I or II disease. Little is known about the outcome of patients who experience a relapse after radiation therapy. METHODS AND MATERIALS Data from 61 patients who relapsed after adjuvant or curative radiation therapy from 17 centers in 11 countries were collected and retrospectively analyzed. Primary outcomes were disease-free and overall survival. Secondary outcomes were time to relapse, stage at relapse, treatment for relapse, and rate of febrile neutropenia during chemotherapy for relapse. RESULTS With a median follow-up of 9.9 years (95% confidence interval [CI], 7.5-10.9), we found a 5-year disease-free survival of 90% (95% CI, 79-95) and a 5-year overall survival of 98% (95% CI, 89-100). Sixty-six percent of patients had stage III disease at time of relapse and 93% of patients fell into the good prognosis group per the International Germ Cell Cancer Collaborative Group classification. The median time to relapse after radiation therapy was 15.6 months (95% CI, 12-23). Twenty-two (36%) patients relapsed more than 2 years after radiation therapy and 7 (11.5%) patients relapsed more than 5 years after radiation therapy. One-third of relapses was detected owing to patients' symptoms, whereas two-thirds of relapses were detected during routine follow-up. The majority (93%) of cases were treated with cisplatin-based chemotherapy. The rate of febrile neutropenia during chemotherapy was 35%. Five patients experienced a second relapse. At last follow-up, 55 patients (90%) were alive without disease. Only 1 patient died owing to disease progression. CONCLUSIONS Cisplatin-based chemotherapy for patients with seminoma who have relapsed after treatment with radiation therapy alone leads to excellent outcomes. Patients and physicians should be aware of possible late relapses after radiation therapy

NetCTLpan: pan-specific MHC class I pathway epitope predictions

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/

Optimized Nonlinear Gradients for Reversed-Phase Liquid Chromatography in Shotgun Proteomics

Reversed-phase liquid chromatography has become the preferred method for separating peptides in most of the mass spectrometry-based proteomics workflows of today. In the way the technique is typically applied, the peptides are released from the chromatography column by the gradual addition of an organic buffer according to a linear function. However, when applied to complex peptide mixtures, this approach leads to unequal spreads of the peptides over the chromatography time. To address this, we investigated the use of nonlinear gradients, customized for each setup at hand. We developed an algorithm to generate optimized gradient functions for shotgun proteomics experiments and evaluated it for two data sets consisting each of four replicate runs of a human complex sample. Our results show that the optimized gradients produce a more even spread of the peptides over the chromatography run, while leading to increased numbers of confident peptide identifications. In addition, the list of peptides identified using nonlinear gradients differed considerably from those found with the linear ones, suggesting that such gradients can be a valuable tool for increasing the proteome coverage of mass spectrometry-based experiments

Phenotype frequencies.

<p>HLA frequencies in the European population. Data obtained from the dbMHC database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038670#pone.0038670-Sayers1" target="_blank">[19]</a>.</p

Epitopes per set for all supertype representatives.

<p>Percentage of predicted epitopes is given for data extracted from the ASTD database as well as for permutated sequences. N/C is the ratio between the normal and cancer percentages. P-values are calculated by two-tailed t-test and adjusted for multiple testing by Bonferroni correction.</p

Hydrophilic amino acids are enriched in cancer.

<p>N/C ratios in relation to Hopp-Woods hydrophilicity scale (A), Wimley-White hydrophobicity scale (B) and to the mean ranking of amino acids based on 38 hydrophobicity scales (C). N/C ratio is the ratio of observed frequencies of the respective amino acids in polypeptides of over-represented transcripts from normal and cancer tissues. If the N/C value >1, the amino acid is more common in normal tissue; If the N/C value <1, the amino acid is more common in cancer. Green bars refer to more hydrophobic amino acids, whereas black bars refer to more hydrophilic amino acids. All N/C ratios larger or smaller than 1 are significant (p<0.001, calculated using the Wilson score <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038670#pone.0038670-Wilson1" target="_blank">[45]</a> and Bonferroni corrected).</p