Analysis of Gene Expression Using Gene Sets Discriminates Cancer Patients with and without Late Radiation Toxicity

BACKGROUND: Radiation is an effective anti-cancer therapy but leads to severe late radiation toxicity in 5%–10% of patients. Assuming that genetic susceptibility impacts this risk, we hypothesized that the cellular response of normal tissue to X-rays could discriminate patients with and without late radiation toxicity. METHODS AND FINDINGS: Prostate carcinoma patients without evidence of cancer 2 y after curative radiotherapy were recruited in the study. Blood samples of 21 patients with severe late complications from radiation and 17 patients without symptoms were collected. Stimulated peripheral lymphocytes were mock-irradiated or irradiated with 2-Gy X-rays. The 24-h radiation response was analyzed by gene expression profiling and used for classification. Classification was performed either on the expression of separate genes or, to augment the classification power, on gene sets consisting of genes grouped together based on function or cellular colocalization. X-ray irradiation altered the expression of radio-responsive genes in both groups. This response was variable across individuals, and the expression of the most significant radio-responsive genes was unlinked to radiation toxicity. The classifier based on the radiation response of separate genes correctly classified 63% of the patients. The classifier based on affected gene sets improved correct classification to 86%, although on the individual level only 21/38 (55%) patients were classified with high certainty. The majority of the discriminative genes and gene sets belonged to the ubiquitin, apoptosis, and stress signaling networks. The apoptotic response appeared more pronounced in patients that did not develop toxicity. In an independent set of 12 patients, the toxicity status of eight was predicted correctly by the gene set classifier. CONCLUSIONS: Gene expression profiling succeeded to some extent in discriminating groups of patients with and without severe late radiotherapy toxicity. Moreover, the discriminative power was enhanced by assessment of functionally or structurally related gene sets. While prediction of individual response requires improvement, this study is a step forward in predicting susceptibility to late radiation toxicity

Somatic Variation of T-Cell Receptor Genes Strongly Associate with HLA Class Restriction

Every person carries a vast repertoire of CD4+ T-helper cells and CD8+ cytotoxic T cells for a healthy immune system. Somatic VDJ recombination at genomic loci that encode the T-cell receptor (TCR) is a key step during T-cell development, but how a single T cell commits to become either CD4+ or CD8+ is poorly understood. To evaluate the influence of TCR sequence variation on CD4+/CD8+ lineage commitment, we sequenced rearranged TCRs for both α and β chains in naïve T cells isolated from healthy donors and investigated gene segment usage and recombination patterns in CD4+ and CD8+ T-cell subsets. Our data demonstrate that most V and J gene segments are strongly biased in the naïve CD4+ and CD8+ subsets with some segments increasing the odds of being CD4+ (or CD8+) up to five-fold. These V and J gene associations are highly reproducible across individuals and independent of classical HLA genotype, explaining ~11% of the observed variance in the CD4+ vs. CD8+ propensity. In addition, we identified a strong independent association of the electrostatic charge of the complementarity determining region 3 (CDR3) in both α and β chains, where a positively charged CDR3 is associated with CD4+ lineage and a negatively charged CDR3 with CD8+ lineage. Our findings suggest that somatic variation in different parts of the TCR influences T-cell lineage commitment in a predominantly additive fashion. This notion can help delineate how certain structural features of the TCR-peptide-HLA complex influence thymic selection

Computational Model Reveals Limited Correlation between Germinal Center B-Cell Subclone Abundancy and Affinity: Implications for Repertoire Sequencing

Immunoglobulin repertoire sequencing has successfully been applied to identify expanded antigen-activated B-cell clones that play a role in the pathogenesis of immune disorders. One challenge is the selection of the Ag-specific B cells from the measured repertoire for downstream analyses. A general feature of an immune response is the expansion of specific clones resulting in a set of subclones with common ancestry varying in abundance and in the number of acquired somatic mutations. The expanded subclones are expected to have BCR affinities for the Ag higher than the affinities of the naive B cells in the background population. For these reasons, several groups successfully proceeded or suggested selecting highly abundant subclones from the repertoire to obtain the Ag-specific B cells. Given the nature of affinity maturation one would expect that abundant subclones are of high affinity but since repertoire sequencing only provides information about abundancies, this can only be verified with additional experiments, which are very labor intensive. Moreover, this would also require knowledge of the Ag, which is often not available for clinical samples. Consequently, in general we do not know if the selected highly abundant subclone(s) are also the high(est) affinity subclones. Such knowledge would likely improve the selection of relevant subclones for further characterization and Ag screening. Therefore, to gain insight in the relation between subclone abundancy and affinity, we developed a computational model that simulates affinity maturation in a single GC while tracking individual subclones in terms of abundancy and affinity. We show that the model correctly captures the overall GC dynamics, and that the amount of expansion is qualitatively comparable to expansion observed from B cells isolated from human lymph nodes. Analysis of the fraction of high- and low-affinity subclones among the unexpanded and expanded subclones reveals a limited correlation between abundancy and affinity and shows that the low abundant subclones are of highest affinity. Thus, our model suggests that selecting highly abundant subclones from repertoire sequencing experiments would not always lead to the high(est) affinity B cells. Consequently, additional or alternative selection approaches need to be applie

In rheumatoid arthritis, synovitis at different inflammatory sites is dominated by shared but patient-specific T cell clones

Genetic and immunological evidence clearly points to a role for T cells in the pathogenesis of rheumatoid arthritis (RA). Selective targeting of such disease-associated T cell clones might be highly effective while having few side effects. However, such selective targeting may only be feasible if the same T cell clones dominate the immune response at different sites of inflammation. We leveraged high-throughput technology to quantitatively assess whether different T cell clones dominate the inflammatory infiltrate at various sites of inflammation in this prototypic autoimmune disease. In 13 RA patients, we performed quantitative next-generation sequencing–based human TCRb repertoire analysis in simultaneously obtained samples from inflamed synovial tissue (ST) from distinct locations within one joint, from multiple joints, and from synovial fluid (SF) and peripheral blood (PB). Identical TCRb clones dominate inflammatory responses in ST samples taken from different locations within a single joint and when sampled in different joints. Although overall ST–SF overlap was comparable to higher ST–ST values, the overlap in dominant TCRb clones in ST–SF comparisons was much lower than ST–ST and comparable to the low ST–PB overlap. In individual RA patients, a limited number of TCRb clones dominate the immune response in the inflamed ST regardless of the location within a joint and which joint undergoes biopsy; in contrast, there is limited overlap of ST with SF or PB TCR repertoires. This limited breadth of the T cell response in ST of the individual RA patient indicates that development of immunotherapies that selectively modulate dominant T cell responses might be feasible

Somatic Variation of T-Cell Receptor Genes Strongly Associate with HLA Class Restriction

Every person carries a vast repertoire of CD4+ T-helper cells and CD8+ cytotoxic T cells for a healthy immune system. Somatic VDJ recombination at genomic loci that encode the T-cell receptor (TCR) is a key step during T-cell development, but how a single T cell commits to become either CD4+ or CD8+ is poorly understood. To evaluate the influence of TCR sequence variation on CD4+/CD8+ lineage commitment, we sequenced rearranged TCRs for both α and β chains in naïve T cells isolated from healthy donors and investigated gene segment usage and recombination patterns in CD4+ and CD8+ T-cell subsets. Our data demonstrate that most V and J gene segments are strongly biased in the naïve CD4+ and CD8+ subsets with some segments increasing the odds of being CD4+ (or CD8+) up to five-fold. These V and J gene associations are highly reproducible across individuals and independent of classical HLA genotype, explaining ~11% of the observed variance in the CD4+ vs. CD8+ propensity. In addition, we identified a strong independent association of the electrostatic charge of the complementarity determining region 3 (CDR3) in both α and β chains, where a positively charged CDR3 is associated with CD4+ lineage and a negatively charged CDR3 with CD8+ lineage. Our findings suggest that somatic variation in different parts of the TCR influences T-cell lineage commitment in a predominantly additive fashion. This notion can help delineate how certain structural features of the TCR-peptide-HLA complex influence thymic selection

Non-response to rituximab therapy in rheumatoid arthritis is associated with incomplete disruption of the B cell receptor repertoire

Objective: To gain more insight into the dynamics of lymphocyte depletion and develop new predictors of clinical response to rituximab in rheumatoid arthritis (RA). Methods: RNA-based next-generation sequencing was used to analyse the B cell receptor (BCR) repertoire in peripheral blood and synovial tissue samples collected from 24 seropositive patients with RA treated with rituximab. Clonal expansion, mutation load and clonal overlap were assessed in samples collected before, at week 4 and at week 16 or 24 after treatment and correlated to the patients' clinical response. Results: After 4 weeks of rituximab-induced B cell depletion, the peripheral blood BCR repertoire of treated patients consisted of fewer, more dominant and more mutated BCR clones. No significant changes in the synovial tissue BCR repertoire were detected until week 16 post-treatment, when a reduced clonal overlap with baseline and an increased mutation load were observed. In patients who were non-responders at month 3 (n=5) using the European League Against Rheumatism response criteria, peripheral blood samples taken at week 4 after rituximab treatment showed more dominant clones compared with moderate responders (n=9) (median (IQR): 36 (27-52) vs 18 (16-26); p<0.01) and more clonal overlap with the baseline (median (IQR): 5% (2%-20%) vs 0% (0%-0%); p≤0.01). Conclusion: Significant changes in BCR clonality are observed in peripheral blood of patients 4 weeks after rituximab treatment, while changes in synovial tissue were observed at later time points. Incomplete depletion of the dominant baseline peripheral blood BCR repertoire in the first month of treatment might predict clinical non-response at 3 months

Expression Profiles of Classifying Gene Sets

<p>Heat map of <i>r</i> values of 72 gene sets that were present in more than 20% of the 500 repeated assessments with 34 patients in the training set of the classifier. These discriminating gene sets were used in a supervised two-dimensional hierarchical clustering of NRs (green) and ORs (red) based on the <i>r</i> values The threshold for being affected was set at |β₂| = 0.4.</p

Validation of the Gene Set Classification with an Independent Patient Set

<div><p>(A) Contingency table of the physician and microarray classification of 12 additional patients. The 72 most discriminating gene sets in the training set were used to predict responder status. Numbers of patients classified with certainty are in parentheses.</p> <p>(B) A principal components analysis plot of the two principal components separating the NRs (green) from the ORs (red). Circles represent the 38 patients of the original training set, and triangles represent the 12 patients of the independent validation set.</p></div

Expression Profiles of Classifying Genes

<p>Heat map of β₂ values of 62 genes that were present in more than 20% of the 500 repeated assessments with 34 patients in the training set of the classifier. These discriminating genes were used in a supervised two-dimensional hierarchical clustering of NRs (green) and ORs (red) based on the β₂ values representing the radiation response.</p

Improved Patient Classification Using Functionally Related Gene Sets

<div><p>(A) Gene classification (in red) and gene set classification (in blue), following the strategy of Michiels et al. [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0030422#pmed-0030422-b025" target="_blank">25</a>], with 95% confidence intervals for the test of proportions. The minimal misclassification rate was 37% ± 2% with gene classification and 14% ± 2% with gene set classification.</p> <p>(B and C) The certainty of microarray classification for each patient was calculated based on (B) genes or (C) functionally related gene sets. The certainty was calculated at the training set size of 32 patients (red arrow in [A]).</p> <p>(D and E) Contingency tables summarizing the concordance between the physician and microarray classifications using (D) genes and (E) gene sets. Numbers of patients classified with certainty (cases where the tolerance limit does not include zero) are in parentheses.</p></div