Enhancing Natural Killer and CD8 + T Cell-Mediated Anticancer Cytotoxicity and Proliferation of CD8 + T Cells with HLA-E Monospecific Monoclonal Antibodies

Cytotoxic NK/CD8+ T cells interact with MHC-I ligands on tumor cells through either activating or inhibiting receptors. One of the inhibitory receptors is CD94/NKG2A. The NK/CD8+ T cell cytotoxic capability is lost when tumor-associated human leukocyte antigen, HLA-E, binds the CD94/NKG2A receptor, resulting in tumor progression and reduced survival. Failure of cancer patients to respond to natural killer (NK) cell therapies could be due to HLA-E overexpression in tumor tissues. Preventing the inhibitory receptor-ligand interaction by either receptor- or ligand-specific monoclonal antibodies (mAbs) is an innovative passive immunotherapeutic strategy for cancer. Since receptors and ligands can be monomeric or homo- or heterodimeric proteins, the efficacy of mAbs may rely on their ability to distinguish monospecific (private) functional epitopes from nonfunctional common (public) epitopes. We developed monospecific anti-HLA-E mAbs (e.g., TFL-033) that recognize only HLA-E-specific epitopes, but not epitopes shared with other HLA class-I loci as occurs with currently available polyreactive anti-HLA-E mAbs. Interestingly the amino acid sequences in the α1 and α2 helices of HLA-E, critical for the recognition of the mAb TFL-033, are strikingly the same sequences recognized by the CD94/NKG2A inhibitory receptors on NK/CD8+ cells. Such monospecific mAbs can block the CD94/NKG2A interaction with HLA-E to restore NK cell and CD8+ anticancer cell cytotoxicity. Furthermore, the HLA-E monospecific mAbs significantly promoted the proliferation of the CD4-/CD8+ T cells. These monospecific mAbs are also invaluable for the specific demonstration of HLA-E on tumor biopsies, potentially indicating those tumors most likely to respond to such therapy. Thus, they can be used to enhance passive immunotherapy once phased preclinical studies and clinical trials are completed. On principle, we postulate that NK cell passive immunotherapy should capitalize on both of these features of monospecific HLA-E mAbs, that is, the specific determination HLA-E expression on a particular tumor and the enhancement of NK cell/CD8+ cytotoxicity if HLA-E positive

Copy number variation signature to predict human ancestry

Abstract Background Copy number variations (CNVs) are genomic structural variants that are found in healthy populations and have been observed to be associated with disease susceptibility. Existing methods for CNV detection are often performed on a sample-by-sample basis, which is not ideal for large datasets where common CNVs must be estimated by comparing the frequency of CNVs in the individual samples. Here we describe a simple and novel approach to locate genome-wide CNVs common to a specific population, using human ancestry as the phenotype. Results We utilized our previously published Genome Alteration Detection Analysis (GADA) algorithm to identify common ancestry CNVs (caCNVs) and built a caCNV model to predict population structure. We identified a 73 caCNV signature using a training set of 225 healthy individuals from European, Asian, and African ancestry. The signature was validated on an independent test set of 300 individuals with similar ancestral background. The error rate in predicting ancestry in this test set was 2% using the 73 caCNV signature. Among the caCNVs identified, several were previously confirmed experimentally to vary by ancestry. Our signature also contains a caCNV region with a single microRNA (MIR270), which represents the first reported variation of microRNA by ancestry. Conclusions We developed a new methodology to identify common CNVs and demonstrated its performance by building a caCNV signature to predict human ancestry with high accuracy. The utility of our approach could be extended to large case–control studies to identify CNV signatures for other phenotypes such as disease susceptibility and drug response

Natural Killer T Cells Infiltrate Neuroblastomas Expressing the Chemokine CCL2

CD1d-restricted Vα24-Jα18–invariant natural killer T cells (iNKTs) are potentially important in tumor immunity. However, little is known about their localization to tumors. We analyzed 98 untreated primary neuroblastomas from patients with metastatic disease (stage 4) for tumor-infiltrating iNKTs using TaqMan® reverse transcription polymerase chain reaction and immunofluorescent microscopy. 52 tumors (53%) contained iNKTs, and oligonucleotide microarray analysis of the iNKT+ and iNKT− tumors revealed that the former expressed higher levels of CCL2/MCP-1, CXCL12/SDF-1, CCL5/RANTES, and CCL21/SLC. Eight tested neuroblastoma cell lines secreted a range of CCL2 (0–21.6 ng/ml), little CXCL12 (≤0.1 ng/ml), and no detectable CCL5 or CCL21. CCR2, the receptor for CCL2, was more frequently expressed by iNKT compared with natural killer and T cells from blood (P < 0.001). Supernatants of neuroblastoma cell lines that produced CCL2 induced in vitro migration of iNKTs from blood of patients and normal adults; this was abrogated by an anti-CCL2 monoclonal antibody. CCL2 expression by tumors was found to inversely correlate with MYCN proto-oncogene amplification and expression (r = 0.5, P < 0.001), and MYCN-high/CCL2-low expression accurately predicted the absence of iNKTs (P < 0.001). In summary, iNKTs migrate toward neuroblastoma cells in a CCL2-dependent manner, preferentially infiltrating MYCN nonamplified tumors that express CCL2

Robust selection of cancer survival signatures from high-throughput genomic data using two-fold subsampling

Identifying relevant signatures for clinical patient outcome is a fundamental task in high-throughput studies. Signatures, composed of features such as mRNAs, miRNAs, SNPs or other molecular variables, are often non-overlapping, even though they have been identified from similar experiments considering samples with the same type of disease. The lack of a consensus is mostly due to the fact that sample sizes are far smaller than the numbers of candidate features to be considered, and therefore signature selection suffers from large variation. We propose a robust signature selection method that enhances the selection stability of penalized regression algorithms for predicting survival risk. Our method is based on an aggregation of multiple, possibly unstable, signatures obtained with the preconditioned lasso algorithm applied to random (internal) subsamples of a given cohort data, where the aggregated signature is shrunken by a simple thresholding strategy. The resulting method, RS-PL, is conceptually simple and easy to apply, relying on parameters automatically tuned by cross validation. Robust signature selection using RS-PL operates within an (external) subsampling framework to estimate the selection probabilities of features in multiple trials of RS-PL. These probabilities are used for identifying reliable features to be included in a signature. Our method was evaluated on microarray data sets from neuroblastoma, lung adenocarcinoma, and breast cancer patients, extracting robust and relevant signatures for predicting survival risk. Signatures obtained by our method achieved high prediction performance and robustness, consistently over the three data sets. Genes with high selection probability in our robust signatures have been reported as cancer-relevant. The ordering of predictor coefficients associated with signatures was well-preserved across multiple trials of RS-PL, demonstrating the capability of our method for identifying a transferable consensus signature. The software is available as an R package rsig at CRAN (http://cran.r-project.org)

Genome wide DNA methylation analysis identifies novel molecular subgroups and predicts survival in neuroblastoma

Background: Neuroblastoma is the most common malignancy in infancy, accounting for 15% of childhood cancer deaths. Outcome for the high-risk disease remains poor. DNA-methylation patterns are significantly altered in all cancer types and can be utilised for disease stratification. Methods: Genome-wide DNA methylation (n = 223), gene expression (n = 130), genetic/clinical data (n = 213), whole-exome sequencing (n = 130) was derived from the TARGET study. Methylation data were derived from HumanMethylation450 BeadChip arrays. t-SNE was used for the segregation of molecular subgroups. A separate validation cohort of 105 cases was studied. Results: Five distinct neuroblastoma molecular subgroups were identified, based on genome-wide DNA-methylation patterns, with unique features in each, including three subgroups associated with known prognostic features and two novel subgroups. As expected, Cluster-4 (infant diagnosis) had significantly better 5-year progression-free survival (PFS) than the four other clusters. However, in addition, the molecular subgrouping identified multiple patient subsets with highly increased risk, most notably infant patients that do not map to Cluster-4 (PFS 50% vs 80% for Cluster-4 infants, P = 0.005), and allowed identification of subgroup-specific methylation differences that may reflect important biological differences within neuroblastoma. Conclusions: Methylation-based clustering of neuroblastoma reveals novel molecular subgroups, with distinct molecular/clinical characteristics and identifies a subgroup of higher-risk infant patients

The genetic landscape of high-risk neuroblastoma

Neuroblastoma is a malignancy of the developing sympathetic nervous system that often presents with widespread metastatic disease, resulting in survival rates of less than 50%1. To determine the spectrum of somatic mutation in high-risk neuroblastoma, we studied 240 cases using a combination of whole exome, genome and transcriptome sequencing as part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative. Here we report a low median exonic mutation frequency of 0.60 per megabase (0.48 non-silent), and remarkably few recurrently mutated genes in these tumors. Genes with significant somatic mutation frequencies included ALK (9.2% of cases), PTPN11 (2.9%), ATRX (2.5%, an additional 7.1% had focal deletions), MYCN (1.7%, a recurrent p.Pro44Leu alteration), and NRAS (0.83%). Rare, potentially pathogenic germline variants were significantly enriched in ALK, CHEK2, PINK1, and BARD1. The relative paucity of recurrent somatic mutations in neuroblastoma challenges current therapeutic strategies reliant upon frequently altered oncogenic drivers

WAVELET FOOTPRINTS AND SPARSE BAYESIAN LEARNING FOR DNA COPY NUMBER CHANGE ANALYSIS

Alterations in the number of DNA copies are very common in tumor cells and may have a very important role in cancer development and progression. New array platforms provide means to analyze the copy number by comparing the hybridization intensities of thousands of DNA sections along the genome. However, detecting and locating the copy number changes from this data is a very challenging task due to the large amount of biological processes that affect hybridization and cannot be controlled. This paper proposes a new technique that exploits the key characteristic that the DNA copy number is piecewise-constant along the genome. First, wavelet footprints are used to obtain a basis for representing the DNA copy number tha