Neoantigens arise from mutations in cancer cells and are important targets of T cell-mediated anti-tumor immunity. Here, we report the first open-label, phase Ib clinical trial of a personalized ...neoantigen-based vaccine, NEO-PV-01, in combination with PD-1 blockade in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. This analysis of 82 patients demonstrated that the regimen was safe, with no treatment-related serious adverse events observed. De novo neoantigen-specific CD4+ and CD8+ T cell responses were observed post-vaccination in all of the patients. The vaccine-induced T cells had a cytotoxic phenotype and were capable of trafficking to the tumor and mediating cell killing. In addition, epitope spread to neoantigens not included in the vaccine was detected post-vaccination. These data support the safety and immunogenicity of this regimen in patients with advanced solid tumors (Clinicaltrials.gov: NCT02897765).
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•The personalized neoantigen vaccine Neo-PV-01 plus nivolumab is feasible and safe•NEO-PV-01 plus nivolumab stimulates durable neoantigen-specific T cell reactivity•NEO-PV-01-specific T cells have cytotoxic potential and can traffic to the tumor•NEO-PV-01 induces epitope spreading consistent with vaccine-mediated tumor cytotoxicity
In a phase Ib clinical trial, Ott et al. demonstrate feasibility, safety, and immunogenicity of the combination of personalized neoantigen vaccines and PD-1 inhibition in patients with advanced solid tumors. Vaccine-induced T cells persist over time, exhibit cytotoxic potential, and can migrate to tumors. Epitope spread and major pathologic tumor responses were detected following vaccination.
Summary
DNA methylation, an epigenetic alteration typically occurring early in cancer development, could aid in the molecular diagnosis of melanoma. We determined technical feasibility for ...high‐throughput DNA‐methylation array‐based profiling using formalin‐fixed paraffin‐embedded tissues for selection of candidate DNA‐methylation differences between melanomas and nevi. Promoter methylation was evaluated in 27 common benign nevi and 22 primary invasive melanomas using a 1505 CpG site microarray. Unsupervised hierarchical clustering distinguished melanomas from nevi; 26 CpG sites in 22 genes were identified with significantly different methylation levels between melanomas and nevi after adjustment for age, sex, and multiple comparisons and with β‐value differences of ≥0.2. Prediction analysis for microarrays identified 12 CpG loci that were highly predictive of melanoma, with area under the receiver operating characteristic curves of >0.95. Of our panel of 22 genes, 14 were statistically significant in an independent sample set of 29 nevi (including dysplastic nevi) and 25 primary invasive melanomas after adjustment for age, sex, and multiple comparisons. This first report of a DNA‐methylation signature discriminating melanomas from nevi indicates that DNA methylation appears promising as an additional tool for enhancing melanoma diagnosis.
T cells use highly diverse receptors (TCRs) to identify tumor cells presenting neoantigens arising from genetic mutations and establish anti-tumor activity. Immunotherapy harnessing ...neoantigen-specific T cells to target tumors has emerged as a promising clinical approach. To assess whether a comprehensive peripheral mononuclear blood cell analysis predicts responses to a personalized neoantigen cancer vaccine combined with anti-PD-1 therapy, we characterize the TCR repertoires and T and B cell frequencies in 21 patients with metastatic melanoma who received this regimen. TCR-α/β-chain sequencing reveals that prolonged progression-free survival (PFS) is strongly associated with increased clonal baseline TCR repertoires and longitudinal repertoire stability. Furthermore, the frequencies of antigen-experienced T and B cells in the peripheral blood correlate with repertoire characteristics. Analysis of these baseline immune features enables prediction of PFS following treatment. This method offers a pragmatic clinical approach to assess patients’ immune state and to direct therapeutic decision making.
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Pre-treatment blood-based factors predict response to immunotherapyTCR repertoire clonality and stability associate with improved clinical outcomesBaseline T and B cell memory phenotypes associate with improved clinical outcomesCombined baseline TCR repertoire and PBMC phenotypes predict immunotherapy response
Poran et al. study peripheral blood cells from metastatic melanoma patients before, during, and after treatment with personalized neoantigen therapy plus anti-PD-1. The combination of T cell receptor repertoire profiling and immunophenotyping of blood cells collected pre-treatment presents a strong predictor for response to treatment in a minimally invasive manner.
We have recently shown that replication forks pause near origins in normal human fibroblasts (NHF1-hTERT) but not glioblastoma T98G cells. This observation led us to question whether other ...differences in the replication program may exist between these cell types that may relate to their genetic integrity. To identify differences, we detected immunoflourescently the sequential incorporation of the nucleotide analogs IdU and CldU into replicating DNA at the start of every hour of a synchronized S phase. We then characterized the patterns of labeled replicating DNA tracks and quantified the percentages and lengths of the tracks found at these hourly intervals. From the directionality of labeling in single extended replicating DNA fibers, tracks were categorized as single bidirectional origins, unidirectional elongations, clusters of origins firing in tandem, or merging forks (terminations). Our analysis showed that the start of S phase is enriched in single bidirectional origins in NHF1-hTERT cells, followed by an increase in clustering during mid S phase and an increase in merging forks during late S phase. Early S phase in T98G cells also largely consisted of single bidirectional origin initiations; however, an increase in clustering was delayed until an hour later, and clusters were shorter in mid/late S phase than in NHF1-hTERT cells. The spike in merging forks also did not occur until an hour later in T98G cells. Our observations suggest models to explain the temporal replication of single and clustered origins, and suggest differences in the replication program in a normal and cancer cell line.
Big data presents the overwhelming challenge of estimating a large number of parameters, which is much larger than the sample size. Even for a simple linear model, when the number of predictors is ...larger than or close to the sample size, such model may be unidentifiable and the least squares estimates of regression coefficients can be unstable. To deal with such issue, we systematically investigate three Bayesian regularization methods with applications in imaging genetics. First, we develop a Bayesian lasso estimator for the covariance matrix and propose a metropolis-based sampling scheme. This development is motivated by functional network exploration for the entire brain from magnetic resonance imaging (MRI) data. Second, we propose a Bayesian generalized low rank regression model (GLRR) for the mean parameter estimation and combine this with factor loading method of covariance estimation to capture the spatial correlation among the responses and jointly estimate the mean and covariance parameters. This development is motivated by performing genome-wide searches for associations between genetic variants and brain imaging phenotypes from data collected by Alzheimer's Disease Neuroimaging Initiative (ADNI). Third, we extend GLRR to longitudinal setting and propose a Bayesian longitudinal low rank regression (L2R2) to account for spatiotemporal correlation among the responses as well as estimation of full-rank coefficient matrix for standard prognostic factors. This development is motivated by genome-wide searches for associations between genetic variants and brain imaging phenotypes observed over time with a primary focus on role of aging and the interaction of age with genotype in affecting brain volume.
We propose a Bayesian generalized low-rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for ...associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We apply GLRR to investigate the impact of 1071 SNPs on top 40 genes reported by AlzGene database on the volumes of 93 regions of interest (ROI) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI). Supplementary materials for this article are available online.
To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) ...model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients’ brain images obtained from the Alzheimer's Disease Neuroimaging Initiative.
•The proposed method L2R2 jointly analyzes high-dimensional longitudinal neuroimaging responses and genetic covariates.•Modeling longitudinal SNP effects on ROI trajectories through SNP-age interactions.•L2R2 identify more longitudinal genetic effects on ROI trajectories for data from the Alzheimer's Disease Neuroimaging Initiative than the competing approaches.•Low-rank decomposition utilizes the variable structures to efficiently reduce the number of parameters for more powerful association analysis.•Modeling spatial–temporal correlations of longitudinal neuroimaging variables to increase detection power.
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