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  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 730-730
    Abstract: Neoantigens arise from tumor-specific, somatic mutations and have the potential to be recognized by T cells that are associated with anti-tumor immune responses. Since they are non-self, they are hypothesized to provide an attractive therapeutic modality because T cells that can respond to those sequences have not undergone thymic selection. The ATLASTM platform enables identification of biologically relevant CD4+ and CD8+ T cell neoantigens in any subject in an unbiased manner, overcoming the limitations of conventional in silico predictive approaches. The ATLAS platform utilizes matched patient tumor biopsy and blood samples to identify recall T cell responses to tumor specific mutations. From patient peripheral blood, CD14+ monocytes were isolated and differentiated into dendritic cells (MDDCs), and T cells were sorted into CD4+ and CD8+ populations and non-specifically expanded. Tumor-specific changes (single nucleotide variants and insertion/deletions) were identified through whole exome sequencing and cloned into E. coli expression vectors with and without co-expressed listeriolysin O to enable presentation via MHC class I or class II, respectively. For each patient, their unique clones were co-cultured with autologous MDDCs in an ordered array, then their CD4+ or CD8+ T cells were added and incubated overnight. T cell activation was determined by measurement of TNF-α and IFN-γ levels in the supernatants by a Meso-Scale Discovery assay. Neoantigens were defined as clones that elicited cytokine responses & gt;2 median absolute deviations from the median of negative control clones. Historically, ATLAS has identified CD4+ and CD8+ T cells responses to up to 15% of mutant polypeptide sequences. Here we will present ATLAS profiling of T cell responses to & gt;2,500 potential neoantigens, across a broad cohort of patients with different tumor types, including tumors with a wide range of mutational burden. T cell responses detected by ATLAS challenge assumptions in the field, with the majority of empirically identified neoantigens not predicted by algorithms, and many predicted neoantigens demonstrating “inhibitory” activity. When exploring neoantigens selected by ATLAS by tumor type, no patterns in overall mutational burden, RNA expression level, or DNA mutant allele frequency have yet been identified. We will also present broader functional analysis, including pathway analysis of proteins containing neoantigens, review of the immunogenicity of known oncogenes and features of immunogenic peptide sequences. The ATLAS platform empirically defines which potential neoantigens created by somatic mutations elicit immune responses in individual patients independently of a patient's HLA type and T cell receptor repertoire. This approach provides the opportunity to identify better targets to include in a personalized vaccine formulation. Citation Format: Jason Dobson, Huilei Xu, Johanna Kaufmann, James Foti, Jin Yuan, Michael O''Keeffe, Crystal Cabral, James Loizeaux, Christopher Warren, Ning Wu, Erick Donis, Kyle Ferber, Pamela Carroll, Jessica B. Flechtner, Wendy Broom. Neoantigen identification using the ATLAS T cell profiling platform highlights the need to empirically define neoantigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 730.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 5718-5718
    Abstract: Neoantigens are emerging as attractive vaccine targets for personalized cancer immunotherapy. As opposed to tumor-associated antigens, neoantigens contain non-synonymous mutations that enable their identification as foreign targets not subject to central tolerance in the thymus. Personalized cancer vaccines leverage neoantigens to specifically direct the immune system to recognize cancer cells for the coordinated attack and destruction of tumors. While in silico methods are commonly used to predict immunogenic neoantigens primarily via putative binding to major histocompatibility complexes (MHC), the positive predictive value of these approaches is low as they cannot account for the complexity of antigen processing, the diversity of MHC class I and class II alleles, and the additional steps of T cell activation. Ex vivo technologies have the potential to overcome the limitations of neoantigen identification by utilizing biologically-relevant testing. ATLAS™ is an unbiased immune response profiling platform that enables comprehensive screening of a tumor mutanome by using a patient's own autologous immune cells, specifically monocyte-derived dendritic cells (MDDC) as antigen presenting cells (APCs) and sorted CD8+ and CD4+ T cells. By utilizing autologous APCs and T cells, ATLAS is agnostic to MHC diversity and assesses preexisting T cell responses to any given mutation. Patient MDDC are pulsed with an ordered array of Escherichia coli expressing patient-specific mutations as short polypeptides. CD8+ and CD4+ T cell response screening is performed using APCs and E. coli with and without pore-forming lysteriolysin O (cLLO) facilitating MHC class I or class II presentation, respectively. Thus, preexisting patient T cell responses to cancer antigens can be characterized by inflammatory cytokine secretion. We utilized a mouse melanoma model to demonstrate the capability of the ATLAS platform for identification of vaccine neoantigens. Whole exome sequencing was performed on B16F10 melanomas resected from C57BL/6 mice, identifying & gt;1600 non-somatic, non-silent mutations. E. coli libraries individually expressing all mutations were constructed and used to screen APCs and T cells from the spleens of B16F10 tumor-bearing mice. Biologically relevant neoantigens were identified by their ability to modulate the secretion of inflammatory cytokines by CD4+ and CD8+ T cells. The significance of the identified neoantigens in comparison to predicted and previously reported B16F10 antigens is described. Top neoantigen candidates were selected and manufactured as synthetic long peptides. Therapeutic vaccination with ATLAS-identified neoantigens in tumor challenge studies is planned and progress will be reported. These studies demonstrate a biologically-relevant approach to improve neoantigen selection for personalized cancer vaccine design enabling improved therapeutic efficacy. Citation Format: Hanna Starobinets, Catarina Nogueira, Kyle Ferber, Huilei Xu, Abha Dhaneshwar, Jason R. Dobson, James Loizeaux, James Foti, Michael O'Keefe, Erick Donis, Wendy Broom, Pamela Carroll, Paul Kirschmeier, Jessica B. Flechtner, Hubert Lam. Ex vivo ATLAS-identification of neoantigens for personalized cancer immunotherapy in mouse melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5718.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 3
    In: Annals of Clinical and Translational Neurology, Wiley, Vol. 10, No. 5 ( 2023-05), p. 765-778
    Abstract: The amyloid probability score (APS) is the model read‐out of the analytically validated mass spectrometry‐based PrecivityAD ® blood test that incorporates the plasma Aβ42/40 ratio, ApoE proteotype, and age to identify the likelihood of brain amyloid plaques among cognitively impaired individuals being evaluated for Alzheimer's disease. Purpose This study aimed to provide additional independent evidence that the pre‐established APS algorithm, along with its cutoff values, discriminates between amyloid positive and negative individuals. Methods The diagnostic performance of the PrecivityAD test was analyzed in a cohort of 200 nonrandomly selected Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL) study participants, who were either cognitively impaired or healthy controls, and for whom a blood sample and amyloid PET imaging were available. Results In a subset of the dataset aligned with the Intended Use population (patients aged 60 and older with CDR ≥0.5), the pre‐established APS algorithm predicted amyloid PET with a sensitivity of 84.9% (CI: 72.9–92.1%) and specificity of 96% (CI: 80.5–99.3%), exclusive of 13 individuals for whom the test was inconclusive. Interpretation The study shows individuals with a high APS are more likely than those with a low APS to have abnormal amounts of amyloid plaques and be on an amyloid accumulation trajectory, a dynamic and evolving process characteristic of progressive AD pathology. Exploratory data suggest APS retains its diagnostic performance in healthy individuals, supporting further screening studies in the cognitively unimpaired.
    Type of Medium: Online Resource
    ISSN: 2328-9503 , 2328-9503
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2023
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  • 4
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    Online Resource
    Wiley ; 2017
    In:  Bulletin of the London Mathematical Society Vol. 49, No. 5 ( 2017-10), p. 784-797
    In: Bulletin of the London Mathematical Society, Wiley, Vol. 49, No. 5 ( 2017-10), p. 784-797
    Type of Medium: Online Resource
    ISSN: 0024-6093
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    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1476985-2
    SSG: 17,1
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  • 5
    Online Resource
    Online Resource
    American Mathematical Society (AMS) ; 2019
    In:  Transactions of the American Mathematical Society Vol. 372, No. 6 ( 2019-4-18), p. 4239-4262
    In: Transactions of the American Mathematical Society, American Mathematical Society (AMS), Vol. 372, No. 6 ( 2019-4-18), p. 4239-4262
    Type of Medium: Online Resource
    ISSN: 0002-9947 , 1088-6850
    URL: Issue
    Language: English
    Publisher: American Mathematical Society (AMS)
    Publication Date: 2019
    detail.hit.zdb_id: 1474637-2
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  • 6
    Online Resource
    Online Resource
    SAGE Publications ; 2014
    In:  Cancer Informatics Vol. 13 ( 2014-01), p. CIN.S20806-
    In: Cancer Informatics, SAGE Publications, Vol. 13 ( 2014-01), p. CIN.S20806-
    Abstract: High-throughput genomic assays are performed using tissue samples with the goal of classifying the samples as normal 〈 pre-malignant 〈 malignant or by stage of cancer using a small set of molecular features. In such cases, molecular features monotonically associated with the ordinal response may be important to disease development; that is, an increase in the phenotypic level (stage of cancer) may be mechanistically linked through a monotonic association with gene expression or methylation levels. Though traditional ordinal response modeling methods exist, they assume independence among the predictor variables and require the number of samples ( n) to exceed the number of covariates ( P) included in the model. In this paper, we describe our ordinalgmifs R package, available from the Comprehensive R Archive Network, which can fit a variety of ordinal response models when the number of predictors ( P) exceeds the sample size ( n). R code illustrating usage is also provided.
    Type of Medium: Online Resource
    ISSN: 1176-9351 , 1176-9351
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2014
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  • 7
    Online Resource
    Online Resource
    SAGE Publications ; 2015
    In:  Cancer Informatics Vol. 14s2 ( 2015-01), p. CIN.S17275-
    In: Cancer Informatics, SAGE Publications, Vol. 14s2 ( 2015-01), p. CIN.S17275-
    Abstract: Researchers have recently shown that penalized models perform well when applied to high-throughput genomic data. Previous researchers introduced the generalized monotone incremental forward stagewise (GMIFS) method for fitting overparameterized logistic regression models. The GMIFS method was subsequently extended by others for fitting several different logit link ordinal response models to high-throughput genomic data. In this study, we further extended the GMIFS method for ordinal response modeling using a complementary log-log link, which allows one to model discrete survival data. We applied our extension to a publicly available microarray gene expression dataset (GSE53733) with a discrete survival outcome. The dataset included 70 primary glioblastoma samples from patients of the German Glioma Network with long-, intermediate-, and short-term overall survival. We tested the performance of our method by examining the prediction accuracy of the fitted model. The method has been implemented as an addition to the ordinalgmifs package in the R programming environment.
    Type of Medium: Online Resource
    ISSN: 1176-9351 , 1176-9351
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
    detail.hit.zdb_id: 2202739-7
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  • 8
    In: Cancer Discovery, American Association for Cancer Research (AACR), Vol. 11, No. 3 ( 2021-03-01), p. 696-713
    Abstract: Neoantigens are critical targets of antitumor T-cell responses. The ATLAS bioassay was developed to identify neoantigens empirically by expressing each unique patient-specific tumor mutation individually in Escherichia coli, pulsing autologous dendritic cells in an ordered array, and testing the patient's T cells for recognition in an overnight assay. Profiling of T cells from patients with lung cancer revealed both stimulatory and inhibitory responses to individual neoantigens. In the murine B16F10 melanoma model, therapeutic immunization with ATLAS-identified stimulatory neoantigens protected animals, whereas immunization with peptides associated with inhibitory ATLAS responses resulted in accelerated tumor growth and abolished efficacy of an otherwise protective vaccine. A planned interim analysis of a clinical study testing a poly-ICLC adjuvanted personalized vaccine containing ATLAS-identified stimulatory neoantigens showed that it is well tolerated. In an adjuvant setting, immunized patients generated both CD4+ and CD8+ T-cell responses, with immune responses to 99% of the vaccinated peptide antigens. Significance: Predicting neoantigens in silico has progressed, but empirical testing shows that T-cell responses are more nuanced than straightforward MHC antigen recognition. The ATLAS bioassay screens tumor mutations to uncover preexisting, patient-relevant neoantigen T-cell responses and reveals a new class of putatively deleterious responses that could affect cancer immunotherapy design. This article is highlighted in the In This Issue feature, p. 521
    Type of Medium: Online Resource
    ISSN: 2159-8274 , 2159-8290
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
    detail.hit.zdb_id: 2607892-2
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  • 9
    In: Computing and Software for Big Science, Springer Science and Business Media LLC, Vol. 3, No. 1 ( 2019-12)
    Type of Medium: Online Resource
    ISSN: 2510-2036 , 2510-2044
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2908677-2
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  • 10
    Online Resource
    Online Resource
    Cambridge University Press (CUP) ; 2017
    In:  Combinatorics, Probability and Computing Vol. 26, No. 6 ( 2017-11), p. 839-849
    In: Combinatorics, Probability and Computing, Cambridge University Press (CUP), Vol. 26, No. 6 ( 2017-11), p. 839-849
    Abstract: A subset C of edges in a k -uniform hypergraph H is a loose Hamilton cycle if C covers all the vertices of H and there exists a cyclic ordering of these vertices such that the edges in C are segments of that order and such that every two consecutive edges share exactly one vertex. The binomial random k -uniform hypergraph H k n,p has vertex set [ n ] and an edge set E obtained by adding each k -tuple e ∈ ( $\binom{[n]}{k}$ ) to E with probability p , independently at random. Here we consider the problem of finding edge-disjoint loose Hamilton cycles covering all but o (| E |) edges, referred to as the packing problem . While it is known that the threshold probability of the appearance of a loose Hamilton cycle in H k n,p is $p=\Theta\biggl(\frac{\log n}{n^{k-1}}\biggr),$ the best known bounds for the packing problem are around p = polylog( n )/ n . Here we make substantial progress and prove the following asymptotically (up to a polylog( n ) factor) best possible result: for p ≥ log C n / n k −1 , a random k -uniform hypergraph H k n,p with high probability contains $N:=(1-o(1))\frac{\binom{n}{k}p}{n/(k-1)}$ edge-disjoint loose Hamilton cycles. Our proof utilizes and modifies the idea of ‘online sprinkling’ recently introduced by Vu and the first author.
    Type of Medium: Online Resource
    ISSN: 0963-5483 , 1469-2163
    Language: English
    Publisher: Cambridge University Press (CUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1481145-5
    SSG: 17,1
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