The detrimental effects of spaceflight and simulated microgravity on the immune system have been extensively documented. We report here microarray gene expression analysis, in concert with quantitative RT-PCR, in young adult C57BL/6NTac mice at 8 weeks of age after exposure to spaceflight aboard the space shuttle (STS-118) for a period of 13 days. Upon conclusion of the mission, thymus lobes were extracted from space flown mice (FLT) as well as age- and sex-matched ground control mice similarly housed in animal enclosure modules (AEM). mRNA was extracted and an automated array analysis for gene expression was performed. Examination of the microarray data revealed 970 individual probes that had a 1.5-fold or greater change. When these data were averaged (n = 4), we identified 12 genes that were significantly up- or down-regulated by at least 1.5-fold after spaceflight (P < or = 0.05). The genes that significantly differed from the AEM controls and that were also confirmed via QRT-PCR were as follows: Rbm3 (up-regulated) and Hsph110, Hsp90aa1, Cxcl10, Stip1, Fkbp4 (down-regulated). QRT-PCR confirmed the microarray results and demonstrated additional gene expression alteration in other T cell related genes, including: Ctla-4, IFN-alpha2a (up-regulated) and CD44 (down-regulated). Together, these data demonstrate that spaceflight induces significant changes in the thymic mRNA expression of genes that regulate stress, glucocorticoid receptor metabolism, and T cell signaling activity. These data explain, in part, the reported systemic compromise of the immune system after exposure to the microgravity of space.
Despite the growing body of clinical evidence to support the utility of comprehensive genomic profiling (CGP) for advanced cancer patients, only a small fraction of individuals receive precision oncology guided treatment strategies. To facilitate globally available CGP solutions, we have developed and validated the PGDx elio plasma complete cell-free DNA (cfDNA) kitted assay, which employs a hybrid capture-based 521-gene, 2.1 Mb targeted panel, combined with automated bioinformatics and reporting. PGDx elio plasma complete detects single nucleotide variants (SNVs), insertions and deletions (indels), amplifications, translocations, microsatellite instability (MSI), blood tumor mutation burden (bTMB), and loss of heterozygosity (LOH). Targets include clinically relevant alterations, such as those contained within ALK, BRAF, BRCA1/2, EGFR, ERBB2, FGFR1/2/3, KIT, KRAS, MET, NRAS, NTRK1/2/3, PIK3CA, RET, and ROS1. With as little as 10 ng of sample input of cfDNA, and sequenced on the NovaSeq 6000, PGDx elio plasma complete achieves deep coverage across the target region combined with automated bioinformatics and reporting algorithms. We assessed the analytical performance of this assay. Analytical specificity in a cohort of 20 non-cancerous donors was 100% for all clinically actionable variants and 99.9% panel-wide. The reportable range of the assay is ≥ 0.10% variant allele frequency (VAF) for SNVs and indels, ≥ 3 fusion reads for translocations, and ≥ 1.15-fold for amplifications. Furthermore, analytical sensitivity studies using dilution series with known positive alterations demonstrated a limit of detection ranging from 0.32-0.78% VAF for actionable mutations, 0.34-1.75% VAF for panel-wide mutations, 0.33% VAF for translocations, and 1.32-fold for amplifications. The assay produces highly reproducible results, with 100% average positive agreement (APA) for clinically actionable alterations and 92.5% APA for panel-wide alterations across operators, days, and runs. Compared to a CGP assay of a similar size, PGDx elio plasma complete achieved a 94.7% positive predictive value and >99.9% negative predictive value across all variant types assessed. The PGDx elio plasma complete assay can be processed manually or through automated liquid handling systems with high concordance, with an overall success rate of 97.8%. Taken together, these data demonstrate that the PGDx elio plasma complete assay is a sensitive, specific, accurate, reproducible, and robust approach to enable CGP to guide both translational biomarker discovery and CGP-informed precision oncology strategies. Citation Format: Kenneth C. Valkenburg, Vito Caropreso, Jesse Fox, Christopher Gault, Andrew Georgiadis, Kelly M. Gerding, Aanavi Karandikar, Cynthia Maddox, Paul McGregor, David Riley, Vuna Fa. Comprehensive liquid biopsy profiling enabled by PGDx elio plasmacomplete to facilitate precision oncology through decentralized access to testing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 72.
Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensitivities and specificities. We recently performed a large feasibility study to assess the performance of 4 biomarkers (aneuploidy, methylation, mutation, and protein) for the detection of cancers from up to 15 organ sites. Specifically, a training and validation set was tested for 3 biomarkers (aneuploidy, methylation, and protein) and the performance was subsequently confirmed in an independent testing set. Methods: We have now further improved the performance of a 4-marker cancer detection blood test by fine-tuning the respective marker calling models and thresholds, exploring prostate-specific antigen (PSA) for prostate cancer detection, and developing an overarching Machine Learning (ML) cancer classifier. To improve the mutation detection, we tested (in triplicate) 200 plasma and buffy samples from young, non-cancer subjects and mutant DNA from cell lines to develop an ML-based mutation calling algorithm. This caller was validated on 186 samples and tested on an independent set of 1388 cancer and non-cancer samples. The calling of cancer-associated DNA methylation events was refined by performing training, validation, and testing across different studies. We also explored models for methylation detection based solely on distribution of methylation signal observed in non-cancer samples. Free and total PSA were investigated as markers for prostate cancer detection by including clinically relevant Gleason scores in the development of the protein-based cancer calling algorithm. Results: In the previous analysis the combination of mutation, aneuploidy, methylation, and protein biomarkers resulted in an overall sensitivity of 61.0% (95% CI: 56.9%-65.0) at a specificity of 98.2% (95% CI: 97.1 – 99.4%). We will present the added performance benefit of ML-based mutation variant calling. PSA derived features were evaluated with the goal of increasing the detectability of high-grade prostate cancers while minimizing the detection of indolent cancers. Lastly, we compared the Boolean logic-based 4-biomarker combination algorithm used in the previous analysis with an ML-based cancer classifier. The results of the modeling, applied to the testing set, will be shared. Conclusions: In summary, improvements in cancer detection performance may be achieved by optimizing each biomarker calling algorithm as well as overarching cancer classifier. When combining these improvements, we believe that a single blood test will provide robust sensitivity for the detection of several cancer types, particularly for earlier-stage disease in real world settings. Citation Format: Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira. Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr IA023.
Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensitivities and specificities. We recently performed a large feasibility study to assess the performance of 4 biomarkers (aneuploidy, methylation, mutation, and protein) for the detection of cancers from up to 15 organ sites. Specifically, a training and validation set was tested for 3 biomarkers (aneuploidy, methylation, and protein) and the performance was subsequently confirmed in an independent testing set. Methods: We have now further improved the performance of a 4-marker cancer detection blood test by fine-tuning the respective marker calling models and thresholds, exploring prostate-specific antigen (PSA) for prostate cancer detection, and developing an overarching Machine Learning (ML) cancer classifier. To improve the mutation detection, we tested (in triplicate) 200 plasma and buffy samples from young, non-cancer subjects and mutant DNA from cell lines to develop an ML-based mutation calling algorithm. This caller was validated on 186 samples and tested on an independent set of 1388 cancer and non-cancer samples. The calling of cancer-associated DNA methylation events was refined by performing training, validation, and testing across different studies. We also explored models for methylation detection based solely on distribution of methylation signal observed in non-cancer samples. Free and total PSA were investigated as markers for prostate cancer detection by including clinically relevant Gleason scores in the development of the protein-based cancer calling algorithm. Results: In the previous analysis the combination of mutation, aneuploidy, methylation, and protein biomarkers resulted in an overall sensitivity of 61.0% (95% CI: 56.9%-65.0) at a specificity of 98.2% (95% CI: 97.1 – 99.4%). We will present the added performance benefit of ML-based mutation variant calling. PSA derived features were evaluated with the goal of increasing the detectability of high-grade prostate cancers while minimizing the detection of indolent cancers. Lastly, we compared the Boolean logic-based 4-biomarker combination algorithm used in the previous analysis with an ML-based cancer classifier. The results of the modeling, applied to the testing set, will be shared. Conclusions: In summary, improvements in cancer detection performance may be achieved by optimizing each biomarker calling algorithm as well as overarching cancer classifier. When combining these improvements, we believe that a single blood test will provide robust sensitivity for the detection of several cancer types, particularly for earlier-stage disease in real world settings. Citation Format: Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira. Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P041.
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