Argo data 1999-2019: two million temperature-salinity profiles and subsurface velocity observations from a global array of profiling floats.

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Wong, A. P. S., Wijffels, S. E., Riser, S. C., Pouliquen, S., Hosoda, S., Roemmich, D., Gilson, J., Johnson, G. C., Martini, K., Murphy, D. J., Scanderbeg, M., Bhaskar, T. V. S. U., Buck, J. J. H., Merceur, F., Carval, T., Maze, G., Cabanes, C., Andre, X., Poffa, N., Yashayaev, I., Barker, P. M., Guinehut, S., Belbeoch, M., Ignaszewski, M., Baringer, M. O., Schmid, C., Lyman, J. M., McTaggart, K. E., Purkey, S. G., Zilberman, N., Alkire, M. B., Swift, D., Owens, W. B., Jayne, S. R., Hersh, C., Robbins, P., West-Mack, D., Bahr, F., Yoshida, S., Sutton, P. J. H., Cancouet, R., Coatanoan, C., Dobbler, D., Juan, A. G., Gourrion, J., Kolodziejczyk, N., Bernard, V., Bourles, B., Claustre, H., D'Ortenzio, F., Le Reste, S., Le Traon, P., Rannou, J., Saout-Grit, C., Speich, S., Thierry, V., Verbrugge, N., Angel-Benavides, I. M., Klein, B., Notarstefano, G., Poulain, P., Velez-Belchi, P., Suga, T., Ando, K., Iwasaska, N., Kobayashi, T., Masuda, S., Oka, E., Sato, K., Nakamura, T., Sato, K., Takatsuki, Y., Yoshida, T., Cowley, R., Lovell, J. L., Oke, P. R., van Wijk, E. M., Carse, F., Donnelly, M., Gould, W. J., Gowers, K., King, B. A., Loch, S. G., Mowat, M., Turton, J., Rama Rao, E. P., Ravichandran, M., Freeland, H. J., Gaboury, I., Gilbert, D., Greenan, B. J. W., Ouellet, M., Ross, T., Tran, A., Dong, M., Liu, Z., Xu, J., Kang, K., Jo, H., Kim, S., & Park, H. Argo data 1999-2019: two million temperature-salinity profiles and subsurface velocity observations from a global array of profiling floats. Frontiers in Marine Science, 7, (2020): 700, doi:10.3389/fmars.2020.00700.In the past two decades, the Argo Program has collected, processed, and distributed over two million vertical profiles of temperature and salinity from the upper two kilometers of the global ocean. A similar number of subsurface velocity observations near 1,000 dbar have also been collected. This paper recounts the history of the global Argo Program, from its aspiration arising out of the World Ocean Circulation Experiment, to the development and implementation of its instrumentation and telecommunication systems, and the various technical problems encountered. We describe the Argo data system and its quality control procedures, and the gradual changes in the vertical resolution and spatial coverage of Argo data from 1999 to 2019. The accuracies of the float data have been assessed by comparison with high-quality shipboard measurements, and are concluded to be 0.002°C for temperature, 2.4 dbar for pressure, and 0.01 PSS-78 for salinity, after delayed-mode adjustments. Finally, the challenges faced by the vision of an expanding Argo Program beyond 2020 are discussed.AW, SR, and other scientists at the University of Washington (UW) were supported by the US Argo Program through the NOAA Grant NA15OAR4320063 to the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) at the UW. SW and other scientists at the Woods Hole Oceanographic Institution (WHOI) were supported by the US Argo Program through the NOAA Grant NA19OAR4320074 (CINAR/WHOI Argo). The Scripps Institution of Oceanography's role in Argo was supported by the US Argo Program through the NOAA Grant NA15OAR4320071 (CIMEC). Euro-Argo scientists were supported by the Monitoring the Oceans and Climate Change with Argo (MOCCA) project, under the Grant Agreement EASME/EMFF/2015/1.2.1.1/SI2.709624 for the European Commission

Development and validation of an integrative pan-solid tumor predictor of PD-1/PD-L1 blockade benefit

Background: Anti-PD-1 and PD-L1 (collectively PD-[L]1) therapies are approved for many advanced solid tumors. Biomarkers beyond PD-L1 immunohistochemistry, microsatellite instability, and tumor mutation burden (TMB) may improve benefit prediction. Methods: Using treatment data and genomic and transcriptomic tumor tissue profiling from an observational trial ( NCT03061305 ), we developed Immunotherapy Response Score (IRS), a pan-tumor predictive model of PD-(L)1 benefit. IRS real-world progression free survival (rwPFS) and overall survival (OS) prediction was validated in an independent cohort of trial patients. Results: Here, by Cox modeling, we develop IRS-which combines TMB with CD274, PDCD1, ADAM12 and TOP2A quantitative expression-to predict pembrolizumab rwPFS (648 patients; 26 tumor types; IRS-High or -Low groups). In the 248 patient validation cohort (248 patients; 24 tumor types; non-pembrolizumab PD-[L]1 monotherapy treatment), median rwPFS and OS are significantly longer in IRS-High vs. IRS-Low patients (rwPFS adjusted hazard ratio [aHR] 0.52, p = 0.003; OS aHR 0.49, p = 0.005); TMB alone does not significantly predict PD-(L)1 rwPFS nor OS. In 146 patients treated with systemic therapy prior to pembrolizumab monotherapy, pembrolizumab rwPFS is only significantly longer than immediately preceding therapy rwPFS in IRS-High patients (interaction test p = 0.001). In propensity matched lung cancer patients treated with first-line pembrolizumab monotherapy or pembrolizumab+chemotherapy, monotherapy rwPFS is significantly shorter in IRS-Low patients, but is not significantly different in IRS-High patients. Across 24,463 molecularly-evaluable trial patients, 7.6% of patients outside of monotherapy PD-(L)1 approved tumor types are IRS-High/TMB-Low. Conclusions: The validated, predictive, pan-tumor IRS model can expand PD-(L)1 monotherapy benefit outside currently approved indications

XRCC1 Polymorphism Associated With Late Toxicity After Radiation Therapy in Breast Cancer Patients

International audiencePURPOSE:To identify single-nucleotide polymorphisms (SNPs) in oxidative stress-related genes associated with risk of late toxicities in breast cancer patients receiving radiation therapy.METHODS AND MATERIALS:Using a 2-stage design, 305 SNPs in 59 candidate genes were investigated in the discovery phase in 753 breast cancer patients from 2 prospective cohorts from Germany. The 10 most promising SNPs in 4 genes were evaluated in the replication phase in up to 1883 breast cancer patients from 6 cohorts identified through the Radiogenomics Consortium. Outcomes of interest were late skin toxicity and fibrosis of the breast, as well as an overall toxicity score (Standardized Total Average Toxicity). Multivariable logistic and linear regression models were used to assess associations between SNPs and late toxicity. A meta-analysis approach was used to summarize evidence.RESULTS:The association of a genetic variant in the base excision repair gene XRCC1, rs2682585, with normal tissue late radiation toxicity was replicated in all tested studies. In the combined analysis of discovery and replication cohorts, carrying the rare allele was associated with a significantly lower risk of skin toxicities (multivariate odds ratio 0.77, 95% confidence interval 0.61-0.96, P=.02) and a decrease in Standardized Total Average Toxicity scores (-0.08, 95% confidence interval -0.15 to -0.02, P=.016).CONCLUSIONS:Using a stage design with replication, we identified a variant allele in the base excision repair gene XRCC1 that could be used in combination with additional variants for developing a test to predict late toxicities after radiation therapy in breast cancer patients

XRCC1 Polymorphism Associated With Late Toxicity After Radiation Therapy in Breast Cancer Patients.

To identify single-nucleotide polymorphisms (SNPs) in oxidative stress-related genes associated with risk of late toxicities in breast cancer patients receiving radiation therapy

Development and validation of an integrative pan-solid tumor predictor of PD-1/PD-L1 blockade benefit.

BACKGROUND: Anti-PD-1 and PD-L1 (collectively PD-[L]1) therapies are approved for many advanced solid tumors. Biomarkers beyond PD-L1 immunohistochemistry, microsatellite instability, and tumor mutation burden (TMB) may improve benefit prediction. METHODS: Using treatment data and genomic and transcriptomic tumor tissue profiling from an observational trial (NCT03061305), we developed Immunotherapy Response Score (IRS), a pan-tumor predictive model of PD-(L)1 benefit. IRS real-world progression free survival (rwPFS) and overall survival (OS) prediction was validated in an independent cohort of trial patients. RESULTS: Here, by Cox modeling, we develop IRS-which combines TMB with CD274, PDCD1, ADAM12 and TOP2A quantitative expression-to predict pembrolizumab rwPFS (648 patients; 26 tumor types; IRS-High or -Low groups). In the 248 patient validation cohort (248 patients; 24 tumor types; non-pembrolizumab PD-[L]1 monotherapy treatment), median rwPFS and OS are significantly longer in IRS-High vs. IRS-Low patients (rwPFS adjusted hazard ratio [aHR] 0.52, p = 0.003; OS aHR 0.49, p = 0.005); TMB alone does not significantly predict PD-(L)1 rwPFS nor OS. In 146 patients treated with systemic therapy prior to pembrolizumab monotherapy, pembrolizumab rwPFS is only significantly longer than immediately preceding therapy rwPFS in IRS-High patients (interaction test p = 0.001). In propensity matched lung cancer patients treated with first-line pembrolizumab monotherapy or pembrolizumab+chemotherapy, monotherapy rwPFS is significantly shorter in IRS-Low patients, but is not significantly different in IRS-High patients. Across 24,463 molecularly-evaluable trial patients, 7.6% of patients outside of monotherapy PD-(L)1 approved tumor types are IRS-High/TMB-Low. CONCLUSIONS: The validated, predictive, pan-tumor IRS model can expand PD-(L)1 monotherapy benefit outside currently approved indications