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Sökning: WFRF:(Gertz B)

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  • Uy, G. L., et al. (författare)
  • Contribution of chemotherapy mobilization to disease control in multiple myeloma treated with autologous hematopoietic cell transplantation
  • 2015
  • Ingår i: Bone Marrow Transplantation. - : Springer Science and Business Media LLC. - 0268-3369 .- 1476-5365. ; 50:12, s. 1513-1518
  • Tidskriftsartikel (refereegranskat)abstract
    • In patients with multiple myeloma (MM) undergoing autologous hematopoietic cell transplantation (auto-HCT), peripheral blood progenitor cells may be collected following mobilization with growth factor alone (GF) or cytotoxic chemotherapy plus GF (CC+GF). It is uncertain whether the method of mobilization affects post-transplant outcomes. We compared these mobilization strategies in a retrospective analysis of 968 patients with MM from the Center for International Blood and Marrow Transplant Research database who received an auto-Ha in the US and Canada between 2007 and 2012. The kinetics of neutrophil engraftment (>= 0.5 x 10(9)/L) was similar between groups (13 vs 13 days, P=0.69) while platelet engraftment (>= 20 x 10(9)/L) was slightly faster with CC+GF (19 vs 18 days, P=0.006). Adjusted 3-year PFS was 43% (95% confidence interval (CI) 38-48) in GF and 40% (95% CI 35-45) in CC+GF, P=0.33. Adjusted 3-year OS was 82% (95% CI 78-86) vs 80% (95% CI 75-84), P=0.43 and adjusted 5-year OS was 62% (95% CI 54-68) vs 60% (95% CI 52-67), P=0.76, for GF and CC+GF, respectively. We conclude that MM patients undergoing auto-Ha have similar outcomes irrespective of the method of mobilization and found no evidence that the addition of chemotherapy to mobilization contributes to disease control.
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  • Yakneen, S, et al. (författare)
  • Butler enables rapid cloud-based analysis of thousands of human genomes
  • 2020
  • Ingår i: Nature biotechnology. - : Springer Science and Business Media LLC. - 1546-1696 .- 1087-0156. ; 38:3, s. 288-
  • Tidskriftsartikel (refereegranskat)abstract
    • We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.
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