Publications about 'multiple myeloma'
Articles in journal or book chapters
  1. A.L. Williams, J.E. Fitzgerald, F. Ivich, E.D. Sontag, and M. Niedre. Comment on In vivo flow cytometry reveals a circadian rhythm of circulating tumor cells. npg Light: Science & Applications, 10:188, 2021. [PDF] Keyword(s): circulating tumor cells, liquid biopsy, cancer, oncology, multiple myeloma, systems biology.
    Correspondence regarding circulating tumor cell detection

  2. A.L. Williams, J.E. Fitzgerald, F. Ivich, E.D. Sontag, and M. Niedre. Short-term circulating tumor cell dynamics in mouse xenograft models and implications for liquid biopsy. Frontiers in Oncology, 10:2447-, 2020. [PDF] [doi:10.3389/fonc.2020.601085] Keyword(s): circulating tumor cells, liquid biopsy, cancer, oncology, multiple myeloma, systems biology.
    Circulating tumor cells (CTCs) are widely studied using liquid biopsy methods that analyze single, fractionally-small peripheral blood (PB) samples. However, little is known about fluctuations in CTC numbers that occur over short timescales in vivo, and how these may affect accurate enumeration from blood samples. Diffuse in vivo flow cytometry (DiFC) developed by the Niedre lab allows continuous, non-invasive counting of rare, green fluorescent protein expressing CTCs in large deeply-seated blood vessels in mice. Here, DiFC is used to study short-term changes in CTC numbers in multiple myeloma and Lewis lung carcinoma xenograft models. Both 35- to 50-minute data sets are analyzed, with intervals corresponding to approximately 1, 5, 10 and 20\% of the PB volume, as well as changes over 24-hour periods. For rare CTCs, the use of short DiFC intervals (corresponding to small PB samples) frequently resulted in no detections. For more abundant CTCs, CTC numbers frequently varied by an order of magnitude or more over the time-scales considered. This variability far exceeded that expected by Poisson statistics, and instead was consistent with rapidly changing mean numbers of CTCs in the PB. Because of these natural temporal changes, accurately enumerating CTCs from fractionally small blood samples is inherently problematic. The problem is likely to be compounded for multicellular CTC clusters or specific CTC subtypes. However, it is also shown that enumeration can be improved by averaging multiple samples, analysis of larger volumes, or development of new methods for enumeration of CTCs directly in vivo.

  3. A. Silva, M. Silva, P. Sudalagunta, A. Distler, T. Jacobson, A. Collins, T. Nguyen, J. Song, D.T. Chen, Lu Chen, C. Cubitt, R. Baz, L. Perez, D. Rebatchouk, W. Dalton, J.M. Greene, R. Gatenby, R. Gillies, E.D. Sontag, M. Meads, and K. Shain. An ex vivo platform for the prediction of clinical response in multiple myeloma. Cancer Research, pp 10.1158/0008-5472.CAN-17-0502, 2017. [PDF] Keyword(s): cancer, multiple myeloma, personalized therapy.
    This paper describes a novel approach for characterization of chemosensitivity and prediction of clinical response in multiple myeloma. It relies upon a patient-specific computational model of clinical response, parameterized by a high-throughput ex vivo assay that quantifies sensitivity of primary MM cells to 31 agents or combinations, in a reconstruction of the tumor microenvironment. The mathematical model, which inherently accounts for intra-tumoral heterogeneity of drug sensitivity, combined with drug- and regimen-specific pharmacokinetics, produces patient-specific predictions of clinical response 5 days post-biopsy.



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Last modified: Wed Apr 17 19:59:03 2024
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