Format

Send to

Choose Destination
Cancer Immunol Immunother. 2010 Sep;59(9):1435-41. doi: 10.1007/s00262-010-0883-4. Epub 2010 Jun 19.

Modeling flow cytometry data for cancer vaccine immune monitoring.

Author information

1
Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.

Abstract

Flow cytometry (FCM) is widely used in cancer research for diagnosis, detection of minimal residual disease, as well as immune monitoring and profiling following immunotherapy. In all these applications, the challenge is to detect extremely rare cell subsets while avoiding spurious positive events. To achieve this objective, it helps to be able to analyze FCM data using multiple markers simultaneously, since the additional information provided often helps to minimize the number of false positive and false negative events, hence increasing both sensitivity and specificity. However, with manual gating, at most two markers can be examined in a single dot plot, and a sequential strategy is often used. As the sequential strategy discards events that fall outside preceding gates at each stage, the effectiveness of the strategy is difficult to evaluate without laborious and painstaking back-gating. Model-based analysis is a promising computational technique that works using information from all marker dimensions simultaneously, and offers an alternative approach to flow analysis that can usefully complement manual gating in the design of optimal gating strategies. Results from model-based analysis will be illustrated with examples from FCM assays commonly used in cancer immunotherapy laboratories.

PMID:
20563720
PMCID:
PMC2892609
DOI:
10.1007/s00262-010-0883-4
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center