Single cell functional proteomics for assessing immune response in cancer therapy: technology, methods, and applications
- Creators
- Ma, Chao
- Fan, Rong
- Elitas, Meltem
Abstract
In the past decade, significant progresses have taken place in the field of cancer immunotherapeutics, which are being developed for most human cancers. New immunotherapeutics, such as Ipilimumab (anti-CTLA-4), have been approved for clinical treatment; cell-based immunotherapies such as adoptive cell transfer (ACT) have either passed the final stage of human studies (e.g., Sipuleucel-T) for the treatment of selected neoplastic malignancies or reached the stage of phase II/III clinical trials. Immunotherapetics has become a sophisticated field. Multimodal therapeutic regimens comprising several functional modules (up to five in the case of ACT) have been developed to provide focused therapeutic responses with improved efficacy and reduced side-effects. However, a major challenge remains: the lack of effective and clinically applicable immune assessment methods. Due to the complexity of antitumor immune responses within patients, it is difficult to provide comprehensive assessment of therapeutic efficacy and mechanism. To address this challenge, new technologies have been developed to directly profile the cellular immune functions and the functional heterogeneity. With the goal to measure the functional proteomics of single immune cells, these technologies are informative, sensitive, high-throughput, and highly multiplex. They have been used to uncover new knowledge of cellular immune functions and have been utilized for rapid, informative, and longitudinal monitoring of immune response in clinical anti-cancer treatment. In addition, new computational tools are required to integrate high-dimensional data sets generated from the comprehensive, single cell level measurements of patient's immune responses to guide accurate and definitive diagnostic decision. These single cell immune function assessment tools will likely contribute to new understanding of therapy mechanism, pre-treatment stratification of patients, and ongoing therapeutic monitoring and assessment.
Additional Information
© 2013 Ma, Fan and Elitas. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc. Received: 01 April 2013; accepted: 13 May 2013; published online: 29 May 2013. Chao Ma acknowledges the support of Rosen Fellowship. Rong Fan acknowledges the support of the NIH LINCS Program Technology U01 Grant (NIH 1 U01 CA164252), the NCI Physical Sciences-Oncology Program Grant (U54 CA143798), and the NCI Howard Temin Pathway to Independence Award (NIH 4R00 CA136759).Attached Files
Published - Chao_2013p133.pdf
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Additional details
- Eprint ID
- 38989
- Resolver ID
- CaltechAUTHORS:20130619-151218794
- Rosen Fellowship
- NIH LINCS Program Technology U01 Grant
- U01 CA164252
- NCI Physical Sciences-Oncology Program Grant
- U54 CA143798
- Howard Temin Pathway to Independence Award
- NIH 4R00 CA136759
- Created
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2013-06-24Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field