PMID- 17937494
OWN - NLM
STAT- MEDLINE
DCOM- 20071206
LR  - 20211020
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Print)
IS  - 1553-734X (Linking)
VI  - 3
IP  - 10
DP  - 2007 Oct
TI  - A statistical framework for modeling HLA-dependent T cell response data.
PG  - 1879-86
LID - e188
AB  - The identification of T cell epitopes and their HLA (human leukocyte antigen) 
      restrictions is important for applications such as the design of cellular 
      vaccines for HIV. Traditional methods for such identification are costly and 
      time-consuming. Recently, a more expeditious laboratory technique using ELISpot 
      assays has been developed that allows for rapid screening of specific responses. 
      However, this assay does not directly provide information concerning the HLA 
      restriction of a response, a critical piece of information for vaccine design. 
      Thus, we introduce, apply, and validate a statistical model for identifying 
      HLA-restricted epitopes from ELISpot data. By looking at patterns across a broad 
      range of donors, in conjunction with our statistical model, we can determine 
      (probabilistically) which of the HLA alleles are likely to be responsible for the 
      observed reactivities. Additionally, we can provide a good estimate of the number 
      of false positives generated by our analysis (i.e., the false discovery rate). 
      This model allows us to learn about new HLA-restricted epitopes from ELISpot data 
      in an efficient, cost-effective, and high-throughput manner. We applied our 
      approach to data from donors infected with HIV and identified many potential new 
      HLA restrictions. Among 134 such predictions, six were confirmed in the lab and 
      the remainder could not be ruled as invalid. These results shed light on the 
      extent of HLA class I promiscuity, which has significant implications for the 
      understanding of HLA class I antigen presentation and vaccine development.
FAU - Listgarten, Jennifer
AU  - Listgarten J
AD  - Microsoft Research, Redmond, Washington, USA.
FAU - Frahm, Nicole
AU  - Frahm N
FAU - Kadie, Carl
AU  - Kadie C
FAU - Brander, Christian
AU  - Brander C
FAU - Heckerman, David
AU  - Heckerman D
LA  - eng
GR  - N01-AL-15422/PHS HHS/United States
GR  - R01-A1-067077/PHS HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
RN  - 0 (Epitopes, T-Lymphocyte)
RN  - 0 (HLA Antigens)
SB  - IM
MH  - Alleles
MH  - Computational Biology/*methods
MH  - Enzyme-Linked Immunosorbent Assay/methods
MH  - Epitopes, T-Lymphocyte/chemistry
MH  - False Positive Reactions
MH  - HLA Antigens/*chemistry
MH  - Humans
MH  - Models, Statistical
MH  - Models, Theoretical
MH  - Reproducibility of Results
MH  - T-Lymphocytes/*immunology
PMC - PMC2014793
COIS- Competing interests. The authors have declared that no competing interests exist.
EDAT- 2007/10/17 09:00
MHDA- 2007/12/07 09:00
PMCR- 2007/10/01
CRDT- 2007/10/17 09:00
PHST- 2007/07/18 00:00 [received]
PHST- 2007/08/14 00:00 [accepted]
PHST- 2007/10/17 09:00 [pubmed]
PHST- 2007/12/07 09:00 [medline]
PHST- 2007/10/17 09:00 [entrez]
PHST- 2007/10/01 00:00 [pmc-release]
AID - 06-PLCB-RA-0286 [pii]
AID - 06-PLCB-RA-0286R2 [pii]
AID - 10.1371/journal.pcbi.0030188 [doi]
PST - ppublish
SO  - PLoS Comput Biol. 2007 Oct;3(10):1879-86. doi: 10.1371/journal.pcbi.0030188.