Central Memory T Cells

We used data from a long-standing natural history study of HIV [3 (link)], from which 384 individuals were selected, with known HIV seroconversion dates (so that an estimated date of infection could be calculated) and long-term clinical follow-up [4 (link)]. For each individual, the time to AIDS diagnosis (“survival time”) was known, and patients were censored upon initiation of treatment or when lost to follow-up. We chose the earliest available cell specimen (<18 months post-infection (p.i.); mean = 7 months p.i.); our previous analysis showed that the timing of the cell specimen had no relationship to survival time. Cells were stained with two polychromatic flow cytometry panels: the first assessed the resting phenotypes of T-cells directly ex vivo [4 (link)] (but was not used for this study), while the second characterized cytokine and other functional responses to HIV, after stimulation with pools of overlapping peptides from HIV-Gag proteins (Chattopadhyay et al., under review). Specifically, the latter panel examined the cytokines IFNg, IL2, and TNF, along with a costimulatory molecule (CD154) upregulated by CD4+ T-cells upon activation and a marker of T-cell degranulation (CD107a). Cell surface markers that define the maturity of the HIV-specific T-cell were examined in this panel as well (CD45RO, CCR7, CD27, and CD57) so that the functional responses could be ascribed to cell types well-described in the literature (e.g., central memory T-cells, senescent T-cells, etc.). Manual analysis of the dataset was performed previously, and showed that no specific characteristics of the anti-HIV T-cell response in the selected samples could predict survival time (specifically, the following characteristics were tested: total magnitude of the HIV Gag or Env specific T-cell responses, magnitude of individual functional responses, polyfunctionality of the CD4 or CD8 response, and differentiation state of the response; these are described in greater detail in a manuscript under review (Chattopadhyay, et al)). It is important to note, however, that the manual data analysis examined only cell characteristics that were known to the biologists a priori. Manual data analysis of all possible combinations of markers in the dataset was not feasible. All selected samples from divided into two equal training- and test-sets randomly and uniformly. No correlation with the clinical outcome was observed across the two sets (pvalue > 0.22). The FlowCAP participants were blinded to the clinical outcomes in the testset, which was used for independently verifying the submitted results.
To ensure the feasibility of the study, members of the FlowCAP organizing committee not participating in the submission of algorithm results performed an independent analysis [5 ]. The raw Gag-stimulated samples were pre-processed using an optimized logicle transformation [6 (link)] and 1000 cells per patient were selected randomly. K-means clustering was used to identify 50 cell types simultaneously in all patients using the pooled cells. For each cluster, the frequency of the cells (i.e., the number of cells in the cluster divided by the total number of cells) was used for the correlative analysis (Cox-proportional hazards model) on the training set. The median expression of each marker for each cell type as well as log-rank p-values for each cluster were calculated (Supplemental Figure 1). The most significant clusters included CD3+CD4+ and CD3+CD8+ populations that also express a range of other markers (e.g., CD27, CCR7, and IL2) and lacked expression of others (e.g., CD57 and TNF). These clusters remained correlated with the clinical outcome in the test set, suggesting that successful identification of correlates of disease progression in this dataset was possible (Supplemental Figure 2).
Data from unstimulated and Gag-stimulated samples were distributed to all participants. The dataset was randomly partitioned into a training set and a test set of equal sizes. The clinical information (survival time and censorship status) of the patients in the test set was not provided to the participants. Each participant provided a vector of final predictions (extracted from one or a combination of several cell types) that was most correlated with the clinical outcome (as measured by a lon-rank test on a Cox proportional hazards regression). The complete source code as well as the required software packages for independent reproduction of the results was also required. The results were evaluated by an independent log-rank test on the blinded test set.