The immunological processes that lead to ?-cell destruction are complex and most probably involve, at singleor multiple stages, the majority of humoral and cell-based effector and regulatory mechanisms that wecurrently have knowledge of. Thus, any single aspect of innate and adaptive immune responses has thepotential to act as a surrogate for loss of ?-cell function. To date, however, no marker(s) have emerged thatare capable of predicting the decline in C-peptide that follows diagnosis of Type 1 diabetes (T1D). In theprevention setting, autoantibodies and HLA genotype can identify non-diabetic subjects at high risk ofprogression to diabetes (usually 35-50%) over a defined period (usually 5 years): but these biomarkers appearto have limited predictive utility in relation to time of onset or rate of decline of ?-cell function. In the context ofboth intervention and prevention trials for T1D, this knowledge gap represents a major limitation: betterbiomarkers or surrogates of ?-cell decline could be used as end-points to make studies shorter (e.g. forprevention they currently last several years) or to stratify for study entry (e.g. rapid or slow progressors). In the context of studying peripheral blood immune cell subsets in patients (treated and placebo) enrolled inthe TrialNet Abatacept intervention study in new-onset T1D, we recently made two discoveries that weconsider have novelty and importance in the T1D biomarker and therapeutic trial effort: 1) C-peptide loss can be predicted by change in specific T-cell populations.2) Co-stimulation blockade with Abatacept not only alters CD4 cell population composition but also altersthe relationship between change in Central Memory and Na ve/Central Memory composition andsubsequent metabolic change. We now seek to test these discoveries in a series of replication and validation experiments conducted withalternative specimen sources as well as different clinical populations representing stages of progression todiabetes. In addition, our findings to date highlight the importance of a subset of CD4 T cells defined bypossession of CD45R0 and CD62L in T1D progression.
We aim to analyze these cells in greater depth andwith greater resolution using polychromatic flow and mass cytometry ( CyTOFTM ) , to examine whether thereis a particular cell cohort, discernible by activation, migration, lineage or other markers, that emerges as moststrongly associated with disease progression. This would not only offer novel biomarker populations, but couldalso lead by extension to novel targeting strategies using tailored biologics. Success in this research projectwill likely lead to further investigation of the cell cohorts identified as associated with disease progression,validated biomarkers that are ready for use in clinical trials, and possibly also yield a means for mechanistic-based risk classification in clinical trials.
We recently made two discoveries that we consider have novelty and importance in the T1Dbiomarker and therapeutic trial effort. We now seek to test these discoveries in a series ofreplication and validation experiments conducted with alternative specimen sources as well asdifferent clinical populations representing stages of progression to diabetes. In addition; ourfindings to date highlight the importance of a subset of CD4 T cells defined by possession ofCD45R0 and CD62L in T1D progression. We aim to analyze these cells in greater depth andwith greater resolution using polychromatic flow and mass cytometry ( CyTOFTM ) ; to examinewhether there is a particular cell cohort; discernible by activation; migration; lineage or othermarkers; that emerges as most strongly associated with disease progression. This would notonly offer novel biomarker populations; but could also lead by extension to novel targetingstrategies using tailored biologics. Success in this research project will likely lead to furtherinvestigation of the cell cohorts identified as associated with disease progression; validatedbiomarkers that are ready for use in clinical trials; and possibly also yield a means formechanistic-based risk classification in clinical trials.
