Immature dendritic cells promote high-avidity tuning of vaccine T cell response

Therapeutic vaccines can elicit tumor-specific cytotoxic T lymphocytes (CTLs), but durable reductions in tumor burden require vaccines that stimulate high-avidity CTLs. Recent advances in immunotherapy responses have led to renewed interest in vaccine approaches, including dendritic cell vaccine strategies. However, dendritic cell requirements for vaccines that generate potent anti-tumor T-cell responses are unclear. Here we use mathematical modeling to show that counterintuitively, increasing levels of immature dendritic cells may lead to selective expansion of high-avidity CTLs. This finding contrasts with traditional dendritic cell vaccine approaches that have sought to harness ex vivo generated mature dendritic cells. We show that the injection of vaccine antigens in the context of increased numbers of immature dendritic cells results in a decreased overall peptide:MHC complex load that favors high-avidity CTL activation and expansion. Overall, our results provide a firm basis for further development of this approach, both alone and in combination with other immunotherapies such as checkpoint blockade.

is the concentration of non-vaccine peptides, and + is the proportion of peptides presented that are 113 vaccine specific.

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In Equations 3 to 6, immature DCs initially enter the mature DC population presenting one vaccine 115 peptide with subsequent peptides presented at rate + as described above. Additionally, surface 116 peptides degrade at rate , which is proportional to the number of presented peptides, . Finally, 117 mature DCs decay at rate . Here, we assume that mature DCs decay faster than iDCs (21).

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T Cells 119 To model the activation and proliferation of T cells both at the lymph node (the volume of which is LN ) 120 and at the tissue site, we first model avidity as a spectrum that varies from j=1 to j=J, corresponding to 121 the lowest and highest avidity states respectively. We then consider several populations: , the 127 In Equation 7, naive CTLs in the lymph node of avidity j are supplied at rate , where is the 128 proportion supplied that have avidity . These naive CTLs also exit the lymph node at rate . The 129 rate at which naive CTLs are activated by mature DCs that have migrated into the lymph node is . The interactions between these cancer cells and effector CTLs are modelled with an ODE system: 159 In Equations 17 to 19, the total cancer population, total = ∑ =0 , grows logistically at rate and with 160 carrying capacity . As a simplifying assumption, we assume that the number of surface peptides is 161 halved after mitosis, resulting in a net compartmental growth rate of for the population of cancer cells presenting peptides, . We also assume that surface peptides are 164 regenerated at rate . To model trogocytosis-mediated MHC stripping, we assume that CTLs and 165 cancer cells presenting peptides interact at rate and additionally assume the number of peptides 166 stripped during this interaction is binomially distributed with probability . For brevity we let , = 167 ൫ ൯ (1 − ) − denote the probability that a CTL will trogocytose MHC:peptides off a cancer 168 cell presenting surface peptides. This allows us to describe the trogocytosis rate as 170 Finally, to model lysis, we let , denote the lysis probability between a cancer cell presenting 171 peptides and an effector CTL of avidity , and assume these interactions occur at rate . To model 172 the lysis probability, we assume that the probability of lysis increases with cognate pMHCs but is also 173 modulated by CTL avidity. This can be modelled by assuming a probability function of the form where is an avidity-dependent rate parameter chosen so that the lysis probability at maximal levels 175 of cognate pMHC expression, i.e., , varies linearly from 1 for the lowest avidity CTL to for the 176 highest avidity CTL.

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Peptide vaccine injection rate 180 Here, we assume that the vaccine is injected systemically at a fixed dose, 0 , and at a regular interval 181 of ζ, which corresponds to the functional form .

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Activation probability 184 The probability of a mature DC presenting vaccine-associated pMHCs activating a naive T cell of 185 avidity j, , , is modelled with a switch: We assume that the vaccine is first administered at = 0, i.e., (0) = 0 , where 0 is the vaccine 197 dose. To determine the initial DC populations, we assume that the system is at steady state when 198 there is no vaccine, which implies (0) = total , and (0) = 0, where total is the total DC population 199 at steady-state conditions.

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To model the scarcity of high-avidity naive T cells, we assume that their availability decreases high-avidity T cells, equates to the model parameter LH . In our simulations, we set LH to 100, which 204 means that for one high-avidity T cell there are 100 low-avidity T cells.
For simplicity, we assume that initially there are zero vaccine-associated effector T cells, i.e., (0) = 206 0, (0) = 0, and (0) = 0. As there are no vaccine-associated effector T cells present initially, we also 207 set the concentration of growth factor to be (0) = 0. 208 Finally, we assume that the total cancer cell concentration is init , with cognate pMHC being normally 209 distributed with mean μ = 148 and variance σ 2 = 49.

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To understand how DC maturation status affects parameter sensitivity, we conduct sensitivity analysis 213 on our modified model. We account for non-linear interactions between parameters by varying all 214 parameters simultaneously using Latin hypercube sampling (n=250) over the ranges shown in Table   215 2, and measure sensitivity by calculating Spearman's rank correlation coefficient (SRCC), ρ, for each 216 parameter against the fold decrease. Table 2 shows SRCC ρ for each parameter.

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Modified mathematical model 225 We previously found that the rate of antigen presentation by DCs determined the therapeutic value of As a simplifying assumption, we assume that induced immature DCs (iDCs) are given at a fixed dose 234 0 , and at dosing intervals of hours after the injection of the peptide vaccine, which leads to the 235 functional form: 237 Figure 1 uses a block diagram to depict the key interactions of our model.

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Increased immature DC levels yields lower peptide:MHC levels and tumor cell reduction 239 In our example, we assume our tumor is a melanoma and assume that our vaccine either targets