Recent clinical findings in chronic myeloid leukemia (CML) patients suggest that the risk of molecular recurrence after stopping tyrosine kinase inhibitors (TKI) treatment substantially depend on an individual, leukemia-specific immune response. However, it is still not possible to prospectively identify patients that will most likely remain in a long-term treatment free remission (TFR). Here, we use a mathematical model for CML, which explicitly includes an anti-leukemic (presumably immunological) effect and apply it to a set of patients (n=60) for whom BCR-ABL/ABL time courses had been quantified before and after TKI stop. We demonstrate that such a feedback control is conceptually necessary to explain long-term remission as observed in about half of the patients. Based on simulation results we classify the patient data sets into three different groups according to their predicted immune system configuration. While one class of patients requires a complete CML eradication to achieve TFR, other patients are able to control the leukemia after treatment cessation. Among them, we identified a third class of patients, which only maintains TFR if an optimal balance between leukemia abundance and immunological activation is achieved before treatment cessation. Further, we demonstrate that the immune response classification of the patients cannot be obtained solely from BCR-ABL measurements before treatment cessation. However, our results strongly suggest that changes in the BCR-ABL dynamics arising after system perturbations, such as TKI dose reduction, holds the information to predict the individual outcome after treatment cessation.

Predicting minimal residual disease (MRD) levels in tyrosine kinase inhibitor (TKI)-treated chronic myeloid leukemia (CML) patients is of major clinical relevance. The reason is that residual leukemic (stem) cells are the source for both, potential relapses of the leukemicclone but also for its clonal evolution and, therefore, for the occurrence of resistance. The state-of-the art method for monitoring MRD in TKI-treated CML is the quantification of BCR-ABL levels in the peripheral blood (PB) by PCR. However, the question is whether BCR-ABL levels in the PB can be used as a reliable estimate for residual leukemic cells at the level of hematopoietic stem cells in the bone marrow (BM). Moreover, once the BCR-ABL levels have been reduced to undetectable levels, information on treatment kinetics is censored by the PCR detection limit. Clearly, BCR-ABL negativity in the PB suggests very low levels of residual disease also in the BM, but whether the MRD level remains at a constant level or decreases further cannot be read from the BCR-ABL negativity itself. Thus, also the prediction of a suitable time point for treatment cessation based on residual disease levels cannot be obtained from PCR monitoring in the PB and currently remains a heuristic decision. To overcome the current lack of a suitable biomarker for residual disease levels in the BM, we propose the application of a computational approach to quantitatively describe and predict long-term BCR-ABL levels. The underlying mathematical model has previously been validated by the comparison to more than 500 long-term BCR-ABL kinetics in the PB from different clinical trials under continuous TKI-treatment [1,2,3]. Here, we present results that show how this computational approach can be used to estimate MRD levels in the BM based on the measurements in the PB. Our results demonstrate that the mathematical model can quantitatively reproduce the cumulative incidence of the loss of deep and major molecular response in a population of patients, as published by Mahon et al. [4] and Rousselot et al. [5]. Furthermore, to demonstrate how the model can be used to predict the BCR-ABL levels and to estimate the molecular relapse probability of individual patients, we compare simulation results with more than 70 individual BCR-ABL-kinetics. For this analysis we use patient data from different clinical studies (e.g. EURO-SKI: NCT01596114, STIM(s): NCT00478985, NCT01343173) where TKI-treatment had been stopped after prolonged deep molecular response periods. Specifically, we propose to combine statistical (non-linear regression) and mechanistic (agent-based) modelling techniques, which allows us to quantify the reliability of model predictions by confidence regions based on the quality (i.e. number and variance) of the clinical measurements and on the particular kinetic response characteristics of individual patients. The proposed approach has the potential to support clinical decision making because it provides quantitative, patient-specific predictions of the treatment response together with a confidence measure, which allows to judge the amount of information that is provided by the theoretical prediction. References [1] Roeder et al. (2006) Dynamic modeling of imatinib-treated chronic myeloid leukemia: functional insights and clinical implications, Nat Med 12(10):1181-4 [2] Horn et al. (2013) Model-based decision rules reduce the risk of molecular relapse after cessation of tyrosine kinase inhibitor therapy in chronic myeloid leukemia, Blood 121(2):378-84. [3] Glauche et al. (2014) Model-Based Characterization of the Molecular Response Dynamics of Tyrosine Kinase Inhibitor (TKI)-Treated CML Patients a Comparison of Imatinib and Dasatinib First-Line Therapy, Blood 124:4562 [4] Mahon et al. (2010) Discontinuation of imatinib in patients with chronic myeloid leukaemia who have maintained complete molecular remission for at least 2 years: the prospective, multicentre Stop Imatinib (STIM) trial. Lancet Oncol 11(11):1029-35 [5] Rousselot 
et al. (2014) Loss of major molecular response as a trigger for restarting TKI therapy in patients with CP- CML who have stopped Imatinib after durable undetectable disease, JCO 32(5):424-431 Disclosures Glauche: Bristol Meyer Squib: Research Funding. von Bubnoff:Amgen: Honoraria; Novartis: Honoraria, Research Funding; BMS: Honoraria. Saussele:ARIAD: Honoraria; Novartis: Honoraria, Other: Travel grants, Research Funding; Pfizer: Honoraria, Other: Travel grants; BMS: Honoraria, Other: Travel grants, Research Funding. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Ariad: Research Funding; Novartis: Honoraria, Research Funding. Guilhot:CELEGENE: Consultancy. Mahon:NOVARTIS PHARMA: Honoraria, Research Funding; BMS: Honoraria; PFIZER: Honoraria; ARIAD: Honoraria. Roeder:Bristol-Myers Squibb: Honoraria, Research Funding.

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